Radiative transfer models (RTMs) designed to reproduce the anisotropy of surface brightness temperature (BT) are particularly useful for applications on Earth’s energy budget when using remote sensing (RS) datasets. Despite the fact that several thermal infrared (TIR) RTMs have been developed, a quantitative analysis comparing the benefits and limits of these models remains necessary. Herein, three modeling frameworks (physical hybrid, analytical parameterization, and kernel driven) have been evaluated comparatively for homogeneous vegetation, a row-planted crop, and a sparse forest. Airborne measurements and the discrete anisotropy radiative transfer (DART) model simulations were retained as the benchmark. Forward modeling and inverse fitting schemes were proposed for the sake of comparison. Results reveal that: 1) in the forward modeling scheme, from airborne measurements, the hybrid model performs better with root-mean-squared errors (RMSEs) of$0.17~^{\circ }$C,$1.57~^{\circ }$C, and$0.38~^{\circ }$C for homogenous, row-planted vineyard, and sparse forest scenes, respectively; the analytical model appears similar performant ($0.17~^{\circ }$C,$0.40~^{\circ }$C) for the homogeneous and sparse forest scenes, but less performant ($2.39~^{\circ }$C) for the row-planted scene and 2) in the inverse fitting scheme, the uncertainties (95% of probability) of model coefficients and predicted directional anisotropies were considered. The kernel-driven model has fewer modeling constraints and statistically performs better for the homogeneous and sparse forest scenes with RMSEs of$0.07~^{\circ }$C and$0.19~^{\circ }$C, respectively, whereas it is less efficient for the row-planted scene with RMSE of$0.80~^{\circ }$C. This study highlights the differences in accuracy between models of different complexity and provides reference information for researchers to improve existing models and for users to choose their best modeling solution.

Sloping terrain distorts the sun-target-sensor geometry, resulting in biases of the optical reflectance measured by remote sensors relative to flat situations. Performing topographic correction (TC) is, therefore, deemed mandatory to foster the full exploitation of satellite images worldwide to support various applications in mountainous regions. Various TC models have already been proposed and developed, while most of them were previously evaluated at local or regional scales using a few images with various evaluation criteria. Therefore, a systematic and comprehensive assessment has yet to be done on these TC models in the global mountainous regions. In the present study, 10523 Landsat 8 OLI images filtered by land cover types and seasons sampled in the global mountains are corrected by ten popular TC models (SE, b correction, VECA, CC, SCS, DS, SCS+C, PLC, Minnaert, and Minnaert+SCS) with a unified evaluation criterion on the Google Earth Engine platform. The outcomes are that: (1) global TC effects on Landsat 8 OLI images generally increase with sun zenith angles and latitudes; (2) six models (SE, b correction, CC, VECA, Minnaert, and Minnaert+SCS) show good adaptability among the ten models for the global mountainous placing a disregard to land cover types and seasons; (3) considering permanent snow and ice, needle-leaved forests in winter, and null values might appear in b correction, SE is deemed to be with the most global adaptability. This study pioneers an evaluation of fashionable TC models concerning mountainous regions worldwide and will be useful for applying TC to Landsat images for the benefit of making global TC products in the future and a fair inter-comparison of OLI surface reflectance measured in various mountainous areas of the globe.

Land Surface Temperature (LST) estimation relies on precise measurements of surface thermal infrared (TIR) radiance. LST, classified as an essential climate variable (ECV), exhibits rapid temporal variations within specific spatial scales, closely tied to illumination and scan angles. To enhance the utility of LST products, this study proposes a novel approach for concurrently reconstructing the temporal profile and angular dependence. The suggested method, known as the visible-thermal envelope method, integrates kernel-driven (KD) and diurnal temperature cycle (DTC) models, addressing surface structure and thermal factors, respectively. Additionally, this method facilitates LST downscaling by leveraging the higher spatial resolution of visible and near-infrared (VNIR) data, assuming temperature differences are homogeneous within coarse pixels. To validate the reliability of the proposed approach, TIR data from the geostationary satellite Himawari 8 are amalgamated with VNIR data from the polar-orbit satellite Sentinel-3A/3B. When compared to field measurements, the reconstructed results exhibit improvements with a total bias of 0.55 K and Root Mean Square Error (RMSE) of 2.14 K. Notably, in contrast to the original uncorrected results, this correction results in an approximate 50% reduction in bias and a 10% decrease in RMSE.

Estimating evapotranspiration (ET) beyond the local or point scale is essential for many water-related studies. By exploiting the relationship between surface biophysical parameters and thermal emission, continuous ET at such larger spatial scales can be obtained. In this study, we applied the EVASPA tool, which provides an ensemble of ET estimates, over southern France. This was done using MODIS data, including Land Surface Temperature, NDVI, and albedo, resulting in 243 ET estimates. Initial evaluations using in-situ flux data yielded reasonable results even when a simple average was used, with a broad absolute and performance range between the member estimates being observed. Additionally, our uncertainty analyses indicated that ensemble-based contextual modelling can provide sufficient spread for enhanced flux simulations. As EVASPA is intended for operational use, this work aims to guide the establishment of an optimal weighting criteria for the members to improve ET estimates.

