Yuhan (Douglas) Rao
Dr. Rao joined NCICS as a Postdoctoral Research Scholar in 2019. His current research at NCICS focuses on generating a blended near-surface air temperature dataset by integrating in situ measurements and satellite observations via innovative statistical models. Dr. Rao is also co-leading a course at NCEI on using machine learning techniques. His broad research interests focus on advanced statistical models, satellite data development/validation, land–atmosphere interaction, and applied research for climate and environment monitoring. Dr. Rao is actively engaged in national and international communities, including Earth Science Information Partners (ESIP), the American Geophysical Union (AGU), and the Young Earth System Scientists community (YESS)—an international early career earth scientist network. He is a member of the AGU Honors & Recognition Committee and member of the YESS Executive Committee.
Dr. Rao received his doctoral 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.
Dr. Rao received both his bachelor’s degree in statistics and master’s degree in cartography and remote sensing from Beijing Normal University. He worked for the GOES-R algorithm working group 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 product using both station measurements and other satellite products. During his PhD program, Douglas was also an instructor for GIS and spatial statistics courses at UMD.
Wang, S., Y. Rao, J. Chen, L. Liu, and W. Wang, 2021: Adopting “difference-in-differences” method to monitor crop response to agrometeorological hazards with satellite data: A case study of dry-hot wind. Remote Sensing, 13. http://dx.doi.org/10.3390/rs13030482
Zhou, J., J. Chen, X. Chen, X. Zhu, Y. Qiu, H. Song, Y. Rao, C. Zhang, X. Cao, and X. Cui, 2021: Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction. Remote Sensing of Environment, 252, 112130. https://doi.org/10.1016/j.rse.2020.112130
Runkle, J. D., M. M. Sugg, R. D. Leeper, Y. Rao, J. L. Mathews, and J. J. Rennie, 2020: Short-term effects of weather parameters on COVID-19 morbidity in select US cities. Science of The Total Environment, 140093. http://dx.doi.org/10.1016/j.scitotenv.2020.140093
Shen, M., N. Jiang, D. Peng, Y. Rao, Y. Huang, Y. H. Fu, W. Yang, X. Zhu, R. Cao, X. Chen, J. Chen, C. Miao, C. Wu, T. Wang, E. Liang, and Y. Tang, 2020: Can changes in autumn phenology facilitate earlier green-up date of northern vegetation? Agricultural and Forest Meteorology, 291, 108077. http://dx.doi.org/10.1016/j.agrformet.2020.108077
Wang, S., J. Chen, Y. Rao, L. Liu, W. Wang, and Q. Dong, 2020: Response of winter wheat to spring frost from a remote sensing perspective: Damage estimation and influential factors. ISPRS Journal of Photogrammetry and Remote Sensing, 168, 221-235. http://dx.doi.org/10.1016/j.isprsjprs.2020.08.014
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
Rao, Y., 2020: A satellite based daily near-surface temperature data records for the Tibetan Plateau. 2020 NOAA Environmental Data Management Workshop, Virtual, August 20, 2020.
Rao, Y., 2020: What we wish we learned in grad school: A workshop to develop a mini data management training. ESIP 2020 Summer Meeting, Virtual, July 22, 2020.
Rao, Y., 2020: Building Machine Learning Tutorials for Earth Science Applications. Poster. ESIP 2020 Winter Meeting, January 9, 2020.
Rao, Y., 2020: Improving surface temperature data quality by leveraging daily satellite observations and machine learning techniques. ESIP 2020 Winter Meeting, January 9, 2020.
Rao, Y., 2019: Integrating long term satellite data and in situ observations to study snow-albedo-temperature feedback over the Tibetan Plateau. American Geophysical Union Fall Meeting, San Francisco, CA, December 12, 2019.
Rao, Y., 2019: Improving surface temperature data quality by leveraging daily satellite observations and machine learning techniques. 2019 American Geophysical Union (AGU) Fall Meeting, San Francisco, CA, December 13, 2019.