Soil moisture deficit estimation using satellite multi-angle brightness temperature

Accurate soil moisture information is critically important for hydrological modelling. Although remote sensing soil moisture measurement has become an important data source, it cannot be used directly in hydrological modelling. A novel study based on nonlinear techniques (a local linear regression (LLR) and two feedforward artificial neural networks (ANNs)) is carried out to estimate soil moisture deficit (SMD), using the Soil Moisture and Ocean Salinity (SMOS) multi-angle brightness temperatures (Tbs) with both horizontal (H) and vertical (V) polarisations. The gamma test is used for the first time to determine the optimum number of Tbs required to construct a reliable smooth model for SMD estimation, and the relationship between model input and output is achieved through error variance estimation. The simulated SMD time series in the study area is from the Xinanjiang hydrological model. The results have shown that LLR model is better at capturing the interrelations between SMD and Tbs than ANNs, with outstanding statistical performances obtained during both training (NSE = 0.88, r = 0.94, RMSE = 0.008 m) and testing phases (NSE = 0.85, r = 0.93, RMSE = 0.009 m). Nevertheless, both ANN training algorithms (radial BFGS and conjugate gradient) have performed well in estimating the SMD data and showed excellent performances compared with those derived directly from the SMOS soil moisture products. This study has also demonstrated the informative capability of the gamma test in the input data selection for model development. These results provide interesting perspectives for data-assimilation in flood-forecasting

Quality Report: Validation of SMOS-BEC experimental products in the Mediterranean Sea. Years 2011-2013 (V.1.0)

7 pages, 2 figures, 2 tablesThis technical note focuses on the comparison of the Sea Surface Salinity (SSS) of the SMOS-BEC experimental products in the Mediterranean currently distributed by BEC at http://cp34-bec.cmima.csic.es with respect to the SSS provided by Argo floatsPeer reviewe

Quality Report: Validation of SMOS-BEC experimental Sea Surface Salinity products in the Arctic Ocean and high latitudes Oceans. Years 2011-2013 (V.1.0)

5 pages, 1 figure, 1 tableThis technical note focuses on the comparison of the Sea Surface Salinity (SSS) of the SMOS-BEC experimental products in high latitudes Ocean and in the Arctic Sea currently distributed by BEC at http://cp34-bec.cmima.csic.es with respect to the SSS provided by Argo floatsPeer reviewe

¿Qué hemos aprendido de SMOS tres años después de su lanzamiento?

II Encuentro de la Oceanografía Física Española (EOF): Conociendo los mares para el beneficio de la sociedad, 14-16 de noviembre de 2012, MadridPeer Reviewe