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Abstract: The Global Ecosystem Dynamics Investigation (GEDI) aims to provide improved characterization of forest structure, and plant area index (PAI) is one of many variables provided in the official GEDI Level 2B (L2B) product suite. However, since release, few quantitative validation studies have been conducted. To reach Stage 1 of the validation hierarchy proposed by the Land Product Validation (LPV) subgroup of the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV), we provide an initial assessment of PAI estimates from GEDI’s L2B product. This is achieved using 18 in situ reference measurements available through the Copernicus Ground Based Observations for Validation (GBOV) service. We show that GEDI L2B PAI retrievals provide a nearly unbiased estimate of effective (PAIe; RMSD = 0.95, bias = 0.02, slope = 1.07), but systematically underestimate PAI (RMSD = 1.42, bias = −0.91, slope = 0.77). This is attributed to an assumed random distribution of plant material in the algorithm. To reach Stage 2 of the CEOS WGCV LPV hierarchy, continued work is needed to validate the product against additional in situ reference measurements covering further locations and time periods.
Abstract: 1. Digital hemispherical photography (DHP) is widely used to derive forest biophysi-cal variables including leaf, plant, and green area index (LAI, PAI and GAI), the fraction of intercepted photosynthetically active radiation (FIPAR), and the frac-tion of vegetation cover (FCOVER). However, the majority of software packages for processing DHP data are based on a graphical user interface, making program-matic analysis difficult. Meanwhile, few natively support analysis of RAW image formats, while none incorporate the propagation or provision of uncertainties. 2. To address these limitations, we present HemiPy, an open-source Python module for deriving forest biophysical variables and uncertainties from DHP images in an automated manner. We assess HemiPy using simulated hemispherical images, in addition to multiannual time-series and litterfall data from several forested National Ecological Observatory Network (NEON) sites, as well as comparison against the CAN-EYE software package. 3. Multiannual time-series of PAI, FIPAR and FCOVER demonstrate HemiPy's out-puts realistically represent expected temporal patterns. Comparison against lit-terfall data reveals reasonable accuracies are achievable, with RMSE values close to the error of ~1 unit typically attributed to optical LAI measurement approaches. HemiPy's PAI, FIPAR and FCOVER outputs demonstrate good agreement with CAN- EYE. Consistent with previous studies, when compared to simulated hemi-spherical images, better agreement is observed for PAI derived using gap fraction near the hinge angle of 57.5° only, as opposed to values derived using gap fraction over a wider range of zenith angles. 4. HemiPy should prove a useful tool for processing DHP images, and its open- source nature means that it can be adopted, extended and further refined by the user community.
Abstract: Accurate monitoring of albedo trends over snow is essential to evaluate the consequences of the global snow cover retreat on Earth’s energy budget. Satellite observations provide the best way to monitor these trends globally, but their uncertainty increases over snow. Besides, different products sometimes show diverging trends. A better assessment of the fitness of satellite products for monitoring snow albedo trends is needed. We analyze the consistency of black-sky albedo estimates from global long-term products over snow: advanced very-high-resolution radiometer (AVHRR)-based (CLARA-A2.1, GLASS-v4.2), moderate resolution imaging spectroradiometer (MODIS)-based (MCD43C3-v6.1/v6, GLASS-v4.2), multiangle imaging spectro radiometer (MISR)-based (MIL3MLSN-v4), and multisensor (C3S-v1/v2). We use MCD43C3-6.1 as the reference based on a previous comparison against in situ measurements. CLARA-A2.1 is the one most consistent with MCD43C3, but has a low coverage in high latitudes and an artificial albedo decrease since 2015. The study shows the limitations of MIL3MLSN, Global Land Surface Satellite (GLASS), and Copernicus Climate Change Service (C3S) multisensor products over snow. MIL3MLSN has a too-low coverage of albedo over snow. GLASS-AVHRR overestimates albedo in regions with seasonal snow due to delayed snowmelt and underestimates it in permanently snow-covered regions. GLASS-MODIS is more consistent with MCD43C3 at mid-latitudes, and also underestimates albedo in regions with permanent snow and has an increase in missing values after 2011. Both the GLASS datasets are temporally inconsistent with the other products. Despite the improvements from v1 to v2, C3S-v2 has the largest negative bias over snow and discontinuities in the transitions between sensors. The study evidences the difficulties of AVHRR products to provide stable snow albedo estimates in polar regions, particularly before 2000.
Abstract: Monitoring snow cover to infer climate change impacts is now feasible using Earth observation data together with reanalysis products derived from Earth system models and data assimilation. Temporal stability becomes essential when these products are used to monitor snow cover changes over time. While the temporal stability of satellite products can be altered when multiple sensors are combined and due to the degradation and orbital drifts in each sensor, the stability of reanalysis datasets can be compromised when new observations are assimilated into the model. This study evaluates the stability of some of the longest satellite-based and reanalysis products (ERA5, 1950–2020, ERA5-Land, 1950–2020, and the National Oceanic and Atmospheric Administration Climate Data Record (NOAA CDR), 1966–2020) by using 527 ground stations as reference data (1950–2020). Stability is assessed with the time series of the annual bias in snow depth and snow cover duration of the products at the different stations. Reanalysis datasets face a trade-off between accuracy and stability when assimilating new data to improve their estimations. The assimilation of new observations in ERA5 improved its accuracy significantly during the recent years (2005–2020) but introduced three negative step discontinuities in 1977–1980, 1991–1992, and 2003–2004. By contrast, ERA5-Land is more stable because it does not assimilate snow observations directly, but this leads to worse accuracy despite having a finer spatial resolution. The NOAA CDR showed a positive artificial trend from around 1992 to 2015 during fall and winter that could be related to changes to the availability of satellite data. The magnitude of most of these artificial trends and/or discontinuities is larger than actual snow cover trends and the stability requirements of the Global Climate Observing System (GCOS). The use of these products in seasons and regions where artificial trends and discontinuities appear should be avoided. The study also updates snow trends (1955–2015) over local sites in the Northern Hemisphere (NH), corroborating the retreat of snow cover, driven mainly by an earlier melt and recently by a later snow onset. In warmer regions such as Europe, snow cover decrease is coincident with a decreasing snow depth due to less snowfall, while in drier regions such as Russia, earlier snowmelt occurs despite increased maximum seasonal snow depth.
Abstract: Multiple satellite products are available to monitor the spatiotemporal dynamics of surface albedo. They are extensively assessed over snow-free surfaces but less over snow. However, snow albedo is critical for climate monitoring applications, so a better understating of the accuracy of these products over snow is needed. This work analyzes long-term (+20 years) products (MCD43C3 v6/v6.1, GLASS-AVHRR, C3S v1/v2) by comparing them against the 11 most spatially representative stations from FLUXNET and BSRN during the snow-free and snow-covered season. Our goal is to understand how the performance of these products is affected by different snow cover conditions to use this information in an upcoming product inter-comparison that extends the analysis spatially and temporally. MCD43C3 has the smallest bias during the snow season (−0.017), and more importantly, the most stable bias with different snow cover conditions. Both v6 and v6.1 have similar performance, with v6.1 just increasing slightly the coverage at high latitudes. On the contrary, the quality of both GLASS-AVHRR and C3S-v1/v2 albedo decreases over snow. Particularly, the bias of both products varies strongly with the snow cover conditions, underestimating albedo over snow and overestimating snow-free albedo. GLASS bias strongly increases during the melting season, which is most likely due to an artificially extended snow season. C3S-v2 has the largest negative bias overall over snow during both the AVHRR (−0.141) and SPOT/VGT (−0.134) period. In addition, despite the improvements from v1 to v2, C3S-v2 still is not consistent enough during the transition from AVHRR to SPOT/VGT.
Abstract: This article presents the evaluation of the Copernicus Sentinel-3 Ocean Land Colour Instrument (OLCI) operational terrestrial products corresponding to the green instantaneous Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and its associated rectified channels. These products are estimated using OLCI spectral measurements acquired at the top of the atmosphere by a physically-based approach and are available operationally at full (300 m) and reduced (1.2 km) spatial resolution daily. The evaluation of the quality of the FAPAR OLCI values was based on the availability of data acquired over several years by Sentinel-3A (S3A) and Sentinel-3B (S3B). The evaluation exercise consisted of several stages: first, an overall comparison of the two S3 platform products was carried out during the tandem phase; second, comparison with an FAPAR climatology derived from the Medium Resolution Imaging Spectrometer (MERIS) provided information on the seasonality of various types of land cover. Then, direct comparisons were made with the same type of FAPAR products retrieved from two sensors, the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Sentinel-2 (S2) Multispectral Instrument (MSI), and with several ground-based estimates. In addition, an analysis of the efficiency of the retrieval algorithm with 3D radiative transfer simulations was performed. The results indicated that the consistency between daily and monthly S3A and S3B on a global scale was very good during the tandem phase (RMSD = 0.01 and a correlation R2 of 0.99 with a bias of 0.003); we found an agreement with a correlation of 0.95 and 0.93 (RMSD = 0.07 and 0.09) with JRC FAPAR S2 and JRC FAPAR MODIS, respectively. Compatibility with the ground-based data was between 0.056 and 0.24 in term of RMSD depending on the type of vegetation with an overall R2 of 0.89. Immler diagrams demonstrate that their variances were lower than the total uncertainties. The quality assurance using 3D radiative transfer model has shown that the apparent performance of the algorithm depends strongly on the type of in-situ measurement and canopy type.
Abstract: Monitoring snow cover extent is now feasible using Earth Observation (EO) data together with reanalysis products (derived from earth system model and data assimilation) to infer climate change impacts. Temporal stability is essential but can be altered by the combination of multiple satellite sensors and their degradation, or by the assimilation of new observations at a certain period in the case of reanalysis. This study evaluates the stability of some longest satellite and reanalysis products (ERA5, 1950–2020, ERA5-Land, 1981–2020, and NOAA CDR, 1966–2020) by using 470 ground stations as reference data (1950–2020). Temporal stability is assessed with the time series of the annual bias in snow depth and snow cover duration of the products at the different stations. Results show that the assimilation of new observations in ERA5 improved significantly its accuracy during the recent years (2005–2020) but introduced three negative step discontinuities starting in 1980, 1992, 2004. By contrast, ERA5-Land is more stable due to the lack of data assimilation, but at expense of worsening its accuracy despite having a finer spatial resolution. In the NOAA CDR, the increasing number of satellite data used introduces a positive trend since 1990–1995 that leads to artificial recovery of snow cover in fall and winter. The magnitude of most of these artificial trends/discontinuities is larger than actual snow cover trends and Global Climate Observing System (GCOS) stability requirements. The stability challenge of reanalysis products is linked to the assimilation of new observations to improve their accuracy or extend their temporal coverage. The study also updates snow trends (1950–2020) over local sites in the North Hemisphere (NH) corroborating the retreat of snow cover, driven mainly by an earlier melt and recently by a later snow onset. In warmer regions such as Europe, snow cover decrease is aggravated by a decreasing snow depth due to less snowfall, while in drier regions such as Russia snow cover retreats despite the increasing snow depth observed.
Abstract: A framework is proposed for assessing the physical consistency between two terrestrial Essential Climate Variables (ECVs) products retrieved from Earth Observation at global scale. The methodology assessed the level of agreement between the temporal variations of Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). The simultaneous changes were classified according to their sign, magnitude and level of confidence, whereby the respective products uncertainties were taken into consideration. A set of proposed agreement metrics were used to identify temporal and spatial biases of non-coherency, non-significance, sensitivity and the overall level of agreement of the temporal changes between two ECVs. We applied the methodology using the Joint Research Center (JRC) Two-stream Inversion Package (TIP) products at 1 km, those provided by the Copernicus Global Land Service (CGLS) based on the SPOT/VGT and Proba-V at 1 km, and the MODIS MCD15A3 at 500 m. In addition, the same analysis was applied with aggregated products at a larger scale over Southern Africa. We found that the CGLS LAI and FAPAR products lacked consistency in their spatial and temporal changes and were severely affected by trends. The MCD15A3 products were characterized by the highest number of non-coherent changes between the two ECVs but temporal inconsistencies were mainly located over the eastern hemisphere. The JRC-TIP products were highly consistent. The results showed the advantages of physically-based retrieval algorithms, in both JRC-TIP and MODIS products, and indicated also that, except for MODIS over forests, aggregated products using an uncertainty-based weighted average led to higher agreement between the ECVs changes.
Abstract: The Sentinel-2 Level 2 Prototype Processor (SL2P) is made available to users for the retrieval of vegetation biophysical variables including leaf area index (LAI) from Multispectral Instrument (MSI) data within the Sentinel Application Platform (SNAP). A limited number of validation exercises have indicated SL2P LAI re-trievals frequently meet user requirements over agricultural environments, but perform comparatively poorly over heterogeneous canopies such as forests. Recently, a modified version of SL2P was developed, using the directional area scattering factor (DASF) to constrain retrievals as an alternative to regularisation (SL2P-D). Whilst SL2P makes use of prior information on expected canopy conditions, SL2P-D is trained using uniform distributions of input parameters to define radiative transfer model (RTM) simulations. Using in situ measure-ments available through the Copernicus Ground Based Observations for Validation (GBOV) service, we per-formed an extensive validation of SL2P and SL2P-D LAI retrievals over 19 sites throughout the United States. For effective LAI (LAIe), SL2P demonstrated good overall performance (RMSD =0.50, NRMSD =31%, bias =-0.10), with all LAI retrievals meeting the Sentinels for Science (SEN4SCI) uncertainty requirements over ho-mogeneous canopies (cultivated crops, grasslands, pasture/hay and shrub/scrub), whilst underestimation occurred over heterogeneous canopies (deciduous forest, evergreen forest, mixed forest, and woody wetlands). SL2P-D retrievals demonstrated reduced bias, slightly improving overall performance when compared with SL2P (RMSD =0.48, NRMSD =30%, bias =-0.05), indicating its retrieval approach appears to offer some advantages over regularisation using prior information, especially at LAIe >3. Additionally, SL2P-D resulted in 32% more valid retrievals than SL2P, with the largest differences observed at LAIe <1. Validation against in situ mea-surements of LAI as opposed to LAIe yielded similar patterns but poorer performance (RMSD =1.08 to 1.13, NRMSD =49% to 52%, bias =-0.64 to -0.68) because the RTM used by SL2P and SL2P-D does not account for foliage clumping. In addition to the retrievals themselves, we examined the relationship between predicted uncertainties and observed differences in retrieved and in situ LAI. With respect to LAIe, SL2P’s predicted un-certainties were conservative, underestimating observed differences in only 35% of cases, whilst those for LAI were unbiased.
Abstract: This paper aims to assess the relationship between the surface reflectance derived fromground based and aircraft measurements. The parameters of the Rahman–Pinty–Verstraete (RPV)and Ross Thick-LiSparse (RTLS) kernel based bi-directional reflectance distribution functions (BRDF),have been derived using actual measurements of the hemispherical-directional reflectance factor(HDRF), collected during different campaigns over the Railroad Valley Playa. The effect of theatmosphere, including that of the diffuse radiation on bi-directional reflectance factor (BRF) parameterretrievals, assessed using 6S model simulations, was negligible for the low turbidity conditions of thesite under investigation (τ550≤0.05). It was also shown that the effects of the diffuse radiation onRPV spectral parameters retrieval is linear for the isotropic parameterρ0and the scattering parameterΘ, and can be described with a second order polynomial for thek-Minnaert parameter. In order toovercome the lack of temporal collocations between aircraft and in-situ measurements, Monte Carlo3-D radiative transfer simulations mimicking in-situ and remote sensing techniques were performedon a synthetic parametric meshed scene defined by merging Landsat and Multiangle ImagingSpectroradiometer (MISR) remote sensing reflectance data. We simulated directional reflectancemeasurements made at different heights for PARABOLA and CAR, and analyzed them accordingto practices adopted for real measurements, consisting of the inversion of BRF functions and thecalculation of the bi-hemispherical reflectance (BHR). The difference of retrievals against the knownbenchmarks of kernel parameters and BHR is presented. We associated an uncertainty of up to 2%with the retrieval of area averaged BHR, independently of flight altitudes and the BRF model usedfor the inversion. As expected, the local nature of PARABOLA data is revealed by the difference ofthe anisotropic kernel parameters with the corresponding parameters retrieved from aircraft loops.The uncertainty of the resultant BHR fell within ±3%.
Abstract: With a growing number of Earth observation (EO) products available through operational programmes such as the European Union’s Copernicus, there is increasing emphasis on product accuracy and uncertainty, necessitating evaluation against in situ reference measurements. Whilst existing reference datasets have proven a valuable resource, they incorporate little data with which products from recent EO instruments can be assessed. A reliance on individual field campaigns has also led to several inconsistencies, whilst limiting the extent to which temporal variations in EO product performance can be captured. Recently established environmental monitoring networks such as the National Ecological Observatory Network (NEON), which collect routine in situ measurements using standardised instruments and protocols, provide a promising opportunity in this respect. The Copernicus Ground Based Observations for Validation (GBOV) service was initiated in recognition of this fact. In the first component of the project, raw observations from existing networks have been collected and processed to provide reference data for a range of EO land products. In this study, we focus on leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FAPAR). Raw digital hemispherical photography (DHP) from twenty NEON sites was processed to derive in situ reference measurements, which were then upscaled to provide high spatial resolution reference maps. Using these data, we assess the recently released Copernicus Global Land Service (CGLS) 300 m Version 1 (V1) products derived from PROBA-V, in addition to existing products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Radiometer Suite (VIIRS). When evaluated against reference data, the CGLS 300 m V1 product demonstrated the best agreement (RMSD = 0.57 for LAI and 0.08 for FAPAR), followed by the Collection 6 VNP15A2H and MOD15A2H products (RMSD = 0.81 to 0.89 for LAI and 0.12 for FAPAR). Differing assumptions of the products and in situ reference measurements, which cause them to be sensitive to slightly different quantities, are thought to explain apparent biases over sparse vegetation and forest environments. To ensure their continued utility, future work should focus on updating the GBOV in situ reference measurements, implementing additional corrections, and improving their geographical representativeness.
Abstract: Surface albedo is a fundamental radiative parameter as it controls the Earth’s energy budget and directly affects the Earth’s climate. Satellite observations have long been used to capture the temporal and spatial variations of surface albedo because of their continuous global coverage. However, space-based albedo products are often affected by errors in the atmospheric correction, multi-angular bi-directional reflectance distribution function (BRDF) modelling, as well as spectral conversions. To validate space-based albedo products, an in situ tower albedometer is often used to provide continuous “ground truth” measurements of surface albedo over an extended area. Since space-based albedo and tower-measured albedo are produced at different spatial scales, they can be directly compared only for specific homogeneous land surfaces. However, most land surfaces are inherently heterogeneous with surface properties that vary over a wide range of spatial scales. In this work, tower-measured albedo products, including both directional hemispherical reflectance (DHR) and bi-hemispherical reflectance (BHR), are upscaled to coarse satellite spatial resolutions using a new method. This strategy uses high-resolution satellite derived surface albedos to fill the gaps between the albedometer’s field-of-view (FoV) and coarse satellite scales. The high-resolution surface albedo is generated from a combination of surface reflectance retrieved from high-resolution Earth Observation (HR-EO) data and moderate resolution imaging spectroradiometer (MODIS) BRDF climatology over a larger area. We implemented a recently developed atmospheric correction method, the Sensor Invariant Atmospheric Correction (SIAC), to retrieve surface reflectance from HR-EO (e.g., Sentinel-2 and Landsat-8) top-of-atmosphere (TOA) reflectance measurements. This SIAC processing provides an estimated uncertainty for the retrieved surface spectral reflectance at the HR-EO pixel level and shows excellent agreement with the standard Landsat 8 Surface Reflectance Code (LaSRC) in retrieving Landsat-8 surface reflectance. Atmospheric correction of Sentinel-2 data is vastly improved by SIAC when compared against the use of in situ AErosol RObotic NETwork (AERONET) data. Based on this, we can trace the uncertainty of tower-measured albedo during its propagation through high-resolution EO measurements up to coarse satellite scales. These upscaled albedo products can then be compared with space-based albedo products over heterogeneous land surfaces. In this study, both tower-measured albedo and upscaled albedo products are examined at Ground Based Observation for Validation (GbOV) stations (https://land.copernicus.eu/global/gbov/), and used to compare with satellite observations, including Copernicus Global Land Service (CGLS) based on ProbaV and VEGETATION 2 data, MODIS and multi-angle imaging spectroradiometer (MISR).
