A surface bidirectional reflectance model has been developed for the correction of surface bidirectional effects in time series of satellite observations, where both sun and viewing angles are varying. The model follows a semiempirical approach and is designed to be applicable to heterogeneous surfaces. It contains only three adjustable parameters describing the surface and can potentially be included in an algorithm of processing and correction of a time series of remote sensing data. The model considers that the observed surface bidirectional reflectance is the sum of two main processes operating at a local scale: (1) a diffuse reflection component taking into account the geometrical structure of opaque reflectors on the surface, and shadowing effects, and (2) a volume scattering contribution by a collection of dispersed facets which simulates the volume scattering properties of canopies and bare soils. Detailed comparisons between the model and in situ observations show satisfactory agreement for most investigated surface types in the visible and near-infrared spectral bands. The model appears therefore as a good candidate to reduce substantially the undesirable fluctuations related to surface bidirectional effects in remotely sensed multitemporal data sets.

Information on land surface properties finds applications in a range of areas related to weather forecasting, environmental research, hazard management and climate monitoring. Remotely sensed observations yield the only means of supplying land surface information with adequate time sampling and a wide spatial coverage. The aim of the Satellite Application Facility for Land Surface Analysis (Land-SAF) is to take full advantage of remotely sensed data to support land, land-atmosphere and biosphere applications, with emphasis on the development and implementation of algorithms that allow operational use of data from European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) sensors. This article provides an overview of the Land-SAF, with brief descriptions of algorithms and validation results. The set of parameters currently estimated and disseminated by the Land-SAF consists of three main groups: (i) the surface radiation budget, including albedo, land surface temperature, and downward short-and longwave fluxes; (ii) the surface water budget (snow cover and evapotranspiration); and (iii) vegetation and wild-fire parameters.

Abstract. The overall objective of the present study is to introduce the new ECOCLIMAP-II database for Europe, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, already implemented at global scale. The ECOCLIMAP programme is a dual database at 1 km resolution that includes an ecosystem classification and a coherent set of land surface parameters that are primarily mandatory in meteorological modelling (notably leaf area index and albedo). Hence, the aim of this innovative physiography is to enhance the quality of initialisation and impose some surface attributes within the scope of weather forecasting and climate related studies. The strategy for implementing ECOCLIMAP-II is to depart from prevalent land cover products such as CLC2000 (Corine Land Cover) and GLC2000 (Global Land Cover) by splitting existing classes into new classes that possess a better regional character by virtue of the climatic environment (latitude, proximity to the sea, topography). The leaf area index (LAI) from MODIS and normalized difference vegetation index (NDVI) from SPOT/Vegetation (a global monitoring system of vegetation) yield the two proxy variables that were considered here in order to perform a multi-year trimmed analysis between 1999 and 2005 using the K-means method. Further, meteorological applications require each land cover type to appear as a partition of fractions of 4 main surface types or tiles (nature, water bodies, sea, urban areas) and, inside the nature tile, fractions of 12 plant functional types (PFTs) representing generic vegetation types -principally broadleaf forest, needleleaf forest, C3 and C4 crops, grassland and bare land -as incorporated by the SVAT model ISBA (Interactions Surface Biosphere Atmosphere) developed at Météo France. This landscape division also forms the cornerstone of a validation exercise. The new ECOCLIMAP-II can be verified with auxiliary land cover products at very fine and coarse resolutions by means of versatile land occupation nomenclatures.

ABSTRACT:The EUMETSAT Satellite Application Facility for Land Surface Analysis operationally delivers estimates of the downwelling shortwave radiation flux, which constitutes an important variable for characterising the surface energy budget. The product is derived from observations provided by the SEVIRI instrument onboard the geostationary satellites of the Meteosat Second Generation series. The spatial coverage of the product includes Europe, Africa, the Middle East, and parts of South America. It is generated every 30 min and distributed to the users in near real-time. For clear sky conditions the flux estimate is determined with a parameterisation of the atmospheric transmittance as a function of the concentration of atmospheric constituents. For overcast sky a simple physical model of the radiation transfer in the cloudatmosphere-surface system is employed, for which the satellite signal supplies the essential input information. The product has been validated with in situ data from six European ground measurement stations. The resulting statistics show a standard deviation of the difference between instantaneous satellite estimates and ground measurements in the order of 40 W m −2 for clear sky data and 110 W m −2 for cloudy sky data. For the complete sample including all data points the standard deviation amounts to 85 W m −2 . The bias between the satellite product and the ground data is small with absolute values of less than 10 W m −2 .