TRISHNA is a thermal mission led in collaboration by CNES (Centre National d'Etudes Spatiales) and ISRO (Indian Space Research Organisation). It will collect optical and thermal datasets globally at a spatial resolution of 57m. The revisit period of 3 times per 8 days at low latitudes, with additional acquisitions towards the poles, and, suggests a high field of view (HFOV) around 35° to fill mission objectives. This will infer angular effects on the time series of measurements.TRISHNA overpass is 12:30 local time, which is highly prone to capture the hot spot phenomena corresponding to a radiometric peak when the view and sun geometries coincide. Previous studies (Lacaze et al.,2000 [1]; Roujean, 2000 [2]; Duffour et al., 2016 [3]) showed that the maximum directional effect arises in the hot spot geometry, thus adding significance to anisotropy effects in LST (Land Surface Temperature) measurements beside HFOV. Hot spot features can lead to LST measurement errors up to 10K. This uncertainty propagates in the estimation of evapotranspiration, which is a significant deliverable of the TRISHNA mission. Therefore, paying attention to the observations that are impacted by the hot spot is mandatory, either for discarding them or to perform an angular correction. Such a decision depends on the width of the hot spot peak and its intensity. On the one hand, data located very near the hot spot geometry may not be processed as hotspot characteristics are susceptible to the architecture of the canopy, and such information will not be available globally.On the other hand, regarding the cloudiness in certain regions, clear data, even in the hot spot, should be harnessed. Therefore, acquiring knowledge through measurements and simulations is mandatory for preparing the TRISHNA mission to be launched in 2026 in the best conditions.Hitherto, several studies about the impact of the hot spot phenomenon on radiometry were broadly concentrated in the optical domain and inadequately in the thermal infrared (TIR) range. The reason for that is the difficulty to accumulate information for TIR over time as the TIR signal is ephemeral and depends on environmental factors (wind, soil wetness, atmospheric humidity) in addition to the structure of the medium, which is the only driver for optical.Directional effects, either thermal or optical, can be resumed by the BRDF (Bidirectional Reflectance Distribution Function) (Julien et al., 2023 [4]). The starting point is collecting directional measurements for various environmental conditions and canopy types. This sustained the implementation of the TIRAMISU (Thermal InfraRed Anisotropy Measurements in India and Southern eUrope) project.

The human living environment of cities area has been rapidly improved from the end of the 20th century to the 21st century. The urban thermal environment and local microclimate have been changed and will affect human health and well-being for a long time, particularly in metropolitan areas. Land surface temperature (LST) for urban areas can be obtained from thermal infrared remote sensing observations, enabling the analysis of spatial and temporal variations in urban heat. However, there is very little published research on the modeling and analyzing land surface emissivity (LSE) for urban surfaces. LSE is a prerequisite for some inversion algorithms of LST such as the split-window algorithm, and it is also an important parameter in urban energy balance. Therefore, we proposed an analytical model for the urban LSE by using geometric optical (GO) and spectral invariant (SI) theories. The proposed model was evaluated based on both the synthetic and measured datasets. Results indicated that the simulation performance of proposed model was satisfactory with root mean squared error (RMSE) of approximately 0.004 and 0.009 when compared with datasets from 3D raytracing model and satellite-based emissivity product, respectively.

The European ECOSTRESS Hub (EEH) funded by European Space Agency targets at generating land surface temperature (LST), evapotranspiration (ET) and gross primary productivity (GPP) from the high-resolution ECOSTRESS observations. In Phase 1 (2020-2022), EEH LST obtained using the split-window and temperature and emissivity separation algorithms achieved good accuracy with an overall RMSE around 2 K. Evaluation of three ET estimates using different models, namely the Surface Energy Balance System (SEBS), Two Source Energy Balance (TSEB) parametric models, and the non-parametric Surface Temperature Initiated Closure (STIC) model, indicated that STIC ET had the highest accuracy (RMSE of ~70 W m-2). In Phase 2 (2023-2026), the surface energy balance will be coupled with photosynthesis through canopy-stomatal conductance. Overall, EEH is promising to advance the science of terrestrial ecosystem processes and facilitate the preparation for the future high-resolution thermal missions.

Topography distorts the angular distribution of the canopy gap fraction (GF). Path length (PL) correction is a simple and effective method to harmonize this distortion and improve canopy reflectance modelling andin situleaf area index measurements for vegetation, including both continuous (e.g., grass and crop) and discrete (e.g., forests) canopies, over sloping terrains. The rigorously theoretical derivation of PL correction for continuous canopies has been implemented. However, for discrete canopies, the PL show a serious heterogeneity, making it nearly impossible to be calculated. In this regard, there is still a need to develop theoretical derivation to evaluate and improve PL correction for forests over sloping terrains. In this study, (1) PL correction is proven to be equivalent to the correction of the canopy GF over sloping terrains, and our strategy concerns the canopy GF as a proxy of PL. (2) PL correction is first proven to be completely valid for forests with the Poisson trees distribution; yet it may produce uncertainty in certain directions for forests with tree distribution deviating from the Poisson model, especially for forests with regular tree distribution. (3) An improved model based on a Nilson and Peterson’s GF model for correcting PL for forests is given in this study. The results show that error produced by the PL correction for some forests can be effectively decreased by the improved model. The variation of directional tree distribution parameter cB(θ) with slope is the main cause of error produced by PL correction for forests. The study is of importance for better understanding and more accurate application of PL theory in topographic corrections andin situleaf area index measurements for forest canopies over sloping terrains.