Abstract: Satellite remotely sensed fraction of photosynthetically active radiation (FPAR) products are widely used in landsurface monitoring and modeling, especially for estimating global terrestrial photosynthetic activity through light use efficiency (LUE) models. PAR absorbed by active chlorophyll (APARchl) is directly linked to vegetation photosynthesis and can be used to estimate ecosystem gross primary production (GPP). Previous studies have demonstrated that solar induced chlorophyll fluorescence has very tight relationship with APARchl at various ecosystems. Therefore, the solar angle normalized SIF (nSIF) is directly related to the fraction of PAR absorbed by chlorophyll (FPARchl). This paper intercompared six space FPAR products from Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Copernicus Global Land Service (CGLS), Multi-angle Imaging SpectroRadiometer (MISR), Earth Polychromatic Imaging Camera (EPIC) and Ocean and Land Colour Instrument (OLCI). Their potential relationships with FPARchl were indirectly evaluated with both spaceborne (Orbiting Carbon Observatory-2, OCO-2 and TROPOspheric Monitoring Instrument, TROPOMI) and airborne (Chlorophyll Fluorescence Imaging Spectrometer, CFIS) nSIF data as well as in situ GPP measurements. Our results show that these FPAR products are different in terms of amplitudes and seasonal variations across biomes. Among six FPAR products, OLCI FPAR shows the best relationships with TROPOMI nSIF740, OCO-2 nSIF757, and CFIS nSIF755. The coefficient of determination (R2) for the relationship between OLCI FPAR and TROPOMI nSIF740 is 0.79 ± 0.17 on a global average. APAR calculated from OLCI also exhibits the best relationship (R2 = 0.79) with in situ GPP over 25 flux towers.
Abstract: NOAA platforms provide the longest period of terrestrial observation since the 1980s. The progress in calibration, atmospheric corrections and physically based land retrieval offers the opportunity to reprocess these data for extending terrestrial product time series. Within the Quality Assurance for Essential Climate Variables (QA4ECV) project, the black-sky Joint Research Centre (JRC)-fraction of absorbed photosynthetically active radiation (FAPAR) algorithm was developed for the AVHRR sensors on-board NOAA-07 to -16 using the Land Surface Reflectance Climate Data Record. The retrieval algorithm was based on the radiative transfer theory, and uncertainties were included in the products. We proposed a time and spatial composite for providing both 10-day and monthly products at 0.05° × 0.05°. Quality control and validation were achieved through benchmarking against third-party products, including Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) datasets produced with the same retrieval algorithm. Past ground-based measurements, providing a proxy of FAPAR, showed good agreement of seasonality values over short homogeneous canopies and mixed vegetation. The average difference between SeaWiFS and QA4ECV monthly products over 2002–2005 is about 0.075 with a standard deviation of 0.091. We proposed a monthly linear bias correction that reduced these statistics to 0.02 and 0.001. The complete harmonized long-term time series was then used to address its fitness for the purpose of analysis of global terrestrial change.
Abstract: The High resOlution Land Atmosphere surface Parameters from Space (HOLAPS) programme provides a modeling system to maximize the use of satellite-based products and ensure internally consistent estimation of surface water and energy fluxes. Leaf area index (LAI) and land surface albedo are two key parameters for estimation of latent and sensible heat fluxes with HOLAPS. Thus, to facilitate the generation of long-term high accuracy latent and sensible heat fluxes, high quality global long-term LAI and land surface albedo datasets are essential. The Quality Assurance for Essential Climate Variables (QA4ECV) project released quality-assured long-term LAI and albedo datasets with traceable and reliable uncertainty information provided in the dataset. Taking MODIS-BNU-LAI and MODIS albedo as reference, different global long-term LAI and albedo datasets including GlobAlbedo, QA4ECV and GLOBMAP were investigated for estimation of latent/sensible heat fluxes with HOLAPS in this study. The results show that all albedo datasets show similar accuracy for estimation of latent and sensible heat fluxes when validated against FLUXNET observations. The QA4ECV LAI leads to worse latent heat flux estimation due to its use of effective LAI rather than green leaf LAI. Sensitivity analysis also shows that the HOLAPS estimated latent heat flux (LE) is more sensitive to uncertainty in LAI than land surface albedo. Overall, the combined use of QA4ECV albedo and GLOBMAP LAI is suggested for estimation of latent/sensible heat fluxes with HOLAPS. The root mean square differences (RMSD) between estimations and FLUXNET measurements are 54 (30) W/m<sup>2</sup> for hourly (monthly) latent heat flux, and 80.5 (24.5) W/m<sup>2</sup> for sensible heat flux, which are comparable to estimates with MODIS and other reported studies.
Abstract: Drought in Australia has widespread impacts on agriculture and ecosystems. Satellite-based Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) has great potential to monitor and assess drought impacts on vegetation greenness and health. Various FAPAR products based on satellite observations have been generated and made available to the public. However, differences remain among these datasets due to different retrieval methodologies and assumptions. The Quality Assurance for Essential Climate Variables (QA4ECV) project recently developed a quality assurance framework to provide understandable and traceable quality information for Essential Climate Variables (ECVs). The QA4ECV FAPAR is one of these ECVs. The aim of this study is to investigate the capability of QA4ECV FAPAR for drought monitoring in Australia. Through spatial and temporal comparison and correlation analysis with widely used Moderate Resolution Imaging Spectroradiometer (MODIS), Satellite Pour l’Observation de la Terre (SPOT)/PROBA-V FAPAR generated by Copernicus Global Land Service (CGLS), and the Standardized Precipitation Evapotranspiration Index (SPEI) drought index, as well as the European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture, the study shows that the QA4ECV FAPAR can support agricultural drought monitoring and assessment in Australia. The traceable and reliable uncertainties associated with the QA4ECV FAPAR provide valuable information for applications that use the QA4ECV FAPAR dataset in the future.
Abstract: This paper presents a framework for assessing the physical consistency between time-series of several satellite-based surface albedo and burned area products at global scale. The methodology evaluates the level of agreement of temporal change between these two Essential Climate Variables (ECVs) taking into account their uncertainties. Several configurations of surface albedo and burned area products, including the ones from MODIS Collection 6, Copernicus Global Land Service (C-GLS), Climate Change Initiative (CCI) and the GlobAlbedo, were analysed over the 2005–2011 period at 0.05°× 0.05° spatial resolution. The study evaluates the temporal and spatial coherence level in the ECV changes, and explores the product dependency impact on the fire-driven radiative forcings. The main findings indicate that the level of agreement between these two ECVs depends on the product combination but also on the albedo broadband. Whereas both GlobAlbedo and MODIS albedo changes are consistent both temporally and spatially when combined with any burned area product, the C-GLS albedo changes are characterised by lower levels of agreement. The results also indicate that surface albedo and burned area product changes are physically coherent for all biomes except grasslands and croplands, when using the MODIS burned area product. Using different combinations of burned area and surface albedo products, fire-induced radiative forcing estimates at the surface can differ, according to the biome, by 26% to 46%. The proposed framework identifies lack of coherency and consistency between the two ECVs. This novel approach allows us to address cross-ECV compatibility, and as such offers a valuable toolset to users.
Abstract: Data from Earth observation (EO) satellites are increasingly used to monitor the environment, understand variability and change, inform evaluations of climate model forecasts, and manage natural resources. Policymakers are progressively relying on the information derived from these datasets to make decisions on mitigating and adapting to climate change. These decisions should be evidence based, which requires confidence in derived products, as well as the reference measurements used to calibrate, validate, or inform product development. In support of the European Union’s Earth Observation Programmes Copernicus Climate Change Service (C3S), the Quality Assurance for Essential Climate Variables (QA4ECV) project fulfilled a gap in the delivery of climate quality satellite-derived datasets, by prototyping a generic system for the implementation and evaluation of quality assurance (QA) measures for satellite-derived ECV climate data record products. The project demonstrated the QA system on six new long-term, climate quality ECV data records for surface albedo, leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), nitrogen dioxide (NO2), formaldehyde (HCHO), and carbon monoxide (CO). The provision of standardised QA information provides data users with evidence-based confidence in the products and enables judgement on the fitness-for-purpose of various ECV data products and their specific applications.
Abstract: This paper presents results of using multi-sensor and multi-angular constraints in the generic Earth Observation-Land Data Assimilation System (EO-LDAS) for reproducing arbitrary bandsets of hyperspectral reflectance at the top-of-canopy (TOC) level by merging observations from multispectral sensors with different spectral characteristics. This is demonstrated by combining Multi-angle Imaging Spectroradiometer (MISR) and Landsat Enhanced Thematic Mapper Plus (ETM+) data to simulate the Compact High Resolution Imaging Spectrometer CHRIS/PROBA hyperspectral signal over an agricultural test site, in Barrax, Spain. However, the method can be more generally applied to any combination of spectral data, providing a tool for merging EO data to any arbitrary hyperspectral bandset. Comparisons are presented using both synthetic and observed MISR and Landsat data, and retrieving surface biophysical properties. We find that when using simulated MISR and Landsat data, the CHRIS/PROBA hyperspectral signal is reproduced with RMSE 0.0001–0.04. LAI is retrieved with r2 from 0.97 to 0.99 and RMSE of from 0.21 to 0.38. The results based on observed MISR and Landsat data have lower performances, with RMSE for the reproduced CHRIS/PROBA hyperspectral signal varying from 0.007 to 0.2. LAI is retrievedwith r2 from 0.7 to 0.9 and RMSE from 0.7 to 1.4. We found that for the data considered here the main spectral variations in the visible and near infrared regions can be described by a limited number of parameters (3–4) that can be estimated from multispectral information. Results show that the method can be used to simulate arbitrary bandsets, which will be of importance to any application which requires combining new and existing streams of new EO data in the optical domain, particularly intercalibration of EO satellites in order to get continuous time series of surface reflectance, across programmes and sensors of different designs.
Abstract: Application of remote sensing datasets in modelling phenology of heterotrophic animals has received little attention. In this work, we compare the predictive power of remote sensing versus temperature-derived variables in modelling peak flight periods of herbivorous insects, as exemplified by nocturnal moths. Moth phenology observations consisted of weekly observations of five focal moth species (Orthosia gothica, Ectropis crepuscularia, Cabera exanthemata, Dysstroma citrata and Operophtera brumata) gathered in a national moth monitoring scheme in Finland. These species were common and widespread and had peak flight periods in different seasons. Temperature-derived data were represented by weekly accumulating growing degree days (GDD) calculated from gridded temperature observations. Remote sensing data were obtained from three sources: (1) snow melt-off date from the MODIS daily snow maps, (2) greening date using the NDWI from MODIS data and (3) dates of start, maximum and end of growing season based on the JRC FAPAR products. Peak phenology observations of moths were related to different explanatory variables by using linear mixed effect models (LMM), with 70% of the data randomly selected for model calibration. Predictive power of models was tested using the remaining 30% of the data. Remote sensing data (snow melt-off and vegetation greening date) showed the highest predictive power in two moth species flying in the early and late spring, whereas in the three other species none of the variables showed reasonable predictive power. Flight period of the spring species coincides with natural events such as snow melt or vegetation greening that can easily be observed using remote sensing techniques. We demonstrate the applicability of our methodology by predictive spatial maps of peak flight phenology covering the entire Finland for two of the focal species. The methods are applicable in situations that require spatial predictions of animal activity, such as the management of populations of insect pest species.
Abstract: The Fraction of Absorbed Photosynthetically-Active Radiation (FAPAR) is an important parameter in climate and carbon cycle studies. In this aper, we use the Earth Observation Land Data Assimilation System (EO-LDAS) framework to retrieve FAPAR from observations of directional surface reflectance measurements from the Multi-angle Imaging SpectroRadiometer(MISR) instrument. The procedure works by interpreting the reflectance data via the semi-discrete Radiative Transfer (RT) model, supported by a prior parameter distribution and a dynamic regularisation model and resulting in an inference of land surface parameters, such as effective Leaf Area Index (LAI), leaf chlorophyll concentration and fraction of senescent leaves, with full uncertainty quantification. The method is demonstrated over three agricultural FLUXNET sites, and the EO-LDAS results are compared with eight years of in situ measurements of FAPAR and LAI, resulting in a total of 24 site years. We additionally compare three ther widely-used EO FAPAR products, namely the MEdium Resolution Imaging Spectrometer (MERIS) Full Resolution, the MISR High Resolution (HR) Joint Research Centre Two-stream Inversion Package (JRC-TIP) and MODIS MCD15 FAPAR products. The EO-LDAS MISR FAPAR retrievals show a high correlation with the ground measurements (r2 > 0.8), as well as the lowest average RMSE (0.14), in line with the MODIS product. As the EO-LDAS solution is effectively interpolated, if only measurements that are coincident with MISR observations are considered, the correlation increases (r2 > 0.85); the RMSE is lower by 4–5%; and the bias is 2% and 7%. The EO-LDAS MISR LAI estimates show a strong correlation with ground-based LAI (average r2 = 0.76), but an underestimate of LAI for optically-thick canopies due to saturation (average RMSE = 2.23). These results suggest that the EO-LDAS approach is successful in retrieving both FAPAR and other land surface parameters. A large part of this success is based on the use of a dynamic regularisation model that counteracts the poor temporal sampling from the MISR instrument.
Abstract: This paper proposes a benchmarking method for assessing the level of spatio-temporal variability of Essential Climate Variable (ECV) products against a reference taking into account acceptance criteria in terms of intensity and physical distance tolerances. This is based on a modified version of the gamma index that could be suitable for fitness-for-purpose assessment given that one can choose various criteria depending on applications. The method is first presented and then applied to both land and atmospheric ECVs. The terrestrial analysis concerns the global surface albedo, using monthly white-sky surface albedo in the visible, near-infrared and shortwave broadband spectral ranges at a spatial resolution of 0.05° using three sources of products. The latter study is conducted using monthly aerosol optical depth (AOD) products at 550 nm at a spatial resolution of 1° with four different datasets at the global scale. The analysis shows how the values of the gamma criteria impact the spatial and temporal results. As an example, if the Global Climate Observing System (GCOS) actual target measurements uncertainty is used as an acceptance criteria for the intensity tolerance the results show that: 1) the seasonal agreement for the surface albedo products varies over 20% to 40% of the terrestrial surface in the shortwave and near-infrared broadband and from 10% to 30% in the visible one and 2) the three aerosols optical depth products agree with the reference one for over 50% of the land surface only when the tolerance distance term is at 224km.
Abstract: Four representative two-stream canopy radiative transfer models were examined and intercompared using the same configuration. Based on the comparison results, two modifications were introduced to the widely used Dickinson-Sellers model and then incorporated into the Community Land Model (CLM4.5). The modified model was tested against Monte-Carlo simulations and produced significant improvements in the simulated canopy transmittance and albedo values. In direct comparison with MODIS albedo data, the modified model shows good performance over most snow/ice-free vegetated areas, especially for regions that are covered by dense canopy. The modified model shows seasonally dependent behavior mainly in the near-infrared band. Thus, the improvements are not present in all seasons. Large biases are still noticeable in sparsely vegetated areas, in particular for the snow/ice covered regions, that is possibly related to the model, the land surface input data, or even the observations themselves. Further studies focusing on the impact of the seasonal changes in leaf optical properties, the parameterizations for snow/ice covered regions and the case of sparsely vegetated areas, are recommended.
Abstract: Earth Observation (EO) land products have been demonstrated to provide a constraint on the terrestrial carbon cycle that is complementary to the record of atmospheric carbon dioxide. We present the Joint Research Centre Two-stream Inversion Package (JRC-TIP) for retrieval of variables characterising the state of the vegetation-soil system. The system provides a set of land surface variables that satisfy all requirements for assimilation into the land component of climate and numerical weather prediction models. Being based on a one dimensional representation of the radiative transfer within the canopy-soil system such as those used in the land surface components of advanced global models, the JRC-TIP products are not only physically consistent internally, but also achieve a high degree of consistency with these global models. Furthermore, the products are provided with full uncertainty information. We describe how these uncertainties are derived in a fully traceable manner without any hidden assumptions from the input observations, which are typically broadband white sky albedo products. Our discussion of the product uncertainty ranges, including the uncertainty reduction, highlights the central role of the leaf area index which describes the density of the canopy. We explain the generation of products aggregated to coarser spatial resolution than that of the native albedo input and describe various approaches to validation of JRC-TIP products, including the comparison against in-situ observations. We present a JRC-TIP processing system that satisfies all operational requirements and explain how it delivers stable climate data records. As many aspects of JRC-TIP are generic the package can serve as an example of a state-of-the-art system for retrieval of EO products, and this contribution can help the user to understand advantages and limitations of such products.
Abstract: Land surface albedo defines the ratio of shortwave radiation absorbed by the surface and controls the surface energy balance, thus is important for environmental and climate scientific communities. Remote sensing is the only means to globally map land surface albedo, however for it to be of use to the aforementioned communities, it must be accurate with respect to Global Climate Observing System (GCOS) requirements. Sources of error are introduced in each step of the provision of land surface albedo products, whereby this study intends to investigate sources of error introduced by the narrowband-to-broadband conversion formulae step. Radiative transfer modeling of vegetation is used to simulate spectral albedo over complex 3D vegetation canopies, then broadband-to-narrow conversion formulae for numerous sensors applied on the spectral albedo to compute broadband albedo, and the accuracy of formulae investigated. Results indicate that the efectiveness of conversion formulae is determined by the sensor, depending on the placement and number of the sensor wavebands, ecosystem complexity and the broadband range of the broadband albedo.
Abstract: Satellite-based retrievals of land surface albedo are essential for climate and environmental modelling communities. To be of use, satellite-retrievals are required to comply to given accuracy requirements, mainly achieved through comparison with in situ measurements. Differences between in situ and satellite-based retrievals depend on their actual difference and their associated uncertainties. It is essential that these uncertainties can be computed to properly understand the differences between satellite-based and in situ measurements of albedo, however quantifying the individual contributions of uncertainty is difficult. This study introduces a model-based framework for assessing the quality of in situ albedo measurements. A 3D Monte Carlo Ray Tracing (MCRT) radiative transfer model is used to simulate field measurements of surface albedo, and is able to identify and quantify potential sources of error in the field measurement. Compliance with the World Meteorological Organisation (WMO) requirement for 3% accuracy is tested. 8 scenarios were investigated, covering a range of ecosystem types and canopy structures, seasons, illumination angles and tree heights. Results indicate that height of measurement above the canopy is the controlling factor in accuracy, with each canopy scenario reaching the WMO requirement at different heights. Increasing canopy heterogeneity and tree height noticeably reduces the accuracy, whereas changing seasonality from summer to winter in a deciduous forest increases accuracy. For canopies with a row structure, illumination angle can significantly impact accuracy as a result of shadowing effects. Tests were made on the potential use of multiple in situ measurements, indicating considerably increased accuracy if two or more in situ measurements can be made.
Abstract: We present the first comparison between new fAPAR and LAI products derived from the GlobAlbedo dataset and the widely-used MODIS fAPAR and LAI products. The GlobAlbedo-derived products are produced using a 1D two-stream radiative transfer (RT) scheme designed explicitly for global parameter retrieval from albedo, with consistency between RT model assumptions and observations, as well as with typical large-scale land surface model RT schemes. The approach does not require biome-specific structural assumptions (e.g., cover, clumping, understory), unlike more detailed 3D RT model approaches. GlobAlbedo-derived values of fAPAR and LAI are compared with MODIS values over 2002–2011 at multiple flux tower sites within selected biomes, over 1200 x 1200 km regions and globally. GlobAlbedo-derived fAPAR and LAI values are temporally more stable than the MODIS values due to the smoothness of the underlying albedo, derived via optimal estimation (assimilation) using an a priori estimate of albedo derived from an albedo “climatology” (composited multi-year albedo observations). Parameters agree closely in timing but with GlobAlbedo values consistently lower than MODIS, particularly for LAI. Larger differences occur in winter (when values are lower) and in the Southern hemisphere. Globally, we find that: GlobAlbedo-derived fAPAR is ~0.9–1.01 x MODIS fAPAR with an intercept of ~0.03; GlobAlbedo-derived LAI is ~0.6 x MODIS LAI with an intercept of ~0.2. Differences arise due to the RT model assumptions underlying the products, meaning care is required in interpreting either set of values, particularly when comparing to fine-scale ground-based estimates. We present global transformations between GlobAlbedo-derived and MODIS products.
