Evaluating maize evapotranspiration using high-resolution UAV-based imagery and FAO-56 dual crop coefficient approach

Abstract

Timely and accurate estimation of crop evapotranspiration (ETc) is essential for efficient irrigation management at the farmland scale. However, effective decision-making for irrigation scheduling requires high spatiotemporal-resolution data to provide within-field heterogeneity information. The objective of this study was to evaluate the use of optical and thermal information obtained from an unmanned aerial vehicle (UAV) to quantify maize (Zea mays L.) ETc within the framework of the FAO-56 dual crop coefficient approach. Canopy temperature-based crop water stress index (CWSI) and number of degrees above canopy threshold (DACT) were used to determine the stress coefficient (Ks). Three types of normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) were adopted to determine basal crop coefficient (Kcb). Then the two forms of stress coefficient in combination with three vegetation indices (VIs) were evaluated to estimate daily crop ETc under different irrigation treatments over two years in the southwest region of Inner Mongolia, China. The results demonstrated that the combination of NDVI and CWSI produced the best estimates of maize ETc, with R2 of 0.84 and root mean square error (RMSE) of 0.50 mm/day. The DACT-based model also performed well, with R2 of 0.77–0.80 and RMSE of 0.53 mm/day. Although the results varied with irrigation levels, the daily mean bias error of ETc predictions over different years indicated acceptable accuracy, with a mean bias error (MBE) of 0.48 mm/day. Sensitivity analyses indicated that ETc models were most sensitive to the slope of the linear regression between Kcb and VIs, followed by soil evaporation constant and influential factor in Ks_DACT. From this study, the combinations of vegetation index and CWSI, as well as DACT, were recommended as alternative approaches for estimating ETc due to intrinsic simplicity and easy interpretation. These ETc models relying on UAV-based multi-sensor data thus show promising potential in farmland-scale applications.