A Bayesian approach for an SAC-D/Aquarius Soil Moisture product
13Th Specialist Meeting microwave Radiometry and remote Sensing of The Environment – MICRORAD 2014 Pasadena, California – USA March 24-27 de 2014
Abstract: In this work, several retrieval algorithms were implemented to retrieve soil moisture (sm) and optical depth (τ ) from Aquarius/SAC-D observations. Currently used sm retrieval
algorithms (H- and V-pol Single Channel Algorithm, SCAH and SCAV; Microwave Polarization Difference Algorithm, MPDA) were computed over Pampas Plains, Argentina. The methodology of a novel Bayesian algorithm developed is also presented, and its results are contrasted with the previous algorithms. Finally, performance metrics for each algorithms were derived using SMOS Level-2 sm and τ as benchmark products. The new Bayesian approach provide the sm retrieval algorithm that exhibited the lowest ubRMSE (0.115 m3 /m3 ), though very close to USDA SCA and SCAV ubRMSE (0.116 m3 /m3 ). Nevertheless, some improvements are discussed in Section 4 that might increase significantly the Bayesian algorithm performance.
Index Terms— Aquarius; soil moisture; Bayesian inference; Markov Chain Monte Carlo.