The anisotropy of land surfaces reflectance of greatly impacts the acquisition and analysis of satellite images. Normalization methods using semi empirical kernel-driven models commonly remove these directional effects. Here, we present a physically-based approach to assess the accuracy of 2 normalization methods of Sentinel-2 (S2) and 3 (S3) images. A ReBeLS-based process fits the parameters of a kernel-driven model with S3 and S2 images simulated by the DART RT model in agricultural and urban areas. Here, normalization methods (c-factor, NDVI-disaggregation) hardly validate their assumptions: surface type and orientation have a great influence. We show the potential of RT models to assess and improve normalization methods.

Three-dimensional (3D) radiative transfer (RT) models are frequently recognized as a prerequisite when using high spatial resolution remote sensing data in heterogeneous surfaces. However, most studies of 3D RT models have been restricted to limited applications due to the low computational efficiency. Therefore, this study proposed a graphic processing unit (GPU)-based solution for the ray tracing 3D RT model. A state-of-the-art graphics and compute application programming interface, Vulkan, was introduced to implement the RT process. A bounding box method was adopted for the computation acceleration. By comparison with a central processing unit (CPU)-based solution, the performance efficiency of the proposed solution is significantly better: the simulation time of a GPU model is significantly reduced by more than 99% when facing a large-scale simulation mission. The simulation accuracy of the two solutions is similar, with root mean squared errors (RMSEs) lower than 0.005, 0.032 and 0.31 K for the red, near-infrared (NIR) and brightness temperature images, respectively. An evaluation based on airborne multiangle measurements also indicated that the accuracy of the proposed solution was satisfactory for simulating the red and NIR bidirectional reflectance factor and brightness temperature directional anisotropies, with RMSEs lower than 0.003, 0.020 and 0.20 K, respectively, when treating the whole scene as a pixel. Considering the simulation accuracy and efficiency, a GPU-based model will be an important supplement to the CPU model.

Canopy clumping index (CI) characterizes the extent of the nonrandom spatial distribution of foliage elements within a canopy and is critical for determining the radiative transfer, photosynthesis, and transpiration processes in the canopy. It is widely perceived that CI increases with zenith angle (θ), because between-crown gaps decrease in size and number with increasing θ. In this study, we demonstrate that this is not always true. Analytical equations between CI and θ are first developed based on widely-used forest canopy gap fraction theories. The results show that the zenith angular variation of CI is closely related to crown projected area or crown shapes (i.e., the ratio of the crown height to its diameter, RHD): CI increases with θ for canopies with “tower” crowns (RHD > 1), but decreases with θ for “umbrella” crowns (RHDin-situmeasurements and multi-angular remote sensing.

Land surface emissivity is required for deriving surface temperature from thermal infrared radiances. When using single-channel or two-channel thermal infrared sensors, information on emissivity may be derived from spectral reflectance measurements through regression models. In this study, we present relationships derived over bare soils for Landsat 7 - ETM+ sensor. Reflectances in ETM+ channels were obtained from soil spectra (between 0.4 and 13 μm) extracted from the ASTER spectral library and the dataset acquired by Lesaignoux et al. (2013). The best relations were obtained between reflectances in the mid-infrared channels (ETM5 and ETM7) and the thermal infrared channel (ETM6) with correlation coefficients of 0.63 and 0.72 respectively. The relations were mostly generated by the variations of soil reflectances due to changes in soil moisture. Correlations were lower when considering the variations due to soil type.

In this paper, we study the estimation of olive tree biophysical properties driven by Sentinel-2 (S2) image inversion. The latter is based on the forward/backward radiative transfer (RT) model. The forward step is done simulating DART on a realistic tree mock-up, whereas the backward is done based on a coupling between the Look UP Table (LUT) and the Markov Chain Monte Carlo (MCMC). The parameters Leaf area index (LAI), chlorophyll (Cab) water (Cw) contents and mesophyll structure (N) are derived. The results are promising, in particular LAI and Cab values are close to those found in literature.

The monitoring of the water cycle at the Earth surface which tightly interacts with the climate change processes as well as a number of practical applications (agriculture, soil and water quality assessment, irrigation and water resource management, etc…) requires surface temperature measurements at local scale. Such is the goal of the Indian-French high spatio-temporal TRISHNA mission (Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment). The scientific objectives of the mission and research work conducted to consolidate the mission specifications are presented. Progress in modelling of surface fluxes is then discussed. The main specifications of the mission such as the revisit, the spatial resolution, the overpass time, the spectral bands and the orbit are analyzed and justified. The resulting baseline of the mission is given.

Satellite Sentinel-2 offers a global coverage of the Earth at the frequency of a few days with pixel size ranging from 10 to 20 meters. Such spatio-temporal resolution fosters an advanced research in agriculture. Accounting for BRDF (Bidirectional Reflectance Distribution Function) information is required both for target monitoring and surface albedo estimate. BRDF sampling being limited from HR (High Resolution) sensors, the added-value of the BRDF 300m from PROBA-V instrument is assessed. Results are shown for the seasonal cycles 2016 and 2017 using parameterized and detailed radiation transfer models. The validation is carried on for anchor ICOS (Integrated Carbon Observation System) stations located on the French territory.

