Using solar-induced chlorophyll fluorescence to predict winter wheat actual evapotranspiration through machine learning and deep learning methods

作者：Yao Li, Xuanang Liu, Xuegui Zhang, Xiaobo Gu, Lianyu Yu, Huanjie Cai, Xiongbiao Peng · 发表于：Agricultural Water Management · 年份：2025 · DOI：10.1016/j.agwat.2025.109322 · 被引用次数：16 · 研究领域：Plant Water Relations and Carbon Dynamics、Remote Sensing in Agriculture、Leaf Properties and Growth Measurement

As the world's largest wheat producer, accurately and timely predicting the actual evapotranspiration (ET c_act ) during the growth period of winter wheat is crucial for improving farmland water use efficiency and yield in China. Solar-Induced Chlorophyll Fluorescence (SIF) is a radiative signal emitted during plant photosynthesis, and ET c_act is largely influenced by photosynthetic efficiency. Therefore, SIF demonstrates significant potential for predicting ET c_act over large spatial scales and long temporal sequences. This study combined meteorological data with two remote sensing variables, Leaf Area Index (LAI) and SIF, to construct four models: Random Forest (RF), Gradient Boosting (GB), Support Vector Regression (SVR) Machine, and Long Short-Term Memory (LSTM) neural networks. These models were applied to predict ET c_act at seven sites across the North China Plain and Guanzhong Plain. The results showed that in the feature importance ranking based on the Maximal Information Coefficient (MIC) method, air temperature (T), LAI, and SIF all had high importance scores (>0.3), making them important features for predicting ET c_act . The simulation accuracy and stability of the RF and LSTM models were higher than those of the SVR and GB models. The LSTM model maintained stable simulation accuracy across both strategies and all sites, with an average R 2 of 0.754 and RMSE of 0.831 mm across all simulation scenarios. Incorporating SIF with LAI and meteorological data signific...