Abstract: This report is the response to a request by the Committee on Space Research of the International Council for Science to prepare a roadmap on observation and integrated Earth-system science for the coming ten years. Its focus is on the combined use of observations and modelling to address the functioning, predictability and projected evolution of interacting components of the Earth system on timescales out to a century or so. It discusses how observations support integrated Earth-system science and its applications, and identifies planned enhancements to the contributing observing systems and other requirements for observations and their processing. All types of observation are considered, but emphasis is placed on those made from space. The origins and development of the integrated view of the Earth system are outlined, noting the interactions between the main components that lead to requirements for integrated science and modelling, and for the observations that guide and support them. What constitutes an Earth-system model is discussed. Summaries are given of key cycles within the Earth system. The nature of Earth observation and the arrangements for international coordination essential for effective operation of global observing systems are introduced. Instances are given of present types of observation, what is already on the roadmap for 2016–2025 and some of the issues to be faced. Observations that are organised on a systematic basis and observations that are made for process understanding and model development, or other research or demonstration purposes, are covered. Specific accounts are given for many of the variables of the Earth system. The current status and prospects for Earth-system modelling are summarized. The evolution towards applying Earth-system models for environmental monitoring and prediction as well as for climate simulation and projection is outlined. General aspects of the improvement of models, whether through refining the representations of processes that are already incorporated or through adding new processes or components, are discussed. Some important elements of Earth-system models are considered more fully. Data assimilation is discussed not only because it uses observations and models to generate datasets for monitoring the Earth system and for initiating and evaluating predictions, in particular through reanalysis, but also because of the feedback it provides on the quality of both the observations and the models employed. Inverse methods for surface-flux or model-parameter estimation are also covered. Reviews are given of the way observations and the processed datasets based on them are used for evaluating models, and of the combined use of observations and models for monitoring and interpreting the behaviour of the Earth system and for predicting and projecting its future. A set of concluding discussions covers general developmental needs, requirements for continuity of space-based observing systems, further long-term requirements for observations and other data, technological advances and data challenges, and the importance of enhanced international co-operation.
Abstract: The RAdiative transfer Model Intercomparison (RAMI) activity focuses on the benchmarking of canopy radiative transfer (RT) models. For the current fourth phase of RAMI, six highly realistic virtual plant environments were constructed on the basis of intensive field data collected from (both deciduous and coniferous) forest stands as well as test sites in Europe and South Africa. Twelve RT modelling groups provided simulations of canopy scale (directional and hemispherically integrated) radiative quantities, as well as a series of binary hemispherical photographs acquired from different locations within the virtual canopies. The simulation results showed much greater variance than those recently analysed for the abstract canopy scenarios of RAMI-IV. Canopy complexity is among the most likely drivers behind operator induced errors that gave rise to the discrepancies. Conformity testing was introduced to separate the simulation results into acceptable and non-acceptable contributions. More specifically, a shared risk approach is used to evaluate the compliance of RT model simulations on the basis of reference data generated with the weighted ensemble averaging technique from ISO-13528. However, using concepts from legal metrology, the uncertainty of this reference solution will be shown to prevent a confident assessment of model performance with respect to the selected tolerance intervals. As an alternative, guarded risk decision rules will be presented to account explicitly for the uncertainty associated with the reference and candidate methods. Both guarded acceptance and guarded rejection approaches are used to make confident statements about the acceptance and/or rejection of RT model simulations with respect to the predefined tolerance intervals.
Abstract: Since 70% of global forests are managed and forests impact the global carbon cycle and the energy exchange with the overlying atmosphere, forest management has the potential to mitigate climate change. Yet, none of the land-surface models used in Earth system models, and therefore none of today’s predictions of future climate, accounts for the interactions between climate and forest management. We addressed this gap in modelling capability by developing and parametrising a version of the ORCHIDEE land-surface model to simulate the biogeochemical and biophysical effects of forest management. The most significant changes between the new branch called ORCHIDEE-CAN (SVN r2290) and the trunk version of ORCHIDEE (SVNr2243) are the allometric-based allocation of carbon to leaf, root, wood, fruit and reserve pools; the transmittance, absorbance and reflectance of radiation within the canopy; and the vertical discretisation of the energy budget calculations. In addition, conceptual changes were introduced towards a better process representation for the interaction of radiation with snow, the hydraulic architecture of plants, the representation of forest management and a numerical solution for the photosynthesis formalism of Farquhar, von Caemmerer and Berry. For consistency reasons, these changes were extensively linked throughout the code. Parametrisation was revisited after introducing 12 new parameter sets that represent specific tree species or genera rather than a group of often distantly related or even unrelated species, as is the case in widely used plant functional types. Performance of the new model was compared against the trunk and validated against independent spatially explicit data for basal area, tree height, canopy structure, gross primary production (GPP), albedo and evapotranspiration over Europe. For all tested variables, ORCHIDEE-CAN outperformed the trunk regarding its ability to reproduce large-scale spatial patterns as well as their inter-annual variability over Europe. Depending on the data stream, ORCHIDEE-CAN had a 67 to 92% chance to reproduce the spatial and temporal variability of the validation data.
Abstract: The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is recognized as an essential climate variable (ECVs), playing a critical role in the estimation of the global energy and carbon balance. With multiple space-borne remote sensing FAPAR global products available from several sources the need for continual comparison and validation has become imperative. In this study, the performance of three global FAPAR algorithms (JRC-TIP, ESA/JRC MGVI and Boston University FAPAR) was evaluated over Europe for the year 2011. Results show an overall agreement among FAPAR products on sites having high and low FAPAR values, except for the north-eastern region of Europe characterized by boreal forest and the transition region with tundra biomes, where the Boston product exceeds values in other products by up to 0.5. Differences in FAPAR estimates over forest biomes suggest that assumptions on structure and optical properties of land surfaces in the different radiative transfer models play an important role in remote-sensing-derived FAPAR products. Uncertainty assessments were carried out using both quality indicators as proposed by the individual product teams as well as independent theoretical uncertainty estimates obtained with the triple collocation error model. The former revealed consistent spatial patterns but large differences in magnitudes (up to 0.1) with systematically lower uncertainties for the Boston product. The latter instead suggests similar uncertainty ranges among the three products. Finally, a comparison with ground estimates for the 2009–2011 period over four European flux tower sites showed consistent, plausible seasonal variations of remote-sensing-derived FAPAR products. Findings suggest that differences in absolute values and inconsistency in uncertainty representation among FAPAR products are still considerable. Standardization frameworks quantifying the impact of different radiative transfer formulations on the estimation of biophysical variables, independent uncertainty estimation methods and well-defined ground measurement protocols need to be put in place before FAPAR products can be reliably fed into existing biogeochemical process models.
Abstract: Angularly resolved measurements of scattered light from surfaces can provide useful information in various fields of research and industry, such as computer graphics, satellite based Earth bservation etc. In practice, empirical or physics-based models are needed to interpolate the measurement results, because a thorough characterization of the surfaces under all relevant conditions may not be feasible. In this work, plain and anodized metal samples were prepared and measured optically for bidirectional reflectance distribution function (BRDF) and mechanically for surface roughness. Two models for BRDF (Torrance–Sparrow model and a polarimetric BRDF model) were fitted to the measured values. A better fit was obtained for plain metal surfaces than for anodized surfaces.
Abstract: A new goniometer for in situ spectro-directional measurements of the reflection and transmission properties of individual leaves is presented. One diffuse and five directional illumination angles can be chosen. A multichannel spectrometer allows simultaneous detection of the scattered light at 80 viewing angles in the spectral range from 400 nm to 1000 nm.
Abstract: Understanding the environmental and biotic drivers of respiration at the ecosystem level is a prerequisite to further improve scenarios of the global carbon cycle. In this study we investigated the relevance of physiological phenology, defined as seasonal changes in plant physiological properties, for explaining the temporal dynamics of ecosystem respiration (RECO) in deciduous forests. Previous studies showed that empirical RECO models can be substantially improved by considering the biotic dependency of RECO on the short-term productivity (e.g., daily gross primary production, GPP) in addition to the well-known environmental controls of temperature and water availability. Here, we use a model-data integration approach to investigate the added value of physiological phenology, represented by the first temporal derivative of GPP, or alternatively of the fraction of absorbed photosynthetically active radiation, for modeling RECO at 19 deciduous broadleaved forests in the FLUXNET La Thuile database. The new data-oriented semiempirical model leads to an 8% decrease in root mean square error (RMSE) and a 6% increase in the modeling efficiency (EF) of modeled RECO when compared to a version of the model that does not consider the physiological phenology. The reduction of the model-observation bias occurred mainly at the monthly time scale, and in spring and summer, while a smaller reduction was observed at the annual time scale. The proposed approach did not improve the model performance at several sites, and we identified as potential causes the plant canopy heterogeneity and the use of air temperature as a driver of ecosystem respiration instead of soil temperature. However, in the majority of sites the model-error remained unchanged regardless of the driving temperature. Overall, our results point toward the potential for improving current approaches for modeling RECO in deciduous forests by including the phenological cycle of the canopy.
Abstract: Croplands cover about 12% of the ice-free terrestrial land surface. Compared with natural ecosystems, croplands have distinct characteristics due to anthropogenic influences. Their global gross primary production (GPP) is not well constrained and estimates vary between 8.2 and 14.2 Pg C yr-1. We quantified global cropland GPP using a light use efficiency (LUE) model, employing satellite observations and survey data of crop types and distribution. A novel step in our analysis was to assign a maximum light use efficiency estimate (ϵ*GPP) to each of the 26 different crop types, instead of taking a uniform value as done in the past. These ϵ*GPP values were calculated based on flux tower CO2 exchange measurements and a literature survey of field studies, and ranged from 1.20 g CMJ-1 to 2.96 g CMJ-1. Global cropland GPP was estimated to be 11.05 Pg C yr-1 in the year 2000. Maize contributed most to this (1.55 Pg C yr-1), and the continent of Asia contributed most with 38.9% of global cropland GPP. In the continental United States, annual cropland GPP (1.28 Pg C yr-1) was close to values reported previously (1.24 Pg C yr-1) constrained by harvest records, but our estimates of ε*GPP values were much higher. Our results are sensitive to satellite information and survey data on crop type and extent, but provide a consistent and data-driven approach to generate a look-up table of ε*GPP for the 26 crop types for potential use in other vegetation models.
Abstract: A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The paper is addressed to scientists, policymakers, and funding agencies who need to have a global picture of the current state of the (diverse) carbon observations. We identify the current state of carbon observations, and the needs and notional requirements for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion of the ground-based observation networks required to reach the high spatial resolution for CO2 and CH4 fluxes, and for carbon stocks for addressing policy-relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over areas such as the southern oceans, tropical forests, and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote-sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO2 proxy measurements such as radiocarbon in CO2 and carbon-fuel combustion tracers. Additionally, a policy-relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. In addition, uncertainties for each observation data-stream should be assessed. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases interoperable, and on the calibration of each component of the system to agreed-upon international scales.
Abstract: The OLIVE (On Line Interactive Validation Exercise) platform is dedicated to the validation of global biophysical products such as LAI (Leaf Area Index) and FAPAR (Fraction of Absorbed Photosynthetically Active Radiation). It was developed under the framework of the CEOS (Committee on Earth Observation Satellites) Land Product Validation (LPV) sub-group. OLIVE has three main objectives: (i) to provide a consistent and centralized information on the definition of the biophysical variables, as well as a description of the main available products and their performances (ii) to provide transparency and traceability by an online validation procedure compliant with the CEOS LPV and QA4EO (Quality Assurance for Earth Observation) recommendations (iii) and finally, to provide a tool to benchmark new products, update product validation results and host new ground measurement sites for accuracy assessment. The functionalities and algorithms of OLIVE are described to provide full transparency of its procedures to the community. The validation process and typical results are illustrated for three FAPAR products: GEOV1 (VEGETATION sensor), MGVIo (MERIS sensor) and MODIS collection 5 FPAR. OLIVE is available on the European Space Agency CAL/VAL portal), including full documentation, validation exercise results, and product extracts.
Abstract: Although forest management is one of the instruments proposed to mitigate climate change, the relationship between forest management and canopy albedo has been ignored so far by climate models. Here we develop an approach that could be implemented in Earth system models. A stand-level forest gap model is combined with a canopy radiation transfer model and satellite-derived model parameters to quantify the effects of forest thinning on summertime canopy albedo. This approach reveals which parameter has the largest affect on summer canopy albedo: we examined the effects of three forest species (pine, beech, oak) and four thinning strategies with a constant forest floor albedo (light to intense thinning regimes) and five different solar zenith angles at five different sites (40° N 9° E-60° N 9° E). During stand establishment, summertime canopy albedo is driven by tree species. In the later stages of stand development, the effect of tree species on summertime canopy albedo decreases in favour of an increasing influence of forest thinning. These trends continue until the end of the rotation, where thinning explains up to 50% of the variance in near-infrared albedo and up to 70% of the variance in visible canopy albedo. The absolute summertime canopy albedo of all species ranges from 0.03 to 0.06 (visible) and 0.20 to 0.28 (near-infrared); thus the albedo needs to be parameterised at species level. In addition, Earth system models need to account for forest management in such a way that structural changes in the canopy are described by changes in leaf area index and crown volume (maximum change of 0.02 visible and 0.05 near-infrared albedo) and that the expression of albedo depends on the solar zenith angle (maximum change of 0.02 visible and 0.05 near-infrared albedo). Earth system models taking into account these parameters would not only be able to examine the spatial effects of forest management but also the total effects of forest management on climate.
Abstract: Climate change is expected to alter vegetation and carbon cycle processes, with implications for ecosystems. Notably, understanding the sensitivity of vegetation to the anomalies of precipitation and temperature over different land cover classes and the corresponding temporal response is essential for improved climate prediction. In this paper, we analyze vegetation response to hydroclimatic forcings using the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) derived from SeaWiFS (Sea-viewing Wide Field-of-view Sensor) (1998–2002) and (Medium Resolution Imaging Spectrometer) (2003–2011) satellite sensors at ∼1-km resolution. Based on land cover and pixel-wise analysis, we quantify the extent of the dependence of the FAPAR and, ultimately, the phenology on the anomalies of precipitation and temperature over Europe. Statistical tests are performed to establish where this correlation may be regarded as statistically significant. Furthermore, we assess a statistical link between the climate variables and a set of phenological metrics defined from FAPAR measurement. Variation in the phenological response to the unusual values of precipitation and temperature can be interpreted as the result of the balanced opposite effects of water and temperature on vegetation processes. Results suggest very different responses for different land cover classes and seasons. Correlation analysis also indicates that European phenology may be quite sensitive to perturbations in precipitation and temperature regimes, such as those induced by climate change.
Abstract: Dynamic global vegetation models (DGVMs) are an essential part of current state-of-the-art Earth system models. In recent years, the complexity of DGVMs has increased by incorporating new important processes like, e.g., nutrient cycling and land cover dynamics, while biogeophysical processes like surface radiation have not been developed much further. Canopy radiation models are however very important for the estimation of absorption and reflected fluxes and are essential for a proper estimation of surface carbon, energy and water fluxes. The present study provides an overview of current implementations of canopy radiation schemes in a couple of state-of-the-art DGVMs and assesses their accuracy in simulating canopy absorption and reflection for a variety of different surface conditions. Systematic deviations in surface albedo and fractions of absorbed photosynthetic active radiation (faPAR) are identified and potential impacts are assessed. The results show clear deviations for both, absorbed and reflected, surface solar radiation fluxes. FaPAR is typically underestimated, which results in an underestimation of gross primary productivity (GPP) for the investigated cases. The deviation can be as large as 25% in extreme cases. Deviations in surface albedo range between -0.15 ≤ Δα ≤ 0.36, with a slight positive bias on the order of Δα ≈ 0.04. Potential radiative forcing caused by albedo deviations is estimated at -1.25 ≤ RF ≤ -0.8 (W m-2), caused by neglect of the diurnal cycle of surface albedo. The present study is the first one that provides an assessment of canopy RT schemes in different currently used DGVMs together with an assessment of the potential impact of the identified deviations. The paper illustrates that there is a general need to improve the canopy radiation schemes in DGVMs and provides different perspectives for their improvement.
Abstract: Satellite remote sensing products of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are routinely used for diverse applications in Earth-System and land-surface modelling and monitoring. The availability of numerous products creates a need to understand the level of consistency between products, and reasons for inconsistencies. We evaluate the consistency of six FAPAR products (MODIS, MERIS, SeaWIFS, MODIS-TIP, SPOT-VEG, and AVHRR) across the Australian continent, using multi-year records. We find that seemingly large differences in FAPAR products over much of Australia can be explained by a simple offset present in certain products. Additional inconsistencies arise fromdifferent sensitivities in FAPAR to changes in vegetation cover. These inconsistencies can in turn be partially attributed to changes in biome type that are relevant to certain products and related model-specific assumptions. The satellite FAPAR products are compared to ~800 observation-based estimates of fractional vegetation cover at field sites across Australia. After accounting for offsets in FAPAR, relatively high agreement occurs at sites classified as grasslands, shrublands and managed land (agriculture). Significant disagreement occurs at sites correctly classified as forests. Consequently, some products show significant differences in FAPAR between regions of similar vegetation cover but different biome classification.We find that all products showamuch lower sensitivity to fractional vegetation cover (range in coefficient of linear regression: 0.28–0.61) than is predicted theoretically (0.96–1.18) using a canopy radiative transfer model. Reasons for this discrepancy are discussed.
Abstract: We present the concept of the Carbon Cycle Data Assimilation System and describe its evolution over the last two decades from an assimilation system around a simple diagnostic model of the terrestrial biosphere to a system for the calibration and initialization of the land component of a comprehensive earth system model. We critically review the capability of this modeling framework to integrate multiple data streams, to assess their mutual consistency and with the model, to reduce uncertainties in the simulation of the terrestrial carbon cycle, to provide, in a traceable manner, reanalysis products with documented uncertainty, and to assist the design of the observational network. We highlight some of the challenges we met and experience we gained, give recommendations for operating the system and suggest directions for future development.
Abstract: The radiation transfer model intercomparison (RAMI) activity aims at assessing the reliability of physics-based radiative transfer (RT) models under controlled experimental conditions. RAMI focuses on computer simulation models that mimic the interactions of radiation with plant canopies. These models are increasingly used in the development of satellite retrieval algorithms for terrestrial essential climate variables (ECVs). Rather than applying ad hoc performance metrics, RAMI-IV makes use of existing ISO standards to enhance the rigor of its protocols evaluating the quality of RT models. ISO-13528 was developed “to determine the performance of individual laboratories for specific tests or measurements.” More specifically, it aims to guarantee that measurement results fall within specified tolerance criteria from a known reference. Of particular interest to RAMI is that ISO-13528 provides guidelines for comparisons where the true value of the target quantity is unknown. In those cases, “truth” must be replaced by a reliable “conventional reference value” to enable absolute performance tests. This contribution will show, for the first time, how the ISO-13528 standard developed by the chemical and physical measurement communities can be applied to proficiency testing of computer simulation models. Step by step, the pre-screening of data, the identification of reference solutions, and the choice of proficiency statistics will be discussed and illustrated with simulation results from the RAMI-IV “abstract canopy” scenarios. Detailed performance statistics of the participating RT models will be provided and the role of the accuracy of the reference solutions as well as the choice of the tolerance criteria will be highlighted.
