Analyzing Effects of Crops on SMAP Satellite-based Soil Moisture using a
Rainfall-Runoff Model 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.