Updating Corn Crop Coefficients with Remote Sensing-Based Actual Evapotranspiration Algorithms

Improving irrigation water management through scheduling is key to securing water and soil resources in agriculture. This study compared the performance of two crop coefficient (Kc) approaches for maize evapotranspiration with a one-source surface energy balance. Also, we evaluated an updated FAO-56 PM crop coefficient approach using remotely sensed Kc using an Eddy Covariance system. A surface irrigated maize field near Fort Collins, Colorado, was the study area. A constellation of space borne remote sensing microsatellites were used to obtain multispectral images to map actual evapotranspiration (ETa). Preliminary results indicate that the single source surface energy balance performed better than the ReSET and FAO-56 crop coefficient approaches by 57% on average. Further, the updated FAO-56 approach, adjusted with remotely-sensed Kc values, showed the potential to augment irrigation scheduling tools. There was a 46% error reduction in maize ETa when the FAO-56 standard crop coefficient curve was improved with Kc values derived from the single source surface energy balance ETa estimates.