In this paper, we investigate the estimation of crop biophysical properties from small footprint LiDAR waveforms inversion. Due to crop heterogeneity within the same agricultural field, a classification on similar waveform clusters is performed before inversion. A Look up table (LUT) approach was adapted then, to derive the height and LAI of maize and wheat crops. The LUT was generated using the Discrete Anisotropic Radiative Transfer (DART) by simulating the LiDAR observations which are used to search for the suitable set of biophysical properties describing the different crops clusters. The results are promising. Crops height is accurately estimated with a root mean square error (RMSE) of 0.06m and 0.03m for maize and wheat, respectively. LAI was well estimated with RMSE of 0.07 and 0.43 for maize and wheat, respectively.

This study aims at presenting for the first time merged GEO (geostationary elevation orbit) MSG and LEO (low elevation orbit) EPS albedo products over Europe issued from an operational production center. The assets of the merging is to complete the high latitude coverage from existing GEO products with a resolution grid adapted. Spectral norms were applied for product matching and also standard atmospheric correction. Still aerosol removal need to be timely performed.

Correction of the directional effects affecting satellite measurements in thermal infrared (TIR) require simple methods robust enough to be easily integrated into data processing chains. The ability of a simple parametric model based on an analytic formulation of the hot spot to estimate directional TIR anisotropy is tested. For this purpose, the necessary data are provided using the deterministic model SCOPE (Soil Canopy Observations, Photochemistry and Energy fluxes, Van der Tol et al. 2009) as a data generator. SCOPE is first validated against experimental energy fluxes and directional temperatures measured over winter wheat and pine stands. Then, the sensitivity of anisotropy to several parameters (leaf area index, water stress, wind speed and hot spot parameter) is illustrated. Finally the parameters of the simplified model are derived by inverting SCOPE simulated data. The comparison of directional anisotropy for zenith viewing angles lower than 35° reveals absolute error lower than 0.3K.

A comparison between the algorithm for Land Aerosol property and Bidirectional reflectance Inversion by Time Series technique (LABITS) and a daily estimation of aerosol optical depth (AOD) algorithm (AERUS-GEO) over land surface using MSG/SEVIRI data over North Africa is presented. To obtain indications about the quantitative performance of two AOD retrieval methods mentioned above, daily SEVIRI AOD values is considered with respect to those measured from the global aerosol-monitoring Aerosol Robotic Network (AERONET) data. The correlation coefficient (R2) between retrieved SEVIRI AOD at 650 nm from the AERUS-GEO algorithm and the AERONET Level 2.0 daily average AOD at 675 nm is 0.80 and root mean square error (RMSE) is 0.044, and R2between retrieved AOD from the LABITS algorithm and AERONET AOD is 0.80 and RMSE is 0.037.

Land surface albedo is considered to be a fundamental quantity for accurate assessment of the surface energy budget. Whereas satellite products usually provide total surface albedo, land surface models often require a separation of snow-free surface albedos of the vegetation canopy and the underlying bare soil. Visible and near infrared vegetation and bare soil albedos are here retrieved from a decade of MODIS data at global scale by using a Kalman-filter-based method. The retrieved quantities show good agreement with those derived in previous studies. The main advantage of the proposed method over other approaches is that it provides albedo components efficiently throughout the year, thus allowing the seasonal cycles to be captured. In a second step, the snow-free vegetation and bare soil albedos derived from the satellite surface albedo product are exploited to evaluate the value added of the use of satellite albedo in Numerical Weather Prediction (NWP) models.

Downwelling radiative fluxes at the surface level are estimated in near real time in the framework of the Satellite Application Facility on Land Surface Analysis (LSA SAF). This is achieved using data acquired by the geostationary Meteosat Second Generation satellite. The method for the retrieval for downwelling shortwave surface fluxes (DSSF) is based on a physical parameterization, which depends on the solar energy arriving to Earth, the cloud coverage, and the atmospheric transmittance, among other parameters. After many years of development and operational activities involving the DSSF product of the LSA SAF, a summary of the retrieval algorithm, some applications, and the recent improvements on the consideration of aerosol effects is presented here.

Vegetation surfaces play an important role in the Earth's energy balance and have a significant impact on the global carbon cycle. The fraction of solar radiation that is absorbed by a vegetation canopy will assess the rate of photosynthesis and besides the amount of carbon flux further fixed or released by this same canopy layer. The radiative transfer scheme within the canopy of ISBA-Ags interactive vegetation model was defined in 1998 by Calvet et al [2] according to a self-shading approach. The incident fluxes at the top of the canopy go through a multilayer vegetation cover. Then, the attenuated flux in the PAR wavelength domain of each layer is used by the Jacobs Model [3] to evaluate the net assimilation (An) of the leaf. Net assimilation of the leaf estimated for each layer is mixed together to derive the average An quantity of the total vegetation cover. A detailed description of the vegetation radiative transfer scheme within the canopy is given in Appendix B of Calvet et al. [2]. The objective of the present study is the improvement of the current scheme and its evaluation. The impact of the new absorbed radiation on the photosynthesis module is considered, in order to obtain better simulations of the Leaf Area Index (LAI) and Gross Primary Production (GPP). These evaluations sustain the value added of an advanced representation of the radiative transfer within the canopy in order to estimate the photosynthesis.

