The gridded Decision Support System for AgroTechnology Transfer (GriDSSAT) model was used to test and verify the SM profile assimilation methodology in two specific modes: (a) first using the best state-of-the art climate inputs available without data assimilation (“open-loop”) (b) then enhancing the open-loop model by assimilating into the model Remote sensing Driven SM Profile (RDSMP) data where available. Toward reaching this goal, this study utilized satellite derived microwave and thermal-infrared coupled SM profiles assimilated into a crop model via the Ensemble Kalman Filter over parts of the Southeastern United States from 2006 to 2010. The goal of this study was to determine if assimilation of satellite-driven SM profiles can improve crop model yield estimations in a commonly used crop model while reducing the reliance on field management information at regional scales. can be assimilated into crop models to reduce input and model related uncertainties. Satellite observations of soil moisture (SM), vegetation index etc. Agricultural simulation models can be a key component in testing new technologies, seeds and cultivars etc., however, inaccurate input information in addition to model related errors adds to model uncertainties. Global food security is one of the most pressing issues of the current century, particularly for developing nations.
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