Evapotranspiration ( $ET$ ) provides a strong connection between surface energy and hydrological cycles. Advancements in remote sensing techniques have increased our understanding of energy and terrestrial water balances as well as the interaction between surface and atmosphere over large areas. In this study, we computed surface energy fluxes using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm and a simplified adaptation of the CIMEC (Calibration using Inverse Modeling at Extreme Conditions) process for automated endmember selection. Our main purpose was to assess and compare the accuracy of the automated calibration of the SEBAL algorithm using two different sources of meteorological input data (ground measurements from an eddy covariance flux tower and reanalysis data from Modern-Era Reanalysis for Research and Applications version 2 (MERRA-2)) to estimate the dry season partitioning of surface energy and water fluxes in a transitional area between tropical rainforest and savanna. The area is located in Brazil and is subject to deforestation and cropland expansion. The SEBAL estimates were validated using eddy covariance measurements (2004 to 2006) from the Large-Scale Biosphere-Atmosphere Experiment in the Amazon (LBA) at the Bananal Javaés (JAV) site. Results indicated a high accuracy for daily ET, using both ground measurements and MERRA-2 reanalysis, suggesting a low sensitivity to meteorological inputs. For daily ET estimates, we found a root mean square error (RMSE) of 0.35 mm day⁻¹ for both observed and reanalysis meteorology using accurate quantiles for endmembers selection, yielding an error lower than 9% (RMSE compared to the average daily ET). Overall, the ET rates in forest areas were 4.2 mm day⁻¹, while in grassland/pasture and agricultural areas we found average rates between 2.0 and 3.2 mm day⁻¹, with significant changes in energy partitioning according to land cover. Thus, results are promising for the use of reanalysis data to estimate regional scale patterns of sensible heat (H) and latent heat (LE) fluxes, especially in areas subject to deforestation.

中文翻译：

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SEBAL算法自动校准的评估，以估算巴西森林-热带稀树草原过渡期的干季表面能分配

蒸散（ $ËŤ$ ）在表面能和水文循环之间建立了牢固的联系。遥感技术的进步增加了我们对能量和陆地水平衡以及大面积地表与大气之间相互作用的理解。在这项研究中，我们使用土地表面能平衡算法（SEBAL）算法和对CIMEC（在极端条件下使用逆建模的校准）过程的简化适应来计算表面能通量，以进行自动端构件选择。我们的主要目的是使用两种不同的气象输入数据源（来自涡流协方差通量塔的地面测量和来自现代时代研究和应用版本2的重新分析数据来评估和比较SEBAL算法自动校准的准确性（ MERRA-2））来估算热带雨林和热带稀树草原之间过渡地区地表能量和水通量的旱季分配。该地区位于巴西，受到森林砍伐和农田扩张的影响。SEBAL估计值是使用BananaalJavaés（JAV）站点的亚马逊大型生物圈-大气实验（LBA）中的涡度协方差测量（2004年至2006年）验证的。结果表明每天的准确性很高ET，同时使用地面测量和MERRA-2重新分析，表明对气象输入的敏感性较低。对于每日ET估算，我们发现使用精确的分位数进行最终成员选择时，观测和重新分析气象学的均方根误差（RMSE）为0.35 mm天^-1，产生的误差低于9％（RMSE与平均每日ET相比） 。总体而言，森林地区的ET发生率是4.2毫米天^-1，而草原/牧草和农业地区的平均ET发生率是2.0到3.2毫米天^-1，根据土地覆盖的能源分配发生了重大变化。因此，使用再分析数据来估算显热（H）和潜热（LE）通量的区域尺度模式，尤其是在森林遭到砍伐的地区，其结果很有希望。