Resources Science ›› 2020, Vol. 42 ›› Issue (10): 1998-2009.doi: 10.18402/resci.2020.10.16

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Retrieval of daily net surface shortwave radiation climatic resources based on machine learning

YING Wangmin1(), LIU Xiaojie1, FANG Shifeng1, LI Xiujuan1, LAI Ming2, ZHANG Xuzhen3, WU Hua1,4()   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. School of Earth Resources, China University of Geosciences (Wuhan), Wuhan 430000, China
    3. Yantai Coastal Zone Geological Survey Center, China Geological Survey, Yantai 264004, China
    4. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-08-25 Revised:2020-10-13 Online:2020-10-25 Published:2020-12-25
  • Contact: WU Hua E-mail:qsy@zju.edu.cn;wuhua@igsnrr.ac.cn

Abstract:

Daily net surface shortwave radiation (DNSSR) is one of the most important parameters in various global land process and hydrological models and is required in climate change, energy balance, ecological, and atmospheric circulation research. This study constructed a daily net surface shortwave radiation model using the random forest (RF) method and MODIS twin-satellite products. 15531 pairs of samples containing 18 independent variables were extracted by matching MODIS twin-satellite products and FLUXNET daily observations. The Bias, RMSE (root mean square error), and R2 for the proposed DNSSR model using the RF method are -0.1W/m2, 27.8 W/m2, and 0.90, respectively. Based on the process, MODIS-DNSSR global distribution in different seasons were presented. Verification with field observations shows that the results are similar to the ERA5 reanalysis data, which are closely related to the seasonal distribution of solar energy. To further verify the results, ERA5-DNSSR were compared with the FLUXNET-DNSSR. The result shows that the proposed DNSSR model has also better accuracy and higher resolution than the ERA5 data. The RF-based DNSSR model has a good retrieval accuracy, high spatial resolution, and good temporal continuity. It can be effectively transplanted to the retrieval of other climatic resources.

Key words: climatic resources, daily net surface shortwave radiation (DNSSR), machine learning, remote sensing, retrieval