用知识引导的机器学习提升美国中西部蒸散量估算精度
Knowledge-Guided Machine Learning Models to Upscale Evapotranspiration in the U.S. Midwest
- 结合遥感与气象数据，用知识引导特征提升树模型预测能力
- LightGBM模型达R²=0.86，误差低于9W/m²，优于其他方法
- 生成2019-2024年500米分辨率日尺度蒸散数据集
蒸散量（ET）在陆气相互作用中至关重要，但跨时空尺度的精确量化仍具挑战。现场测量方法如涡动相关（EC）或气象站估算仅能获取单点数据，难以满足农业对大范围田块级ET估计的需求。本研究融合基于树的机器学习与知识引导的特征工程，利用多光谱遥感、格网化气象数据及EC观测数据，实现美国中西部地区蒸散量的升尺度估算。对比了随机森林、CatBoost、XGBoost、LightGBM四种树模型及简单前馈神经网络，在5折交叉验证与按站点-年份-生物群落分层的训练测试划分下评估性能。结果表明，采用知识引导特征的LightGBM表现最佳，组内5折验证的R²为0.86，均方误差（MSE）为14.99 W m⁻²，平均绝对误差（MAE）为8.82 W m⁻²。特征重要性分析显示，知识引导特征在预测中起关键作用。基于最优模型，生成了2019–2024年期间500米空间分辨率、日时间分辨率的网格化蒸散数据产品，与州级气象站估算结果对比表现出领先水平。
原文摘要 · Abstract (English)
Evapotranspiration (ET) plays a critical role in the land-atmosphere interactions, yet its accurate quantification across various spatiotemporal scales remains a challenge. In situ measurement approaches, like eddy covariance (EC) or weather station-based ET estimation, allow for measuring ET at a single location. Agricultural uses of ET require estimates for each field over broad areas, making it infeasible to deploy sensing systems at each location. This study integrates tree-based and knowledge-guided machine learning (ML) techniques with multispectral remote sensing data, griddled meteorology and EC data to upscale ET across the Midwest United States. We compare four tree-based models - Random Forest, CatBoost, XGBoost, LightGBM - and a simple feed-forward artificial neural network in combination with features engineered using knowledge-guided ML principles. Models were trained and tested on EC towers located in the Midwest of the United States using k-fold cross validation with k=5 and site-year, biome stratified train-test split to avoid data leakage. Results show that LightGBM with knowledge-guided features outperformed other methods with an R2=0.86, MSE=14.99 W m^-2 and MAE = 8.82 W m^-2 according to grouped k-fold validation (k=5). Feature importance analysis shows that knowledge-guided features were most important for predicting evapotranspiration. Using the best performing model, we provide a data product at 500 m spatial and one-day temporal resolution for gridded ET for the period of 2019-2024. Intercomparison between the new gridded product and state-level weather station-based ET estimates show best-in-class correspondence.
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