Comparison of Dobson and Mironov Dielectric Models in the SMOS Soil Moisture Retrieval Algorithm - Archive ouverte HAL
Article Dans Une Revue IEEE Transactions on Geoscience and Remote Sensing Année : 2015

Comparison of Dobson and Mironov Dielectric Models in the SMOS Soil Moisture Retrieval Algorithm

Résumé

The Soil Moisture and Ocean Salinity (SMOS) mission provides global surface soil moisture over the continental land surfaces. The retrieval algorithm is based on the comparison between the observations of the L-band (1.4 GHz) brightness temperatures (TB) and the simulated TB data using the L-band Microwave Emission of the Biosphere (L-MEB) model. The L-MEB model includes a dielectric model for the computation of the soil dielectric constant. Since the beginning of the mission, the Dobson model has been used in the operational SMOS algorithm. Recently, a new model of the soil dielectric constant has been developed by Mironov et al. and is now considered. This paper is the first evaluation of these two models based on the actual SMOS observations. First, both Dobson and Mironov models were modified to ensure that the SMOS retrieval algorithm converges to realistic soil moisture retrievals (symmetrization for negative soil moisture values was applied). Second, soil moisture was retrieved over several sites using both Dobson and Mironov models to compute the soil dielectric constant and were compared with in situ measurements. At a global scale, the use of the Mironov model leads to higher retrieved soil moisture than when using the Dobson model (0.033 m3/m3 on average). However, the comparisons of the two model output with in situ measurements over various test sites do not demonstrate a superior performance of one model over the other.
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Dates et versions

hal-01114866 , version 1 (10-02-2015)

Identifiants

Citer

Arnaud Mialon, Philippe Richaume, Delphine Leroux, Simone Bircher, Al Bitar Ahmad, et al.. Comparison of Dobson and Mironov Dielectric Models in the SMOS Soil Moisture Retrieval Algorithm. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53 (6), pp.3084-3094. ⟨10.1109/TGRS.2014.2368585⟩. ⟨hal-01114866⟩
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