Accurate estimation of evapotranspiration ($\mathit{ET}$) is essential for several applications in water resources management. $\mathit{ET}$ models using remote sensing data have flourished in recent years allowing spatial and temporal assessments at unprecedented resolutions. This study presents geeSEBAL, a new tool for automated estimation of $\mathit{ET}$, based on the Surface Energy Balance Algorithm for Land (SEBAL) and a simplified version of the CIMEC (Calibration using Inverse Modeling at Extreme Conditions) process for the endmembers selection, developed within the Google Earth Engine (GEE) environment. The tool framework is introduced, and case studies across multiple biomes in Brazil are presented by comparing daily $\mathit{ET}$ estimates with eddy covariance (EC) data from 10 flux towers. Based on 224 Landsat images using ERA5 Land as meteorological inputs, daily $\mathit{ET}$ estimates of geeSEBAL yielded an average root mean squared difference (RMSD) of 0.67 mm day⁻¹ when compared to EC data corrected for the energy balance closure. Additional analyses indicate a low geeSEBAL sensitivity to meteorological inputs, yielding an average RMSD of 0.71 mm day⁻¹ when driven by in situ meteorological measurements. On the other hand, we found a higher sensitivity of the automated CIMEC algorithm to the selection of endmembers for internal calibration. For instance, by adjusting the endmembers percentiles to tropical biomes we found an error that was 36% lower compared to the standard CIMEC percentiles. Finally, we assessed the long-term effects (1984–2020) of land cover changes on surface energy fluxes and water use in agriculture for key areas in Brazil, from deforested areas in the Amazon to irrigated crops in the Pampas and Cerrado biomes. A comparison with a land surface temperature-based (SSEBop) and a vegetation-based (MOD16) model was also performed to assess relative advantages and disadvantages. This analysis showed that geeSEBAL has a significant potential for long-term assessment of $\mathit{ET}$ in data-scarce areas, due to its lower sensitivity to meteorological inputs. geeSEBAL codes are written in Python and JavaScript and are freely available on GitHub (https://github.com/et-brasil/geesebal). geeSEBAL also includes a graphical user interface (https://etbrasil.org/geesebal), allowing important advances in water resources management at regional scales.

精确估算蒸散量 ($\mathit{ET}$) 对于水资源管理中的一些应用是必不可少的。 $\mathit{ET}$近年来，使用遥感数据的模型蓬勃发展，允许以前所未有的分辨率进行空间和时间评估。本研究介绍了 geeSEBAL，一种用于自动估计$\mathit{ET}$，基于陆地表面能量平衡算法 (SEBAL) 和 CIMEC（在极端条件下使用逆向建模校准）过程的简化版本，用于端元选择，在谷歌地球引擎 (GEE) 环境中开发。介绍了工具框架，并通过每日比较呈现巴西多个生物群落的案例研究$\mathit{ET}$使用来自 10 个通量塔的涡流协方差 (EC) 数据进行估计。基于使用 ERA5 Land 作为气象输入的 224 幅 Landsat 图像，每天$\mathit{ET}$与针对能量平衡闭合校正的 EC 数据相比，geeSEBAL 的估计产生了 0.67 mm day ^-1的平均均方根差 (RMSD) 。其他分析表明，geeSEBAL 对气象输入的敏感性较低，平均 RMSD 为 0.71 mm day ^-1当由现场气象测量驱动时。另一方面，我们发现自动 CIMEC 算法对内部校准端元的选择具有更高的灵敏度。例如，通过将端元百分位数调整为热带生物群落，我们发现误差比标准 CIMEC 百分位数低 36%。最后，我们评估了土地覆盖变化对巴西关键地区的表面能通量和农业用水的长期影响（1984-2020），从亚马逊的森林砍伐地区到潘帕斯和塞拉多生物群落的灌溉作物。还与基于地表温度 (SSEBop) 和基于植被 (MOD16) 的模型进行了比较，以评估相对优势和劣势。该分析表明，geeSEBAL 在长期评估$\mathit{ET}$在数据稀缺地区，由于其对气象输入的敏感性较低。geeSEBAL 代码是用 Python 和 JavaScript 编写的，可在 GitHub ( https://github.com/et-brasil/geesebal )上免费获得。geeSEBAL 还包括一个图形用户界面 ( https://etbrasil.org/geesebal )，允许在区域尺度的水资源管理方面取得重要进展。