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

Variable rate irrigation (VRI) requires accurate knowledge of crop water demands at the sub-field level. Existing VRI practices commonly use one or more variables like soil electrical conductivity, historical yields, and topographic maps to delineate variable zones. However, these data sets do not quantify within season variability in demands. Crop coefficients are widely used help estimate evapotranspiration (ET) different stages a crop’s growth cycle, past research has shown how remotely sensed can identify differences regional field levels. amount spatial temporal variation level (i.e. single center pivot system) been researched. This study aims compare ET estimates from two remote sensing platforms variations aggregated ET. Vegetation indices reference were collected Kansas State University’s Southwest Research Extension Center (SWREC) Water Technology Farms during 2020 corn growing season. Weekly Normalized Difference Index (NDVI) Soil Adjusted (SAVI) aerial imagery combined with empirical equations existing literature both basal 1-meter resolution. These 30 m resolution compared Landsat Provisional Actual dataset. Finally, actual images using k-means clustering stationary speed zones determine if there is enough build schedules. An equivalence test demonstrated that sources produce significantly coefficient estimates. datasets moderately correlated Pearson’s product-moment correlation ranging -0.95 0.86. Both imaging showed high first 5-6 weeks after emergence, becoming spatially uniform later may irrigators make informed management decisions needed validate integrate them into an decision support system.