Data from: Enhancing Evapotranspiration Estimates in Composite Terrain Through the Integration of Satellite Remote Sensing and Eddy Covariance Measurements

Abstract: Accurate evaluation of water resource systems is essential for informed planning and decision-making. Evapotranspiration (ET), a key component of water resource management, is often estimated using remote sensing techniques; however, such estimates can be subject to significant uncertainties under certain conditions. In this study, we present a novel approach to improving the accuracy of ET estimates in composite terrains. The methodology involves optimizing the Surface Energy Balance Algorithm for Land (SEBAL-OPT) by integrating ground-based eddy covariance (EC) flux tower data into the satellite-based ET retrieval process. The approach was evaluated at four sites in California, each representing different land uses. Parameter optimization was achieved through Bayesian inference using the Differential Evolution Adaptive Metropolis (DREAM) algorithm, which minimized discrepancies between ET estimates derived from Landsat 8 and 9 imagery and the observed ET from EC measurements. Results from the global sensitivity analysis identified solar radiation and hot/cold pixel selection as the most sensitive parameters in the SEBAL algorithm, highlighting their critical role in reducing uncertainty in ET estimates. SEBAL-OPT demonstrated significantly improved accuracy, with root mean square error (RMSE) values ranging from 0.72 mm to 1.33 mm, compared to the original SEBAL parameterization (SEBAL-ORG), which produced RMSE values between 1.03 mm and 2.14 mm. This approach highlights that, when properly calibrated, the model can be effectively applied across diverse agricultural landscapes, regardless of the specific land use at individual sites. These findings have significant implications for water resource planning, agricultural water management, and water rights adjudication and could be applied to other remote sensing of ET models.摘要：水资源系统的精准评估是开展科学规划与决策的重要前提。蒸散发（Evapotranspiration, ET）作为水资源管理的核心环节之一，通常可通过遥感技术进行估算，但在特定条件下，这类估算结果往往存在显著不确定性。本研究提出了一种提升复合地形区域ET估算精度的新方法：通过将地面涡度协方差（eddy covariance, EC）通量塔数据整合入卫星遥感ET反演流程，对地表能量平衡算法（Surface Energy Balance Algorithm for Land, SEBAL）进行优化，得到优化版SEBAL（SEBAL-OPT）。本方法在美国加利福尼亚州的4个代表不同土地利用类型的站点开展了验证。参数优化采用差分进化自适应Metropolis（Differential Evolution Adaptive Metropolis, DREAM）算法结合贝叶斯推断完成，通过最小化Landsat 8与Landsat 9遥感影像反演的ET估算值与EC通量塔实测ET值之间的偏差，实现参数校准。全局敏感性分析结果显示，太阳辐射与冷热像元选取是SEBAL算法中最敏感的参数，表明二者对降低ET估算不确定性具有关键作用。与原始SEBAL参数化方案（SEBAL-ORG，其均方根误差（root mean square error, RMSE）介于1.03 mm至2.14 mm之间）相比，优化后的SEBAL-OPT模型估算精度显著提升，RMSE范围为0.72 mm至1.33 mm。本研究表明，经过合理校准后，该模型可有效应用于多样的农业景观区域，无需考虑单个站点的具体土地利用类型。本研究结果对水资源规划、农业用水管理以及水权裁决具有重要参考价值，同时可推广应用于其他遥感ET估算模型。