Airborne broadband albedo and leaf area index (LAI) measurements were carried out simultaneously in the subarctic area of Sodankylä during the SNORTEX campaign in springs 2008 - 2010. Two pairs of pyranometers were attached to the helicopter on either side and a wide-optics camera was looking orthogonally downwards for LAI retrieval. The albedo depended on the LAI according to a previously developed model. The measurement altitude did not affect the albedo vs. LAI relationship. The winter time albedo value of snow varied in a wide range both seasonally and diurnally. The albedo values of open areas could also differ from each other distinctly during the same day depending on the land cover class.

This paper presents an innovative method to estimate the daily aerosol optical thickness (AOT) on a continental scale, using MSG observation. The approach takes best benefit of the high temporal resolution of the observing system, in order to disentangle the surface and aerosol angular signatures. For such, a semi-empirical BRDF model that mimics both surface and aerosols signals is inverted according to a recursive scheme. The method is not instrument-specific and could be reproduced with other GEO and LEO observing systems. Herein, field of application is restricted to MSG/SEVIRI data over mid latitude and African regions. The reliability of AOT estimates is demonstrated through comparisons with local AERONET ground measurements and at regional scale against MODIS. The method appears very promising for tracking anthropogenic emissions in the troposphere and also for estimating dust events over bright surfaces. Furthermore, high temporal AOT retrievals at moderate spatial resolution stress the effects of the density of urbanization and potentially on motor vehicle traffic on air quality. Finally, this study suggests that this approach is appropriate for multi-sensor data fusion, for the simultaneous retrieval of surface albedo and AOT, and also to generate these products in near-real time with a very high generation frequency.

The European Meteorological Satellite Organization (EUMETSAT) maintains a number of decentralized processing centers dedicated to different scientific themes. The Portuguese Meteorological Institute hosts the Satellite Application Facility on Land Surface Analysis (LSA-SAF). The primary objective of the LSA-SAF is to provide added-value products for the meteorological and environmental science communities with main applications in the fields of climate modeling, environmental management, natural hazards management, and climate change detection. Since 2005 data from Meteosat Second Generation satellite are routinely processed in near real time by the LSA-SAF operational system in Lisbon. Presently, the delivered operational products comprise land surface albedo and temperature, shortwave and long-wave downwelling radiation fluxes, vegetation parameters and snow cover. After more than ten years (1999–2011) of research, development, and progressive operational activities, a summary of characteristics and performances of albedo and downwelling shortwave (DSSF) products is presented (Sections 2 and 3). The relevance of LSA-SAF albedo product is analyzed through a weather forecast model (ALADIN) in order to account for the inter-annual spatial and temporal variability (Section 4). The added value brought by the use of LSA-SAF shortwave and long-wave products is also diagnosed through SURFEX Land Surface Models (LSM) simulations with the surface temperature, the water content and the energy fluxes (Section 4). Perspectives of the LSA-SAF project are finally stressed (Section 5–6).

This study is devoted to an assessment of the quality performance of the Satellite Application Facility on Land Surface Analysis (LSA-SAF) albedo products, which are generated from the high frequency of observations now offered by the Meteosat Second Generation (MSG) geostationary satellite series. Land surface albedo products are compared against in situ measurements and the analogous products from the Collection 4 of Moderate Resolution Imaging Spectroradiometer (MODIS) in domains in European and Northern African regions. We find a good consistency with MODIS broadband shortwave and near-infrared directional-hemispherical reflectance (DHR) over midlatitude regions. Comparative statistics indicate biases below 0.01 in absolute and 5% in relative terms, and an average root-mean-square error (RMSE) around 0.03 for all situations, including in snow cases. The comparison for shortwave bihemispherical reflectance (BHR) shows an even better match over herbaceous cover with respective values for bias and RMSE of 0.007 and 0.012. Broadband visible BHR from LSA-SAF overestimates MODIS equivalent albedo product by 20%, which seems to be caused by remaining uncertainties in narrowband-to-broadband conversion, residual aerosol signals, and the use of a different bidirectional reflectance distribution function (BRDF) model. The commendable statistical results obtained over bright desert targets such as Northern Africa are worth noticing. Assuming that the MODIS product is a good reference, a relative bias of 10% can be judged satisfactory for the LSA-SAF snow-free broadband DHR values. In addition, quarter-hourly MSG observations better capture snow episodes, although some discrepancies appear for areas with high vegetation cover.

The European Meteorological Satellite Organization (EUMETSAT) maintains a number of decentralized processing centers dedicated to different scientific themes. The Portuguese Meteorological Institute hosts the Satellite Application Facility on Land Surface Analysis (LSA-SAF). The primary objective of the LSA-SAF is to provide added-value products for the meteorological and environmental science communities with main applications in the fields of climate modeling, environmental management, natural hazards management, and climate change detection. Since 2005 data from Meteosat Second Generation satellite are routinely processed in near real time by the LSA-SAF operational system in Lisbon. Presently, the delivered operational products comprise land surface albedo and temperature, shortwave and long-wave downwelling radiation fluxes, vegetation parameters and snow cover. After about seven years (1999–2007) of research, development, and progressive operational activities, a summary of the surface albedo product characteristics and performances is presented. The relevance of LSA-SAF albedo product is analyzed through a weather forecast model (ALADIN) in order to account for the inter-annual spatial and temporal variability. Results clearly show a positive impact on the 12-hour forecast of 2m temperatures. Moreover, in order to improve the quality of LSA-SAF products, an operational MSG aerosol product is under development that will allow a near real time atmospheric correction of the MSG radiance. Results show a significant enhancement of the consistency of the surface albedo.

