Abstract
Abstract. The first products derived over France in 2010 from the L-band brightness temperatures (Tb) measured by the SMOS (Soil Moisture and Ocean Salinity) satellite, launched in November 2009, were compared with the surface soil moisture (SSM) estimates produced by the C-band Advanced Scatterometer, ASCAT, launched in 2006 on board METOP-A. SMOS and ASCAT SSM products were compared with the simulations of the ISBA-A-gs model and with in situ measurements from the SMOSMANIA network, including 21 stations located in southern France. ASCAT tended to correlate better than SMOS with ISBA-A-gs. The significant anomaly correlation coefficients between in situ observations and the SMOS (ASCAT) product ranged from 0.23 to 0.48 (0.35 to 0.96). However, in wet conditions, similar results between the two satellite products were found. An attempt was made to derive SSM from regressed empirical logarithmic equations using a combination of SMOS Tb at different incidence angles and different polarizations, and the Leaf Area Index (LAI) modeled by ISBA-A-gs. The analysis of the intercept coefficient of the regression showed an impact of topography. A similar analysis applied to ASCAT and SMOS SSM values showed a more limited impact of topography on the intercept coefficient of the SMOS SSM product, while fewer residual geographic patterns were found for the ASCAT SSM.
Highlights
Soil moisture plays a key role in the hydrological cycle and in land-atmosphere interactions
SMOS and ASCAT surface soil moisture (SSM) products were evaluated using the SMOSMANIA in situ observations in southern France, and the ISBA-A-gs SSM simulations over the whole of France, for the year 2010
As described in Sect. 2.4.1, the satellite data were projected onto the ISBA-A-gs grid, and the SMOS and ASCAT time series for each SMOSMANIA site were taken www.hydrol-earth-syst-sci.net/16/423/2012/
Summary
Soil moisture plays a key role in the hydrological cycle and in land-atmosphere interactions. A number of studies have shown the importance of soil moisture in many applications: atmospheric reanalyses and weather forecast (Beljaars et al, 1996; Frennessy and Shukla, 1999; Diermeyer, 2000), land surface and crop growth modelling (Diermeyer et al, 1999; Georgakakos and Carpenter, 2006; de Wit and van Diepen, 2007; Guerif and Duke, 2000), hydrometeorology (Eltahir, 1998, among others) and improvement of flood prediction (Brocca et al, 2010a). Microwave remote sensing is able to provide quantitative information about the water content of a shallow near surface layer (Schmugge, 1983), in the low-frequency microwave range, from 1 to 10 GHz. In the last few years, significant progress towards operational soil moisture monitoring has been made (Wagner et al, 2007)
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