Implications of soil and canopy temperature uncertainty in the estimation of surface energy fluxes using TSEB2T and high-resolution imagery in commercial vineyards

Ayman Nassar; Alfonso Torres-Rua; William Kustas; Hector Nieto; Mac McKee; Lawrence Hipps; Joseph Alfieri; John Prueger; Maria Mar Alsina; Lynn McKee; Calvin Coopmans; Luis Sanchez; Nick Dokoozlian

doi:10.1117/12.2558715

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26 May 2020 Implications of soil and canopy temperature uncertainty in the estimation of surface energy fluxes using TSEB2T and high-resolution imagery in commercial vineyards

Ayman Nassar, Alfonso Torres-Rua, William Kustas, Hector Nieto, Mac McKee, Lawrence Hipps, Joseph Alfieri, John Prueger, Maria Mar Alsina, Lynn McKee, Calvin Coopmans, Luis Sanchez, Nick Dokoozlian

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Proceedings Volume 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V; 114140F (2020) https://doi.org/10.1117/12.2558715
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

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-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_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 NDVI_c uncertainties were used simultaneously, the domain of the observed relative error corresponding to the absolute values of |ΔLE| was between 0% and 4%.

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Ayman Nassar, Alfonso Torres-Rua, William Kustas, Hector Nieto, Mac McKee, Lawrence Hipps, Joseph Alfieri, John Prueger, Maria Mar Alsina, Lynn McKee, Calvin Coopmans, Luis Sanchez, and Nick Dokoozlian "Implications of soil and canopy temperature uncertainty in the estimation of surface energy fluxes using TSEB2T and high-resolution imagery in commercial vineyards", Proc. SPIE 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V, 114140F (26 May 2020); https://doi.org/10.1117/12.2558715

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