Publications
A list is also available @Google Scholar; * denotes corresponding author.
[18] 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
[17] 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
[16] 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
[15] 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
[14] Gaber, M., Kang, Y.*, Schurgers, G., and Keenan, T (2023).: Using automated machine learning for the upscaling of gross primary productivity, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2023-141,
[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