A machine learning-driven semi-mechanistic model for estimating actual evapotranspiration: Integrating photosynthetic indicators with vapor pressure deficit

Yao Li, Xiongbiao Peng, Zhunqiao Liu, Xiaoliang Lu, Xiaobo Gu, Lianyu Yu, Jiatun Xu and Huanjie Cai

Agricultural Water Management, 2025, vol. 315, issue C

Abstract: Accurate estimation of actual crop evapotranspiration (ETc act) is essential for optimizing water resource management and irrigation strategies, particularly in arid and semi-arid agricultural regions. Traditional models rely on extensive meteorological data, limiting their applicability in data-scarce areas. This study used on-site ground observation data with a 30-minute temporal resolution from a winter wheat field at the Yangling Station on the Guanzhong Plain, China, to evaluate the performance of machine learning-driven semi-mechanistic models driven by three machine learning methods (Ridge regression, Random Forest, and Support Vector Machine) in estimating ETc act. These machine learning-driven semi-mechanistic models integrate photosynthetic indicators (Gross Primary Production, GPP; solar-induced chlorophyll fluorescence, SIF; near-infrared reflectance of vegetation, NIRv) with the square root of vapor pressure deficit (VPD0.5) to enhance ETc act estimation accuracy. The results showed that among the photosynthetic indicators, GPP and SIF exhibited a strong correlation with ETc act. When combined with VPD0.5, their correlation with ETc act further increased by 0.10 and 0.05, respectively, while their response time to ETc act variations was reduced by 2 hours and 1 hour. Notably, NIRv exhibited the weakest correlation with ETc act, with a Pearson correlation coefficient of only 0.31, significantly lower than SIF (0.78) and GPP (0.69), indicating its limited effectiveness as an independent predictor. Furthermore, machine learning-driven semi-mechanistic models driven by machine learning achieved higher accuracy in ETc act estimation than single-factor machine learning models and the Penman-Monteith equation incorporating the single crop coefficient method. Among them, the RF model based on SIF × VPD0.5 achieved the best performance, with an R2 of 0.86 and an RMSE of 0.69 mm/day. This study demonstrates that machine learning-driven semi-mechanistic models can significantly improve ETc act estimation accuracy while reducing dependence on meteorological data. The proposed approach provides a new theoretical framework for improving water resource management and irrigation efficiency in arid and semi-arid agricultural regions, while also offering a scientific basis for future ETc act estimation methods integrating remote sensing data.

Keywords: Winter wheat; Eddy covariance system; Water resource management; Gross primary production; Crop water use efficiency; Solar-induced chlorophyll fluorescence (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:315:y:2025:i:c:s037837742500277x

DOI: 10.1016/j.agwat.2025.109563

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