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An algorithm using in situ measurements for training a neural network (NN) to retrieve soil moisture (SM) from SMOS observations is discussed. The in situ data are measurements of the SM content in the 0–5 cm depth layer from the SCAN, SNOTEL and USCRN networks. It is shown that this approach can be used to retrieve SM at continental scale in North America. The NN retrieval (NN inSitu ) is evaluated against in situ data not used during the training phase and against maps of the SMOS level 3 SM product and ECMWF SM models. NN inSitu SM values are closer to ECMWF values for wet areas. A method to use NNs as a tool to classify in situ sites representative of the remote sensing observations scale is briefly discussed.

International audience; In this study, we evaluated a new SMOS (Soil Moisture and Ocean Salinity) soil moisture (SM) product, developed by the collaboration of INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d'Etudes Spatiales de la BIOsphere), against the operational SMOS level 3 SM product (SMOSL3). This new product (hereinafter referred to as SMOS-INRA-CESBIO, i.e. SMOSIC in short) differs from SMOSL3 three ways: (i) the SMOSIC algorithm considers the pixel as homogeneous and does not take into account the heterogeneity of the pixel; (ii) uses a new calibration of the effective scattering albedo and soil roughness parameters; (iii) no time correlation is applied on the optical depth. The evaluation was done over North America using the (European Center for Medium range Weather Forecasting) ECMWF SM simulation as a reference, using data for 2011. A better performance of the SMOSIC SM product with respect to ECMWF was found: (i) SMOSIC had higher correlation coefficients (temporal dynamics) and lower unbiased RMSD (absolute values) values with ECMWF over most of the study area and (ii) the spatial patterns of the SMOSIC temporal mean SM maps were in a better agreement with ECMWF.

We introduced an approach to simulate and separate atmospheric contribution in ground-based thermal-infrared (TIR) camera measurements. Different from the traditional approach which uses the look-up table built from 1-D radiative transfer model (RTM), this approach directly simulates 3-D ray propagations and interactions in the heterogeneous urban environment by using the Discrete Anisotropic Radiative Transfer (DART) model. The atmospheric turbid cells that occupy every part of the urban scene are created using the vertical constituent distribution and the optical property profiles in the existing databases or from the actual meteorological measurements. The two components of atmospheric effects on the TIR at-sensor radiance are attenuated transmission and path thermal emission. Taking both into account, the at-surface radiance corresponding to the signal emitted only from the urban surface can be derived.