Abstract: This paper describes the combination of terrestrial vegetation observations from two sensors, providing a historical dataset used for an in-depth analysis of the corresponding spatio-temporal patterns. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is an important variable suitable for regional to large-scale monitoring of climate impacts on vegetation. In this work, we create an extensive dataset of FAPAR using a 10-day product at ∼1 km resolution from September, 1997, to April, 2012, combining information from two sensors: the NASA/Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the European Space Agency (ESA)/Medium Resolution Imaging Spectrometer Instrument (MERIS). The proposed methodology reduces the noise, fills the gaps and corrects for the spurious trends in the data, providing a time-consistent coverage of FAPAR. We develop a fast merging method and evaluate its performance over Europe and the Horn of Africa.
Abstract: A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The goal of this study is to identify the current state of carbon observations and needs for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion (by several orders of magnitude) of the ground-based observation networks required to reach the high spatial resolution for CO2 and CH4 fluxes, and for carbon stocks for addressing policy relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over remote areas such as the southern oceans, tropical forests and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in-situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO2 proxy measurements such as radiocarbon in CO2 and carbon-fuel combustion tracers. Additionally, a policy relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases inter-operable, and on the calibration of each component of the system to agreed-upon international scales.
Abstract: [1] Leaf area index (LAI) is a critical variable for land surface and climate modeling studies. Several global LAI products exist and it is important to know how these products perform and what their uncertainties are. Five major global LAI products: MODIS, GEOV1, GLASS, GLOBMAP, and JRC-TIP were compared between 2003 and 2010 at a 0.01° spatial resolution and with a monthly time step. The daily Land-SAF product was used as a regional reference in order to evaluate the performance of other global products in Africa. Cross-sensor LAI conversion equations were derived for different biome types. Product uncertainties were assessed by looking into the product quantitative quality indicators (QQIs) attached to MODIS, GEOV1, and JRC-TIP. MODIS, GEOV1, GLASS, and GLOBMAP are generally consistent and show strong linear relationships between the products (R2 > 0.74), with typical deviations of < 0.5 for non-forest and < 1.0 for forest biomes. JRC-TIP, the only effective LAI product, is about half the values of the other LAI products. The average uncertainties and relative uncertainties are in the order: MODIS (0.17, 11.5%) < GEOV1 (0.24, 26.6%) < Land-SAF (0.36, 37.8%) < JRC-TIP (0.43, 114.3%). The highest relative uncertainties usually appear in ecological transition zones. More than 75% of MODIS, GEOV1, JRC-TIP, and Land-SAF pixels are within the absolute uncertainty requirements (± 0.5) set by the Global Climate Observing System (GCOS), whereas more than 78.5% of MODIS and 45.0% of GEOV1 pixels are within the threshold for relative uncertainty (20%). This study reveals that the discrepancies are mainly due to differences between definitions, retrieval algorithms, and input data. Future product development and validation studies should focus on areas (e.g., sparsely vegetated and savanna areas) and periods (e.g., winter time) with higher uncertainties.
Abstract: Terrestrial productivity in semi-arid woodlands is strongly susceptible to changes in precipitation, and semi-arid woodlands constitute an important element of the global water and carbon cycles. Here, we use the Carbon Cycle Data Assimilation System (CCDAS) to investigate the key parameters controlling ecological and hydrological activities for a semi-arid savanna woodland site in Maun, Botswana. Twenty-four eco-hydrological process parameters of a terrestrial ecosystem model are optimized against two data streams separately and simultaneously: daily averaged latent heat flux (LHF) derived from eddy covariance measurements, and decadal fraction of absorbed photosynthetically active radiation (FAPAR) derived from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Assimilation of both data streams LHF and FAPAR for the years 2000 and 2001 leads to improved agreement between measured and simulated quantities not only for LHF and FAPAR, but also for photosynthetic CO2 uptake. The mean uncertainty reduction (relative to the prior) over all parameters is 14.9% for the simultaneous assimilation of LHF and FAPAR, 8.5% for assimilating LHF only, and 6.1% for assimilating FAPAR only. The set of parameters with the highest uncertainty reduction is similar between assimilating only FAPAR or only LHF. The highest uncertainty reduction for all three cases is found for a parameter quantifying maximum plant-available soil moisture. This indicates that not only LHF but also satellite-derived FAPAR data can be used to constrain and indirectly observe hydrological quantities.
Abstract: In this work we present a new land cover map of South America derived from the 300 m resolution MEdium Resolution Imaging Spectrometer (MERIS) sensor using data from 2009 and 2010. The results are compared to those from similar continental products from SPOT VGT (Global Land Cover 2000 - GLC2000) and from MODIS (MODerate Imaging Spectrometer). We use the new map to assess the major land cover changes that have occurred since the year 2000 using the GLC2000 map (Bartholomé and Belward 2005) as historical reference. An assessment of the product is undertaken using finer spatial resolution data from Landsat Thematic Mapper.
Abstract: Satellite-derived time series of the fraction of absorbed photosynthetically active radiation (fAPAR) are widely used to monitor vegetation dynamics and to detect vegetation anomalies. Several global data sets are available for this purpose. They are produced using different algorithms and/or satellite sensors. This paper compares and analyzes three multitemporal fAPAR data sets derived from SPOT-VEGETATION instrument by explicitly distinguishing between spatial and temporal agreement. The first two data sets are currently used by the Joint Research Centre—Monitoring Agricultural ResourceS Unit (JRC-MARS) for operational yield forecasting and food security assessments. The third time series (named GEOV1) is from a new processing algorithm developed within the European FP7 Geoland2 project. The comparative analysis was conducted for the years 2003 and 2004 over three $10^{circ} times 10^{circ}$ regions with different eco-climatic characteristics (Niger, Brazil, and France). Our study revealed that GEOV1 fAPAR estimates were systematically higher than those of JRC-MARS. The spatial analysis showed moderate to high agreement between data sets with specific seasonality in the three study regions. The temporal agreement showed spatial (and land cover-related) variability spanning from very low to almost perfect. Large differences were observed in regions and periods with large cloud occurrence where GEOV1 provides more reliable and smooth temporal profiles due to improved cloud screening and longer compositing periods. Other sources of disagreement between data sets were identified in differences in the fAPAR retrieval algorithm definitions.
Abstract: The terrestrial biosphere is currently a strong sink for anthropogenic CO2 emissions. Through the radiative properties of CO2, the strength of this sink has a direct influence on the radiative budget of the global climate system. The accurate assessment of this sink and its evolution under a changing climate is, hence, paramount for any efficient management strategies of the terrestrial carbon sink to avoid dangerous climate change. Unfortunately, simulations of carbon and water fluxes with terrestrial biosphere models exhibit large uncertainties. A considerable fraction of this uncertainty reflects uncertainty in the parameter values of the process formulations within the models. This paper describes the systematic calibration of the process parameters of a terrestrial biosphere model against two observational data streams: remotely sensed FAPAR (fraction of absorbed photosynthetically active radiation) provided by the MERIS (ESA’s Medium Resolution Imaging Spectrometer) sensor and in situ measurements of atmospheric CO2 provided by the GLOBALVIEW flask sampling network.We use the Carbon Cycle Data Assimilation System (CCDAS) to systematically calibrate some 70 parameters of the terrestrial BETHY (Biosphere Energy Transfer Hydrology) model. The simultaneous assimilation of all observations provides parameter estimates and uncertainty ranges that are consistent with the observational information. In a subsequent step these parameter uncertainties are propagated through the model to uncertainty ranges for predicted carbon fluxes. We demonstrate the consistent assimilation at global scale, where the global MERIS FAPAR product and atmospheric CO2 are used simultaneously. The assimilation improves the match to independent observations.We quantify how MERIS data improve the accuracy of the current and future (net and gross) carbon flux estimates (within and beyond the assimilation period). We further demonstrate the use of an interactive mission benefit analysis tool built around CCDAS to support the design of future space missions.We find that, for long-term averages, the benefit of FAPAR data is most pronounced for hydrological quantities, and moderate for quantities related to carbon fluxes from ecosystems. The benefit for hydrological quantities is highest for semi-arid tropical or sub-tropical regions. Length of mission or sensor resolution is of minor importance.
Abstract: Terrestrial productivity in semi-arid woodlands is strongly susceptible to changes in precipitation, and semi-arid woodlands constitute an important element of the global water and carbon cycles. Here, we use the Carbon Cycle Data Assimilation System (CCDAS) to investigate the mechanisms controlling ecological and hydrogical activities for a semi-arid savanna woodland site in Maun, Botswana. Twenty-four eco-hydrological process parameters of a terrestrial ecosystem model are optimized against two data streams either separately or simultaneously: daily averaged latent heat flux (LHF) derived from eddy covariance measurement, and decadal fraction of absorbed photosynthetically active radiation (FAPAR) derived from Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Assimilation of both LHF and FAPAR for the years 2000 and 2001 leads to improved agreement between measured and simulated quantities not only for LHF and FAPAR, but also for photosynthetic CO2 uptake. The closest agreement is found for each observed data stream when only the same data stream is assimilated. The mean uncertainty reduction (relative to the prior) over all parameters is 16.1% for the simultaneous assimilation of LHF and FAPAR, 9.2% for assimilating LHF only, and 7.8% for assimilating FAPAR only. Furthermore, the set of parameters with the highest uncertainty reduction is similar between assimilating only FAPAR or only LHF. The highest uncertainty reduction is found for a parameter describing maximum plant-available soil moisture for all three cases. This indicates that not only LHF but also satellite-derived FAPAR data can be used to constrain and indirectly observe hydrological quantities.
Abstract: Current methods for estimating vegetation parameters are generally sub-optimal in the way they exploit information and do not generally consider uncertainties. We look forward to a future where operational data assimilation schemes improve estimates by tracking land surface processes and exploiting multiple types of observations. Data assimilation schemes seek to combine observations and models in a statistically optimal way taking into account uncertainty in both, but have not yet been much exploited in this area. The EO-LDAS scheme and prototype, developed under ESA funding, is designed to exploit the anticipated wealth of data that will be available under GMES missions, such as the Sentinel family of satellites, to provide improved mapping of land surface biophysical parameters. This paper describes the EO-LDAS implementation, and explores some of its core functionality. EO-LDAS is a weak constraint variational data assimilation system. The prototype provides a mechanism for constraint based on a prior estimate of the state vector, a linear dynamic model, and Earth Observation data (top-of-canopy reflectance here). The observation operator is a non-linear optical radiative transfer model for a vegetation canopy with a soil lower boundary, operating over the range 400 to 2500 nm. Adjoint codes for all model and operator components are provided in the prototype by automatic differentiation of the computer codes. In this paper, EO-LDAS is applied to the problem of daily estimation of six of the parameters controlling the radiative transfer operator over the course of a year (>2000 state vector elements). Zero and first order process model constraints are implemented and explored as the dynamic model. The assimilation estimates all state vector elements simultaneously. This is performed in the context of a typical Sentinel-2 MSI operating scenario, using synthetic MSI observations simulated with the observation operator, with uncertainties typical of those achieved by optical sensors supposed for the data. The experiments consider a baseline state vector estimation case where dynamic constraints are applied, and assess the impact of dynamic constraints on the a posteriori uncertainties. The results demonstrate that reductions in uncertainty by a factor of up to two might be obtained by applying the sorts of dynamic constraints used here. The hyperparameter (dynamic model uncertainty) required to control the assimilation are estimated by a cross-validation exercise. The result of the assimilation is seen to be robust to missing observations with quite large data gaps.
Abstract: The terrestrial biosphere is currently a strong sink for anthropogenic CO2 emissions. Through the radiative properties of CO2 the strength of this sink has a direct influence on the radiative budget of the global climate system. The accurate assessment of this sink and its evolution under a changing climate is, hence, paramount for any efficient management strategies of the terrestrial carbon sink to avoid dangerous climate change. Unfortunately, simulations of carbon and water fluxes with terrestrial biosphere models exhibit large uncertainties. A considerable fraction of this uncertainty is reflecting uncertainty in the parameter values of the process formulations within the models. This paper describes the systematic calibration of the process parameters of a terrestrial biosphere model against two observational data streams: remotely sensed FAPAR provided by the MERIS sensor and in situ measurements of atmospheric CO2 provided by the GLOBALVIEW flask sampling network. We use the Carbon Cycle Data Assimilation System (CCDAS) to systematically calibrate some 70 parameters of the terrestrial biosphere model BETHY. The simultaneous assimilation of all observations provides parameter estimates and uncertainty ranges that are consistent with the observational information. In a subsequent step these parameter uncertainties are propagated through the model to uncertainty ranges for predicted carbon fluxes. We demonstrate the consistent assimilation for two different set-ups: first at site-scale, where MERIS FAPAR observations at a range of sites are used as simultaneous constraints, and second at global scale, where the global MERIS FAPAR product and atmospheric CO2 are used simultaneously. On both scales the assimilation improves the match to independent observations. We quantify how MERIS data improve the accuracy of the current and future (net and gross) carbon flux estimates (within and beyond the assimilation period). We further demonstrate the use of an interactive mission benefit analysis tool built around CCDAS to support the design of future space missions. We find that, for long-term averages, the benefit of FAPAR data is most pronounced for hydrological quantities, and moderate for quantities related to carbon fluxes from ecosystems. The benefit for hydrological quantities is highest for semi-arid tropical or sub-tropical regions. Length of mission or sensor resolution is of minor importance.
Abstract: The Joint Research Centre Two-stream Inversion Package (JRC-TIP) makes use of white sky albedo products— derived fromMODIS andMISR observations in the visible and near-infrared domain—to deliver consistent sets of information about the terrestrial environments that gave rise to these data. The baseline version of the JRC-TIP operates at a spatial resolution of 0.01° and yields estimates of the Probability Distribution Functions (PDFs) of the effective canopy Leaf Area Index (LAI), the canopy background albedo, the vegetation scattering properties, as well as, the absorbed, reflected and transmitted fluxes of the vegetation canopy. In this contribution the evaluation efforts of the JRC-TIP products are extended to the deciduous forest site of Hainich (Germany) wheremultiannual datasets of in-situ estimates of canopy transmission—derived from LAI-2000 observations—are available. As a Fluxnet site,Hainich offers access to camera acquisitions fromfixed locations in and above the canopy that are being used in phenological studies. These images qualitatively confirm the seasonal patterns of the effective LAI, canopy transmission and canopy absorption products (in the visible range of the solar spectrum) derived with the JRC-TIP. Making use of the LAI-2000 observations it is found that 3/4 of the JRC-TIP products lie within a ±0.15 interval around the in-situ estimates of canopy transmission and absorption. The largest discrepancies occur at the end of the senescence phasewhen the scattering properties of the vegetation (evidenced by the pictures) and the images qualitatively confirm the seasonal patterns of the effective LAI, canopy transmission and canopy absorption products (in the visible range of the solar spectrum) derivedwith the JRC-TIP.Making use of the LAI-2000 observations it is found that 3/4 of the JRC-TIP products liewithin a±0.15 interval around the in-situ estimates of canopy transmission and absorption. The largest discrepancies occur at the end of the senescence phase when the scattering properties of the vegetation (evidenced by the pictures) and the effective LAI (also derived from LAI-2000 measurements) are experiencing large simultaneous changes. It was also found that the seasonal pattern of vegetation scattering properties derived fromMISR observations in the near-infrared varies together with the Excess Green index computed from the various channels of the camera data acquired at the top of the canopy.
Abstract: We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5° × 0.5° spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 ± 7 J × 1018 yr−1), H (164 ± 15 J × 1018 yr−1), and GPP (119 ± 6 Pg C yr−1) were similar to independent estimates. Our global TER estimate (96 ± 6 Pg C yr−1) was likely underestimated by 5–10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.
Abstract: This contribution illustrates results from a large scale application of the Joint Research Centre Two-stream Inversion Package (JRC-TIP), using MODIS broadband visible and near-infrared white sky surface albedos as inputs. The discussion focuses on products (based on the mean and one-sigma values of the Probability Distribution Functions (PDFs)) obtained during the summer and winter. This paper discusses the retrieved model parameters including the effective Leaf Area Index (LAI), the background brightness and the scattering efficiency of the vegetation elements. The similarity between the derived LAI seasonal maps and earlier distributions of this variable comforts us in the quality of the albedo products as well as in the ability of the JRC TIP to interpret the latter meaningfully. The opportunity to generate global maps of new products, such as the background albedo, underscores the advantages of using state of the art algorithmic approaches capable of fully exploiting accurate satellite remote sensing datasets. The detailed analyses of the retrieval uncertainties highlight the central role and contribution of the LAI, the main process parameter to interpret radiation transfer observations over vegetated surfaces. The estimation of the radiation fluxes that are absorbed, transmitted and scattered by the vegetation layer and its background is achieved on the basis of the retrieved PDFs of the model parameters. Results from this latter step are discussed in the companion paper.
Abstract: The two-stream model parameters and associated uncertainties retrieved by inversion against MODIS broadband visible and near-infrared white sky surface albedos were discussed in a companion paper. The present paper concentrates on the partitioning of the solar radiation fluxes delivered by the Joint Research Centre Two-stream Inversion Package (JRC-TIP). The estimation of the various flux fractions related to the vegetation and the background layers separately capitalizes on the probability density functions of the model parameters discussed in the companion paper. The propagation of uncertainties from the observations to the model parameters is achieved via the Hessian of the cost function and yields a covariance matrix of posterior parameter uncertainties. This matrix is propagated to the radiation fluxes via the model`s Jacobian matrix of first derivatives. Results exhibit a rather good spatiotemporal consistency given that the prior values on the model parameters are not specified as a function of land cover type and/or vegetation phenological states. A specific investigation based on a scenario imposing stringent conditions of leaf absorbing and scattering properties highlights the impact of such constraints that are, as a matter of fact, currently adopted in vegetation index approaches. Special attention is also given to snow-covered and snow-contaminated areas since these regions encompass significant reflectance changes that strongly affect land surface processes. A definite asset of the JRC-TIP lies in its capability to control and ultimately relax a number of assumptions that are often implicit in traditional approaches. These features greatly help us understand the discrepancies between the different data sets of land surface properties and fluxes that are currently available. Through a series of selected examples, the inverse procedure implemented in the JRC-TIP is shown to be robust, reliable, and compliant with large-scale processing requirements. Furthermore, this package ensures the physical consistency between the set of observations, the two-stream model parameters, and radiation fluxes. It also documents the retrieval of associated uncertainties.
Abstract: Geophysical time series often feature missing data or data acquired at irregular times. Procedures are needed to either resample these series at systematic time intervals or to generate reasonable estimates at specified times in order to meet specific user requirements or to facilitate subsequent analyses. Interpolation methods have long been used to address this problem, taking into account the fact that available measurements also include errors of measurement or uncertainties. This paper inspects some of the currently used approaches to fill gaps and smooth time series (smoothing splines, Singular Spectrum Analysis and Lomb-Scargle) by comparing their performance in either reconstructing the original record or in minimizing the Mean Absolute Error (MAE) between the underlying model and the available data, using both artificially-generated series or well-known publicly available records. Some methods make no assumption on the type of variability in the data while others hypothesize the presence of at least some dominant frequencies. It will be seen that each method exhibits advantages and drawbacks, and that the choice of an approach largely depends on the properties of the underlying time series and the objective of the research.
Abstract: Drought as an intermittent disturbance of the water cycle interacts with the carbon cycle differently than the ‘gradual’ climate change. During drought plants respond physiologically and structurally to prevent excessive water loss according to species-specific water use strategies. This has consequences for carbon uptake by photosynthesis and release by total ecosystem respiration. After a drought the disturbances in the reservoirs of moisture, organic matter and nutrients in the soil and carbohydrates in plants lead to longer-term effects in plant carbon cycling, and potentially mortality. Direct and carry-over effects, mortality and consequently species competition in response to drought are strongly related to the survival strategies of species. Here we review the state of the art of the understanding of the relation between soil moisture drought and the interactions with the carbon cycle of the terrestrial ecosystems. We argue that plant strategies must be given an adequate role in global vegetation models if the effects of drought on the carbon cycle are to be described in a way that justifies the interacting processes.
