Real evapotranspiration (ETr) plays a key role in water balance, especially for dry forest vegetation (Caatinga) and irrigated agricultural areas, as is the case in the Petrolina region located in the Brazilian semiarid region (BSAR). Population growth increases the need for food and the use of water resources, which are scarcer in semiarid regions. Thus, knowledge of the energy balance (EB) and radiative balance are indispensable for the management of water resources, especially in irrigated agricultural areas. Currently, one of the biggest challenges in the application of algorithms for estimating ETr based on satellite images is the calibration of the EB. The goal of this study was to evaluate the applicability of Geographic Resources Analysis Support System (GRASS) Python surface EB algorithm for land (GP-SEBAL) to calculate ETr for Caatinga vegetation and agricultural areas in the BSAR. GP-SEBAL was applied to two images from Landsat 8 obtained in 2013 and compared with manual SEBAL and observed data. In this study, kappa coefficient accuracy was used to compare the ground truth data and predicted land use and land cover. The performance of algorithms in determining the EB components, land-surface temperature (LST), net radiation (R_n), soil heat flux (G), latent heat flux (L_E), sensible heat flux (H), and ETr were compared with statistical indices and uncertainty percentages (PUs) were calculated. In addition, a sensitivity analysis using a deviation of 30 W/m² with an interval of 5 W/m² was used to analyze the sensitivity of ETr. The results showed a strong correlation between GP-SEBAL and manual SEBAL for ETr, LST, R_n, L_H, and H. The values suggest the regularity of estimating the EB in an automated manner. The results indicate a small PU difference in the estimated ETr values calculated using GP-SEBAL and SEBAL for two images of 10.87% and 20.11% for Caatinga vegetation and 15.01% and 35.16% for agriculture, respectively. We conclude that GP-SEBAL is an efficient automated algorithm for estimating ETr that uses a less complex process with a lower incidence of errors and considerably faster execution time than classical applications of SEBAL.

真正的蒸散量（ETr）在水平衡中起着关键作用，尤其是对于干燥的森林植被（Caatinga）和灌溉的农业地区，如巴西半干旱地区（Petrolina）的Petrolina地区（BSAR）的情况。人口增长增加了对食物的需求和对水资源的利用，而这在半干旱地区却很少。因此，能量平衡（EB）和辐射平衡的知识对于水资源的管理是必不可少的，尤其是在农业灌溉地区。当前，基于卫星图像估算ETr的算法应用中的最大挑战之一是EB的校准。这项研究的目的是评估地理资源分析支持系统（GRASS）Python地表EB算法在土地上（GP-SEBAL）的适用性，以计算BSAR中Caatinga植被和农业区的ETr。将GP-SEBAL应用于2013年获得的Landsat 8的两幅图像，并与手动SEBAL和观察到的数据进行了比较。在这项研究中，卡帕系数的准确性用于比较地面真实数据和预测的土地利用和土地覆盖。算法在确定EB分量，地表温度（LST），净辐射（R 卡伯系数的准确性用于比较地面真实数据和预测的土地利用和土地覆盖。算法在确定EB分量，地表温度（LST），净辐射（R 卡伯系数的准确性用于比较地面真实数据和预测的土地利用和土地覆盖。算法在确定EB分量，地表温度（LST），净辐射（R_n），土壤热通量（G），潜热通量（L _E），显热通量（H）和ETr与统计指标进行比较，并计算不确定度百分比（PU）。此外，使用偏差为30 W / m ²且间隔为5 W / m ²的灵敏度分析来分析ETr的灵敏度。结果表明，对于ETr，LST，R _n和L _H，GP-SEBAL和手动SEBAL之间有很强的相关性，这些值表明以自动方式估算EB的规律性。结果表明，使用GP-SEBAL和SEBAL计算的两个图像中，Caatinga植被的10.87％和20.11％的图像以及农业的15.01％和35.16％的ETR估计值的PU差异很小。我们得出的结论是，与SEBAL的传统应用相比，GP-SEBAL是一种用于估算ETr的高效自动化算法，该算法使用的过程较少复杂，错误发生率较低，执行时间明显更长。