Exploring the Potential of SMAP Soil Moisture for Improving Real-time Streamflow Prediction in the U.S. Corn Belt

Abstract

L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and hence flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity), and two distributed hydrologic models with measurements from in~situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in~situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflow predictions.