Abstract
Over the past two decades, remote sensing has made possible the routine global monitoring of surface soil moisture. Regional agricultural drought monitoring is one of the most logical application areas for such monitoring. However, remote sensing alone provides soil moisture information for only the top few centimeters of the soil profile, while agricultural drought monitoring requires knowledge of the amount of water present in the entire root zone. The assimilation of remotely sensed soil moisture products into continuous soil water balance models provides a way of addressing this shortcoming. Here, we describe the assimilation of NASA's soil moisture active passive (SMAP) surface soil moisture data into the United States Department of Agriculture Foreign Agricultural Service (USDA FAS) Palmer model and assess the impact of SMAP on USDA FAS drought monitoring capabilities. The assimilation of SMAP is specifically designed to enhance the model skill and the USDA FAS drought capabilities by correcting for random errors inherent in its rainfall forcing data. The performance of this SMAP-based assimilation system is evaluated using two approaches. At global scale, the accuracy of the system is assessed by examining the lagged correlation agreement between soil moisture and the normalized difference vegetation index (NDVI). Additional regional-scale evaluation using in situ- based soil moisture estimates is carried out at seven of the SMAP core Cal/Val sites located in the USA. Both types of analysis demonstrate the value of assimilating SMAP into the USDA FAS Palmer model and its potential to enhance operational USDA FAS root-zone soil moisture information.
Highlights
T HE US Department of Agriculture (USDA) Foreign Agricultural Service (FAS) is tasked with enhancing the international market competitiveness of United States agricultural exports
The specific values of R were determined using error values derived from published soil moisture active passive (SMAP) calibration and validation (Cal/Val) results
Accurate and on-time global RZSM anomaly information is essential for USDA FAS as it aids their decision-making capabilities related to short- and long-term agricultural drought impacts on expected yield production and global food security
Summary
Given the individual shortcomings of modeled and remotely sensed SM products, data assimilation strategies have been developed for the optimal integration of satellite SM retrievals into continuous water balance models If properly constructed, these strategies yield optimal RZSM estimates with continuous coverage in both time and space. Numerous papers have already demonstrated the benefit of assimilating satellite-retrieved observations into physically-based land surface models [3]–[10] Much of this past research utilized an ensemble Kalman filter (EnKF) approach. The application developed to enhance the USDA FAS drought monitoring and forecasting capabilities utilizes SM observations derived from SMAP and their assimilation into the USDA FAS PM using a one-dimensional (1-D) EnKF (hereinafter referred to as the “PM+SMAP” data assimilation system) This system represents one of the first truly operational uses of SMAP SM products.
Sign-in/Register to view highlights & summary 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 callMore From: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Disclaimer: 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.