Abstract: The Moderate Resolution Imaging Spectroradiometer (MODIS) white-sky surface albedos are compared with similar products generated on the basis of the Multiangle Imaging SpectroRadiometer (MISR) surface bidirectional reflectance factor (BRF) model parameters available for the year 2005. The analysis is achieved using global-scale statistics to characterize the broad patterns of these two independent albedo datasets. The results obtained in M. Taberner et al. have shown that robust statistics can be established and that both datasets are highly correlated. As a result, the slight but consistent biases and trends identified in this paper, derived from statistics obtained on a global basis, should be considered sufficiently reliable to merit further investigation. The present paper reports on the zonal- and seasonal-mean differences retrieved from the analysis of the MODIS and MISR surface albedo broadband products. The MISR − MODIS differences exhibit a systematic positive bias or offset in the range of 0.01–0.03 depending on the spectral domain of interest. Results obtained in the visible domain exhibit a well-marked and very consistent meridional trend featuring a “smile effect” such that the MISR − MODIS differences reach maxima at the highest latitudes in both hemispheres. The analysis of seasonal variations observed in MISR and MODIS albedo products reveals that, in the visible domain, the MODIS albedos generate weaker seasonal changes than MISR and that the differences increase poleward from the equatorial regions. A detailed investigation of MODIS and MISR aerosol optical depth retrievals suggests that this large-scale meridional trend is probably not caused by differences in the aerosol load estimated by each instrument. The scale and regularity of the meridional trend suggests that this may be due to the particular sampling regime of each instrument in the viewing azimuthal planes and/or approximations in the atmospheric correction processes. If this is the case, then either MODIS is underestimating, or MISR overestimating, the surface anisotropy or both.
Abstract: The objective of this paper is to present a series of improvements on the Joint Research Centre Two-stream Inversion Package (JRC-TIP) that enhance its effectiveness to generate reliable surface products and associated uncertainties from surface albedo values. Lookup tables (LUTs) are built in the observation space from the JRC-TIP and are used to store solutions obtained from off-line dedicated procedures on selected sets of prior conditions. This new approach drastically limits the occurrence of questionable solutions, revealed by outliers in the retrievals, often associated with local instead of global minima and ensures that the retrieved values are insensitive to small variations in the input albedo values. This TIP table-based approach also reduces considerably the computing time requirement, which is a definite asset in the systematic application of the TIP against large data sets of surface albedo products.
Abstract: Global products of the fraction of absorbed photosynthetically active radiation (FAPAR) are operationally available from a variety of space agencies. A proper validation of these products is essential and hinges on the acquisition of accurate ground-based FAPAR estimates of the vegetation contained within the field of view of the space sensor at the time of satellite overpass. Often remotely sensed FAPAR products are defined with respect to theoretical rather than ambient illumination conditions which complicates in situ validation efforts. Similarly, the spatial complexity and substantial heights of certain plant environments may prevent the reliable sampling of certain radiation fluxes. As a consequence, many field campaigns are carried out on agricultural crops or within young tree plantations where canopy height is not an issue. This contribution compares different approaches for estimating instantaneous FAPAR in tall, open-canopy forest stands under a variety of architectural, spectral and illumination related conditions. The bias associated with these estimations is separated into a sampling error and a transfer bias. The former relates to the impact of both the number and location of the measurements whereas the latter addresses the quality of the theory that relates these measurements to the actual canopy FAPAR. Among the various methods tested it was the 2-flux FAPAR estimator (1 − T) that performs best in open forest canopies under typical summer conditions. The quality of the 1 − T canopy FAPAR estimator changes, however, with illumination conditions, foliage colour and especially with the background brightness. Similarly, the smaller the size of the area for which the FAPAR is to be estimated the larger the variability of the bias is going to be (and this irrespective of the choice of in situ estimation techniques). Evidence is provided that working under overcast sky conditions will reduce the sampling error but may well increase the transfer bias when compared to clear sky conditions. A parametric relationship is developed that allows to predict the instantaneous canopy FAPAR for arbitrary diffuse-to-total-incident-radiation ratios (at any given solar zenith angle). This approach has a similar transfer bias as the 1 − T method when the forest floor is dark but dramatically outperforms the 2-flux approach under snowy background conditions (RMSE = 0.9934 versus 0.5801, respectively). The number of samples acquired was found to be crucial in reducing the variability of the bias of a given FAPAR estimator. Both random and grid-based sampling schemes result in similar FAPAR biases but do not lend themselves easily to the acquisition of hundreds of data points needed for reliable estimations under direct-only illumination conditions. Transect sampling—which is shown to deliver best results if carried out at ninety degrees to the solar azimuth angle—appears ideally suited to acquire the necessary numbers of samples enabling the generation of accurate quasi-instantaneous FAPAR estimates in open-canopy forests.
Abstract: Photosynthesis by terrestrial plants is the main driver of the global carbon cycle, and the presence of actively photosynthesizing vegetation can now be observed from space. However, challenges remain when translating remotely sensed data into carbon fluxes. One reason is that the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), which documents the presence of photosynthetically active vegetation, relates more directly to leaf development and leaf phenology than to photosynthetic rates. Here, we present a new approach for linking FAPAR and vegetation-to-atmosphere carbon fluxes through variational data assimilation. The scheme extends the Carbon Cycle Data Assimilation System (CCDAS) by a newly developed, globally applicable and generic leaf phenology model, which includes both temperature and water-driven leaf development. CCDAS is run for seven sites, six of them included in the FLUXNET network. Optimization is carried out simultaneously for all sites against 20 months of daily FAPAR from the Medium Resolution Imaging Spectrometer on board the European Space Agency`s ENVISAT platform. Fourteen parameters related to phenology and 24 related to photosynthesis are optimized simultaneously, and their posterior uncertainties are computed. We find that with one parameter set for all sites, the model is able to reproduce the observed FAPAR spanning boreal, temperate, humid-tropical, and semiarid climates. Assimilation of FAPAR has led to reduced uncertainty (by >10%) of 10 of the 38 parameters, including one parameter related to photosynthesis, and a moderate reduction in net primary productivity uncertainty. The approach can easily be extended to regional or global studies and to the assimilation of further remotely sensed data.
Abstract: More than half of the solar energy absorbed by land surfaces is currently used to evaporate water. Climate change is expected to intensify the hydrological cycle and to alter evapotranspiration, with implications for ecosystem services and feedback to regional and global climate. Evapotranspiration changes may already be under way, but direct observational constraints are lacking at the global scale. Until such evidence is available, changes in the water cycle on land—a key diagnostic criterion of the effects of climate change and variability—remain uncertain. Here we provide a data-driven estimate of global land evapotranspiration from 1982 to 2008, compiled using a global monitoring network, meteorological and remote-sensing observations, and a machine-learning algorithm. In addition, we have assessed evapotranspiration variations over the same time period using an ensemble of process-based land-surface models. Our results suggest that global annual evapotranspiration increased on average by 7.1 ± 1.0 millimetres per year per decade from 1982 to 1997. After that, coincident with the last major El Niño event in 1998, the global evapotranspiration increase seems to have ceased until 2008. This change was driven primarily by moisture limitation in the Southern Hemisphere, particularly Africa and Australia. In these regions, microwave satellite observations indicate that soil moisture decreased from 1998 to 2008. Hence, increasing soil-moisture limitations on evapotranspiration largely explain the recent decline of the global land-evapotranspiration trend. Whether the changing behaviour of evapotranspiration is representative of natural climate variability or reflects a more permanent reorganization of the land water cycle is a key question for earth system science.
Abstract: Information retrieval from spatiotemporal data cubes is key to earth system sciences. Respective analyses need to consider two fundamental issues: First, natural phenomena fluctuate on different time scales. Second, these characteristic temporal patterns induce multiple geographical gradients. Here we propose an integrated approach of subsignal extraction and dimensionality reduction to extract geographical gradients on multiple time scales. The approach is exemplified using global remote sensing estimates of photosynthetic activity. A wide range of partly well interpretable gradients is retrieved. For instance, well known climate-induced anomalies in FAPAR over Africa and South America during the last severe ENSO event are identified. Also, the precise geographical patterns of the annual–seasonal cycle and its phasing are isolated. Other features lead to new questions on the underlying environmental dynamics. Our method can provide benchmarks for comparisons of data cubes, model runs, and thus be used as a basis for sophisticated model performance evaluations.
Abstract: We use eddy covariance measurements of net ecosystem productivity (NEP) from 21 FLUXNET sites (153 site-years of data) to investigate relationships between phenology and productivity (in terms of both NEP and gross ecosystem photosynthesis, GEP) in temperate and boreal forests. Results are used to evaluate the plausibility of four different conceptual models. Phenological indicators were derived from the eddy covariance time series, and from remote sensing and models. We examine spatial patterns (across sites) and temporal patterns (across years); an important conclusion is that it is likely that neither of these accurately represents how productivity will respond to future phenological shifts resulting from ongoing climate change. In spring and autumn, increased GEP resulting from an ‘extra’ day tends to be offset by concurrent, but smaller, increases in ecosystem respiration, and thus the effect on NEP is still positive. Spring productivity anomalies appear to have carry-over effects that translate to productivity anomalies in the following autumn, but it is not clear that these result directly from phenological anomalies. Finally, the productivity of evergreen needleleaf forests is less sensitive to phenology than is productivity of deciduous broadleaf forests. This has implications for how climate change may drive shifts in competition within mixed-species stands.
Abstract: Earth Observation from space offers the opportunity to produce time-series of geophysical products that can be used to assess the state and changes of land surfaces. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is used to monitor the state and evolution of terrestrial vegetation, and also constitutes a state variable in advanced Earth system models that contain a detailed enough description of the terrestrial biosphere. This present study reports a 12-year (1998–2009) time series of FAPAR derived from the combination of two satellite-based sensors. We find that FAPAR exhibits large-scale inter-annual variations and multi-year trends. The fraction of land grid cells showing positive anomalies, as computed by the deviation from the 12-year climatology, shows a rapid decrease in the early part of the analysis period (until 2004). Large negative anomalies can be associated with previously reported large-scale climate events, such as global land drying associated with El Niño Southern Oscillation 2000–2003, or the European drought of 2003 or recent Australian droughts The present analysis demonstrates that FAPAR is an important global variable suitable for large-scale monitoring of climate impacts on the terrestrial biosphere.
Abstract: Earth observation systems provide high-quality tools to review the state of terrestrial surfaces at the global scale over long periods. In the special context of monitoring land degradation and desertification, long time series of remote sensing products are needed to evaluate the changes in terrestrial surfaces. As an example, plant photosynthesis in terrestrial environments can be documented from spectral measurements made in space. Advances in the understanding of radiation transfer, and the availability of high performance instruments, have led to the development of a new generation of geophysical products providing reliable, accurate information on the state and evolution of terrestrial environments. Specifically, a series of optimized algorithms has been developed and used to estimate the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) for a suite of recent instruments. This paper summarizes the methodology and performance of these FAPAR algorithms and presents various examples of applications showing an analysis of seasonal cycles and maps of vegetation activity anomalies in Europe and the Sahel.
Abstract: Long-term precipitation chemistry have been recorded in the rural area of Banizoumbou (Niger), representative of a semi-arid savanna ecosystem. A total of 305 rainfall samples (~90% of the total annual rainfall) were collected from June 1994 to September 2005. From ionic chromatography, pH major inorganic and organic ions were detected. Rainwater chemistry is controlled by soil/dust emissions associated with terrigeneous elements represented by SO, Ca, Carbonates, K and Mg. It is found that calcium and carbonates represent ~40% of the total ionic charge. The second highest contribution is nitrogenous, with annual Volume Weighed Mean (VWM) for NO and NH concentrations of 11.6 and 18.1 μeq.l, respectively. This is the signature of ammonia sources from animals and NO emissions from savannas soil-particles rain-induced. The mean annual NH and NO air concentration are of 6 ppbv and 2.6 ppbv, respectively. The annual VWM precipitation concentration of sodium and chloride are both of 8.7 μeq.l−1 which reflects the marine signature of monsoonal and humid air masses. The median pH value is of 6.05. Acidity is neutralized by mineral dust, mainly carbonates, and/or dissolved gases such NH. High level of organic acidity with 8μeq.l and 5.2 μeq.l of formate and acetate were also found. The analysis of monthly Black Carbon emissions and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) values show that both biogenic emission from vegetation and biomass burning could explain the rainfall organic acidity content. The interannual variability of the VWM concentrations around the mean (1994–2005) is between ±5% and ±30% and mainly due to variations of sources strength and rainfall spatio-temporal distribution. From 1994 to 2005, the total mean wet deposition flux in the Sahelian region is of 60.1 mmol.m.yr ±25%. Finally, Banizoumbou measurements are compared to other long-term measurements of precipitation chemistry in the wet savanna of Lamto (Côte d`Ivoire) and in the forested zone of Zoétélé (Cameroon). The total chemical loading presents a maximum in the dry savanna and a minimum in the forest (from 143.7, 100.2 to 86.6 μeq.l), associated with the gradient of terrigeneous sources. The wet deposition fluxes present an opposite trend, with 60.0 mmol.m.yr in Banizoumbou, 108.6 mmol.m.yr in Lamto and 162.9 mmol.m.yr in Zoétélé, controlled by rainfall gradient along the ecosystems transect.
Abstract: We are comparing spatially explicit process-model based estimates of the terrestrial carbon balance and its components over Africa and confront them with remote sensing based proxies of vegetation productivity and atmospheric inversions of land-atmosphere net carbon exchange. Particular emphasis is on characterizing the patterns of interannual variability of carbon fluxes and analyzing the factors and processes responsible for it. For this purpose simulations with the terrestrial biosphere models ORCHIDEE, LPJ-DGVM, LPJ-Guess and JULES have been performed using a standardized modeling protocol and a uniform set of corrected climate forcing data. While the models differ concerning the absolute magnitude of carbon fluxes, we find several robust patterns of interannual variability among the models. Models exhibit largest interannual variability in southern and eastern Africa, regions which are primarily covered by herbaceous vegetation. Interannual variability of the net carbon balance appears to be more strongly influenced by gross primary production than by ecosystem respiration. A principal component analysis indicates that moisture is the main driving factor of interannual gross primary production variability for those regions. On the contrary in a large part of the inner tropics radiation appears to be limiting in two models. These patterns are partly corroborated by remotely sensed vegetation properties from the SeaWiFS satellite sensor. Inverse atmospheric modeling estimates of surface carbon fluxes are less conclusive at this point, implying the need for a denser network of observation stations over Africa.
Abstract: The main goal of this study is to help bridge the gap between available remote sensing products and large-scale global climate models. We present results from the application of an inversion method conducted using both MODerate resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging SpectroRadiometer (MISR) derived broadband visible and near-infrared surface albedo products. This contribution is an extension of earlier efforts to optimally retrieve land surface fluxes and associated two-stream model parameters (Pinty et al., 2007). It addresses complex geophysical scenarios involving snow occurrence in mid and high-latitude evergreen and deciduous forest canopy systems. The detection of snow during the winter and spring seasons is based on the MODIS snow product. This information is used by our package to adapt the prior values, specifically the maximum likelihood and width of the 2-D probability density functions (PDF) characterizing the background conditions of the forest floor. Our results (delivered as a Gaussian approximation of the PDFs of the retrieved model parameter values and radiant fluxes) illustrate the capability of the inversion package to retrieve meaningful land vegetation fluxes and associated model parameters during the year, despite the rather high temporal variability in the input products, in large part due to the occurrence of snow events. As a matter of fact, most of this temporal variability, as well as the small differences between the MODIS and MISR broadband albedos, appear to be largely captured by the albedo of the forest canopy background.
Abstract: This paper examines the rationale for and implications of using a near-infrared band to estimate the absorption of visible light by vegetation canopies. The benefits of using near-infrared observations have already been documented extensively in the literature, notably in the context of applications based on vegetation indices. These include, for instance, a degree of normalization with respect to undesirable perturbing factors. Our intent here is twofold: provide the theoretical basis to justify using measurements outside the main absorption band of vegetation for the purpose of retrieving canopy properties, and uncover the implications of doing so. On the basis of simple radiation transfer considerations, we conclude that near-infrared observations are critical to ensure the accurate retrieval of absorption estimates in the visible domain, and that observations within the absorption band help control the perturbing effect of the soil background. The analytical approach implemented here is conceptually similar to a scale analysis which permits us assessing the most significant contributions to the absorption and scattering processes in the vast majority of geophysical situations. Our final conclusions derived from a series of intermediate steps that need to be performed first. To this end, we illustrate in Section 2 the fact that a suitably-defined one-dimensional radiation transfer model can always be setup to represent accurately the reflected, transmitted and absorbed fraction of vertical fluxes in any vegetation volume at medium spatial resolutions (100 m or lower), and this irrespective of the local variability exhibited by the canopy attributes. This finding is exploited throughout the paper to show that 1) measurements performed in the near-infrared band are needed to ensure a large dynamic range in albedo for dense canopy conditions, by contrast to the visible domain, 2) measurements in the visible domain are effective to remove the contribution due to the background below vegetation for low to intermediate LAI conditions. This is made possible thanks to the soil line concept and the spectral invariance of the interception process, and 3) the estimation of visible light absorption in a canopy on the basis of combinations of spectral bands (as implemented in traditional vegetation indices) hinges on spectral correlations between variables, most notably those controlling the absorbing and scattering properties of the soil and leaves. A series of implications and consequences is drawn from our analysis and, in particular, the suggestion to adopt modern interpretation techniques, superseding the commonly used vegetation index approaches. These advances allow us to improve on current approaches, in particular by lifting some of the hypotheses associated with approaches based on combinations of spectral bands.
Abstract: We are comparing spatially explicit process-model based estimates of the terrestrial carbon balance and its components over Africa and confront them with remote sensing based proxies of vegetation productivity and atmospheric inversions of land-atmosphere net carbon exchange. Particular emphasis is on characterizing the patterns of interannual variability of carbon fluxes and analyzing the factors and processes responsible for it. For this purpose simulations with the terrestrial biosphere models ORCHIDEE, LPJ-DGVM, LPJ-Guess and JULES have been performed using a standardized modeling protocol and a uniform set of corrected climate forcing data. While the models differ concerning the absolute magnitude of carbon fluxes, we find several robust patterns of interannual variability among the models. Models exhibit largest interannual variability in southern and eastern Africa, regions which are primarily covered by herbaceous vegetation. Interannual variability of the net carbon balance appears to be more strongly influenced by gross primary production than by ecosystem respiration. A principal component analysis indicates that moisture is the main driving factor of interannual gross primary production variability for those regions. On the contrary in a large part of the inner tropics radiation appears to be limiting in two models. These patterns are corroborated by remotely sensed vegetation properties from the SeaWiFS satellite sensor. Inverse atmospheric modeling estimates of surface carbon fluxes are less conclusive at this point, implying the need for a denser network of observation stations over Africa.
Abstract: We present an approach to estimate gross primary production (GPP) using a remotely sensed biophysical vegetation product (fraction of absorbed photosynthetically active radiation, FAPAR) from the European Commission Joint Research Centre (JRC) in conjunction with GPP estimates from eddy covariance measurement towers in Europe. By analysing the relationship between the cumulative growing season FAPAR and annual GPP by vegetation type, we find that the former can be used to accurately predict the latter. The root mean square error of prediction is of the order of 250 gC m yr. The cumulative growing season FAPAR integrates over a number of effects relevant for GPP such as the length of the growing season, the vegetation`s response to environmental conditions and the amount of light harvested that is available for photosynthesis. We corroborate the proposed GPP estimate (noted FAPAR-based productivity assessment+land cover, FPA+LC) on the continental scale with results from the MOD17+radiation-use efficiency model, an artificial neural network up-scaling approach (ANN) and the Lund–Potsdam–Jena managed Land biosphere model (LPJmL). The closest agreement of the mean spatial GPP pattern among the four models is between FPA+LC and ANN ( = 0.74). At least some of the discrepancy between FPA-LC and the other models result from biases of meteorological forcing fields for MOD17+, ANN and LPJmL. Our analysis further implies that meteorological information is to a large degree redundant for GPP estimation when using the JRC-FAPAR. A major advantage of the FPA+LC approach presented in this paper lies in its simplicity and that it requires no additional meteorological input driver data that commonly introduce substantial uncertainty. We find that results from different data-oriented models may be robust enough to evaluate process-oriented models regarding the mean spatial pattern of GPP, while there is too little consensus among the diagnostic models for such purpose regarding inter-annual variability.