This work presents the ECOCLIMAP-II database for western Africa, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, implemented at global scale. ECOCLIMAP-II is a dual database at 1-km resolution that comprises an ecosystem classification and a coherent set of land surface parameters. This new physiographic information was especially developed in the framework of the African Monsoon Multidisciplinary Analysis (AMMA) programme. Criteria for coherence between prevalent land cover classifications and the satellite leaf area index (LAI) between 2000 and 2007 constitute the analysis tools for setting up the ECOCLIMAP-II. The LAI and inferred fraction of vegetation are spatially distributed per land cover unit. The vegetation fraction is handled to split the land surface albedo into vegetation and soil albedo components. The reliability of the physiographic details is also confirmed through verification with land cover products of higher resolutions.

Large discrepancies are observed between snow albedo in Numerical Weather Prediction (NWP) models and from satellite observations in the case of high vegetation. Knowledge of the Bidirectional Reflectance Distribution Function (BRDF) of snow-forest system is required to solve the problem. The 3-years SNORTEX (Snow Reflectance Transition Experiment) campaign acquires from 2008 in situ measurements of snow and forest properties in support to the development of modelling tools and to validate coarse resolution satellite products (POLDER, MODIS, MERIS, METOP). The measurement scheme and some first example results are presented from the Intensive Observing Period (IOP) of 2008, which can be decomposed into airborne and ground operations. Multi-temporal BRDF at a metric resolution were acquired from OSIRIS (airPOLDER) onboard a helicopter and from ground with FigiFiGo spectrogoniometer. The same helicopter embarked a pair of UV sensors, pyranometers and a wide-optics camera. Ground component includes exhaustive snow measurements.

The European Meteorological Satellite Organization (EUMETSAT) maintains a number of decentralized processing centers dedicated to different scientific themes. The Satellite Application Facility on Land Surface Analysis (LSA-SAF) is hosted by the Portuguese Meteorological Institute. Its objective is to provide value added products for the meteorological and environmental science communities with main applications in the fields of climate modeling, environmental management, natural hazards management, and climate change detection. Since 2005 data from Meteosat Second Generation satellite are routinely processed in near real time by the Land-SAF operational system in Lisbon. Presently, the delivered operational products comprise land surface albedo and temperature, short-wave and long-wave downwelling radiation fluxes, and snow cover. Within the project consortium Meteo-France is responsible for the land surface albedo and down-welling short-wave radiation flux products. After about seven years (1999-2007) of research, development, and progressive operational activities, a summary of the surface albedo and down-welling short-wave radiation product characteristics and performances is presented on the basis of the end of the initial operational phase (February 2007).

Land surface albedo determines the repartition of downwelling solar radiation into components that are either reflected back to the atmosphere or absorbed by the surface. As more sophisticated soil-vegetation-atmosphere transfer schemes are being implemented in numerical meteorological models, it will become increasingly important to accurately characterize the spatial and temporal albedo variations. Within the scope of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Land Surface Analysis, we have developed a daily albedo product, which is derived in near real time from observations provided by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument onboard the geostationary satellites of the Meteosat Second Generation series. The basic algorithm concept comprises an atmospheric correction scheme, the inversion of a linear semi-empirical model of the bidirectional reflectance distribution function, the angular integration of the bidirectional reflectance distribution function to obtain spectral albedo, and the application of suitable conversion relations to derive broadband albedo estimates. The reflectance model inversion is performed each day based on the available set of clear-sky observations. In addition, constraints on the model parameters are taken into account in the inversion process. By specifying these constraints according to the previous model output in a recursive manner, a complete spatial coverage of the resulting albedo maps is achieved while, at the same time, preserving a high temporal resolution. This paper primarily concentrates on the description of the methodology. In addition, examples for the obtained albedo maps and time series, as well as the first validation results, are presented.

In this contribution we give a short overview of the status of the surface albedo and down-welling short-wave radiation flux products which are developed by Meteo-France in the framework of the Satellite Application Facility on Land Surface Analysis (Land-SAF). Both products are based on the 0.6 mum, 0.8 mum, and 1.6 mum channels of the Meteosat/SEVIRI instrument and are calculated and distributed in near real time. The methodology is briefly described, product examples are shown, and first validation results are discussed.

This paper describes the prototyping algorithm developed for retrieving the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) using MSG data in the framework of Satellite Application Facility on Land Surface Analysis (LSA SAF). The prototyping relies on the Roujean and Bréon (1995) method, which is based on simulations of visible and near infrared reflectance values in an optimal geometry. A relationship is found between a vegetation index and daily FAPAR. The algorithm has been applied to one year of MSG BRDF data since August 2005, using a temporal frequency of 5-days, and then validated against a set of operational satellite FAPAR products such as MODIS, MERIS, SeaWiFS and CYCLOPES, as well as with ground-truth information. MSG FAPAR products present a reliable spatial and temporal distribution of retrievals. The inter-comparison of the MSG FAPAR product with different operational products shows the large existing differences among FAPAR products. However, the best consistency is found between MSG and MERIS (only July 2006 was evaluated) or between MSG and CYCLOPES (available only for 2003). At a pixel level, the RMS over selected validation sites is lower than 0.1 between MSG and MERIS or MSG and CYCLOPES products, which demonstrates the competitiveness of the proposed prototype.

