Rate My Professor Yanghui Kang

Yanghui Kang is an assistant professor in the Department of Biological Systems Engineering at Virginia Tech, where she leads the Ecosystem Intelligence Lab since fall 2024. She earned a Ph.D. in Geography from the University of Wisconsin-Madison in 2020, M.S. in Computer Sciences in 2018 and M.S. in Environment and Resources in 2013 from the same university, and a B.S. in Geographic Information Science from Beijing Normal University in 2011. Prior roles include postdoctoral researcher advised by Trevor Keenan at the University of California, Berkeley from 2021 to 2024, postdoctoral fellow at the USDA Agricultural Research Service Hydrology and Remote Sensing Laboratory from 2020 to 2021, research scientist at Agrograph from 2019 to 2020, and research assistant on the OpenET project from 2018 to 2019. The Ecosystem Intelligence Lab advances predictive understanding of terrestrial ecosystem dynamics using satellite remote sensing, artificial intelligence, and process-based modeling to support adaptive land and resource management amid climate change.

Kang's research focuses on remote sensing of terrestrial ecosystems, agroecosystem monitoring and modeling, nature-based climate solutions, and quantifying ecosystem-atmosphere exchanges of carbon, water, and energy. She is principal investigator on the NASA Early Career Investigator Program in Earth Science award from 2024 to 2027, titled 'Enabling Robust Monitoring of Forest and Grassland Climate Solutions: Ground-to-Space Data Integration with Deep Learning.' Additional honors include the IOP Publication Top Cited Award for her 2020 paper 'Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest' in Environmental Research Letters, SCINet Fellowship from USDA Agricultural Research Service in 2020-2021, and NASA Earth and Space Science Fellowship from 2012 to 2015. Key publications comprise 'Using automated machine learning for the upscaling of gross primary productivity' (Biogeosciences, 2024, Gaber et al.), 'Correcting confounding canopy structure, biochemistry and soil background effects improves leaf area index estimates across diverse ecosystems from Sentinel-2 imagery' (Remote Sensing of Environment, 2024, Wan et al.), 'Net fluxes of broadband shortwave and photosynthetically active radiation complement NDVI... to explain gross photosynthesis variability' (Remote Sensing of Environment, 2024, Mallick et al.), 'A data-driven approach to estimate Leaf Area Index for Landsat images over the contiguous US' (Remote Sensing of Environment, 2021), 'Field-level Crop Yield Mapping with Landsat Using A Hierarchical Data Assimilation Approach' (Remote Sensing of Environment, 2019), and the aforementioned 2020 maize yield prediction study. Her contributions inform sustainable management strategies and ecosystem resilience.