Literature DB >> 33758458

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

Ayman Nassar1, Alfonso Torres-Rua1, William Kustas2, Hector Nieto3, Mac McKee1, Lawrence Hipps4, Joseph Alfieri2, John Prueger5, Maria Mar Alsina6, Lynn McKee2, Calvin Coopmans7, Luis Sanchez6, Nick Dokoozlian6.   

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 (Ts and Tc) to estimate surface energy fluxes including Rn, H, LE, and G. The estimation of Ts and Tc 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 Ts and Tc 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 NDVIs and NDVIc. The observed relative error of LE corresponding to NDVIs uncertainty was between -1% and 2%, while for NDVIc uncertainty, the relative error was between -2.2% and 1.2%. However, when the combined NDVIs and NDVIc uncertainties were used simultaneously, the domain of the observed relative error corresponding to the absolute values of |ΔLE| was between 0% and 4%.

Entities:  

Keywords:  LST uncertainty; Monte Carlo simulation; TSEB2T; land surface temperature (LST); sensitivity analysis; soil and canopy temperature (Ts, Tc); surface energy fluxes

Year:  2020        PMID: 33758458      PMCID: PMC7982302          DOI: 10.1117/12.2558715

Source DB:  PubMed          Journal:  Proc SPIE Int Soc Opt Eng        ISSN: 0277-786X


  4 in total

1.  Estimation of surface thermal emissivity in a vineyard for UAV microbolometer thermal cameras using NASA HyTES hyperspectral thermal, Landsat and AggieAir optical data.

Authors:  Alfonso Torres-Rua; Mahyar Aboutalebi; Timothy Wright; Ayman Nassar; Pierre Guillevic; Lawrence Hipps; Feng Gao; Kevin Jim; Maria Mar Alsina; Calvin Coopmans; Mac McKee; William Kustas
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2019-05-14

2.  Implications of sensor inconsistencies and remote sensing error in the use of small unmanned aerial systems for generation of information products for agricultural management.

Authors:  Mac McKee; Ayman Nassar; Alfonso Torres-Rua; Mahyar Aboutalebi; William Kustas
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2018-05-21

3.  Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards.

Authors:  Ayman Nassar; Alfonso Torres-Rua; William Kustas; Hector Nieto; Mac McKee; Lawrence Hipps; David Stevens; Joseph Alfieri; John Prueger; Maria Mar Alsina; Lynn McKee; Calvin Coopmans; Luis Sanchez; Nick Dokoozlian
Journal:  Remote Sens (Basel)       Date:  2020       Impact factor: 4.848

4.  Vicarious Calibration of sUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature.

Authors:  Alfonso Torres-Rua
Journal:  Sensors (Basel)       Date:  2017-06-26       Impact factor: 3.576

  4 in total
  2 in total

1.  Development of High Performance Computing Tools for Estimation of High-Resolution Surface Energy Balance Products Using sUAS Information.

Authors:  Ayman Nassar; Alfonso Torres; Venkatesh Merwade; Sayan Dey; Lan Zhao; I Luk Kim; William P Kustas; Hector Nieto; Lawrence Hipps; Rui Gao; Joseph Alfieri; John Prueger; Maria Mar Alsina; Lynn McKee; Calvin Coopmans; Luis Sanchez; Nick Dokoozlian; Nicolas Bambach Ortiz; Andrew J Mcelrone
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2021-04-12

2.  Assessing Daily Evapotranspiration Methodologies from One-Time-of-Day sUAS and EC Information in the GRAPEX Project.

Authors:  Ayman Nassar; Alfonso Torres-Rua; William Kustas; Joseph Alfieri; Lawrence Hipps; John Prueger; Héctor Nieto; Maria Mar Alsina; William White; Lynn McKee; Calvin Coopmans; Luis Sanchez; Nick Dokoozlian
Journal:  Remote Sens (Basel)       Date:  2021-07-23       Impact factor: 5.349

  2 in total

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