Abstract: In the tropics and subtropics, most fires are set by humans for a wide range of purposes. The total amount of burned area and fire emissions reflects a complex interaction between climate, human activities, and ecosystem processes. Here we used satellite-derived data sets of active fire detections, burned area, precipitation, and the fraction of absorbed photosynthetically active radiation (fAPAR) during 1998–2006 to investigate this interaction. The total number of active fire detections and burned area was highest in areas that had intermediate levels of both net primary production (NPP; 500–1000 g C m year) and precipitation (1000–2000 mm year), with limits imposed by the length of the fire season in wetter ecosystems and by fuel availability in drier ecosystems. For wet tropical forest ecosystems we developed a metric called the fire-driven deforestation potential (FDP) that integrated information about the length and intensity of the dry season. FDP partly explained the spatial and interannual pattern of fire-driven deforestation across tropical forest regions. This climate-fire link in combination with higher precipitation rates in the interior of the Amazon suggests that a negative feedback on fire-driven deforestation may exist as the deforestation front moves inward. In Africa, compared to the Amazon, a smaller fraction of the tropical forest area had FDP values sufficiently low to prevent fire use. Tropical forests in mainland Asia were highly vulnerable to fire, whereas forest areas in equatorial Asia had, on average, the lowest FDP values. FDP and active fire detections substantially increased in forests of equatorial Asia, however, during El Niño periods. In contrast to these wet ecosystems we found a positive relationship between precipitation, fAPAR, NPP, and active fire detections in arid ecosystems. This relationship was strongest in northern Australia and arid regions in Africa. Highest levels of fire activity were observed in savanna ecosystems that were limited neither by fuel nor by the length of the fire season. However, relations between annual precipitation or drought extent and active fire detections were often poor here, hinting at the important role of other factors, including land managers, in controlling spatial and temporal variability of fire.
Abstract: Approaches combining satellite-based remote sensing data with ecosystem modelling offer potential for the accurate assessment of changes in forest carbon balances, for example, in support of emission credits under the Kyoto Protocol. We investigate the feasibility of two alternative methods of using satellite-derived data to constrain the behaviour of a dynamic ecosystem model, in order to improve the model`s predictions of the net primary production (NPP) of conifer forests in northern Europe (4–30°E, 55–70°N). The ecosystem model incorporates a detailed description of forest stand structure and biogeochemical processes. The satellite product comprises multi-spectral reflectance data from the VEGETATION sensor. The first method combines satellite-based estimates of FPAR, the fraction of incoming photosynthetically active radiation absorbed by vegetation, with the model`s predictions of the efficiency with which trees use the incoming radiation to fix carbon. Results obtained using this method averaged 0.22 kg C m yr for the NPP of conifer and mixed forests across the study area, and compared well with forest-inventory-based estimates for Sweden. The second method uses forest stand descriptions derived by application of an inverse radiation transfer scheme to VEGETATION data to prescribe stand structure in the ecosystem model simulations. Predictions obtained by this method averaged 0.31 kg C m yr, somewhat high compared to forest inventory data for central and northern Sweden. Simulations by the ecosystem model when driven only by climate, CO and soils data, but unconstrained by satellite information, yielded an average NPP of 0.41 kg C m yr, which is likely to be an overestimate. Summed over the study area, the NPP estimates amounted to 0.16–0.23 Gt C yr, around 6–9% of the NPP of all boreal forest globally or 0.3–0.4% of terrestrial NPP globally. The investigated methods of combining process modelling and products derived from remote sensing data offer promise as a step towards the development of operational tools for monitoring forest carbon balances at large scales.
Abstract: This paper discusses the accuracy of the operational Medium Resolution Imaging Spectrometer (MERIS) Level 2 land product which corresponds to the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). The FAPAR value is estimated from daily MERIS spectral measurements acquired at the top-of-atmosphere, using a physically based approach. The products are operationally available at the reduced spatial resolution, i.e. 1.2 km, and can be computed at the full spatial resolution, i.e. at 300 m, from the top-of-atmosphere MERIS data by using the same algorithm. The quality assessment of the MERIS FAPAR products capitalizes on the availability of five years of data acquired globally. The actual validation exercise is performed in two steps including, first, an analysis of the accuracy of the FAPAR algorithm itself with respect to the spectral measurements uncertainties and, second, with a direct comparison of the FAPAR time series against ground-based estimations as well as similar FAPAR products derived from other optical sensor data. The results indicate that the impact of top-of-atmosphere radiance uncertainties on the operational MERIS FAPAR products accuracy is expected to be at about 5–10% and the agreement with the ground-based estimates over different canopy types is achieved within ± 0.1.
Abstract: A long-term measurement of precipitation chemistry has been carried-out in a rural area of Banizoumbou, in the Sahel (Niger), representative of the african semi-arid savanna ecosystem. A total of 305 rainfall samples, representing 90% of the total annual rainfall, were collected with an automatic wet-only rain sampler from June 1994 to September 2005. Using ionic chromatography, pH major inorganic and organic ions were analyzed. Rainwater chemistry at the site is controlled by soil dust emissions associated to a strong terrigeneous contribution represented by SO, Ca, Carbonates, K and Mg. Calcium and carbonates represent about 40% of the total ionic charge of precipitation. The second highest contribution is nitrogenous, with annual Volume Weighed Mean (VWM) NO and NH, concentrations of 11.6 and 18.1 μeq.l, respectively. This is thesignature of ammonia sources related to animals and NO emissions from savannas soils rain-induced, at the beginning of the rainy season. The mean annual NH and NO air concentration are of 6 ppbv and 2.6 ppbv, respectively. The annual VWM precipitation concentration of sodium and chloride are both of 8.7 μeq.l and reflects the marine signature from the monsoon humid air masses coming from the ocean. The mean pH value, calculated from the VWM of H, is 5.64. Acidity is neutralized by mineral dust, mainly carbonates, and/or dissolved gases such NH. High level of organic acidity with 8 μeq.l and 5.2 μeq.l of formate and acetate were found, respectively. The analysis of monthly Black Carbon emissions and FAPAR values show that both biogenic emission from vegetation and biomass burning sources could explain the organic acidity content of the precipitation. The interannual variability of the VWM concentrations around the mean (1994–2005) presents fluctuations between ±5% and ±30% mainly attributed to the variations of sources strength associated with rainfall spatio-temporal distribution. From 1994 to 2005, the total mean wet deposition flux in the Sahelian region is 60.1 mmol.m.yr and fluctuates around ±25%. Finally, Banizoumbou measurements, are compared to other long-term measurements of precipitation chemistry in the wet savanna of Lamto (Côte d`Ivoire) and in the forested zone of Zoétélé (Cameroon). The total chemical loadings presents a strong negative gradient from the dry savanna to the forest (143.7, 100.2 to 86.6 μeq.l), associated with the gradient of terrigeneous compounds sources. The wet deposition fluxes present an opposite gradient, with 60.0 mmol.m.yr in Banizoumbou, 108.6 mmol.m.yr in Lamto and 162.9 mmol.m.yr in Zoétélé, controlled by the rainfall gradient along the ecosystems transect.
Abstract: The phenology of vegetation, which describes the seasonal evolution of plants, can be effectively monitored from space. This approach offers important advantages compared to field observations, as quantitative information can be derived for any location worldwide over a number of years, thereby offering a consistent overview of the fate of the observed biomes and their relations with the climate and the environment. This manuscript describes a method to define the start, end, and length of ‘growing seasons’ based on the statistical analysis of time series of the biogeophysical quantity known as the Fraction of Absorbed Photosynthetically Active Radiation derived from an analysis of SeaWiFS data. Results are discussed for various biomes.
Abstract: The divergence of horizontal radiation in vegetation canopies is generally considered to be of negligible consequence in algorithms designed for the physically-based interpretation of space borne observations. However, non-zero horizontal radiation balances are likely to occur if the internal variability of a vegetation target and the typical distances that photons may travel horizontally within such three-dimensional (3-D) media extend to spatial scales that are similar to or larger than those of the nominal footprint of the measuring sensor. Detailed radiative transfer simulations in 3-D coniferous forest environments are presented to document the typical distances that photons may travel in such media, and to quantify the impact that the resulting net horizontal fluxes may have with respect to the local and domain-averaged canopy reflectance. Based on these simulations it is possible to identify a fine spatial resolution limit beyond which pixel-based interpretations of remote sensing data over tall forested areas should be avoided because the horizontal radiation transport at the surface may contribute to 10% or more of the measured reflectance signature of the target pixel.
Abstract: We present a computer-efficient software package enabling us to assimilate operational remote-sensing flux products into a state-of-the-art two-stream radiation transfer scheme suitable for climate models. This package implements the adjoint and Hessian codes, generated using automatic differentiation techniques, of a cost function balancing (1) the deviation from the a priori knowledge on the model parameter values and (2) the misfit between the observed remote-sensing fluxes and the two-stream model simulations. The individual weights of these contributions are specified notably via covariance matrices of the uncertainties in the a priori knowledge on the model parameters and the measurements. The proposed procedure delivers a Gaussian approximation of the PDFs of the retrieved model parameter values. The a posteriori covariance matrix is further exploited to evaluate, in turn, the posterior probability density functions of the radiant fluxes simulated by the two-stream model, including those that are not measured, for example, the fraction of radiation absorbed in the ground. Applications are conducted using Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer (MISR) broadband surface albedo products. It turns out that the differences between these two albedo sets may translate into discernible signatures on some retrieved model parameters. Meanwhile, adding the Joint Research Centre (JRC)-Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Sea-viewing Wide Field-of-view Sensor (SeaWiFS) products into the measurements yields a significant reduction of uncertainties. Results from these applications indicate that the products retrieved from the two-stream inversion procedure (1) exhibit much less variability than those generated by the operational algorithms for the LAI and FAPAR, and (2) are in good agreement with the available ground-based estimates.
Abstract: Understanding the carbon dynamics of the terrestrial biosphere during climate fluctuations is a prerequisite for any reliable modeling of the climate-carbon cycle feedback. We drive a terrestrial vegetation model with observed climate data to show that most of the fluctuations in atmospheric CO are consistent with the modeled shift in the balance between carbon uptake by terrestrial plants and carbon loss through soil and plant respiration. Simulated anomalies of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) during the last two El Niño events also agree well with satellite observations. Our model results suggest that changes in net primary productivity (NPP) are mainly responsible for the observed anomalies in the atmospheric CO growth rate. Changes in heterotrophic respiration (R) mostly happen in the same direction, but with smaller amplitude. We attribute the unusual acceleration of the atmospheric CO growth rate during 2002–2003 to a coincidence of moderate El Niño conditions in the tropics with a strong NPP decrease at northern mid latitudes, only partially compensated by decreased R.
Abstract: The capability of the non-linear Rahman–Pinty–Verstraete (RPV) model to 1) accurately fit a large variety of Bidirectional Reflectance Factor (BRF) fields and 2) return parameter values of interest for land surface applications motivate the development of a computer efficient inversion package. The present paper describes such a package based on the 3 and 4 parameter versions of the RPV model. This software environment implements the adjoint code, generated using automatic differentiation techniques, of the cost function. This cost function itself balances two main contributions reflecting 1) the a priori knowledge on the model parameter values and, 2) BRF uncertainties together with the requirement to minimize the mismatch between the measurements and the RPV simulations. The individual weights of these contributions is specified notably via covariance matrices of the uncertainties in the a priori knowledge on the model parameters and the observations. This package also reports on the probability density functions of the retrieved model parameter values that thus permit the user to evaluate the a posteriori uncertainties on these retrievals. This is achieved by evaluating the Hessian of the cost function at its minimum. Results from a variety of tests are shown in order to document and analyze software performance against complex synthetic BRF fields simulated by radiation transfer models as well as against actual MISR-derived surface BRF products.
Abstract: This paper describes the evaluation and performance of the Medium Resolution Imaging Spectrometer (MERIS) Global Vegetation Index (MGVI) algorithm that is implemented in the MERIS ground segment as the primary land surface product. MGVI output values represent the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) which acts as an indicator of the presence and state of the vegetation canopy. The retrieval algorithm was optimized to be insensitive to the overlying atmosphere, the underlying soil as well as angular effects, using radiative transfer models. This physically based approach for retrieving land biophysical parameters can be extended to a series of sensors; the resulting algorithms are designed to deliver similar geophysical products that are directly comparable and ultimately generate long time series of FAPAR. After presenting the MGVI algorithm, we analyze actual results by inter-comparing the FAPAR values derived from MERIS to similar products derived from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) that have been generated at the European Commission Joint Research Centre (EC-JRC). The FAPAR products derived from MERIS and SeaWiFS are compared on several dates in 2002 when both instruments simultaneously observed the same geographical regions. The consistency and temporal continuity of the products are then evaluated by comparing the time series of FAPAR values over local sites in Europe during the year 2003 from the two instruments. Finally, multi-annual FAPAR time series obtained by merging MERIS and SeaWiFS products demonstrate the feasibility of monitoring the state of land surfaces with FAPAR products before and after the European drought event in 2003.
Abstract: This paper discusses the quality and the accuracy of the Joint Research Center (JRC) fraction of absorbed photosynthetically active radiation (FAPAR) products generated from an analysis of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data. The FAPAR value acts as an indicator of the presence and state of the vegetation and it can be estimated from remote sensing measurements using a physically based approach. The quality of the SeaWiFS FAPAR products assessed in this paper capitalizes on the availability of a 6-year FAPAR time series over the full globe. This evaluation exercise is performed in two phases involving, first, an analysis of the verisimilitude of the FAPAR products under documented environmental conditions and, second, a direct comparison of the FAPAR values with ground-based estimations where and when the latter are available. This second phase is conducted following a careful analysis of problems arising for performing such a comparison. This results in the grouping of available field information into broad categories representing different radiative transfer regimes. This strategy greatly helps the interpretation of the results since it recognizes the various levels of difficulty and sources of uncertainty associated with the radiative sampling of different types of vegetation canopies.
Abstract: Remote sensing products, such as the fraction of reflected solar radiation flux, as well as the amount of radiation absorbed in the photosynthetically active spectral region and the Leaf Area Index (LAI), are operationally available from Space Agencies. Climate models may benefit from these products provided their one dimensional (1-D) radiation transfer schemes effectively represent the three dimensional (3-D) effects implied by the internal spatial variability of vegetation canopies, e.g., the leaf area density, at all scales and resolutions involved (say from 1 to 100 kilometers). Failing to do so leads to inherent inconsistencies between the domain-averaged reflected and absorbed fluxes, and the implied Leaf Area Index. We propose a comprehensive approach which introduces a parameterization of the internal variability of the LAI in the 1-D representation of the radiation scheme, called a domain-averaged structure factor, and provides a description of the radiant fluxes fully consistent with the LAI specified by remote sensing. We take this opportunity to revisit and update the two-stream formulations implemented in climate models to accurately estimate the fractions of radiation absorbed separately by the vegetation canopy and the underlying surface. This is achieved by isolating the contributions of the vegetation canopy alone, the background as seen through the canopy gaps and the multiple scattering between the vegetation layer and the background. The performance of this formulation is evaluated against results from Monte Carlo simulations relative to explicit realistic 3-D canopies to show that the proposed scheme correctly simulates both the amplitude and the angular variations of all radiant fluxes with respect to the solar zenith angle.
Abstract: The state of terrestrial vegetation has been monitored using remote sensing data for decades. Information was often derived from empirical tools, like vegetation indices, which are very sensitive to perturbations and often depend on the spectral properties of the sensor. Advances in the understanding of radiation transfer and the availability of higher performance instruments have stimulated the development of a new generation of geophysical products poised to provide reliable, accurate information on the state and evolution of terrestrial environments. A series of optimized algorithms have been developed for documenting biophysical activities, using a physically based approach (specifically, to estimate Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)) for various instruments. The outline of the methodology will be summarized and the results from an application conducted with SeaWiFS data will be presented.
Abstract: Multi-annual time series of remote sensing data acquired over Europe from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Medium Resolution Imaging Spectrometer (MERIS) instruments were analysed to assess the state of health of vegetation in Spring 2004, compared to previous years. These data show (1) the dramatic impact of the 2003 drought on a variety of land cover types in Europe and (2) the recovery of most ecosystems to a normal situation in early 2004.
Abstract: Identifying the mechanisms driving interannual fluctuations of atmospheric carbon dioxide is necessary for predicting future CO concentrations and climate change. A possible clue comes from a well-established positive correlation between atmospheric CO growth rates and the El Niño-Southern Oscillation phenomenon. Most tropical droughts are also linked to El Niño, suggesting carbon losses from drought as a major cause for interannual CO variations. A lag correlation between 7-month running means of monthly atmospheric CO concentrations and Niño 3 sea surface temperatures for the period 1979-2003 peaks at a lag of four months with a correlation of 0.49, and a significance level above 99.9% (assuming 42 independent measurements).
Abstract: We devise a computer efficient and flexible inversion technique to retrieve vegetation canopy parameters, in particular the Leaf Area Index, from the radiance field emerging at the top of a structurally heterogeneous systems overlying an anisotropic spatially uniform surface background. The proposed inversion strategy focuses on a reanalysis of multiangle and multispectral measurements unhindered by the many specific constraints imposed by the operational application of the current algorithms and their associated limitations on data staging. This technique capitalizes on the decoupling between contributions due to the canopy only and those invoking the background reflectance properties. These contributions are decomposed into the wavelength dependent and independent contributions. A quasi-linear relationship is thus obtained between the radiance/reflectance emerging from the top of the canopy layer and the background reflectance. Although all individual contributions can be estimated from accurate three-dimensional radiation transfer models, we propose appropriate approximations in order to estimate the minor terms. These approximations exploit the relatively limited dependency exhibited by these relatively smaller contributions with respect to the azimuthal coordinate. Moreover, additional mathematical developments are proposed to further approximate these terms by their corresponding solutions obtained in the limit case of a plane-parallel turbid medium scenario. They require defining effective values of the state variables entering the plane-parallel turbid medium model. The resulting reflectance of a three-dimensional spatially heterogeneous vegetation layer is driven by a sum of contributions that can be precomputed offline on the basis of the three-dimensional and plane-parallel homogeneous turbid medium model capabilities. The decoupling of the intrinsic vegetation and the background contributions allows many of the contributions to be precomputed and stored in look-up tables. This development yields a simple and computer efficient inversion scheme that allows us to jointly retrieve the values of the main vegetation layer attributes and the underlying background radiative properties. Demonstration tests based on actual multiangular and multispectral data set are currently being investigated.
Abstract: Parametric bi-directional reflectance factor (BRF) models are often inverted against remote sensing data to describe the amplitude and shape of the anisotropy of geophysical media. For a given geophysical situation, this approach hinges on the availability of (1) a suitable inversion procedure delivering the most probable values of the coefficients entering these models, and (2) reliable and accurate observational data acquired under suitable geometries of illumination and observation. The Rahman, Pinty Verstraete (RPV) model one of the parametric BRF models, idealizes the BRF values through a simple non-linear equation requiring the values of three parameters to be estimated from the remote sensing data. One parameter describes the amplitude of the BRF and the other two jointly represent the angular shape of the anisotropy. The computational load and efficiency required to perform the inversion of the RPV model are critical for operational application. Most, if not all, of the suggested inversion procedures apply to linear versions of parametric models, including a linearized version of the RPV model. Although this approach enables fast inversion methods to be implemented, the linearization prevents the model from representing adequately the wide diversity of observed BRF fields. The present paper describes a computationally efficient inversion method that can be used with the original, i.e., non-linear, version of the RPV model. This method can be applied to retrieve the three model parameters of the original RPV model from an analysis of actual BRF data, while offering the opportunity of specifying the desired accuracy. The procedure delivers ranges of parameter values that depend on the user requirements and capitalizes on the performance of this non-linear model. Two applications are made with laboratory measurements and BRF data sets measured using the multi-angle imaging spectroradiometer (MISR) instrument of the Jet Propulsion Laboratory (JPL) on board the NASA EOS Terra platform.
