517 Physically Constrained Inversion of Radiative Transfer Models in L Band for High-Resolution Retrievals of Soil Moisture and Vegetation Optical Depth from Space

Tuesday, 14 January 2020
Hall B (Boston Convention and Exhibition Center)
Ardeshir Ebtehaj, Univ. of Minnesota Twin Cities, Minneapolis, MN; and L. Gao and M. Sadeghi

L-band (1.4 GHz) spaceborne radiometers provide an unprecedented opportunity to monitor surface soil moisture (SM) and vegetation optical depth (VOD). However, simultaneously retrieval of SM and VOD is an ill-posed problem for single band radiometers on board the ESA's Soil Moisture and Ocean Salinity (SMOS) and NASA’s Soil Moisture Active Passive (SMAP) satellites. This research presents the results of two different variants of a new physically constrained multichannel algorithm (CMCA) for high-resolution inversion of radiative transfer (RT) equation in L-band, which relies on the fundamental assumption that VOD changes slowly over a local window of time or space. The algorithm (i) narrows down the retrievals to a priori physical/climatological properties of soil and overlying canopy, (ii) optimally assimilates multiple sources of surface temperatures into the inversion, (iii) uses the Sobolev-norm regularization to account for the slow temporal changes of vegetation biomass, and (iv) properly weights the observational brightness temperatures to reduce the uncertainty and increase the resolution of retrievals. The combined (C-CMCA) and spatially constrained (S-CMCA) versions of the algorithm are implemented for inversion of zeroth- and first-order approximations of the RT equation using the SMAP radiometers. Validating the retrievals against the ground gauge data over the Contiguous United States (CONUS) shows that the algorithm recovers high-resolution features of SM and VOD at 1 to 5 km and, on average, the monthly root mean squared error (rmse) and bias are reduced by 13% and 50% when compared to the SMAP enhanced products. The results demonstrate that the retrievals are robust to the background water contamination in the vicinity of coastal areas and over lowland floodplains and can capture the expected coupling between the SM and climatology of vegetation density.
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