Mr. Rui Gao Profile

Rui Gao

at Utah State Univ

SPIE Involvement:

Author

Rui is a Ph.D. student at Utah State University. Providing scientific support for precision agriculture management and learning more about the interaction between agriculture and the ecosystem are Rui's interests. Currently, Rui is working on energy-flux estimation based on the super high-resolution imagery captured by the AggieAir platform, a type of small unmanned system (sUAS).

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

Development of high performance computing tools for estimation of high-resolution surface energy balance products using sUAS information

Ayman Nassar, Alfonso Torres-Rua, Venkatesh Merwade, Sayan Dey, Lan Zhao, I. Luk Kim, William 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 Mcelrone

Proceedings Volume 11747, 117470K (2021) https://doi.org/10.1117/12.2587763

KEYWORDS: Atmospheric modeling, Performance modeling, Data processing, Data modeling, Computing systems, Remote sensing, MATLAB, Excel, Atmospheric sensing, Atmospheric monitoring

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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.

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