Evaluating WISE Performance for Maize Corn when Updated with Remote Sensing-based Actual Crop Coefficients: Full and Deficit Irrigation
Edson Costa-Filho, José L. Chávez, Allan A. Andales, and Ansley J. Brown
Several decision-making tools for irrigation scheduling, such as the Water Irrigation Scheduler for Efficient application (WISE), rely on the soil-water deficit method, with tabulated crop coefficients (Kc) to estimate crop evapotranspiration (ETc). However, agricultural fields often experience varying ETc rates due to surface and management conditions that depart from ideal. Remote sensing (RS) techniques could improve irrigation scheduling by accounting for near real-time on-site conditions. This project aims to evaluate the effects of RS-based Kc in improving corn irrigation scheduling within the WISE tool. 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.