Dr. Alex White Profile

Dr. Alex White

at Agricultural Research Ctr

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Publications (5)

Proceedings Article | 4 June 2022 Presentation + Paper

Exploratory analysis of vineyard leaf water potential against sUAS multispectral and temperature information

Rui Gao, Maria Mar Alsina, Alfonso Torres-Rua, Lawrence Hipps, William Kustas, William White, Martha Anderson, Joseph Alfieri, Nick Dokoozlian, Hector Nieto, Feng Gao, Lynn McKee, John Prueger, Luis Sanchez, Andrew Mcelrone, Nicolas Bambach Ortiz, Calvin Coopmans, Ian Gowing

Proceedings Volume 12114, 121140K (2022) https://doi.org/10.1117/12.2622995

KEYWORDS: Near infrared, Vegetation, Remote sensing, Data modeling, Sensors, Agriculture, Performance modeling, Statistical analysis, Feature extraction

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Proceedings Article | 30 May 2022 Presentation

Assessment of TSEB-PT and -2T in ET partitioning estimation over California commercial vineyards based on sUAS information

Rui Gao, Alfonso F. Torres-Rua, Lawrence Hipps, William P. Kustas, Martha Anderson, William A. White, Joseph Alfieri, Maria Mar Alsina, Nick Dokoozlian, Hector Nieto, Feng Gao, Lynn G. McKee, John H. Prueger, Luis Sanchez, Andrew J. Mcelrone, Nicolas Bambach-Ortiz, Calvin Coopmans, Ian Gowing

Proceedings Volume 12114, 121140I (2022) https://doi.org/10.1117/12.2622993

KEYWORDS: Remote sensing, Soil science, Satellites, Performance modeling, Climatology, Carbon, Atmospheric sensing, Agriculture

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Proceedings Article | 12 April 2021 Presentation + Paper

Evapotranspiration partitioning assessment using a machine-learning-based leaf area index and the two-source energy balance model with sUAV information

Rui Gao, Alfonso Torres-Rua, Ayman Nassar, Joseph Alfieri, Mahyar Aboutalebi, Lawrence Hipps, Nicolas Bambach Ortiz, Andrew McElrone, Calvin Coopmans, William Kustas, William White, Lynn McKee, Maria del Mar Alsina, Nick Dokoozlian, Luis Sanchez, John Prueger, Hector Nieto, Nurit Agam

Proceedings Volume 11747, 117470N (2021) https://doi.org/10.1117/12.2586259

KEYWORDS: Data modeling, Vegetation, Biological research, Systems modeling, Remote sensing, Machine learning, Ecosystems, Carbon, Autoregressive models, Atmospheric sensing

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Accurate quantification of the partitioning of evapotranspiration (ET) into transpiration and evaporation fluxes is necessary to understanding ecosystem interactions among carbon, water, and energy flux components. ET partitioning can also support the description of atmosphere and land interactions and provide unique insights into vegetation water status. Previous studies have identified leaf area index (LAI) estimation as a key descriptor of biomass conditions needed for the estimation of transpiration and evaporation. LAI estimation in clumped vegetation systems, such as vineyards and orchards, has proven challenging and is strongly related to crop phenological status and canopy management. In this study, a feature extraction model based on previous research was built to generate a total of 202 preliminary variables at a 3.6-by-3.6- meter-grid scale based on submeter-resolution information from a small Unmanned Aerial Vehicle (sUAV) in four commercial vineyards across California. Using these variables, a machine learning model called eXtreme Gradient Boosting (XGBoost) was successfully built for LAI estimation. The XGBoost built-in function requires only six variables relating to vegetation indices and temperature to produce high-accuracy LAI estimation for the vineyard. Using the sixvariable XGBoost-based LAI map, two versions of the Two-Source Energy Balance (TSEB) model, TSEB-PT and TSEB- 2T were used for energy balance and ET partitioning. Comparing these results with the Eddy-Covariance (EC) tower data, showed that TSEB-PT outperforms TSEB-2T on the estimation of sensible heat flux (within 13% relative error) and surface heat flux (within 34% relative error), while TSEB-2T outperforms TSEB-PT on the estimation of net radiation (within 14% relative error) and latent heat flux (within 2% relative error). For the mature vineyard (north block), TSEB-2T performs better than TSEB-PT in partitioning the canopy latent heat flux with 6.8% relative error and soil latent heat flux with 21.7% relative error; however, for the younger vineyard (south block), TSEB-PT performs better than TSEB-2T in partitioning the canopy latent heat flux with 11.7% relative error and soil latent heat flux with 39.3% relative error.

Proceedings Article | 14 May 2020 Presentation + Paper

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 Ticlavilca, Mahyar Aboutalebi, Hector Nieto, Maria Mar Alsina, Alex White, John Prueger, Joseph Alfieri, Lawrence Hipps, Lynn McKee, William Kustas, Calvin Coopmans, Nick Dokoozlian

Proceedings Volume 11414, 114140B (2020) https://doi.org/10.1117/12.2558824

KEYWORDS: Unmanned aerial vehicles, Vegetation, Earth observing sensors, Landsat, Data modeling, Agriculture, Thermal modeling, Sensors, Cameras, Water

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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.

Proceedings Article | 27 April 2020 Presentation

Deriving daily evapotranspiration from multiple unmanned aerial vehicle (UAV) thermal imageries and high-frequency ground thermal measurements (Conference Presentation)

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

Proceedings Volume 11414, 114140E (2020) https://doi.org/10.1117/12.2558630

KEYWORDS: Unmanned aerial vehicles, Thermography, Wind energy, Remote sensing, Thermal modeling, Agriculture, Shortwaves, Soil science, Temperature sensors, Sensors

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Evapotranspiration (ET) derived from remote sensing-based models represents ½ hourly to hourly value that is upscaled to a daily scale for practical applications in the fields of agricultural and water management. Several upscaling methods such as Gaussian fitting curve, sine approach, and evaporative fraction approach have been developed to extrapolate remote sensing-based ET values to daily scale by assuming constant daytime ratios (e.g., self-preservation of available energy partitioning). This simple assumption can result in uncertainties in the performance of those methods and can be violated for unstable conditions such as a cloudy day. Studies showed that, for example, diurnal variation of incoming shortwave radiation will change from a Gaussian distribution to a multimodal distribution on a cloudy day. Besides, when remote-sensing ET outputs are directly upscaled to daily scale and compared with eddy covariance measurements, a fixed footprint of eddy covariance is assumed, while the actual eddy covariance footprint is dynamic and changes with wind speed, direction and atmospheric stability. In this study, a new method is proposed to spatially and temporally simulate canopy and soil temperature for each time step (e.g., 1-hour) based on the temperature pattern recorded by IRT temperature sensors and UAV initial temperatures at the specific time of day. Next, the Two-Source Energy Balance (TSEB) model is executed for each time step of the daytime period (usually when net radiation >100 W/m^2) to calculate energy balance components. The integration of TSEB outputs over the daytime period leads to estimations of daily energy balance components. Since cloudy conditions affect temperatures recorded by IRT sensors, the proposed model is not sensitive to weather conditions. In addition, the proposed model physically simulates ET at each time step instead of directly extrapolating ET from a single remote sensing observation and model output This feature solves the limitations of comparing the direct extrapolation methods of instantaneous ET against eddy covariance measurements. The proposed approach is applied on 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) conducted since 2014 over multiple vineyards located in California. The estimated ET values from the TSEB model at hourly time steps and integrated over the daytime period are compared to eddy covariance measurements of ET. Additionally, hourly model output integrated over the daytime period compared to the different upscaling methods are presented and discussed.

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