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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
WANG Mengyu 西北农林科技大学 资源环境学院, 杨凌;华中师范大学 城市与环境科学学院
ZHAO Feng 华中师范大学 城市与环境科学学院
PANG Yong 中国林业科学研究院资源信息研究所;国家林业和草原局林业遥感与信息技术重点实验室
MENG Ran 华中农业大学 资源与环境学院

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College of Natural Resources and Environment, Northwest A&F University, Yangling
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