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Full-Text Articles in Engineering
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 F. Torres-Rua, William Kustas, Héctor Nieto, Mac Mckee, Lawrence Hipps, Joseph Alfieri, John H. Prueger, Maria Mar Alsina, Lynn Mckee, Calvin Coopmans, Luis Sanchez, 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, Ayman Nassar, Alfonso F. Torres-Rua, William Kustas, Héctor Nieto, Mac Mckee, Lawrence Hipps, Joseph Alfieri, John H. Prueger, Maria Mar Alsina, Lynn Mckee, Calvin Coopmans, Luis Sanchez, Nick Dokoozlian
Civil and Environmental Engineering Faculty Publications
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 …
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 F. Torres-Rua, Andres Ticlavilca, Mahyar Aboutalebi, Héctor Nieto, Maria Mar Alsina, Alex White, John H. Prueger, Joseph Alfieri, Lawrence Hipps, Lynn Mckee, William Kustas, Calvin Coopmans, 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, Alfonso F. Torres-Rua, Andres Ticlavilca, Mahyar Aboutalebi, Héctor Nieto, Maria Mar Alsina, Alex White, John H. Prueger, Joseph Alfieri, Lawrence Hipps, Lynn Mckee, William Kustas, Calvin Coopmans, Nick Dokoozlian
Civil and Environmental Engineering Faculty Publications
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 …
Estimation Of Soil Moisture At Different Soil Levels Using Machine Learning Techniques And Unmanned Aerial Vehicle (Uav) Multispectral Imagery, Mahyar Aboutalebi, L. Niel Allen, Alfonso F. Torres-Rua, Mac Mckee, Calvin Coopmans
Estimation Of Soil Moisture At Different Soil Levels Using Machine Learning Techniques And Unmanned Aerial Vehicle (Uav) Multispectral Imagery, Mahyar Aboutalebi, L. Niel Allen, Alfonso F. Torres-Rua, Mac Mckee, Calvin Coopmans
AggieAir Publications
Soil moisture is a key component of water balance models. Physically, it is a nonlinear function of parameters that are not easily measured spatially, such as soil texture and soil type. Thus, several studies have been conducted on the estimation of soil moisture using remotely sensed data and data mining techniques such as artificial neural networks (ANNs) and support vector machines (SVMs). However, all models developed based on these techniques are limited to site-specific applications where they are trained and their parameters are tuned. Moreover, since the system of non-linear equations produced by and conducted in the machine learning process …