Spatially distributed information of actual Evapotranspiration (ET) is essentially required for various applications in agriculture, as well as studies related to water balance. An Energy Balance model that uses optical and thermal remote sensing datasets is appropriate for the spatially distributed estimation of ET, which is primarily controlled by local environmental conditions. In this study, we use the Surface Energy Balance System (SEBS), which is an Energy Balance model, to estimate an improved actual ET, by integrating canopy height information obtained from ICESat/GLAS, the first space-borne LiDAR satellite. Our model estimates latent heat flux (LE) as a residual of energy balance, while sensible heat flux (H) is estimated based on Monin–Obukhov similarity theory. The spatial variability of H and LE estimated from Landsat TM/ETM+ images were compared with eddy covariance (EC) based in situ flux tower observations, in order to document the SEBS model uncertainties in three forest sites in the East and Southeast Asian ecosystem. We identified that canopy height (h_c) is one of the critical parameters that can induce uncertainties in SEBS model estimations. Therefore, h_c obtained from LiDAR was introduced in parameterization of the roughness length for momentum transfer (z_0m). In situ based h_c obtained from flux tower stations, and the normalized difference vegetation index based formulation of h_c proposed by Su (2001), were also introduced in the SEBS z_0m parameterization. Root mean square error of LiDAR integrated SEBS (SEBS_LiDar) for estimated H values reduced by (20, 45, and 23)% compared to Landsat integrated SEBS (SEBS_Landsat), and (18, 7, and 9)% compared to In situ integrated SEBS (SEBS_Insitu) at the three selected forest sites (SMC, QYZ, and SMF), respectively, while the mean coefficient of determination values for the estimated LE improved by (9.09 and 5.88)% compared to SEBS_Landsat and SEBS_Insitu, respectively, with reference to ground-based flux tower observations. Our analysis reveals that LiDAR-based parameterization of z_0m can significantly increase the accuracy of estimated roughness lengths within an ET model, which can therefore improve the accuracy of the estimated turbulent heat fluxes. SEBS_LiDAR was further evaluated for the spatially distributed mapping of actual ET. We have observed that ET estimated by SEBS_Landsat overestimated in forest sites compared to EC observations; however, LiDAR-integrated SEBS marginally improved the accuracy of ET, thereby leading to an improvement in the mean correlation value of 0.87 and mean normalized standard deviation value of 0.93 approaching unity based on Taylor diagram, which more closely approximates the actual ground-based measurements. The proposed methodology overcomes the current limitation in the energy balance models in estimating roughness lengths, by providing accurate formulation of h_c by specifically incorporating LiDAR measurements in areas of tall vegetation canopies. The operational improvement in roughness lengths by next-generation SEBS_LiDAR model provides an opportunity to improve the estimation accuracy of water and energy fluxes, specifically ET. Overall, our proposed SEBS_LiDAR model provides a benchmark that is useful to policy makers and water resource managers in devising a plan for sustainable use of water resources in the East Asian region, as well as other similar ecosystems.

实际蒸散量 (ET) 的空间分布信息对于农业中的各种应用以及与水平衡相关的研究都是必不可少的。使用光学和热遥感数据集的能量平衡模型适用于主要受当地环境条件控制的 ET 的空间分布估计。在这项研究中，我们使用表面能量平衡系统 (SEBS)，这是一种能量平衡模型，通过整合从第一颗星载 LiDAR 卫星 ICESat/GLAS 获得的冠层高度信息来估计改进的实际 ET。我们的模型将潜热通量 ( LE )估计为能量平衡的残差，而感热通量 ( H) 是基于 Monin-Obukhov 相似理论估计的。将从 Landsat TM/ETM+ 图像估计的H和LE的空间变异性与基于原位通量塔观测的涡流协方差 (EC) 进行比较，以记录东亚和东南亚生态系统中三个森林地点的 SEBS 模型不确定性。我们确定冠层高度 ( h _c ) 是可能导致 SEBS 模型估计不确定性的关键参数之一。因此，从 LiDAR 获得的h _c被引入到用于动量传递的粗糙度长度 ( z _0m ) 的参数化中。基于原位的h _c从通量塔站获得的，并且所述的归一化植被指数基于制剂ħ _Ç苏（2001），提出了在SEBS z依次还引入_0米参数化。LiDAR 集成 SEBS (SEBS _LiDar ) 的均方根误差与 Landsat 集成 SEBS (SEBS _Landsat )相比，估计H值降低了 ( _20、45和 23)% ，与In相比降低了 ( _18、7和 9)%分别在三个选定的森林地点（SMC、QYZ 和 SMF）的原位综合 SEBS（SEBS _Insitu），而与 SEBS _Landsat和 SEBS相比，估计的 LE 的平均确定系数提高了（9.09 和 5.88）%_原位，分别与参考基于地面的通量塔观测。我们的分析表明，基于 LiDAR 的z _0m参数化可以显着提高 ET 模型中估计粗糙度长度的准确性，因此可以提高估计湍流热通量的准确性。SEBS _LiDAR进一步评估了实际 ET 的空间分布映射。我们观察到 SEBS _Landsat估计的 ET与 EC 观察结果相比，在森林地点高估；然而，激光雷达集成的 SEBS 略微提高了 ET 的精度，从而导致平均相关值提高 0.87，平均归一化标准偏差值提高 0.93，基于泰勒图接近统一，更接近实际的地面测量. 所提出的方法克服了能量平衡模型在估计粗糙度长度方面的当前限制，通过在高植被冠层区域专门结合 LiDAR 测量提供h _c的准确公式。下一代 SEBS _LiDAR对粗糙度长度的操作改进模型提供了一个机会来提高水和能量通量的估计精度，特别是 ET。总的来说，我们提出的 SEBS _LiDAR模型提供了一个基准，对决策者和水资源管理者制定东亚地区以及其他类似生态系统的水资源可持续利用计划很有用。