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The European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission was launched on November 2, 2009. Providing accurate soil moisture (SM) estimation is one of its main scientific objectives. Since the end of the commissioning phase, preliminary global SMOS SM data [Level 2 (L2) product] are distributed to users. In this paper, we carried out a first assessment of the reliability of this product through a comparison with in situ observed and modeled SM over three different sites: One is located in Luxemburg, and two are located in Italy. The period from August 1, 2010, to July 1, 2011, has been analyzed, giving us the opportunity to evaluate the satellite response to different SM states. The selected period is important for hydrological predictions as it is typically characterized by a sequence of transitions from dry to wet and from wet to dry conditions. In order to compare SMOS and ground SM measurements, a two-step approach has been applied. First, an exponential filter has been applied to approximate root-zone SM, and second, a cumulative distribution function matching has been employed to remove systematic differences between satellite and in situ observations and model simulations of SM. Our results indicate rather good reliability of the filtered and bias-corrected SM estimates derived from the first SMOS L2 products. Bearing in mind that an updated/advanced version of the SMOS SM product has been recently produced, our preliminary results already seem to confirm the potential of SMOS for monitoring of water in soils. [ABSTRACT FROM AUTHOR]

The small hyperspectral imager Compact High- Resolution Imaging Spectrometer (CHRIS) is the most important instrument for Earth observation included in the payload of the European Space Agency Third-Part Mission Project for On-Board Autonomy (PROBA)-1 satellite. This instrument has provided dozens of images in several target areas in the world, and a good number of acquisitions are available for the test site of Frascati and Tor Vergata, Italy. This paper reports several results concerning the generation of thematic maps obtained from CHRIS mode-3 imagery. The potential of the use of different configurations for the input vector exploiting multispectral, multiangular, and multitemporal measurements has been investigated, and the results have been evaluated and compared in terms of accuracy in the classification. The core of the decision task has been developed using the neural network methodology. Indeed, this approach is characterized by a particular ease in performing nonlinear mapping of a multidimensional set of inputs into the output one. [ABSTRACT FROM AUTHOR]