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

Authors: ZHANG, YU - Northwest A&f University; HAN, WENTING - Northwest A&f University; Zhang, Huihui; NIU, XIAOTAO - Northwest A&f University; SHAO, GUOMIN - Northwest A&f University

Submitted to: Agricultural Water Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/30/2022
Publication Date: 11/7/2022
Citation: Zhang, Y., Han, W., Zhang, H., Niu, X., Shao, G. 2022. Evaluating maize evapotranspiration using high-resolution UAV-based imagery and FAO-56 dual crop coefficient approach. Agricultural Water Management. 275. Article e108004. https://doi.org/10.1016/j.agwat.2022.108004.
DOI: https://doi.org/10.1016/j.agwat.2022.108004

Interpretive Summary: Timely and accurate estimation of crop evapotranspiration (ETc) is essential for efficient irrigation management at the farmland scale. UAV remote sensing has great potential to map crop ETc. How to use UAV optical and thermal data to estimate critical parameters in the FAO-56 dual crop coefficient method has not been addressed. In this study, we first chose three vegetation indices from UAV multi-spectral imagery to predict crop coefficient; and two stress coefficients using UAV thermal data derived crop water stress index (CWSI) and the Number of Degrees Above Canopy Threshold (DACT). Then we evaluate the performance of models for daily crop ET estimation using the two stress coefficients in combination with the three VIs. We found that the combination of NDVI and CWSI produced the best estimates of maize ETc. UAV-based multi-sensor data will provide the spatial distribution of crop coefficient and ETc within a field and support precision irrigation management at farm scales.

Technical 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 stress coefficient. 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 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 RMSE of 0.50 mm/day. The DACT-based model also performed well. Although the results varied with irrigation levels, the daily mean bias error of ETc predictions over different years indicated acceptable accuracy. 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.