High spatial resolution soil moisture improves crop yield estimation in the midwestern United States

Abstract

<p>The global food supply system is under increasing pressure due to population growth and more extreme climate events. Developing forecast models for accurate prediction of crop yields is helpful for early warning of food crise. Amid the different environmental predictors, soil moisture (SM) information is an important agricultural drought indictor, but the operational microwave SM products have generally low spatial resolution, challenging the effective characterization of the spatial heterogeneity in SM. In this study, empowered by the ability of hourly land surface temperature (LST) from Geostationary Operational Environmental Satellites (GOES) to inform moisture status, we firstly downscaled SM data using machine learning algorithms. Then, by designing three sets of experiment configurations using either downscaled SM, coarse-resolution SM, or precipitation observation, we assess the performance of downscaled SM in estimating crop yields variability using three mainstream machine learning algorithms and two traditional regression algorithms. Our research shows that downscaled SM based on high temporal resolution GOES-LST demonstrates outstanding performance in characterizing the spatial variation of SM. With respect to yield estimation, downscaled high-resolution SM outperformed coarse-resolution SM and precipitation products, with the average R2 between the estimated crop yields and the yield records being 0.814, 0.809, and 0.805, respectively. In addition, we find that among the five algorithms, the non-linear machine learning algorithms exceed the linear algorithms, with the average R2 being 0.827 and 0.783, respectively. Our research demonstrates the great potential of infusing different satellite information to improve the monitoring of crop growing status and yield prediction.</p>