Abstract: The recent availability of quasi-simultaneous multispectral and multidirectional measurements from space, as provided by the Multi-angle Imaging SpectroRadiometer (MISR) on board the Terra platform, offers new and unique opportunities to document the anisotropy of land surfaces at critical solar wavelengths. This paper presents simple physical principles supporting the interpretation of the anisotropy of spectral radiances exiting terrestrial surfaces in terms of a signature of surface heterogeneity. The shape of the anisotropy function is represented with two model parameter values which may be mapped and interpreted in their own right. The value of one of these parameters also permits identifying geophysical conditions where the surface heterogeneity becomes significant and where three-dimensional (3-D) radiation transfer effects have to be explicitly accounted for. This paper documents these findings on the basis of results from a number of 3-D radiation transfer model simulations. The latter are used to perform an extensive sensitivity study which includes issues related to the scale of investigation. A preliminary validation of these results, conducted with a dataset collected by the AirMISR instrument over the Konza prairie, is also discussed.
Abstract: The Multi-angle Imaging SpectroRadiometer (MISR) instrument on board the Terra platform offers the capability of acquiring reflectance data on any earth target in four spectral bands, from nine different directions, in at most seven minutes, at a spatial resolution adequate for the monitoring of the status of terrestrial surfaces. This paper describes the implementation of a physical and mathematical approach to design a simple two-dimensional algorithm dedicated to the interpretation of data collected by this instrument. One dimension fully exploits the spectral information in the blue, red and near- infrared bands while the other dimension capitalizes on the multi- angular capability of MISR to assess the anisotropic behavior of terrestrial surfaces with respect to solar radiation. The spectral information is derived following an approach proposed for single angle instruments, such as the MEdium Resolution Imaging Spectrometer (MERIS), the Global Imager (GLI), the Sea-viewing Wide Field-of-view Sensor (SeaWIFS) and VEGETATION. The access to simultaneous multiangular observations from MISR allows extending this approach. This strategy delivers an estimate of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), which pertains to vegetation photosynthetic activity and is a measure of the presence and density of vegetation. As shown in Part I, the angular shape of the reflectance is strongly related to the architecture of the vegetation and, under some favorable conditions, permits an assessment of surface heterogeneity. The proposed VEGetation Activity and Structure (VEGAS) algorithm for MISR therefore delivers two axes of information representing a) FAPAR and b) vegetation structure at MISR subpixel reso- lution. Its application should improve the present knowledge of vegetation characteristics at regional and global scales.
Abstract: We present a methodology to generate, from SeaWiFS data, 2-km resolution maps of the globe that optimally combine the full spatial resolution Local Area Coverage imagery received at ground stations, and the sub-sampled Global Area Coverage data recorded on board the satellite. Monthly maps display estimates of the Fraction of Absorbed Photosynthetically Active Radiation over terrestrial surfaces and the surface chlorophyll concentration over oceanic areas.
Abstract: Optimized vegetation indices provide a convenient approach to estimate crucial plant properties on the basis of satellite data. This paper describes the steps followed to implement an index optimized to estimate the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) on the basis of data generated by the SeaWiFS instrument, and the preliminary results obtained. Index values arecomputed on the basis of top of atmosphere bidirectional reflectance factor values in the blue, red and near-infrared domains, as well as information on the geometry of illumination and observation. Results obtained with SeaWiFS data are used to evaluate the performance of the index. This case study documents the ability of the index to discriminate between various surface types, and its insensitivity to changes in the geometrical conditions of observation and to atmospheric effects. The operational environment set up at SAI to process SeaWiFS data is outlined and selected standard retrievals resulting from a monthly composite analysis are shown as examples of the products generated.
Abstract: This paper describes the implementation of a physical and mathematical approach to designing advanced vegetation indices optimized for future sensors operating in the solar domain such as the medium resolution imaging spectrometer (MERIS), the global imager (GLI), and the VEGETATION instrument, and proposes an initial evaluation of such indices. These optimized indices address sensor-specific issues such as dependencies with respect to the actual spectral response of the sensor as well as the natural sensitivity of remote sensing measurements to illumination and observing geometry, to atmospheric absorption and scattering effects, and to soil color or brightness changes. The derivation of vegetation index formulae optimized to estimate the same vegetation property fraction of absorbed photosynthetically active radiation (FAPAR) from data generated by different sensors allows the comparison of their relative performances compared with existing vegetation indices, both from a theoretical and experimental point of view and permits the creation of global products, as well as the constitution of long time series from multiple sensors.
Abstract: This paper describes the physical and mathematical approach followed to design a vegetation index optimized for the Medium Resolution Imaging Spectrometer (MERIS) sensor, i.e. the MERIS Global Vegetation Index (MGVI). It complements an earlier feasibility study presented elsewhere in this issue by Govaerts and collaborators. Specifically, the crucial issue of the dependency of the vegetation index on changes in illumination and observing geometries is addressed, together with the atmospheric contamination problem. The derivation of the optimal MGVI index formulae allows a comparison of its performance with that of the widely used Normalized Difference Vegetation Index (NDVI), both from a theoretical and an experimental point of view. Data collected by the MOS/IRS-P3 instrument since March 1996 in spectral bands analogous to those that will be available from MERIS can be used to evaluate the MVGI.
Abstract: Vegetation indices constitute a simple and convenient approach to extract useful information from satellite remote sensing data, provided they are designed to address the needs of specific applications and take advantage of the characteristics of particular instruments. Two factors motivate the development of better spectral indices at this time. The first one is the upcoming arrival of a new generation of advanced Earth observation sensors such as the Medium Resolution Imaging Spectrometer (MERIS) on Envisat, the VEGETATION instrument on the SPOT-4 platform, and GLI on ADEOS II, among others. The second is the recent publication of methodological papers on the design and evaluation of optimal spectral indices. The present contribution describes preliminary results obtained in the definition of a spectral index optimized to monitor the state of terrestrial vegetation, where the fraction of absorbed photosynthetically active radiation in plant canopies is considered the key observable physical process. The specifications of the MERIS instrument are used as an example, but the approach can be extended to other sensors. These results are encouraging and show the feasibility of defining optimal indices that exploit advanced characteristics of new instruments to meet the needs of specific applications.
Abstract: An advanced bidirectional reflectance factor model is developed to account for the architectural effects exhibited by homogeneous vegetation canopies for the first orders of light scattering. The characterization of the canopy allows the simulation of the relevant scattering processes as a function of the number, size, and orientation of the leaves, as well as the total height of the canopy. A turbid medium approach is used to represent the contribution to the total reflectance due to the light scattering at orders higher than 1. This model therefore incorporates two previously separate approaches to the problem of describing light scattering in plant canopies and enhances existing models relying on parameterized formulae to account for the hot spot effect in the extinction coefficient. Simulation results using this model compare quite favorably with those produced with a Monte Carlo ray-tracing model for a variety of vegetation cases. The semidiscrete model is also inverted against a well-documented data set of bidirectional reflectance factors taken over a soybean canopy. It is shown that the inversion of the model against a small subset of these measurements leads to reasonable values for the retrieved canopy parameters. These values are used in a direct mode to simulate the bidirectional reflectance factors for solar and viewing conditions significantly different from those available in the subset of soybean data and compared with the full set of actual measurements.
Abstract: Satellite remote sensing data constitute a significant potential source of information on our environment, provided they can be adequately interpreted. Vegetation indexes, a subset of the class of spectral indexes, represent one of the most commonly used approaches to analyze data in the optical domain. An optimal spectral index is very sensitive to the desired information (e.g. the amount of vegetation), and as insensitive as possible to perturbing factors (such as soil color changes or atmospheric effects). Since both the desired signal and the perturbing factors vary spectrally, and since the instruments themselves only provide data for particular spectral bands, optimal indexes should be designed for specific applications and particular instruments. This paper describes a rational approach to the design of an optimal index to estimate vegetation properties on the basis of the red and near-infrared reflectances of the AVHRR instrument, taking into account the perturbing effects of soil brightness changes, atmospheric absorption and scattering. The rationale behind the Global Environment Monitoring index (GEMI) is explained, and this index is proposed as an alternative to the Normalized Difference Vegetation Index (NDVI) for global applications. The techniques described here are generally applicable to any multispectral sensor and application.
Abstract: A new semiempirical model to describe the bidirectional reflectance of arbitrary natural surfaces using only three parameters has been developed. This model successfully accounts for the observed variability of reflectance measurements in laboratory and field conditions, ranging from bare soil to full canopy cover, in both the visible and the near-infrared bands. Coupled with a simple atmospheric radiation transfer model, this model has been inverted against actual NOAA/advanced very high resolution radiometer (AVHRR) data from several desert sites in northern Africa. This procedure allows the retrieval of surface properties and average amounts of atmospheric constituents (aerosol optical thickness and water vapor) for the duration of the measurement period. Further work is required to expand the usability of the coupled model to other locations and shorter periods of time, but the paper demonstrates the feasibility of inverting a coupled surface-atmosphere model against existing AVHRR data and documents the current limits of this approach.
Abstract: Absorption and scattering processes in the atmosphere affect the transfer of solar radiation along its double path between the Sun, the Earth`s surface, and the satellite sensor. These effects must be taken into account if reliable and accurate information on the surface must be retrieved from satellite remote sensing data. One approach consists in characterizing the state of the atmosphere from independent observations and correcting the data with the help of radiation transfer models. This approach requires a detailed and accurate description of the composition of the atmosphere (e.g., aerosol and water vapor profiles in the case of advanced very high resolution radiometer data) at the time of the satellite overpass and requires significant computer resources. An alternative method is to attempt to simultaneously retrieve surface and atmospheric parameters by inverting a coupled surface-atmosphere model against remote sensing data. This study describes such a coupled model and the results of its inversion against synthetic data, using a nonlinear inversion technique. The results obtained are encouraging in that realistic directional reflectances at the top of the atmosphere can be produced, and the inversion of the model against these synthetic data is capable of estimating surface and atmospheric variables. The accuracy of the retrieval is studied as a function of the amount of noise added to the data. It is shown that some surface or atmospheric parameters are easier to retrieve than others with such a coupled model, and that although it appears to be difficult to accurately and reliably estimate the water vapor amount from channel 2, there is a definite possibility of retrieving the aerosol loading from simulated channel 1 data, if the type of aerosol can be assumed.
Abstract: One of the objectives of the EU-FP7 project QA4ECV (Quality Assurance for Essential Climate variables, www.qa4ecv.eu, under contract No 607405) project is to produce a long and consistent data record of global surface albedo. Thus, level-1 and level-2 data from several sensors, namely NOAAAVHRR, several geostationary satellites and MODIS have been collected and pre-processed to ingest into our optimal estimation algorithm. Our primary output product is a daily global map of two types of albedo bi-hemispherical diffuse reflectance known colloquially as white sky albedo and direct hemispherical reflectance known as black sky albedo over three broadbands (vis: 0.4 - 0.7µm; nir: 0.7 - 3µm; sw:0.4 - 3µm) and, with a spatial resolution of 0.05° × 0.05°. From this product, we derive other up-scaled products such as monthly and 0.5° × 0.5°. The inter-comparison against third party products and in-situ data show a very good overall agreement and confirm the accuracy and the consistency of our output albedos.
Abstract: This paper presents a novel framework for assessing the physical consistency between two terrestrial Essential Climate Variables (ECVs) products retrieved from Earth Observation at global scale. The proposed methodology assesses the level of temporal and spatial agreement between them using multitemporal data. This is based on the analysis of their temporal changes taking into account their associated uncertainties. In this study we will show examples on the agreement between Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) retrieved by the Joint Research Center (JRC) Two-stream Inversion Package (TIP) using 17 years of MODIS surface broadband albedo products at 1km and the ones from the Copernicus Global Land Service based on the SPOT/VGT and Proba-V. This framework can identify the spatial and temporal consistency levels and provides potential information when dataset selection are used for global modeling studies.
Abstract: Envisat's Medium Resolution Imaging Spectrometer (MERIS) acquired multi-spectral imagery of the Earth in the optical domain over terrestrial surfaces for a decade at global scale. For the last ten years, scientists have used multi-spectral data or terrestrial geophysical products for characterizing the state of the global system and its variability. Our paper shows highlights of several achievements of the use of MERIS data over terrestrial surfaces but specifically focuses on regional to global scale applications. We first summarize daily operational biophysical parameters and present examples of their uses for the monitoring of land surface states and changes, especially related to ECVs. In addition, specific projects for deriving a series of land cover maps will be presented and we conclude on the MERIS data exploitation and highlights future applications.
Abstract: The study has developed an interactive mission benefit analysis (MBA) tool that allows instantaneous evaluation of a range of potential mission designs. The designs are evaluated in terms of their constraint on carbon and water fluxes through calibration of a terrestrial bisphere model. The constraint is quantified by methematically rigorous uncertainty propagation in CCDAS. Applying the MBA tool, the study showed that the benefit of FAPAR data is most pronounced for hydrological quantities and moderate for quantities related to carbon fluxes from ecosystems. In semi-arid regions, where vegetation is strongly water limited, the constraint delivered by FAPAR for hydrological quantities was especially large, as documented by the results for Africa and Australia. Sensor resolution is less critical for successful data assimilation, and with even relatively short time series of only a few years, significant uncertainty reduction can be achieved.
Abstract: We present and evaluate results of a validation study of the MERIS Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product delivered by the operational MGVI algorithm. Our approach is based on the application of an inversion method conducted using both MODIS and MISR derived broadband visible and near-infrared surface albedo products available during a full seasonal cycle over a variety of selected FLUXNET stations. This investigation includes complex geophysical scenarios involving snow occurrence in mid and high-latitude evergreen and deciduous forest canopy systems. The occurrence of snow during the winter and spring seasons is based on the analysis of the MODIS snow products which assimilation by our package translates into an adaptation of the prior values, both the maximum likelihood and width of the 2-D probability density functions (PDF), characterizing the background conditions of the forest floor. The inversion package delivers meaningful land vegetation fluxes (such as the FAPAR) and associated model parameters (such as the effective LAI) along the year despite the rather high variability in the input products. Our results illustrate the remarkable agreement between the MERIS FAPAR values delivered by the operational processor and those values retrieved independently from our inversion package using the MODIS and MISR surface albedo products.
Abstract: The current and future strength of the terrestrial carbon sink has a crucial influence on the expected degree of climate warming earth is going to face. Usually, Earth Observation (EO) by its very nature focusses on diagnosing the current state of the planet. However, it is possible to use EO products in data assimilation systems to improve not only the diagnostics of the current state, but also the accuracy of future predictions. This contribution reports from an on-going ESA funded study in which the MERIS FAPAR product is assimilated into a terrestrial biosphere model within the global Carbon Cycle Data Assimilation System (see www.CCDAS.org). Results are presented from a range of selected sites spanning the major biomes of the globe, and show how the inclusion of MERIS products results in improved accuracy of the regional carbon flux estimates. They also show the uncertainty in the predicted carbon sink of those regions for selected climate scenarios until 2039. It concludes with a conceptual outlook for the inclusion of land surface temperatures from AATSR to improve estimates of in particular the effects of drought on carbon uptake and water status of land vegetation.
Abstract: The assessment of the characteristics of both terrestrial surfaces and their temporal evolution requires high level products derived from space measurements. The accuracies of these products are in addition needed in the context of global change issues and regional terrestrial surfaces monitoring. Specifically the operational MEdium Resolution Imaging Spectrometer Instrument (MERIS) Level 2 land products, corresponding to the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), are available at both full (300 m) and reduce (1.2 km) resolution. The FAPAR value is estimated from daily MERIS spectral measurements acquired at the top-of-atmosphere, using a physically based approach. The quality assessment of the MERIS FAPAR products capitalizes on the availability of more than five years of data acquired globally. The validation is performed with a direct comparison of the FAPAR time series against ground-based estimations as well as similar FAPAR products derived from other optical sensor data. In the context of future instrument, we also analyze the impact of the spatial resolution on the performance results.
Abstract: This contribution gives an overview of the Medium Resolution Imaging Spectrometer (MERIS) global land product corresponding to the biophysical variable of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). This product can be used in large-scale biosphere modeling for better estimating the carbon fluxes since they directly represent the amount of solar energy which serves as a ‘battery’ during the photosynthetic process. The daily FAPAR value is operationally estimated from MERIS data and the (demonstration) global products, recently produced at European Space Research Institute (ESRIN) by the grid on demand system, are first compared against the Joint Research Centre (JRC) SeaWiFS global datasets which is available since September 1997. The second part presents a first evaluation against the simulations by the Biosphere Energy Transfer Hydrology Scheme (BETHY) model for a 10 year period and over 3 regional windows. The results show a good agreement between both space remote sensing data and model simulations which promotes the assimilation of the MERIS FAPAR products into a Carbon Cycle Data Assimilation System (CCDAS).
Abstract: This paper discusses the validation of the operational Medium Resolution Imaging Spectrometer (MERIS) land product which corresponds to the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). This biophysical variable acts as an indicator of the presence and state of the vegetation and it is currently estimated from MERIS data at both reduced and full resolution using a physically-based approach. The quality of the MERIS FAPAR products, derived from the MERIS Global Vegetation Index (MGVI) algorithm, capitalizes on the availability of MERIS data since June 2002. The validation protocol to assess the accuracy of FAPAR product includes (1) the estimates of theoretical uncertainties (versus the algorithm formulae and instrument calibration performance), (2) the assessment of the performance for detecting various events (verisimilitude) against the time-series of well-known land surfaces and (3) the direct comparisons of the FAPAR MERIS values to similar products generated by other independent sensors, like the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the MODerate Resolution Imaging Spectroradiometer (MODIS), and against ground-estimates of FAPAR performed over various vegetation types.
Abstract: This paper discusses the validation of the operational Medium Resolution Imaging Spectrometer (MERIS) land product which corresponds to the biophysical variable of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). The FAPAR value acts as an indicator of the presence and state of the vegetation and it is currently estimated from MERIS data at both reduced and full resolution using a physically-based approach. The quality of the MERIS FAPAR products, derived from the MERIS Global Vegetation Index (MGVI) algorithm, capitalizes on the availability of MERIS data since June 2002. The validation protocol to assess the accuracy of FAPAR product is done 1) by analyzing the estimates of theoretical uncertainties (versus the algorithm formulae and instrument calibration performance), 2) by assessing the performance for detecting expected event using long time-series of FAPAR data over a well-known land surfaces and 3) by comparing the FAPAR MERIS values to similar products generated by other independent sensors like the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the MODerate Resolution Imaging Spectro-radiometer (MODIS) and against ground-estimates of FAPAR which have been performed over various types of land.
Abstract: Earth observation systems have been shown to be high-quality tools to review the state of terrestrial surfaces at the global scale over last decades. In the special context of monitoring land degradation and desertification, long time series of remote sensing products are needed to evaluate the changes in terrestrial surfaces. For example, the vegetation photosynthesis activities on terrestrial environments can be documented from spectral measurements made in space. Advances in the understanding of radiation transfer and the availability of high performance instruments have led to the development of a new generation of geophysical products providing reliable, accurate information on the state and evolution of terrestrial environments. Specifically, a series of optimized algorithms have been developed to estimate the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) for a suite of recent instruments. Such an approach allows the synergistic use of FAPAR products derived from different sensors and the construction of FAPAR time series independent from the life time of these specific sensors. This paper summarizes the methodology and performance of the FAPAR algorithm and presents various examples of applications showing an analysis of seasonal cycle and maps of vegetation activity anomalies.
Abstract: This contribution outlines the methodology used to assess the accuracy of the biophysical land product available from the Medium Resolution Imaging Spectrometer (MERIS) Level 2 products, namely the MERIS Global Vegetation Index (MGVI), and the results of the first validation phase. The MGVI is designed to generate values representative of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) of vegetation over terrestrial surfaces. The first evaluation presented here is made by inter-comparing the MGVI to similar products derived from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and generated at the European Commission Joint Research Center (EC-JRC). This assessment uses various data sets acquired quasi-simultaneously over the same geographical regions by both instruments during the year 2002. The analyses show acceptable agreement given the expected range of accuracies for daily products.
