Publications
A list is also available @Google Scholar; * denotes corresponding author.
[20] Wan, L., et al. [including Kang, Y.] (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
[19] Gaber, M., Kang, Y.*, Schurgers, G., and Keenan, T (2024).: Using automated machine learning for the upscaling of gross primary productivity, Biogeosciences, 21, 2447-2472. https://doi.org/10.5194/bg-2023-141
[18] Mallick, K., et al. [including Kang, Y.] (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
[17] Volk, J.M., et al. [including Kang, Y.] (2024): Assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications. Nat Water 1–13. https://doi.org/10.1038/s44221-023-00181-7
[16] Kang, Y.*, Ozdogan, M., Gao, F., Anderson, M., and Keenan, T. (2023): An Operational Data-Driven Framework For Developing High-Resolution Leaf Area Index Products, in: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2185–2187, https://doi.org/10.1109/IGARSS52108.2023.10283064
[15] Zhou, J., Yang, Q., Liu, L., Kang, Y., Jia, X., Chen, M., Ghosh, R., Xu, S., Jiang, C., Guan, K., Kumar, V., and 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
[14] 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 Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-337
[13] 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
[12] 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. https://doi.org/10.1007/s00271-022-00798-8
[11] 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.2021.112408
[10] Melton, F. S., et al. [including Kang, Y.]. (2021). OpenET: Filling a Critical Data Gap in Water Management for the Western United States. JAWRA Journal of the American Water Resources Association, 1–24. https://doi.org/10.1111/1752-1688.12956
[9] Ma, Y., Zhang, Z., Kang, Y., Özdoğan. (2021) Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach. Remote Sensing of Environment 259, 112408. https://doi.org/10.1088/1748-9326/ab7df9
[8] 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. Environ. Res. Lett. 15:064005. https://doi.org/10.1088/1748-9326/ab7df9 (IOP Top Cited Paper Award)
[7] Ren, T., Liu, Z., Zhang, L., Liu, D., Xi, X., Kang, Y., Zhao, Y., Zhang, C., Li, S., Zhang, X. (2020). Early Identification of Seed Maize and Common Maize Production Fields Using Sentinel-2 Images. Remote Sens 12(13): 2140. https://doi.org/10.3390/rs12132140
[6] Chakraborty, R., Daloz, A. S., L’Ecuyer, T., Hicks, A., Young, S., Kang, Y., Shah, M. (2020). A Relational Vulnerability Analytic: Exploring hybrid methodologies in Human Dimensions of Climate Change research in the Himalayas. In Himalayan Weather and Climate and their impact on the environment. Springer International Publishing. https://doi.org/10.1007/978-3-030-29684-1_24
[5] 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
[4] Levitan, N., Kang, Y., Özdoğan, M., Magliulo, V., Castillo, P., Moshary, F., Gross, B. (2019) Evaluation of the Uncertainty in Satellite-based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and their Potential in Coupling with Crop Growth Models. Remote Sensing 11(16): 1982. https://doi.org/10.3390/rs11161928
[3] Kang, Y., Özdoğan, M., Zipper, S.C., Román, M.O., Walker, J., Hong, S. Y., Marshall, M., Magliulo, V., Moreno, J., Alonso, L., Miyata, A., Kimball, B., and Loheide S. P., II. (2016). How Universal is the Relationship between Remotely Sensed 1 Vegetation Indices and Crop Leaf Area Index? A Global Assessment. Remote Sens 8(7): 597. https://doi.org/10.3390/rs8070597
[2] Marshall, M., Okuto, E., Kang, Y., Opiyo, E., Ahmed, M. (2015). Assessment of Earth Observation Based Long-term Global vegetation Records for Agro-ecosystems. Biogeoscience. 12, 9081-9120. https://doi.org/10.5194/bg-13-625-2016
[1] Kang, Y., Wang J., Zhou H., Liu Y. (2013). Soil Surface Roughness Estimation Using Multiangular Remote Sensing Observations: A Preliminary Study. Journal of Remote Sensing (China) 19(1): 001–013. http://dx.doi.org/10.11834/jrs.20131385