Yanghui Kang

GEOG170 Digital Globe: GIScience and Technology Summer 2019, Fall 2015 (UW-Madison)

My research aims to advance the monitoring of natural and managed ecosystems and deepen our understanding of vegetation-atmosphere interactions. Using satellite remote sensing, machine learning, and process-based models, our group quantifies changes in canopy structure, carbon, and water dynamics, assessing their interactions with the climate across temporal and spatial scales. By building novel quantitative frameworks of ecosystems, we aim to directly support sustainable land and water resource management, and climate mitigation efforts.

Our current focus areas include ecosystem-atmosphere carbon, water, and energy exchanges; holistic assessment of Nature-based Climate Solutions; agroecosystem modeling; remote sensing of vegetation states and functions.

Selected recent publications 

(*advisee)

• Gaber, M.*, Kang, Y., Schurgers, G., Keenan, F. (2024) Using automated machine learning for the upscaling of gross primary productivity. Biogeosciences, 21, 2447–2472. https://doi.org/10.5194/bg-21-2447-2024
• Wan, L., Ryu, Y., Dechant, B., Hwang, Y., Feng, H., Kang, Y., Jeong, S., Lee, J., Choi, C., Bae, J., 2024. 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 309, 114224. https://doi.org/10.1016/j.rse.2024.114224
• Mallick, K., Verfaillie, J., Wang, T., Ortiz, A.A., Szutu, D., Yi, K., Kang, Y., Shortt, R., Hu, T., Sulis, M., Szantoi, Z., Boulet, G., Fisher, J.B., Baldocchi, D., 2024. 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. Remote Sensing of Environment 307, 114123. https://doi.org/10.1016/j.rse.2024.114123
• Huntington, J., et al [29 authors including Kang, Y.] (2024) Assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications. Nature Water, 1-13. https://doi.org/10.1038/s44221-023-00181-7
• Nakagawa, R.*, Chau, M.*, Calzaretta, J.*, Keenan, T., Vahabi, P., Todeschini, A., Bassiouni, M., Kang, Y. (2023) Upscaling Global Hourly GPP with Temporal Fusion Transformer (TFT). CVPR 2023 Workshop on Multimodal Learning for Earth and Environment. https://doi.org/10.48550/arXiv.2306.13815
• Zhou, J., Yang, Q., Liu, L., Kang, Y., Jia, X., Chen, M., Ghosh, R., Xu, S., Jiang, C., Guan, K., Kumar, V., Jin, Z. (2023) A deep transfer learning framework for mapping high spatiotemporal resolution LAI. ISPRS Journal of Photogrammetry and Remote Sensing 206, 30–48. https://doi.org/10.1016/j.isprsjprs.2023.10.017
• Kang, Y., Gaber, M., Bassiouni, M., Lu, X., and Keenan, T (2023) CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO2 fertilization. Earth System Science Data Discussion. [preprint], https://doi.org/10.5194/essd-2023-337
• Kang, Y., Gao, F., Anderson, M., Kustas, W., Yang, Y., White, W., Torres-Rua, A., Alsina, M., Nieto, H., Karneli, A. (2022) Evaluating satellite LAI in California vineyards for improving water use estimation. Irrigation Science 40, 531–55.* https://doi.org/10.1007/s00271-022-00798-8
• Kang, Y., Özdoğan, M, Gao, F., Anderson, M., White, W., Yang, Y., Yang, Y., Erickson, T. (2021) A data-driven approach to estimate Leaf Area Index for Landsat images over the contiguous US. Remote Sensing of Environment 258, 112383. https://doi.org/10.1016/j.rse.2021.112383
• Kang, Y., Ozdogan, M., Zhu, X., Ye, Z., Hain, C.R., Anderson, M.C. (2020). Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest. Environmental Research Letters. 15:064005. https://doi.org/10.1088/1748-9326/ab7df9 (IOP Pulibciation Top Cited Award)
• Kang, Y., Özdoğan, M. (2019). Field-level Crop Yield Mapping with Landsat Using A Hierarchical Data Assimilation Approach. Remote Sensing of Environment 228: 144 – 163. https://doi.org/10.1016/j.rse.2019.04.005

2024 - 2027, NASA Early Career Investigator Program in Earth Science. Enabling Robust Monitoring of Forest and Grassland Climate Solutions: Ground-to-Space Data Integration with Deep Learning (PI)