Estimation of evapotranspiration and energy fluxes using a deep-learning-based high-resolution emissivity model and the two-source energy balance model with sUAS information

Alfonso Torres-Rua; Andres M. Ticlavilca; Mahyar Aboutalebi; Hector Nieto; Maria Mar Alsina; Alex White; John H. Prueger; Joseph Alfieri; Lawrence Hipps; Lynn McKee; William Kustas; Calvin Coopmans; Nick Dokoozlian

doi:10.1117/12.2558824

14 May 2020 Estimation of evapotranspiration and energy fluxes using a deep-learning-based high-resolution emissivity model and the two-source energy balance model with sUAS information

Alfonso Torres-Rua, Andres M. Ticlavilca, Mahyar Aboutalebi, Hector Nieto, Maria Mar Alsina, Alex White, John H. Prueger, Joseph Alfieri, Lawrence Hipps, Lynn McKee, William Kustas, Calvin Coopmans, Nick Dokoozlian

Author Affiliations +

Proceedings Volume 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V; 114140B (2020) https://doi.org/10.1117/12.2558824
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Surface temperature is necessary for the estimation of energy fluxes and evapotranspiration from satellites and airborne data sources. For example, the Two-Source Energy Balance (TSEB) model uses thermal information to quantify canopy and soil temperatures as well as their respective energy balance components. While surface (also called kinematic) temperature is desirable for energy balance analysis, obtaining this temperature is not straightforward due to a lack of spatially estimated narrowband (sensor-specific) and broadband emissivities of vegetation and soil, further complicated by spectral characteristics of the UAV thermal camera. This study presents an effort to spatially model narrowband and broadband emissivities for a microbolometer thermal camera at UAV information resolution (~0.15 m) based on Landsat and NASA HyTES information using a deep learning (DL) model. The DL model is calibrated using equivalent optical Landsat / UAV spectral information to spatially estimate narrowband emissivity values of vegetation and soil in the 7–14- nm range at UAV resolution. The resulting DL narrowband emissivity values were then used to estimate broadband emissivity based on a developed narrowband-broadband emissivity relationship using the MODIS UCSB Emissivity Library database. The narrowband and broadband emissivities were incorporated into the TSEB model to determine their impact on the estimation of instantaneous energy balance components against ground measurements. The proposed effort was applied to information collected by the Utah State University AggieAir small Unmanned Aerial Systems (sUAS) Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) over a vineyard located in Lodi, California. A comparison of resulting energy balance component estimates, with and without the inclusion of high-resolution narrowband and broadband emissivities, against eddy covariance (EC) measurements under different scenarios are presented and discussed.

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Alfonso Torres-Rua, Andres M. Ticlavilca, Mahyar Aboutalebi, Hector Nieto, Maria Mar Alsina, Alex White, John H. Prueger, Joseph Alfieri, Lawrence Hipps, Lynn McKee, William Kustas, Calvin Coopmans, and Nick Dokoozlian "Estimation of evapotranspiration and energy fluxes using a deep-learning-based high-resolution emissivity model and the two-source energy balance model with sUAS information", Proc. SPIE 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V, 114140B (14 May 2020); https://doi.org/10.1117/12.2558824

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