Yanghui Kang

Assistant Professor

Virginia Tech

United States

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About

Yanghui Kang is an Assistant Professor at Virginia Tech in the United States. His research focuses on spatiotemporal modeling of primary productivity, remote sensing techniques for leaf area index estimation, and the assessment of satellite data for water resource management. Recent articles include studies on CO2 fertilization effects, satellite-based evapotranspiration accuracy, and machine learning applications in agriculture.

Articles (10)

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2025

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2024

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2024

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2024

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2024

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2023

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2022

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2022

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2021

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2021

Collaborators (5)

Qi Yang

University of Minnesota

UNITED STATES

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Guy Schurgers

Københavns Universitet

DENMARK

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Ayse Kilic

Professor

University of Nebraska - Lincoln

UNITED STATES

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John Volk

Desert Research Institute

UNITED STATES

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Alfonso Torres-Rua

Assistant Professor

Utah State University

UNITED STATES

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Yanghui Kang

Research Interests

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Articles (10)

CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO2 fertilization

Correcting confounding canopy structure, biochemistry and soil background effects improves leaf area index estimates across diverse ecosystems from Sentinel-2 imagery

Net fluxes of broadband shortwave and photosynthetically active radiation complement NDVI and near infrared reflectance of vegetation to explain gross photosynthesis variability across ecosystems and climate

Using automated machine learning for the upscaling of gross primary productivity

Assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications

A deep transfer learning framework for mapping high spatiotemporal resolution LAI

Evaluation of satellite Leaf Area Index in California vineyards for improving water use estimation

OpenET: Filling a Critical Data Gap in Water Management for the Western United States

A data-driven approach to estimate leaf area index for Landsat images over the contiguous US

Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach

Collaborators (5)

Qi Yang

Guy Schurgers

Ayse Kilic

John Volk

Alfonso Torres-Rua

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