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Wu Y, Shi K*, Chen Z, Liu S et al. Developing improved time-Series DMSP-OLS-Like data (1992–2019) in China by integrating DMSP-OLS and SNPP-VIIRS[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60, 4407714.
Chen, G., T. Iwasaki, H. Qin, and W. Sha, 2014: Evaluation of the Warm-Season Diurnal Variability over East Asia in Recent Reanalyses JRA-55, ERA-Interim, NCEP CFSR, and NASA MERRA J. Climate, 27(14), 5517-5537 (DOI: 10.1175/JCLI-D-14-00005.1).
Onogi, K., J. Tsutsui, H. Koide, M. Sakamoto, S. Kobayashi, H. Hatsushika, T. Matsumoto, N. Yamazaki, H. Kamahori, K. Takahashi, S. Kadokura, K. Wada, K. Kato, R. Oyama, T. Ose, N. Mannoji, and R. Taira, 2007: The JRA-25 Reanalysis J. Met. Soc. Jap., 85(3), 369-432 (DOI: 10.2151/jmsj.85.369).
Nguyen, P., E.J. Shearer, H. Tran, M. Ombadi, N. Hayatbini, T. Palacios, P. Huynh, G. Updegraff, K. Hsu, B. Kuligowski, W.S. Logan, and S. Sorooshian, The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data, Nature Scientific Data, Vol. 6, Article 180296, 2019. doi: https://doi.org/10.1038/sdata.2018.296
Chen, J., Gao, M., Cheng, S., Hou, W., Song, M., Liu, X., & Liu, Y. (2022). Global 1 km× 1 km gridded revised real gross domestic product and electricity consumption during 1992–2019 based on calibrated nighttime light data. Scientific Data, 9(1), 1-14.
Yang, K., Chen, Y.Y., He, J., Zhao, L., Lu, H., Qin, J., Zheng, D.H., Li, X. (2020). Development of a daily soil moisture product for the period of 2002–2011 in Chinese Mainland. Science China - Earth Sciences, doi: 10.1007/s11430-019-9588-5
1. Yao, P.P., Lu, H., Zhao, T.J., Wu, S.L., Shi, J.C., Yang K., Cosh, M.H., Zhang, P. (2022). A global daily soil moisture dataset derived from Chinese FengYun-3B Microwave Radiation Imager (MWRI) (2010-2019) . Scientific Data. (Under Review)( 查看 | Bibtex格式) 2. Yao, P.P., Lu, H., Shi, J.C., Zhao, T.J., Yang K., Cosh, M.H., Gianotti, D.J.S., & Entekhabi, D. (2021). A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002-2019). Scientific Data, 8, 143 (2021). https://doi.org/10.1038/s41597-021-00925-8( 查看 | Bibtex格式) 3. Yao, P.P., Shi, J.C., Zhao, T.J., Lu, H. & Al-Yaari, A. (2017). Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index. Remote Sensing 9(1), 35.( 查看 | Bibtex格式)
Yao, P.P., Lu, H., Shi, J.C., Zhao, T.J., Yang K., Cosh, M.H., Gianotti, D.J.S., & Entekhabi, D. (2021). A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002-2019). Scientific Data, 8, 143 (2021). https://doi.org/10.1038/s41597-021-00925-8
Xie, Pingping; Joyce, Robert; Wu, Shaorong; Yoo, S.-H.; Yarosh, Yelena; Sun, Fengying; Lin, Roger, NOAA CDR Program (2019): NOAA Climate Data Record (CDR) of CPC Morphing Technique (CMORPH) High Resolution Global Precipitation Estimates, Version 1 [indicate subset]. NOAA National Centers for Environmental Information. https://doi.org/10.25921/w9va-q159 [access date]
Youngryel Ryu, Chongya Jiang, Hideki Kobayashi, Matteo Detto,MODIS-derived global land products of shortwave radiation and diffuse and total photosynthetically active radiation at 5km resolution from 2000,Remote Sensing of Environment,Volume 204, 2018,Pages 812-825,ISSN 0034-4257,https://doi.org/10.1016/j.rse.2017.09.021.
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