Maize Crop Coefficient Estimated from UAV-Measured Multispectral Vegetation Indices

The rapid, accurate, and real-time estimation of crop coefficients at the farm scale is one of the key prerequisites in precision agricultural water management. This study aimed to map the maize crop coefficient (K(c)) with improved accuracy under different levels of deficit irrigation. The proposed method for estimating the K(c) is based on multispectral images of high spatial resolution taken using an unmanned aerial vehicle (UAV). The analysis was performed on five experimental plots using K(c) values measured from the daily soil water balance in Ordos, Inner Mongolia, China. To accurately estimate the K(c), the fraction of vegetation cover (f(c)) derived from the normalized difference vegetation index (NDVI) was used to compare with field measurements, and the stress coefficients (K(s)) calculated from two vegetation index (VI) regression models were compared. The results showed that the NDVI values under different levels of deficit irrigation had no significant difference in the reproductive stage but changed significantly in the maturation stage, with a decrease of 0.09 with 72% water applied difference. The f(c) calculated from the NDVI had a high correlation with field measurement data, with a coefficient of determination (R(2)) of 0.93. The ratios of transformed chlorophyll absorption in reflectance index (TCARI) to renormalized difference vegetation index (RDVI) and TCARI to soil-adjusted vegetation index (SAVI) were used, respectively, to establish two types of K(s) regression models to retrieve K(c). Compared to the TCARI/SAVI model, the TCARI/RDVI model under different levels of deficit irrigation had better correlation with K(c), with R(2) and root-mean-square error (RMSE) values ranging from 0.68 to 0.80 and from 0.140 to 0.232, respectively. Compared to K(c) calculated from on-site measurements, the K(c) values retrieved from the VI regression models established in this study had greater ability to assess the field variability of soil and crops. Overall, use of the UAV-measured multispectral vegetation index approach could improve water management at the farm scale.