High Spatial Resolution Soil Moisture Improves Crop Yield Estimation

Abstract

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 crises. Amid the different environmental predictors, soil moisture (SM) is an important agricultural drought indicator. However, currentoperational microwave SM products have generally low spatial resolution, challenging the effective characterization of SM spatial heterogeneity. In this study, empowered by the hourly land surface temperature (LST) observations from geostationary operational environmental satellites (GOES), we first spatially-downscaleSM using machine learning (ML) algorithms. Then, by designing three sets of experiment respectively using downscaled SM, coarse-resolution SM, and precipitation observation, we assess the comparative performance of downscaled SM among its counterparts in estimating crop yield variability, based on three mainstream ML 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 outperformscoarse-resolution SM and precipitation products, with the average R2 between the crop yield estimatesand the yield records being 0.814, 0.809, and 0.805, respectively. In addition, we find that among the five algorithms, the nonlinear ML algorithms exceed the linear algorithms in crop yield estimation, 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.

Keywords

• Land surface temperature (LST)
• machine learning (ML)
• soil moisture (SM) downscaling
• yield estimation