This paper gives an overview of the ECOCLIMAP-II database, which is a new surface parameters global dataset at a 1 km resolution. The aim of this database is to initialize soil-vegetation-atmosphere-transfer (SVAT) schemes in meteorological and climate modeling. The overall objective of this paper is then to provide elements of the method used to derive surface parameters at 1 km resolution departing from existing land cover maps, and satellite information. The new concept of classification relies on the analysis of time series of SPOT/ VEGETATION NDVI, in order to define homogeneous ecosystems from a criterion of functioning. For the European product shown here, the more recent CORINE land cover is considered to bring up improvement in regional legends. Surface parameters are derived for each of these ecosystems based on look-up tables. The key parameter is the leaf area index (LAI) since, at the exception of albedo, derivation of other parameter depends on it. This database can be used in meso-scale models,NWP models and GCM models.

This study is devoted to a global verification of the reliability of the POLDER-derived BRDF and albedo products over land surfaces based on an exhaustive review of similar existing products. A global comparison of POLDER albedos is carried on at 0.5 degree resolution with satellite-derived products and Numerical Weather Prediction (NWP) models. In general, a large underestimate of the albedos from climate databases are noticed over desert and semi-desert regions. Also, large discrepancies are noticed for snow. NWP albedos show overestimates of 0.4 over mountainous regions, compared to either POLDER or MODIS. Reverse trend is observed at high latitudes for snow beneath forest canopies. As a conclusion, it comes out the multi-angular capabilities offered by POLDER yields a unique tool to estimate surface albedo within an the accuracy required by the users.

This paper aims at demonstrating the possibility of merging data from various medium-resolution spaceborne sensors to produce a consistent time series of surface bidirectional reflectance distribution function (BRDF) and albedo products. The spectral, directional, temporal, and spatial aspects of the multisensor fusion are presented. Emphasis is then given on the spectral normalization for the fusion of Medium Resolution Imaging Spectrometer Instrument (MERIS) data with Moderate Resolution Imaging Spectroradiometer (MODIS) and VEGETATION (VGT) data. Two methods are evaluated: a simple statistical method, which relies on a linear regression using all the available spectral bands, and a more innovative method called the spectral mode method, which is based on the restitution of the surface spectral signature by a combination of universal spectral functions. Analysis with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data and satellite products reveals that the spectral mode method is more efficient. This approach is used to merge top-of-canopy bidirectional reflectances from MERIS and VGT for the restitution of BRDF and albedo over a subset of West Africa. Compared to the products obtained with MERIS alone, the fusion with VGT demonstrates an improvement of the spatial coverage and a reduction of product uncertainty by about a third

In the framework of the EUMETSAT Satellite Application Facility for Land Surface Analysis (Land-SAF) we develop surface albedo and short-wave radiation products generated from observations provided by the SEVIRI instrument on board of the geo-stationary Meteosat Second Generation satellite. For the albedo product cloud-free top-of-atmosphere reflectances in the instruments 0.6 /spl mu/m, 0.8 /spl mu/m, and 1.6 /spl mu/m channels are atmospherically corrected with a simplified and fast radiative transfer code. The algorithm then exploits the diurnal variation of the illumination angle, which provides information on the angular variation of reflectance, in order to invert a linear kernel-based bi-directional reflectance model. Several albedo variants are derived by adequately integrating the constrained model functions. The down-welling short-wave radiation at the surface essentially depends on cloud cover and hence to derive a reliable estimate of this quantity, the cloud mask represents an important piece of information. Depending on the presence of clouds and potentially on cloud type, different physically based parameterization schemes are applied to approximate the radiative transfer problem. In the case of cloudy sky the top-of-atmosphere albedo is the most important input quantity. It is derived from the satellite measurements by applying a narrow - to broadband conversion and a suitable angular reflectance model.

The objective of the Cyclopes project is to provide coherent estimates of bio-physical variables such as land surface albedo and leaf area index at regional and global scales and to demonstrate their application in the fields of land cover change detection and carbon cycle modeling. An essential element of the project strategy is to provide sensor-independent estimates by exploiting the synergy of an ensemble of medium resolution imaging instruments. The differences in the orbital configurations (angular sampling, frequency of observations) and sensor characteristics (spectral sensitivity, pixel size) potentially offer complementary information allowing us to increase the precision of the variable estimates. However, from a technical point of view these differences pose a number of problems and for the implementation in operational processing chains a compromise between the optimal use of the available information and the practical feasibility needs to be found. In this contribution we outline possible scenarios for achieving the multi-sensor data fusion.

In atmospheric models, the initialization of soil-vegetation-atmosphere transfer scheme (SVAT) requires a few surface parameters like leaf area index (LAI), fraction of vegetation and albedo. These parameters are currently based on look-up tables and land cover maps. This paper presents different means to derive land cover maps, LAI and albedo over France using the SPOT/VEGETATION data for the year 2000. The geometric and radiometric quality of SPOT/VEGETATION leads to an improvement of surface parameters compared to AVHRR products.

