A Bayesian Approach for a SAC-D/Aquarius Soil Moisture Product
[10-Sep-2015] Bruscantini, C.A., Grings, F., Barber, M., Perna, P., and Karszenbaum, H.
Presented at the 13th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment
In this work, several retrieval algorithms were implemented to retrieve soil moisture (sm) and optical depth (Ï) 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 Ï as benchmark products. The new Bayesian approach provide the sm retrieval algorithm that exhibited the lowest ubRMSE (0.115m3/m3), though very close to USDA SCA and SCAV ubRMSE (0.116m3/m3). Nevertheless, some improvements are discussed in Section 4 that might increase significantly the Bayesian algorithm performance.