The Application of AMSR-E Soil Moisture for Improved Global Agricultural Assessment and Forecasting
Soil moisture is estimated by the U. S. Department of Agriculture (USDA) Production Estimates and Crop Assessment Division (PECAD) by utilizing a modified two-layer Palmer water balance model derived from temperature and precipitation observations. It is envisaged that these soil moisture estimates can be improved by integrating passive microwave data which has greater temporal frequency and covers larger spatial domains than available in the past. By integrating direct observations from the EOS Advanced Microwave Scanning Radiometer (AMSR-E) into the current PECAD soil moisture model, more accurate soil moisture and correspondingly crop yield estimates may be possible. This paper presents a methodology for soil moisture data assimilation using a simple bias correction and 1D Ensemble Kalman Filter data assimilation algorithm. An outline of the technical approach is presented.
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