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    • Improving the quality of remotely sensed precipitation product from GPM satellites by using a spatial random forest

    • In the field of satellite remote sensing precipitation, experts have developed a two-stage spatial random forest SRF-SRF method, which effectively improves the accuracy of precipitation observation and provides a solution for large-scale continuous precipitation observation.
    • Vol. 28, Issue 2, Pages: 414-425(2024)   

      Received:28 April 2021

      Published:07 February 2024

    • DOI: 10.11834/jrs.20221222     

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  • Hu B J,Li W,Chen C F and Hu Z Z. 2024. Improving the quality of remotely sensed precipitation product from GPM satellites by using a spatial random forest. National Remote Sensing Bulletin, 28(2):414-425 DOI: 10.11834/jrs.20221222.
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相关作者

Baojian HU 山东科技大学 测绘与空间信息学院
Wei LI 湖州中核勘测规划设计有限公司
Chuanfa CHEN 山东科技大学 测绘与空间信息学院
Zhanzhan HU 山东科技大学 测绘与空间信息学院
SHI Yiwen 上海师范大学 环境与地理科学学院
YU Qinping 上海师范大学 环境与地理科学学院
LIN Wenpeng 上海师范大学 环境与地理科学学院;上海长三角城市湿地生态系统国家野外科学观测研究站
CHEN Jin 北京师范大学 地理科学学部

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School of Environmental and Geographical Sciences, Shanghai Normal University
Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station
Faculty of Geography Science, Beijing Normal University
School of Geography and Planning, Sun Yat-sen University
Institute of Geographic Sciences and Resources, Chinese Academy of Sciences
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