Lead Researchers: Edson Costa-Filho, José L. Chávez, Allan A. Andales, and Ansley J. Brown, Colorado State University
In this study, RS-based Kc were developed based on the land surface energy balance and the reflectance-based Kc approaches. Satellite and unmanned aerial vehicle system imagery were acquired with a pixel spatial resolution of 3 m and 0.08 m, respectively from mid-July to late-September in 2020. The research took place on two corn fields (East and West) located at the Irrigation Innovation Consortium headquarters site in Fort Collins, CO. The West field (16 acres) was not water-stressed, while the East field (18 acres) was water-stressed (approximately 50% of full irrigation). Micro-meteorological data were collected on-site at a height of 3 m above ground level. RS-based Kc resulting values were used to estimate daily ETc rates; which were evaluated with latent heat fluxes from an Eddy Covariance Energy Balance system installed at the West field’s northwest corner. An initial analysis was performed using August PlanetDove satellite imagery. Preliminary results indicate that RS-based Kc values improved the estimation of daily ETc when compared to ETc obtained using tabulated Kc values. On average, the land surface energy balance and reflectance-based Kc methods improved the estimation of ETc by 33% and 6%, respectively. Further research steps will include the analysis of the complete dataset using calculated ETc through the soil water balance and measured soil water content data per field. In addition, developed RS-based Kc sets will be integrated into WISE for assessment of soil water deficit estimates.