Estimation of transpiration coefficient and aboveground biomass in maize using time-series UAV multispectral imagery

Estimating spatial variation in crop transpiration coefficients (CT_c) and aboveground biomass (AGB) rapidly and accurately by remote sensing can facilitate precision irrigation management in semiarid regions. This study developed and assessed a novel machine learning (ML) method for estimating CT_c and AGB using time-series unmanned aerial vehicle (UAV)-based multispectral vegetation indices (VIs) of maize under several irrigation treatments at the field scale. Four ML regression methods: multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), and adaptive boosting regression (ABR), were used to address the complex relationship between CT_c and VIs. AGB was then estimated using exponential, logistic, sigmoid, and linear equations because of their clear mathematical formulations based on the optimal CT_c estimation model. The UAV VIs-derived CT_c using the RFR estimation model yielded the highest accuracy (R² = 0.91, RMSE = 0.0526, and nRMSE = 9.07%). The normalized difference red-edge index, transformed chlorophyll absorption in reflectance index, and simple ratio contributed significantly to the RFR-based CT_c model. The accuracy of AGB estimation using nonlinear methods was higher than that using the linear method. The exponential method yielded the highest accuracy (R² = 0.76, RMSE = 282.8 g m⁻², and nRMSE = 39.24%) in both the 2018 and 2019 growing seasons. The study confirms that AGB estimation models based on cumulative CT_c performed well under several irrigation treatments using high-resolution time-series UAV multispectral VIs and can support irrigation management with high spatial precision at a field scale.