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Implications of Soil and Canopy Temperature Uncertainty in the Estimation of Surface Energy Fluxes Using TSEB2T and High- resolution Imagery in Commercial Vineyards

机译:TSEB2T和高分辨率图像在商业葡萄园中估算土壤和冠层温度不确定性对表面能通量的影响

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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_s and T_c) to estimate surface energy fluxes including Rn, H, LE, and G. The estimation of T_s and T_c 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_s and T_c 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-USD A 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_S and NDVI_C. The observed relative error of LE corresponding to NDVI_S uncertainty was between -1% and 2%, while for NDVI_C uncertainty, the relative error was between -2.2% and 1.2%. However, when the combined NDVI_S and NDVL_c uncertainties were used simultaneously, the domain of the observed relative error corresponding to the absolute values of |ALE| was between 0% and 4%.
机译:使用基于热遥感的能量平​​衡模型(例如,TSEB2T)估算表面能通量涉及使用局部微气象输入数据,包括空气温度,风速和入射太阳辐射,以及植被覆盖度和准确的地表温度( LST)。基于物理的双温度双源能量平衡(TSEB2T)模型将土壤和冠层温度(T_s和T_c)分开,以估算包括Rn,H,LE和G在内的表面能通量。 TSEB2T模型依赖于复合地表温度与植被指数(即NDVI)之间的线性关系。尽管冠层和土壤温度是TSEB2T模型中的控制变量,但它们受到NDVI阈值的影响,其中NDVI阈值的不确定性可能会降低表面能通量估计的准确性。因此,在这项研究工作中,将通过对用于定义冠层和土壤温度的NDVI阈值进行蒙特卡罗模拟,来检验T_s和T_c估计中的不确定性对表面能通量的影响。所使用的空间信息可从犹他州立大学的AggieAir sUAS计划从加利福尼亚洛迪附近的葡萄园获得的多光谱图像获得,这是ARS-USD A农业研究服务的葡萄遥感大气廓线和蒸散实验(GRAPEX)项目的一部分。结果表明LE对NDVI_S和NDVI_C的不确定性略有敏感。与NDVI_S不确定度相对应的LE的相对误差在-1%和2%之间,而对于NDVI_C不确定度,相对误差在-2.2%和1.2%之间。但是,当同时使用组合的NDVI_S和NDVL_c不确定性时,观察到的相对误差的范围对应于| ALE |的绝对值。介于0%和4%之间。

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