A downscaling algorithmfor SMOS which combines MODIS Visible/Infrared data and SMOS horizontal brightness temperatures at 42.5◦ incidence angle into high-resolution soil moisture maps has been shown to nicely reproduce soil moisture dynamics at a 1 km spatial scale. The core of this algorithm is a linking model that depicts the synergy between SMOS and MODIS observations and their sensitivity to soil moisture. In this work, the impact of adding SMOS observations at horizontal and vertical polarizations and at multiple incidence angles to this linking model has been evaluated using 6 months of observations over the Murrumbidgee catchment, South-East Australia, and a robust alternative formulation is proposed. Results show that adding SMOS observations at multiple incidence angles and both polarizations the algorithm is more stable over time and its minimization error is reduced. By comparing with in situ data, a remarkable improvement of the linear regression between downscaled and in situ data is also observed (slope of 0.95).

Peer Reviewed