Assimilation of Synchronous and Asynchronous Active/Passive Microwave Observations at Different Spatial Scales for Improved Soil Moisture and Crop Growth

Monday, 15 December 2014: 1:55 PM
Pang-Wei Liu1, Jasmeet Judge1, Alejandro Monsivais-Huertero2, Susan C Steele-Dunne3, Tara E Bongiovanni1, Rajat Bindlish4 and Thomas J Jackson5, (1)University of Florida, Center for Remote Sensing, Agricultural and Biological Engineering, Gainesville, FL, United States, (2)National Polytechnic Institute, ESME Ticoman/CDA, Mexico City, Mexico, (3)Delft University of Technology, Delft, 5612, Netherlands, (4)U. S. Dept. of Agriculture, Beltsville, MD, United States, (5)USDA ARS, Beltsville, MD, United States
Assimilation of active and passive (AP) microwave observations at L-band in the crop simulation models is able to improve estimates of soil moisture (SM) and crop growth in the models. These observations provide complementary information for dynamic heterogeneous landscapes. Active observations are more sensitive to soil surface roughness and vegetation structure, while passive observations are more sensitive to SM. These observations may be available at different spatial and temporal resolutions from different satellite platforms. For example, the present ESA Soil Moisture Ocean Salinity (SMOS) mission provides passive observations at 1.41 GHz at 25 km every 2-3 days, while the NASA/CONAE Aquarius mission provides L-band AP observations at spatial resolution of 150 km with a repeat coverage of 7 days for global SM products. The planned NASA Soil Moisture Active Passive mission (SMAP) will provide AP observations at 1.26 and 1.41 GHz at the spatial resolutions of 3 and 30 km, respectively, with a repeat coverage of 2-3 days, starting early 2015.

The goal of this study is to develop an Ensemble Kalman Filter-based methodology that assimilates synchronously and asynchronously available backscattering coefficients (σ0) and brightness temperatures (TB) at different spatial scales from SMOS and Aquarius. The Decision Support System for Agrotechnology Transfer (DSSAT) that contains a suite of crop simulation models will be linked to microwave emission and scattering models (DSSAT-A-P) for the assimilation. The methodology will be implemented in the rain fed agricultural region of the Brazilian La Plata Basin in South America, where soybean is the primary crop. The augmented state vector will include both model states and parameters related to soil and vegetation during the growing season. The methodology will be evaluated using a synthetic experiment and also using observations from SMOS and Aquarius. In preliminary results with synthetic experiment, using asynchronous observations improved SM by 0.05 and 0.04 m3/m3 and vegetation biomass by 0.2 and 0.27 kg/m2, using passive-only and both AP observations, respectively.