Evapotranspiration partitioning estimates from 8 methods from 47 NEON sites, 2019-2021

This dataset provides daily estimates of evapotranspiration (ET) and the transpiration-to-evapotranspiration ratio (T/ET) across 47 terrestrial National Ecological Observatory Network (NEON) sites spanning diverse environmental and biome conditions in the United States across three years of data (2019-2021). Daily ET is reported in both energy units (MJ m⁻² day⁻¹) and equivalent water depth (mm day⁻¹), assuming a constant latent heat of vaporization of 2.45 MJ/kg. The primary method uses a hybrid recurrent neural network–Penman–Monteith framework (RNN-PM), which integrates physically based surface energy balance constraints with data-driven learning to partition ET into transpiration and evaporation components. Model inputs include in situ meteorological observations (air temperature, vapor pressure deficit, wind speed, and radiation) combined with satellite-derived land surface temperature, leaf area index, and soil moisture. For benchmarking and uncertainty assessment, T/ET estimates from seven additional models are included: Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), Penman-Monteith (P-M), Two-Source Energy Balance (TSEB), Support Vector Regression (SVR), and Categorical Boosting (CatBoost), among others—spanning empirical, machine-learning, and process-based approaches (see methods section or linked publication for detailed descriptions). Data Package Contents: The dataset a csv files containing daily ET and T/ET estimates for each site and model, along with associated metadata files these variables. Data can be accessed using common spreadsheet software (e.g., Microsoft Excel, LibreOffice) or programming environments such as R or Python. Together, these data support cross-site comparisons of ecosystem water use, evaluation of ET partitioning methods, and development of improved land–atmosphere exchange models.本数据集提供了美国境内47个陆地国家生态观测站网络（National Ecological Observatory Network, NEON）站点的每日蒸散量（evapotranspiration, ET）及蒸腾与蒸散比值（transpiration-to-evapotranspiration ratio, T/ET）估算值，数据覆盖2019-2021年三年时间，涵盖多样化的环境与生物群区条件。每日蒸散量以能量单位（兆焦每平方米每日，MJ m⁻² day⁻¹）和等效水深（毫米每日，mm day⁻¹）两种形式报告，假设汽化潜热恒为2.45 MJ/kg。本数据集采用的核心方法为混合循环神经网络-彭曼-蒙特斯框架（hybrid recurrent neural network–Penman–Monteith framework, RNN-PM），该框架将基于物理过程的地表能量平衡约束与数据驱动学习相结合，将蒸散量划分为蒸腾与蒸发两个组分。模型输入包含原位气象观测数据（气温、水汽压差、风速与辐射），以及卫星反演的地表温度、叶面积指数与土壤湿度数据。为开展基准测试与不确定性评估，本数据集还纳入了7种额外模型的T/ET估算结果，包括普里斯特利-泰勒喷气推进实验室模型（Priestley-Taylor Jet Propulsion Laboratory, PT-JPL）、彭曼-蒙特斯模型（Penman-Monteith, P-M）、双源能量平衡模型（Two-Source Energy Balance, TSEB）、支持向量回归（Support Vector Regression, SVR）与分类提升模型（Categorical Boosting, CatBoost）等，涵盖经验模型、机器学习模型与基于物理过程的模型三类方法（详细描述参见方法章节或关联发表论文）。数据集内容说明：本数据集包含用于存储各站点与各模型每日ET及T/ET估算值的CSV文件，以及上述变量的相关元数据文件。用户可通过常见电子表格软件（如Microsoft Excel、LibreOffice）或R、Python等编程环境获取数据。本数据集可用于开展跨站点生态系统用水比较、蒸散量拆分方法的评估，以及改进型陆-气交换模型的开发。