The CYTTARES database (CYclopes Training and Testing Algorithm Reference Ensemble of Sites) is composed of many sites representing the whole biome over the globe. This selection is based on ECOCLIMAP dataset and uses various specific criteria, such as existing networks site homogeneity, topography, geographical location. 532 sites are selected over the globe, including 137 sites from AERONET to future atmospheric correction and from VALERI for a comparison with the available ground observations. Then, 98 additional sites come from the existing international networks (FLUXNET, ILTER, MAB). Manually sites finally enable to complete the database. Each site is associated to biophysical variables, which will enable to validate the SAIL-PROSPECT model inversion within the European CYCLOPES project.

In the framework of the EUMETSAT Satellite Application Facility for land surface analysis (Land-SAF) we develop a surface albedo product which is generated from observations provided by the SEVIRI instrument on board of the geostationary MSG satellite. Cloud-cleared top-of-atmosphere reflectances in the 0.6 /spl mu/m, 0.8 /spl mu/m, and 1.6 /spl mu/m channels are atmospherically corrected with a simplified and fast radiative transfer code (SMAC). The algorithm for the determination of albedo relies on the inversion of a linear kernel-based BRDF model. Spectral directional-hemispherical and bi-hemispherical albedos as well as associated error estimates are derived. Broadband albedo values are derived from the spectral quantities by using suitable linear regression coefficients. In the case of poor angular sampling the information content provided by the MSG observations needs to be augmented by a priori information, e.g. by using a BRDF data base derived from measurements of instruments with multiple view angle capabilities such as POLDER. A method based on Bayes' Theorem has been developed and integrated for that purpose. In addition, this approach makes it possible to add the information coming from an additional sensor such as the EPS/AVHRR instrument which is planned to be included in the Land-SAF activities. First applications of the prototype to simulated MSG images as well as to real SPOT/VEGETATION data are presented.

A statistical inversion method is first presented in support to the application of kernel-based BRDF (bi-directional reflectance distribution function) models for the calculation of the surface albedo. We present an operational procedure for the inversion of a kernel-driven BRDF model and further albedo retrieval to be applicable to the SEVIRI/MSG reflectance measurements. The processing steps applied to space-borne POLDER sensor data were as follows: (1) quality control, (2) accumulation of a priori information on model coefficients of directional hemispherical reflectance, (3) implementation of the BRDF model inversion methods based on the biased estimation instead of usual non-biased least solution, which has too big a variance in this case. The data control procedure consists both in filtering inputs of reflectance data and output of model coefficients based on analysis criteria determined by Fisher statistics. A multi-criteria procedure follows considering in particular the shape of the reflectance angular signature: (1) T-statistics, (2) the bowl shape index, (3) the dome shape index, (4) the white sky albedo (bi-hemispherical reflectance), (5) the black sky albedo variance (directional hemispherical reflectance). The procedure is applied to POLDER data corresponding to the 16 classes of IGBP land cover classification. The statistical results include mean values and covariance matrix for the spectral BRDF model coefficients.

Leaf area index (LAI) retrieval from remote sensing is a very active research field. In heterogeneous canopies, simulations with the canopy reflectance model five-scale show that nadir view vegetation indices, such as NDVI and simple ratio (SR), based on near infrared and red reflectance are more closely related to the gap fraction at the solar zenith angle than the LAI. The gap fraction is important in canopy light interception. At the solar zenith angle, it can be used to estimate the amount of sunlit LAI. But the knowledge or LAI and foliage heterogeneity are both needed to estimate shaded leaves that are also important in the carbon cycle. In our previous studies, we developed a methodology to retrieve the foliage heterogeneity, represented by a clumping index, from remote sensing. The retrieval is accomplished with an anisotropy index using broadband directional reflectance at the hotspot and at the darkspot. The combination of nadir, hotspot, and darkspot views allows the LAI retrieval for a given cover type. However, directional measurements are not usually acquired with the same sun-target-sensor geometry, so the anisotropy kernel-based four-scale linear model for anisotropy reflectance (FLAIR) is used to interpolate the directional reflectance from ADEOS-POLDER data to acquire hotspot and darkspot reflectance at a common geometry in the near infrared band. A landcover map at 1-km based on SPOT-VGT data acquired in 1998 and directional POLDER data acquired over Canada in June 1997, are used to map the clumping index. The results show consistent clumping index values compared to in-situ values, and that SR and the anisotropy index are not correlated to each other, indicating that both indices are related to different canopy properties.

A new method is proposed for the reduction of the noise-like fluctuations associated with variations of Sun-target-sensor geometry in multitemporal AVHRR data in the visible and near-infrared bands. Its principle is to adjust, over a monthly period, a three-parameter model of surface bidirectional reflectance on a time series of cloud-free atmospherically corrected AVHRR data. One parameter of the model represents the surface reflectance corrected for angular effects. Time profiles of corrected reflectances are obtained by making the monthly period slide over the annual vegetation cycle. This procedure is applied to an annual cycle of AVHRR data on seven test sites in France representative of bare soils, agricultural crops, and forested thematic areas. The method is evaluated according to two criteria, which are the ability of the bidirectional reflectance model to reduce the amount of noise-like temporal fluctuations of AVHRR data, and the stability of the retrieved parameters when random noise is artificially added to the original AVHRR data set. The perturbations induced by the coupling between diffuse sky radiance effects and the non-Lambertian behavior of ground reflectance are also discussed. The method is proved to give satisfactory results, and can potentially be used to compare satellite-derived reflectances obtained not only at different times, but also at different places and with different sensors.>