Nassar Ayman, Torres-Rua Alfonso, Kustas William, Nieto Hector, McKee Mac, Hipps Lawrence, Alfieri Joseph, Prueger John, Alsina Maria Mar, McKee Lynn, Coopmans Calvin, Sanchez Luis, Dokoozlian Nick
Department of Civil and Environmental Engineering, Utah State University, Logan, UT 84322, USA.
U. S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA.
Proc SPIE Int Soc Opt Eng. 2020 Apr-May;11414. doi: 10.1117/12.2558715. Epub 2020 May 26.
Estimation of surface energy fluxes using thermal remote sensing-based energy balance models (e.g., TSEB2T) involves the use of local micrometeorological input data of air temperature, wind speed, and incoming solar radiation, as well as vegetation cover and accurate land surface temperature (LST). The physically based Two-source Energy Balance with a Dual Temperature (TSEB2T) model separates soil and canopy temperature (T and T) to estimate surface energy fluxes including R, H, LE, and G. The estimation of T and T components for the TSEB2T model relies on the linear relationship between the composite land surface temperature and a vegetation index, namely NDVI. While canopy and soil temperatures are controlling variables in the TSEB2T model, they are influenced by the NDVI threshold values, where the uncertainties in their estimation can degrade the accuracy of surface energy flux estimation. Therefore, in this research effort, the effect of uncertainty in T and T estimation on surface energy fluxes will be examined by applying a Monte Carlo simulation on NDVI thresholds used to define canopy and soil temperatures. The spatial information used is available from multispectral imagery acquired by the AggieAir sUAS Program at Utah State University over vineyards near Lodi, California as part of the ARS-USDA Agricultural Research Service's Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. The results indicate that LE is slightly sensitive to the uncertainty of NDVI and NDVI. The observed relative error of LE corresponding to NDVI uncertainty was between -1% and 2%, while for NDVI uncertainty, the relative error was between -2.2% and 1.2%. However, when the combined NDVI and NDVI uncertainties were used simultaneously, the domain of the observed relative error corresponding to the absolute values of |ΔLE| was between 0% and 4%.
利用基于热遥感的能量平衡模型（如TSEB2T）估算表面能量通量，需要使用当地的微气象输入数据，包括气温、风速和入射太阳辐射，以及植被覆盖度和准确的陆地表面温度（LST）。基于物理的双源双温度能量平衡（TSEB2T）模型将土壤和冠层温度（T和T）分开，以估算包括R、H、LE和G在内的表面能量通量。TSEB2T模型中T和T分量的估算依赖于复合陆地表面温度与植被指数（即归一化植被指数NDVI）之间的线性关系。虽然冠层和土壤温度是TSEB2T模型中的控制变量，但它们受NDVI阈值的影响，其估算中的不确定性会降低表面能量通量估算的准确性。因此，在本研究中，将通过对用于定义冠层和土壤温度的NDVI阈值进行蒙特卡洛模拟，来研究T和T估算中的不确定性对表面能量通量的影响。所使用的空间信息来自犹他州立大学AggieAir sUAS项目获取的多光谱图像，该图像拍摄于加利福尼亚州洛迪附近的葡萄园，是美国农业部农业研究局葡萄遥感大气剖面与蒸散实验（GRAPEX）项目的一部分。结果表明，蒸散量（LE）对NDVI和NDVI的不确定性略有敏感。对应于NDVI不确定性的LE观测相对误差在-1%至2%之间，而对于NDVI不确定性，相对误差在-2.2%至1.2%之间。然而，当同时使用NDVI和NDVI的组合不确定性时，对应于|ΔLE|绝对值的观测相对误差范围在0%至4%之间。
