Estimating water use efficiency in maize: a UAV-based approach integrating multisensory data with SEBAL evapotranspiration modeling

Abstract

Rapid, accurate, and non-destructive estimation of crop water use efficiency (WUE) at the field scale is crucial not only for evaluating water efficient cultivars and practices in scientific research but also for optimizing irrigation schedule in agricultural production. The current lack of efficient methods for high-throughput phenotyping WUE hinders development of sustainable agriculture under globally intensified water scarcity. This study aimed to utilize unmanned aerial vehicle (UAV) multisensory remote sensing data combined with a process model to achieve rapid WUE determination via accurate daily-scale evapotranspiration and aboveground biomass (AGB) estimates. First, vegetation indices, canopy temperature, and canopy structural parameters were extracted from multispectral (MS), thermal imaging (TIR), and radar data and combined with an automated machine learning (AutoML) for AGB estimation. The beta function was then employed to accurately estimate AGB accumulation at a daily step (AGB_daily) over the entire growth period. The daily evapotranspiration (ET_daily) was calculated by the surface energy balance algorithm for land (SEBAL) model driven by MS, TIR, and meteorological data. Finally, the WUE was determined by the ratio of AGB_daily to ET_daily. Multisensory data fusion and further integration with process-based model proved effective for simultaneously estimating AGB_daily, ET_daily, and WUE with R² values of 0.71, 0.93, and 0.79, respectively. Notably, the proposed WUE estimation method can capture different temporal pattern between cultivars with different levels of tolerance to drought. We applied this approach to screen water efficient cultivars and found that appropriate reduction of irrigation can improve WUE. In conclusion, this study shows promising perspective in the use of a UAV-based approach integrating multisensory data with SEBAL evapotranspiration modeling for monitoring and evaluating water consumption and utilization in maize.