This study aims to improve estimates of crop water use for corn and soybean across Minnesota by combining satellite-based evapotranspiration data with ground-based sensor measurements. We used remote sensing platforms, including OpenET and Climate Engine, alongside cropland and irrigation datasets, to characterize how corn and soybean crops use water throughout the growing season across Minnesota counties, including the state's sandy, irrigation-dependent Central Sands region. By comparing these satellite-based estimates against ground measurements from eddy covariance towers at University of Minnesota research sites, the team developed region-specific crop coefficients tailored to Minnesota's climate, soils, and growing conditions. These localized coefficients are intended to support more accurate irrigation scheduling, helping growers apply water more efficiently while informing water resource planning and groundwater management across the state.
Research Team: Samikshya Subedi, Vasudha Sharma, Bryan Runck, Brent Dalzell, and Joshua Gamble
Funding: LCCMR and USDA-ARS