Evaluating spatial and temporal variations in sub-field level crop water demands

Abstract

Existing Variable Rate Irrigation (VRI) practices use soil electrical conductivity, historical yields, and topographic maps to delineate variable rate zones. However, these methods are not conducive to tracking within season variability in crop water demands. With increasing remote sensing data availability, in-season maps of crop coefficients and evapotranspiration (ET) may help inform variable rate irrigation schedules. Although, the amount of spatial and temporal variation in crop coefficients at the sub-field level has not been widely researched. This study aims to compare subfield ET estimates from two remote sensing platforms and quantify spatial and temporal variations in sub-field level ET. Vegetation indices and reference ET data were collected at Kansas State University’s Southwest Research Extension Center (SWREC) and two Water Technology Farms during the 2020 corn growing season. Weekly maps of the normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI) from manned aerial imagery were combined with empirical equations to estimate both basal and combined crop coefficients at a 1-meter resolution. These coefficients were combined with local reference ET estimates, aggregated to a 30-meter resolution, and compared to the Landsat Provisional Actual Evapotranspiration dataset. Finally, actual ET estimates from aerial images were aggregated using K-means clustering and stationary variable speed zones to determine if there is enough variation in actual ET at the sub-field level to build variable rate irrigation schedules. An equivalence test demonstrated that the aerial imagery and Landsat data sources produce significantly different crop coefficient estimates. However, the two datasets were moderately correlated with Pearson’s product-moment correlation coefficients ranging from -0.95 at the SWREC to 0.63 and 0.86 at the two Water Technology Farms. Both the aerial imaging and Landsat datasets showed high variability in crop coefficients during the first 5-6 weeks after emergence, with these coefficients becoming more spatially uniform later in the growing season. These crop coefficients may help irrigators make more informed irrigation management decisions during the growing season. However, more research is needed to validate these remotely sensed ET estimates and integrate them into an irrigation decision support system.