Improving Soil Moisture Estimation via Assimilation of Remote Sensing Product into the DSSAT Crop Model and Its Effect on Agricultural Drought Monitoring

Accurate knowledge of soil moisture is crucial for agricultural drought monitoring. Data assimilation has proven to be a promising technique improving estimation, and various studies have been conducted on data based land surface models. However, crop growth models, which are ideal tools simulation applications, rarely used assimilation. Moreover, the role in monitoring seldom investigated. In present work, we assimilated European Space Agency (ESA) Climate Change Initiative (CCI) product into Decision Support System Agro-technology Transfer (DSSAT) model estimate root-zone moisture, evaluated effect The results demonstrate that estimates were significantly improved after Root-zone had better agreement with situ observation. Compared index modeled without remotely-sensed observations, could improve at least one level performed when compared winter wheat yield. conclusion, model-based effectively improves estimation further strengthens moisture-based indices