Abstract. Polarization and Directionality of the Earth's Reflectances (POLDER) is a new instrument devoted to the global observation of the polarization and directionality of solar radiation reflected by the Earth-atmosphere system. It will fly onboard the ADEOS platform in 1996. This paper outlines the improvements expected from POLDER in the description of atmospheric aerosols and water vapor over land, and of surface bidirectional reflectances. It then gives a detailed description of the operational algorithms which are implemented in the "land surface and atmosphere over land" processing line. This line is part of an effort initiated by Centre National d'Etudes Spatiales (the French Space Agency) to develop lines of products in order to facilitate the exploration of POLDER's new capabilities by the international science community. Emphasis is given in this paper to the presentation of the principles, physical rationale, and elements of validation of the algorithms of this processing line. The main products are (1) for each orbit segment, the amount and type of aerosols, the water vapor content, and bidirectional reflectances corrected for atmospheric effects, and (2) every 10 days, global maps of surface directional signatures, of hemispherical surface reflectances, and of parameters describing the statistical distribution of aerosol and water vapor content. These products will be made available to all interested investigators. The most innovative algorithms of the processing line are (1) cloud detection, based on a series of tests involving reflectance thresholds, oxygen pressure estimates, and analysis of polarized radiance in the rainbow direction, (2) retrieval of aerosol optical thickness and type from directional polarized radiance measurements, and (3) retrieval of surface directional signature through an adjustment of a time series of directional reflectance measurements with a semiempirical bidirectional reflectance model. IntroductionBefore the end of the century, a series of Earth-orbiting satellites will carry several advanced, well-calibrated instruments designed to provide global observations of the Earth's oceans, land, and atmosphere.

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 an innovative method for obtaining a daily estimate of a qualitycontrolled aerosol optical thickness (AOT) of a vertical column of the atmosphere over the continents. Because properties of land surface are more stationary than the atmosphere, the temporal dimension is exploited for simultaneous retrieval of the surface and aerosol bidirectional reflectance distribution function (BRDF) coming from a kerneldriven reflectance model. Off-zenith geometry of illumination enhances the forward scattering peak of the aerosol, which improves the retrieval of AOT from the aerosol BRDF. The solution is obtained through an unconstrained linear inversion procedure and perpetuated in time using a Kalman filter. On the basis of numerical experiments using the 6S atmospheric code, the validity of the BRDF model is demonstrated. The application is carried out with data from the Spinning Enhanced Visible and Infra Red Imager

A Bidirectional Reflectance Distribution Function (Ê) catalog of different crops (mainly wheat, alfalfa, sunflower and maize) has been acquired thanks to Alpilles/ReSeDA campaign, over the whole crop cycles in 1997. This was achieved using the airborne POLDER sensor. The aim of this study is to test the ability of neural network techniques to accurately estimate canopy biophysical variables from reflectance data. The biophysical variables of interest considered are cover fraction and leaf area index. A well known and validated canopy radiative transfer model (Ë Á Ä) is first used to simulate two BRDF databases: (1) a learning data set allow to train the neural networks; (2) the second data set allow a first validation of this technique. In a second time, we use ReSeDA POLDER products and apply the calibrated neural networks to derive biophysical variables estimates. These estimates are then compared to in situ measurements for the 16 acquisition dates and different fields and crops. We also compare AE AE Ø AE AE Ø performances versus a NDVI-based technique.