Yuhan (Douglas) Rao
Yuhan (Douglas) Rao received his PhD degree in Geographical Sciences from University of Maryland, College Park, in 2019. His dissertation focused on using machine learning and satellite observations to reduce the uncertainty of regional near surface air temperature datasets. Douglas received both his bachelor’s degree in Statistics and master’s degree in cartography and remote sensing from Beijing Normal University. He has also been working for the GOES-R algorithm working group (AWG) as a research assistant at the Cooperative Institute for Climate and Satellites–Maryland at UMD, where he supported the validation of the GOES-R ABI land surface temperature (LST) product using both station measurements and other satellite products. During his PhD program, Douglas taught GIS and spatial statistics courses at UMD.
Dr. Rao joined NCICS as a Postdoctoral Research Scholar in 2019. His current research at NCICS focuses on generating a blended temperature dataset by integrating station measurements and satellite observations via innovative statistical models. His broad research interests focus on advanced statistical models, satellite data development/validation, and applied research for climate and environment monitoring.
Dr. Rao is actively engaged in local and national community. He is a fellow for the Earth Science Information Partners (ESIP) machine learning cluster, where he is developing training tutorials to promote Earth-science-oriented machine learning applications. He has also been an active member of the American Geophysical Union (AGU), where he is a member of AGU Global Environmental Change section Executive Committee. He is also the co-chair of the AGU Student and Early Career Scientist Conference.
Rao, Y., S. Liang, D. Wang, Y. Yu, Z. Song, Y. Zhou, M. Shen, and B. Xu, 2019: Estimating daily average surface air temperature using satellite land surface temperature and top-of-atmosphere radiation products over the Tibetan Plateau. Remote Sensing of Environment, 234, 111462. http://dx.doi.org/10.1016/j.rse.2019.111462
Zhang, C., L. Ma, J. Chen, Y. Rao, Y. Zhou, and X. Chen, 2019: Assessing the impact of endmember variability on linear Spectral Mixture Analysis (LSMA): A theoretical and simulation analysis. Remote Sensing of Environment, 235, 111471. http://dx.doi.org/10.1016/j.rse.2019.111471
Rao, Y., Liang, S., & Yu, Y., 2018. Land surface air temperature data are considerably different among BEST‐LAND, CRU‐TEM4v, NASA‐GISS, and NOAA‐NCEI. Journal of Geophysical Research: Atmospheres, 123(11), 5881-5900.
Chen, J., Y. Rao, M. Shen, C. Wang, Y. Zhou, L. Ma, Y. Tang, and X. Yang, 2016: A simple method for detecting phenological change from time series of vegetation index. IEEE Transactions on Geoscience and Remote Sensing, 54, 3436-3449. http://dx.doi.org/10.1109/TGRS.2016.2518167
Li, J., Y. Rao, Q. Sun, X. Wu, J. Jin, Y. Bi, J. Chen, F. Lei, Q. Liu, Z. Duan, J. Ma, G. F. Gao, D. Liu, and W. Liu, 2015: Identification of climate factors related to human infection with avian influenza A H7N9 and H5N1 viruses in China. Scientific Reports, 5, 18094. http://dx.doi.org/10.1038/srep18094
Rao, Y., X. Zhu, J. Chen, and J. Wang, 2015: An improved method for producing high spatial-resolution NDVI time series datasets with multi-temporal MODIS NDVI Data and landsat TM/ETM+ images. Remote Sensing, 7. http://dx.doi.org/10.3390/rs70607865
Wang, Y., Y. Rao, X. Wu, H. Zhao, and J. Chen, 2015: A method for screening climate change-sensitive infectious diseases. International Journal of Environmental Research and Public Health, 12, 767-783. http://dx.doi.org/10.3390/ijerph120100767