Evaluating the Operational Application of SMAP for Global Agricultural Drought Monitoring

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.

Keywords

• Agricultural drought
• data assimilation
• hydrologic modeling
• soil moisture
• soil moisture active passive (SMAP)
• United States Department of Agriculture Foreign Agricultural Service (USDA FAS)