A new semi-empirical model for soil moisture content retrieval by ASAR and TM data in vegetation-covered areas

Active microwave and passive optical remote sensing data have demonstrated their respective advantages in inversion of surface soil moisture content. A new semi-empirical model is presented for soil moisture content retrieval in vegetation-covered areas, using ENVISAT-ASAR and LANDSAT-TM data collaboratively. Derivation of the algorithm is based on simplification of the Michigan Microwave Canopy Scattering Model (MIMICS). In the model, the ground surface is divided into a canopy layer and a soil layer, and empirical relationships simulated among vegetation water mass W (c) , the backscatter coefficient sigma (pq1), the bidirectional scattering coefficient sigma (pq2) and the extinction coefficient tau (p) . The key input parameters of the semi-empirical model are reduced to only the leaf area index (LAI), which can be easily inverted by the optical model PROSAIL, allowing coupling of the microwave and optical models to be achieved. Also, vegetation RMS height (S (veg)) is introduced to correct for the radar-shadow effect caused by over-laying vegetation. Analysis of the parameter sensitivity of the semi-empirical model showed that when the regional Leaf Area Index is small (LAIa (c) 1/23), the model is more applicable. Soil moisture distribution in the study area was mapped using the semi-empirical model and field ground measurements used for model validation. This showed that, after correction of the radar-shadow effect, the average relative error (E (r) ) between ground-measured and semi-empirical model-derived estimates of soil moisture decreased from 17.6% to 10.4%, while the RMS reduced from 0.055 to 0.031 g cm(-3). The accuracy of soil moisture estimates from the semi-empirical model is much better than for the MIMICS model (E (r) = 22.7%, RMS = 0.068 g cm(-3)), showing that the semi-empirical model is efficient at obtaining regional surface soil moisture contents when LAI is small.