Yuhan "Douglas" Rao

Rao, Y., Redmon, R., & Khin, E. (2024, March 9). Community-Driven Development of Tools to Improve AI-Readiness of the Open Environmental Data. https://doi.org/10.5194/egusphere-egu24-14071

Rao, Y., Redmon, R., Dale, K., Haupt, S. E., Hopkinson, A., Bostrom, A., … Kihn, E. A. (2023). Developing Digital Twins for Earth Systems: Purpose, Requisites, and Benefits. https://doi.org/10.48550/ARXIV.2306.11175

Hanson, B., Stall, S., Cutcher-Gershenfeld, J., Vrouwenvelder, K., Wirz, C., Rao, Y., & Peng, G. (2023, November 2). Garbage in, garbage out: mitigating risks and maximizing benefits of AI in research. NATURE, Vol. 623, pp. 28–31. https://doi.org/10.1038/d41586-023-03316-8

NASA TOPS Open Science 101. (2023, December 6). Zenodo. https://doi.org/10.5281/zenodo.10161527

NASA TOPS Open Science 101. (2023, December 6). Zenodo. https://doi.org/10.5281/zenodo.10161526

Almarzouq, B., Azevedo, F., Batalha, N., Bayer, J., Bell, T., Bhogal, S., … Yehudi, Y. (2023, February 22). Opensciency - A core open science curriculum by and for the research community. https://doi.org/10.5281/ZENODO.7662732

Wertis, L., Sugg, M. M., Runkle, J. D., & Rao, D. (2023). Socio-Environmental Determinants of Mental and Behavioral Disorders in Youth: A Machine Learning Approach. GEOHEALTH, 7(9). https://doi.org/10.1029/2023gh000839

McGovern, A., Gagne, D. J., Wirz, C. D., Ebert-Uphoff, I., Bostrom, A., Rao, Y., … Peterson, T. (2023). Trustworthy Artificial Intelligence for Environmental Sciences An Innovative Approach for Summer School. BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 104(6), E1222–E1231. https://doi.org/10.1175/BAMS-D-22-0225.1

Sun, Z., Sandoval, L., Crystal-Ornelas, R., Mousavi, S. M., Wang, J., Lin, C., … John, A. (2022). [Review of A review of Earth Artificial Intelligence]. COMPUTERS & GEOSCIENCES, 159. https://doi.org/10.1016/j.cageo.2022.105034

Jain, S., Mindlin, J., Koren, G., Gulizia, C., Steadman, C., Langendijk, G. S., … Rabanal, V. (2022, August). Are We at Risk of Losing the Current Generation of Climate Researchers to Data Science? AGU ADVANCES, Vol. 3. https://doi.org/10.1029/2022AV000676

Dong, Q., Chen, X., Chen, J., Yin, D., Zhang, C., Xu, F., … Stein, A. (2022). Bias of area counted from sub-pixel map: Origin and correction. SCIENCE OF REMOTE SENSING, 6. https://doi.org/10.1016/j.srs.2022.100069

Watson-Parris, D., Rao, Y., Olivie, D., Seland, O., Nowack, P., Camps-Valls, G., … Roesch, C. (2022). ClimateBench v1.0: A Benchmark for Data-Driven Climate Projections. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS, 14(10). https://doi.org/10.1029/2021MS002954

Hills, D. J., Damerow, J. E., Ahmmed, B., Catolico, N., Chakraborty, S., Coward, C. M., … Yao, T. (2022, April). Earth and Space Science Informatics Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science. EARTH AND SPACE SCIENCE, Vol. 9. https://doi.org/10.1029/2021EA002108

McGovern, A., Gagne, D., Ebert-Uphoff, I., Bostrom, A., Wirz, C., Rao, D., … Haynes, K. (2022, June). Trustworthy Artificial Intelligence for Environmental Science (TAI4ES) Summer School 2022. https://doi.org/10.5281/ZENODO.6784187

Jiang, N., Shen, M., Ciais, P., Campioli, M., Penuelas, J., Korner, C., … Zhao, W. (2022, August 14). Warming does not delay the start of autumnal leaf coloration but slows its progress rate. GLOBAL ECOLOGY AND BIOGEOGRAPHY, Vol. 8. https://doi.org/10.1111/geb.13581

Voisin, N., Bennett, A., Fang, Y., Nearing, G., Nijssen, B., & Rao, Y. (2021). A science paradigm shift is needed for Earth and Environmental Systems Sciences (EESS) to integrate Knowledge-Guided Artificial Intelligence (KGAI) and lead new EESS-KGAI theories. https://doi.org/10.2172/1769651

Wang, S., Rao, Y., Chen, J., Liu, L., & Wang, W. (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(3). https://doi.org/10.3390/rs13030482

