Abstract
Soil moisture is an effective variable for agricultural drought monitoring, and data assimilation is a useful tool to improve soil moisture estimates. In this study, we assimilated remotely sensed soil moisture (SM) and leaf area index (LAI) into DSSAT-CSM-Wheat crop growth model to estimate soil moisture. The results showed that compared to open-loop scenario, assimilating LAI independently could slightly improve soil moisture accuracy with reduction in average RMSE (root mean square error) by 4%, and the average RMSEs were decreased by 7% and 10% for SM and LAI+SM assimilations, respectively. The yield differences with observation were decreased by 379 kg/ha for LAI assimilation, 592 kg/ha for SM assimilation and 866 kg/ha for LAI+SM assimilation. Assimilating LAI and SM jointly received best performances in soil moisture and yield estimation. Hence, assimilating remotely sensed data into crop growth model provides a robust method to improve soil moisture for agricultural drought monitoring.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
