Maize Crop Coefficient Estimated from UAV-Measured Multispectral Vegetation Indices

Abstract

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 (<i>K_c</i>) with improved accuracy under different levels of deficit irrigation. The proposed method for estimating the <i>K_c</i> 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 <i>K_c</i> values measured from the daily soil water balance in Ordos, Inner Mongolia, China. To accurately estimate the <i>K_c</i>, the fraction of vegetation cover (<i>f_c</i>) derived from the normalized difference vegetation index (<i>NDVI</i>) was used to compare with field measurements, and the stress coefficients (<i>K_s</i>) calculated from two vegetation index (VI) regression models were compared. The results showed that the <i>NDVI</i> 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 <i>f_c</i> calculated from the <i>NDVI</i> had a high correlation with field measurement data, with a coefficient of determination (R²) of 0.93. The ratios of transformed chlorophyll absorption in reflectance index (<i>TCARI</i>) to renormalized difference vegetation index (<i>RDVI</i>) and <i>TCARI</i> to soil-adjusted vegetation index (<i>SAVI</i>) were used, respectively, to establish two types of <i>K_s</i> regression models to retrieve <i>K_c</i>. Compared to the <i>TCARI</i>/<i>SAVI</i> model, the <i>TCARI</i>/<i>RDVI</i> model under different levels of deficit irrigation had better correlation with <i>K_c</i>, with R² and root-mean-square error (RMSE) values ranging from 0.68 to 0.80 and from 0.140 to 0.232, respectively. Compared to <i>K_c</i> calculated from on-site measurements, the <i>K_c</i> 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.