The utility of multi-angle optical remote sensing for terrestrial carbon cycle estimation is demonstrated through theoretical development, POLDER data analysis, and a case study of carbon cycle in a boreal forest. Progress in canopy-level photosynthesis modeling suggests that simpler big-leaf photosynthesis models are giving ways to more complex sunlit/shaded leaf separation models. This advancement in ecological modeling has increased the demand for advanced description of canopy architecture. Such demand may be mostly met through the use of multi-angle remote sensing techniques. In addition to leaf area index (LAI), another canopy parameter, the foliage clumping index, can be derived from multi-angle remote sensing. These two parameters are the basis for separating sunlit and shaded leaves. As leaf photosynthesis is nonlinearly related to incident radiation, such separation avoids the problems of big-leaf models that only make use of the total radiation absorption by the canopy without considering the distribution of radiation among leaves. A practical conclusion is that the traditional way of mapping the net primary productivity (NPP) through its correlation with the remotely sensed fraction of photosynthetically active radiation (FPAR) absorbed by plant canopies is only a very crude approximation and could be replaced with mapping LAI and clumping index and modeling NPP with advanced photosynthesis models. This is a step forward in remote sensing applications because single-angle remote sensing can only acquire information on the effective LAI related to the canopy gap fraction in the viewing direction and the amount of shaded leaf area is unknown. D

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Abstract. We have measured spectral albedo, as well as ancillary parameters, of seasonal European Arctic snow at Sodankylä, Finland (67°22' N, 26°39' E). The springtime intensive melt period was observed during the Snow Reflectance Transition Experiment (SNORTEX) in April 2009. The upwelling and downwelling spectral irradiance, measured at 290–550 nm with a double monochromator spectroradiometer, revealed albedo values of ~0.5–0.7 for the ultraviolet and visible range, both under clear sky and variable cloudiness. During the most intensive snowmelt period of four days, albedo decreased from 0.65 to 0.45 at 330 nm, and from 0.72 to 0.53 at 450 nm. In the literature, the UV and VIS albedo for clean snow are ~0.97–0.99, consistent with the extremely small absorption coefficient of ice in this spectral region. Our low albedo values were supported by two independent simultaneous broadband albedo measurements, and simulated albedo data. We explain the low albedo values to be due to (i) large snow grain sizes up to ~3 mm in diameter; (ii) meltwater surrounding the grains and increasing the effective grain size; (iii) absorption caused by impurities in the snow, with concentration of elemental carbon (black carbon) in snow of 87 ppb, and organic carbon 2894 ppb, at the time of albedo measurements. The high concentrations of carbon, detected by the thermal–optical method, were due to air masses originating from the Kola Peninsula, Russia, where mining and refining industries are located.

[1] This paper presents a pragmatic method to produce global maps of vegetation parameters, which offer essential data for weather forecast and climate modeling. The crucial variables are leaf area index (LAI), fractional vegetation cover (FVC), and fraction of absorbed photosynthetically active radiation (fAPAR). The approach relies on the use of spectral and directional vegetation indices simulated by a bidirectional reflectance model and calibrated against sets of satellite data. The model belongs to the kernel-driven category, and the coefficients obtained, as the result of linear inversion, are the basis of the proposed method. The strategy presented relies upon the existence of suitable angular measurements to derive each biophysical parameter. An application is shown with the global POLDER/ADEOS-I database. Special attention is given here to the future production of LAI and fAPAR since the albedo is a product already disseminated by the POLDER production center. Terrestrial ecosystems show a high level of aggregation and, in practice, only effective LAI can be measured. Therefore a correction factor, namely the clumping index, must be applied to help resolve the scaling issue. Clumping corrections are performed biome by biome, using empirical equations where it appears that LAI assessments for boreal and tropical forests would otherwise be significantly inaccurate. However, the effect of clumping will be less on FVC and fAPAR. The relevance of the proposed method is demonstrated through a comparison of POLDER-derived LAI values with a varied set of ground LAI measurements, including their coherence with the corresponding fAPAR.