Shen, M., Jiang, N., Peng, D., Rao, Y., Huang, Y., Fu, Y. H., … Tang, Y. (2020). Can changes in autumn phenology facilitate earlier green-up date of northern vegetation? AGRICULTURAL AND FOREST METEOROLOGY, 291. https://doi.org/10.1016/j.agrformet.2020.108077

Wang, S., Chen, J., Rao, Y., Liu, L., Wang, W., & Dong, Q. (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. https://doi.org/10.1016/j.isprsjprs.2020.08.014

Runkle, J. D., Sugg, M. M., Leeper, R. D., Rao, Y., Matthews, J. L., & Rennie, J. J. (2020). Short-term effects of specific humidity and temperature on COVID-19 morbidity in select US cities. SCIENCE OF THE TOTAL ENVIRONMENT, 740. https://doi.org/10.1016/j.scitotenv.2020.140093

Liu, Y., Yu, Y., Yu, P., Wang, H., & Rao, Y. (2019). Enterprise LST Algorithm Development and Its Evaluation with NOAA 20 Data. Remote Sensing, 11(17), 2003. https://doi.org/10.3390/rs11172003

Rao, Y., Liang, S., Wang, D., Yu, Y., Song, Z., Zhou, Y., … Xu, B. (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. https://doi.org/10.1016/j.rse.2019.111462

Li, Y., Chen, J., & Rao, Y. (2018). A practical sampling method for assessing accuracy of detected land cover/land use change: Theoretical analysis and simulation experiments. ISPRS Journal of Photogrammetry and Remote Sensing, 144, 379–389. https://doi.org/10.1016/j.isprsjprs.2018.08.006

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. https://doi.org/10.1029/2018JD028355

Chen, J., Rao, Y., Shen, M., Wang, C., Zhou, Y., Ma, L., … Yang, Xi. (2016). A Simple Method for Detecting Phenological Change From Time Series of Vegetation Index. IEEE Transactions on Geoscience and Remote Sensing, 54(6), 3436–3449. https://doi.org/10.1109/tgrs.2016.2518167

Lu, M., Chen, J., Tang, H., Rao, Y., Yang, P., & Wu, W. (2016). Land cover change detection by integrating object-based data blending model of Landsat and MODIS. Remote Sensing of Environment, 184, 374–386. https://doi.org/10.1016/j.rse.2016.07.028

Wang, Y., Rao, Y., Wu, X., Zhao, H., & Chen, J. (2015). A method for screening climate change-sensitive infectious diseases. International Journal of Environmental Research and Public Health, 12(1), 767–783. https://doi.org/10.3390/ijerph120100767

Wang, J., Cao, X., Chen, J., Liu, D., & Rao, Y. (2015). A quantitative assessment of multiple scattering in plant-soil mixtures and the implications on nonlinear spectral unmixing models. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). https://doi.org/10.1109/igarss.2015.7326129

Rao, Y., Zhu, X., Chen, J., & Wang, J. (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(6), 7865–7891. https://doi.org/10.3390/rs70607865

Li, J., Rao, Y., Sun, Q., Wu, X., Jin, J., Bi, Y., … Liu, W. (2015). Identification of climate factors related to human infection with avian influenza A H7N9 and H5N1 viruses in China. Scientific Reports, 5. https://doi.org/10.1038/srep18094

Wang, C., Cao, R., Chen, J., Rao, Y., & Tang, Y. (2015). Temperature sensitivity of spring vegetation phenology correlates to within-spring warming speed over the Northern Hemisphere. Ecological Indicators, 50, 62–68. https://doi.org/10.1016/j.ecolind.2014.11.004

Chen, X., Li, W., Chen, J., Rao, Y., & Yamaguchi, Y. (2014). A combination of TsHARP and thin plate spline interpolation for spatial sharpening of thermal imagery. Remote Sensing, 6(4), 2845–2863. https://doi.org/10.3390/rs6042845

Chen, X., Li, W., Chen, J., Zhan, W., & Rao, Y. (2014). A simple error estimation method for linear-regression-based thermal sharpening techniques with the consideration of scale difference. Geo-Spatial Information Science, 17(1), 54–59. https://doi.org/10.1080/10095020.2014.889546

Fan, B., Guo, L., Li, N., Chen, J., Lin, H., Zhang, X., … Ma, L. (2014). Earlier vegetation green-up has reduced spring dust storms. Scientific Reports, 4. https://doi.org/10.1038/srep06749

Rao, Y., Chen, J., Chen, X., & Wang, J. (2013). Quantitative assessment of the different methods addressing the endmember variability. International Geoscience and Remote Sensing Symposium (IGARSS), 3317–3320. https://doi.org/10.1109/IGARSS.2013.6723537

混合像元分解技术及其进展. 遥感学报, 20(5), 1102. Retrieved from http://www.jors.cn/jrs/ch/reader/view_abstract.aspx?file_no=r16169&flag=1