Abstract: The C3S Climate Data Records (CDR) include various Essential Climate Variables (ECVs) that are derived from space-borne sensors, including from Copernicus Sentinel sensors, and models. One module of the F4P platform focuses on the benchmarking of data sets and algorithms, in addition to radiative transfer models used towards understanding potential discrepancies between CDR records. In this context, the CLEO (Copernicus Land Observation quality assurance) working group of the Knowledge for Sustainable Development and Food Security Unit (D.6) of the Directorate D of the European Commission, Joint Research Centre (JRC), is actively involved in the quality assurance of EO products, algorithms and models. A key contribution of the F4P component is to assess the feasibility to satisfy these requests and establish priorities for implementation. This report describes the release 1.1.0 of the Copernicus Climate Change Service (C3S) Fitness For Purpose (F4P) benchmark platform. The present software architecture was developed and deployed in support to the C3SF4P benchmarking activities requirements. The main proposal of the C3SF4P platform is to provide a wide range of statistical tools allowing the investigation and identification of technical functionalities aiming at verifying the quality of the essential climate variable products and their uncertainties.
Abstract: The Joint Research Centre (JRC) retrieval algorithm is used to derive the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) from daily spectral measurements acquired by Advanced Very High Resolution Radiometer (AVHRR) onboard a series of National Oceanic and Atmospheric Administration (NOAA) platforms (Gobron 2017). The inputs data are the surface Bidirectional Reflectance Factors (BRFs), derived from the normalised surface reflectances provided by the Land Long Term Data Record (LTDR) project (http://ltdr.nascom.nasa.gov, Franch et al. (2017)). The methodology itself is based on previous JRC-FAPAR algorithms such as the ones developed for the Medium Resolution Instrument Sensor (MERIS) and the Ocean Land Colour Instrument (OLCI), except surface reflectances instead of top of atmosphere ones are used as inputs. The uncertainty computations follow the main principles de- scribed into the Quality Assurance Framework For Earth Observation (QA4EO) guidelines (QA4EO 2012), e.g. using the uncertainties propagation theory. This report concerns the validation of the QA4ECV-FAPAR-AVHRR products through quality control at global scale from daily to 10-days and monthly period at 0.05 deg. x0.05 deg. and 0.5 deg. x 0.5deg. spatial scale, with comparisons at local scale against other space products, i.e. LTDR AVHRR AVH15 (Claverie et al. 2016) and Two-stream Inversion Package (TIP) products (Pinty et al. 2011), using as inputs the MODIS Collection 6 surface albedo and `green' a priori, and ground-based measurements.
Abstract: The Global Climate Observing System (GCOS) has specified the need to systematically produce and validate global leaf area index (LAI) products. This document provides recommendations on good practices for the validation of global LAI products. Internationally accepted definitions of LAI and associated quantities are provided to ensure thematic compatibility across products and reference datasets. A survey of current validation capacity indicates that progress is being made towards the use of standard spatial sampling and in situ measurement methods, but there is less standardisation with respect to performing and reporting statistically robust comparisons. Three comparison approaches are identified: direct validation, indirect validation, and completeness. Direct validation, corresponds to the comparison of temporally and spatially concurrent satellite-derived product and up-scaled in situ reference LAI estimates. Indirect validation, consisting of inter-comparisons of products with ensembles of other products, using a stratified spatial sampling is proposed as a means for quantifying product precision as well as the representativeness of direct validation sites for a given biome. Completeness, corresponding to the frequency and continuity of LAI products, is quantified using a standard set of metrics applied to multi-year products. Finally, the need for an open access facility for performing validation as well as accessing reference LAI maps and ensemble LAI estimates from products is identified.
Abstract: Physically-based algorithm for deriving Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) products has been designed and optimized by the Joint Research Centre (JRC) for various Earth Observation (EO) sensors (Gobron et al. 2000). Among them, VITO implements JRC-FAPAR for 1) Moderate Resolution Imaging Spectroradiometer (MODIS) data at 250 m of spatial resolution and 2) VEGETATION at 1 km. They process data over Europe for delivering them in near-real time to MARS project1. The corresponding algorithms are published in Gobron et al. (2006b, Gobron et al. (2006a) and Gobron et al. (2002), respectively. Note that these above algorithms have been optimized using nominal spectral responses of the first MODIS intrument, i.e. on board the TERRA platform and for VEGETATION 1. We propose here to evaluate VITO implementation processing chain using data over Europe by comparing their products to the those computed with original codes at JRC. The first exercice (see Section 4.1) presents results of direct comparison using daily MODIS data at 250 m over a small region (see Table 2). The second analysis (see Section 4.2) concerns the comparison using VEGETATION data at 1 km over Europe (see Table 2). Section 4.3 presents additional comparisons over Europe between VEGETATION, MODIS and MERIS at 1 km.
Abstract: This Algorithm Theoretical Basis document (ATBd) describes the Joint Research Center (JRC)- procedure used to retrieve information of absorbed photosynthetical radiation by the vegetated terrestrial surfaces from an analysis of the Top Of Atmosphere (TOA) data acquired by Medium Resolution Imaging Spectrometer (MERIS) on board ENVISAT. The code of the proposed algorithm takes the form of a set of several formulae which transform calibrated spectral directional reflectances into a single numerical value. These formulae are designed to extract the green Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in the plant canopy from the measurements and the rectified channels in the red and near-infrared bands. The methodology described in this document has been optimized to assess the presence on the ground of healthy live green vegetation. The main optimization procedure has been constrained to provide an estimate of FAPAR in the plant canopy, although the outputs are expected to be used in a wide range of applications. As example, such FAPAR data sets have been used into Carbon Cycle Data Assimilation (Kaminski et al. 2010; Knorr et al. 2010) or have been analysis for monitoring state of biosphere (Jung et al. 2010; Gobron et al. 2010). Regional data can also be served as a proxy for monitoring quasi near-real time the European drought (Rossi et al. 2010) or have been used for studying phenology (Richardson et al. 2010). This algorithm delivers, in addition to the FAPAR product, the so-called rectified reflectance values in the red and near-infrared spectral bands. These are virtual reflectances largely decontaminated from atmospheric and angular effects. It also provides a categorization of pixel types thanks to a pre-processing identification based on multi-spectral properties. These two virtual reflectances are also computed over bare soils using specific coefficients. This document identifies the sources of input data, outlines the physical principles and mathematical background justifying this approach, describes the proposed algorithm, and lists the assumptions and limitations of this technique.
Abstract: This Algorithm Theoretical Basis document (ATBd) describes the Joint Research Center (JRC)- procedure used to retrieve information of absorbed photosynthetical radiation by the vegetated terrestrial surfaces from an analysis of the Top Of Atmosphere (TOA) data acquired by the Landsat 7 Enhanced Thematic Mapper (ETM+) instrument. The corresponding data consist of eight spectral bands, with a spatial resolution of 30 meters for bands 1 to 5 and band 7 whereas the resolution for band 6 (thermal infrared) is 60 meters and resolution for band 8 (panchromatic) is 15 meters. Approximate scene size is 170 km north-south by 183 km east-west. The code of the proposed algorithm takes the form of a set of several formulae which transform calibrated spectral directional reflectances into a single numerical value. These formulae are designed to extract the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in the plant canopy from the measurements. The methodology described in this document has been optimized to assess the presence on the ground of healthy live green vegetation. The optimization procedure has been constrained to provide an estimate of FAPAR in the plant canopy, although the outputs are expected to be used in a wide range of applications. This algorithm delivers, in addition to the FAPAR product, the so-called rectified reflectance values in the red and near-infrared spectral bands (Landsat 7 ETM+ Band 3 and Band 4). These are virtual reflectances largely decontaminated from atmospheric and angular effects. It also provides a categorization of pixel types thanks to a pre-processing identification based on multi-spectral properties. This document identifies the sources of input data, outlines the physical principles and mathematical background justifying this approach, describes the proposed algorithm, and lists the assumptions and limitations of this technique. Finally, one application using one image is presented for illustrating the use of this algorithm.
Abstract: This document describes the format of the products of the Medium Resolution Imaging Spectrometer (MERIS) Level 3 aggregated products. These data are operationally processed and produced at the Grid Processing-on Demand (G-POD) of European Space Research Institute (ESRIN) using the European Commission – DG Joint Research Centre (JRC) algorithm and software.
Abstract: This document overviews the content and the preliminary results of a specific work package entitled “Land Surface Indicators from Space” of the “Land Use and Landscapes” from the Joint Research Center (JRC) and the European Environment Agency (EEA) 2006 work plan described in Kennedy and Stanners (2005).
Abstract: The Medium Resolution Imaging Spectrometer (MERIS) Global Vegetation Index (MGVI) has been optimized to generate numerical values between 0 and 1 representative of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) of the vegetation over terrestrial surfaces. This bio-geophysical variable plays a critical role in the photosynthetic process and is regularly used in diagnostic and predictive models to compute the primary productivity of the plant cover. This project aims to assess the accuracy and the quality of the MGVI product by comparing this operational Level-2 geophysical product to similar ones generated by other independent sensors co-located and quasi simultaneously acquired data. These comparisons mainly concerns the MERIS Level-2 products generated at reduced spatial resolution.
Abstract: This Algorithm Theoretical Basis document (ATBd) describes the Joint Research Centre (JRC) procedure used to retrieve information on the nature and properties of vegetated terrestrial surfaces from an analysis of the Top Of Atmosphere (TOA) data acquired at 250 m spatial resolution by the MODerate Resolution Imaging Spectroradiometer (MODIS), on board the Terra platform of National Aeronautics and Space Administration (NASA). The software takes the form of a set of several formulae which transform calibrated spectral directional reflectances into a single numerical value approximating the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in the plant canopy. The methodology described in this document has been optimized to assess the presence on the ground of healthy live green vegetation. The optimization procedure has been constrained to provide an estimate of FAPAR in the plant canopy, although the outputs are expected to be used in a wide range of applications. This algorithm delivers, in addition to the FAPAR product, the so-called rectified reflectance values in the red and near-infrared spectral bands (MODIS Band 1 and Band 2). The present procedure generates the rectified reflectance values at 250 m spatial resolution using MODIS data in the blue band at 500 m. These rectified channels are virtual reflectances decontaminated at best from atmospheric and angular effects. This document identifies the sources of input data, outlines the physical principles and mathematical background justifying the approach, describes the proposed algorithm, presents some results with actual data, and lists the assumptions and limitations of this technique.
Abstract: This Algorithm Theoretical Basis document (ATBd) describes the Joint Research Center (JRC)- procedure used to retrieve information on the nature and properties of vegetated terrestrial surfaces from an analysis of the Top Of Atmosphere (TOA) data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board of the Terra platform of National Aeronautics and Space Administration (NASA). The code takes the form of a set of several formulae which transform calibrated spectral directional reflectances into a single numerical value. These formulae are designed to extract the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in the plant canopy from the measurements. The methodology described in this document has been optimized to assess the presence on the ground of healthy live green vegetation. The optimization procedure has been constrained to provide an estimate of FAPAR in the plant canopy, although the outputs are expected to be used in a wide range of applications. This algorithm delivers, in addition to the FAPAR product, the so-called rectified reflectance values in the red and near-infrared spectral bands (MODIS Band 1 and Band 2). These are virtual reflectances largely decontaminated from atmospheric and angular effects. This document identifies the sources of input data, outlines the physical principles and mathematical background justifying this approach, describes the proposed algorithm, and lists the assumptions and limitations of this technique.
Abstract: The Medium Resolution Imaging Spectrometer (MERIS) Global Vegetation Index (MGVI) has been calibrated so as to generate numerical values (between 0 and 1) which correspond to the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) of vegetation over terrestrial surfaces. FAPAR, a bio-geophysical variable, is intimately connected to the photosynthetic process and is regularly used in the estimation of plant primary productivity using diagnostic and predictive models. This project aims at delivering high level FAPAR products with enhanced temporal information thanks to a dedicated composite procedure. Incidentally, this composite algorithm yields to enhanced spatial coverage as well. The level 3 FAPAR product is a summary of the level 2 daily information and is generated with an algorithm that reduces the high temporal frequency variability.
Abstract: This Algorithm Theoretical Basis Document (ATBD) describes an algorithm used to retrieve information on the nature and properties of vegetated terrestrial surfaces from an analysis of Level 1B data which are generated by the Medium Resolution Imaging Spectrometer (MERIS) of the European Space Agency (ESA). This algorithm delivers, in addition to the FAPAR product, the so-called rectified reflectance values in the red and near-infrared spectral bands.
Abstract: This Algorithm Theoretical Basis Document (ATBD) describes an algorithm used to retrieve information on the nature and properties of vegetated terrestrial surfaces from an analysis of Level 1B data which are generated by the Medium Resolution Imaging Spectrometer (MERIS) of the European Space Agency (ESA). This algorithm delivers, in addition to the FAPAR product, the so-called rectified reflectance values in the red and near-infrared spectral bands.
Abstract: This Algorithm Theoretical Basis Document (ATBD) describes an algorithm used to retrieve information on the nature and properties of vegetated terrestrial surfaces from an analysis of time series of surface product values. The time composite algorithm which is proposed here follows a simple strategy that can be used for various surface products such as the daily surface albedo and, more specifically, the instantaneous FAPAR and “rectified” products extracted from data taken by the SeaWiFS, MERIS, VEGETATION, and GLI sensors.
Abstract: This guide summarises the background, design, and use of the STARS group FAPAR algorithm software. The code was developed in IDL and C++, and is available as a package. The code was developed in such a manner that the end user will be able to manipulate the user interface easily, whilst the main algorithm components are protected behind the interface. The addition of new sensors, using existing generalised code has been facilitated. The addition of new, sensor specific code, when developments require it, has been incorporated, especially within the C++ code. The intention is that this code be made available on the internet for downloading by potential users.
Abstract: The atmosphere is largely but not totally transparent in the visible and near-infrared spectral regions, so that the Earth surface is observable through the atmosphere in these spectral bands. As a result, the proper interpretation of satellite measurements in terms of atmospheric properties requires the knowledge or specification of the state of the surface. Conversely, the exploitation of space measurements to characterize the surface of the Earth must take the various atmospheric processes that affect the transfer of radiation into account (absorption, scattering). Since radiation can be scattered multiple times between the atmosphere and the surface, these two components of the Earth system are intimately coupled from a radiative point of view. The accurate description of radiation transfer in such a system, and in particular the analysis and interpretation of satellite data must therefore be based on coupled surface-atmosphere models. This report discusses in detail the feasibility of characterizing the BRFs of selected surface types through the parametric BRF model originally proposed by Rahman et al. and a modification suggested by Martonchik et al. Two new representations of the surface reflection phase function are evaluated. Optimal values of the model parameters may be retrieved by inversion with a linear least squares optimization scheme after a suitable transformation of the model. These modified Rahman models appear to have performances comparable to the original version. The sensitivity of the albedo estimated by inversion of one of the new parametric models against data gathered from a single azimuthal plane is investigated. The estimates are found reliable, but display sometimes slightly higher errors for azimuth angles near the cross plane. The potential and limitation of predicting bidirectional reflectance factors at other observation geometries than those which were used for the parametric model inversion are investigated. The model parameters appear quite insensitive to observational conditions, except for an erratic behavior of the asymmetry factor when the data are acquired in the cross plane only. The study suggests that the extraction of model parameters may lead to improved land surface classification. This family of parametric BRF models is proposed as part of the land surface product algorithm of the Multi-angle Imaging Spectro-Radiometer (MISR) of NASA/JPL, due to fly in 1998 on the EOSAMI platform. These parametric functions could also find important applications in the processing and interpretation of all satellite data in the solar spectral range in which observation geometries are variable, notably MODIS, Vegetation, POLDER, ATSR, GLI, AVHRR, and others. Finally, extensions of the Rahman model to represent diffuse specular surface reflections and variable hot spot effects, are developed. These extensions are shown to address some limitations of the basic Rahman model uncovered in this report. All proposed parametric models are evaluated against a large number of reflectance factor measurements obtained in the field, as well as data generated by simulations based on state-of-the-art radiative transfer models.
Abstract: The Paris Agreement establishes a transparency framework for anthropogenic carbon dioxide (CO2) emissions. It&amp;amp;#039;s core component are inventory-based national greenhouse gas emission reports, which are complemented by independent estimates derived from atmospheric CO2 measurements combined with inverse modelling. It is, however, not known whether such a Monitoring and Verification Support (MVS) capacity is capable of constraining estimates of fossil-fuel emissions to an extent that is sufficient to provide valuable additional information. The CO2 Monitoring Mission (CO2M), planned as a constellation of satellites measuring column-integrated atmospheric CO2 concentration (XCO2), is expected to become a key component of such an MVS capacity. Here we provide a novel assessment of the potential of a comprehensive data assimilation system using simulated XCO2 and other observations to constrain fossil fuel CO2 emission estimates for an exemplary 1-week period in 2008. We find that CO2M enables useful weekly estimates of country-scale fossil fuel emissions independent of national inventories. When extrapolated from the weekly to the annual scale, uncertainties in emissions are comparable to uncertainties in inventories, so that estimates from inventories and from the MVS capacity can be used for mutual verification. We further demonstrate an alternative, synergistic mode of operation, with the purpose of delivering a best fossil fuel emission estimate. In this mode, the assimilation system uses not only XCO2 and the other data streams of the previous (verification) mode, but also the inventory information. Finally, we identify further steps towards an operational MVS capacity.
Abstract: The analysis of temporal geospatial data has provided important insights into global vegetation dynamics, particularly the interaction among different variables such as precipitation and vegetation indices. Nevertheless, this analysis is not a straightforward task due to the complex relationships among different systems driving the dynamics of the observed variables. Aiming at automatically extracting information from temporal geospatial data, we propose a new approach to detect stochastic and deterministic patterns embedded into time series and illustrate its effectiveness through an analysis of global geospatial precipitation and vegetation data captured over a 14 year period. By knowing such patterns, we can find similarities in the behavior of different systems even if these systems are characterized by different dynamics. In addition, we developed a novel determinism measure to evaluate the relative contribution of stochastic and deterministic patterns in a time series. Analyses showed that this measure permitted the detection of regions on the global map where the radiation absorbed by the vegetation and the incidence of rain occur with similar patterns of stochasticity. The methods developed in this study are generally applicable to any spatiotemporal data set and may be of particular interest for the analysis of the vast amount of remotely sensed geospatial data currently being collected routinely as part of national and international monitoring programs.
Abstract: Croatia is positioned in the transitional zone of South-eastern Europe and the Mediterranean, a region with frequent occurrence of severe droughts and dry spells, which makes it particularly vulnerable to the impact of climate change. It distends across three main European biogeographical regions – Continental, Alpine and Mediterranean – and therefore has a very high level of forest diversity comprised of 11 out of 14 European forest types. This makes it very convenient to study the exposure of main European forest types to extreme climatic events in the southern limits of their species distribution range. This study provides an assessment of responses of forest vegetation under episodes of climatic anomalies consisting of the most severe historical dry and warm spells in 2000 and 2003, together with the extremely rainy season in 2005 across Croatian territory. The question of interest in this study was to reveal how the existing forest types across the territorial gradient respond to highly expressed extreme climatic variations and to infer some clues about related adaptive strategies. The regional scale approach was applied which previously performed structural delineation of forest cover into eleven main forest groups or bioclimates and were examined considering functional differences. Responses of eleven bioclimatic types were analyzed by time series (1998–2005) of monthly FAPAR (The Fraction of Absorbed Photosynthetic Active Radiation) coverage with a spatial resolution of 1.2 km, freely available from JRC FAPAR project. To quantify and differentiate the vegetation response in the considered years we adopted indices of resilience (resistance, recovery, resilience and relative resilience). We also provided a modified approach by applying these indices on a seasonal scale to examine the relationship between variations of phenology and ecosystem responses. The results confirmed the modification of seasonality of photosynthetic activity in relation to the altitudinal and spatial gradient. At the intra-seasonal scale, we distinguished specific opportunistic behavior of the common beech and oak forest types to alternating climatic conditions. Beech forest types show a very high ability to shift their phenology to earlier spring warming as a consequence of global warming. However, continental and Mediterranean oak forest types and in particular Aleppo pine and holm oak forests showed a higher increase of FAPAR during rainy events. The revealed capability of some tree species to better exploit rainfall in very wet periodic episodes has to be further evaluated in drawing conclusions about the overall resilience of forests under future climate change scenarios.
