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

文献类型： 外文期刊

作者： Chang, Wushuai 1 ; Gu, Shenghao 1 ; Wang, Baiyan 1 ; Hu, Shuping 3 ; Li, Ruiqi 2 ; Guo, Xinyu 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

2.Hebei Agr Univ, Coll Agron, Baoding 071001, Peoples R China

3.Inner Mongolia Agr Univ, Hohhot 010019, Peoples R China

关键词： Phenotyping; Smart agriculture; Crop growth; Water balance; UAV

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 237 卷

页码：

收录情况： SCI

摘要： 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 (AGBdaily) over the entire growth period. The daily evapotranspiration (ETdaily) 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 AGBdaily to ETdaily. Multisensory data fusion and further integration with process-based model proved effective for simultaneously estimating AGBdaily, ETdaily, 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 multi-sensory data with SEBAL evapotranspiration modeling for monitoring and evaluating water consumption and utilization in maize.

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