Multi-sensor Precipitation Merging

Multi-Sensor Precipitation Merging is a precipitation estimation methodology that integrates data from multiple observational sources, such as ground-based radars, satellite sensors, and in-situ measurements, to produce a unified, spatially and temporally consistent rainfall product with improved accuracy and coverage compared to single-sensor approaches.

29

Publications

2.2K

Citations

138

Authors

68

Institutions

Publications per year

2017–2026

19

Authors

138

  1. GT
    Guoqiang Tang

    University of Saskatchewan, Canada

    3 publications · 283 citations

    3

    h-index

  2. AB
    Ali Behrangi

    University of Arizona, United States

    3 publications · 242 citations

    3

    h-index

  3. JD
    Jianzhi Dong

    Tianjin University, China

    2 publications · 72 citations

    2

    h-index

  4. YX
    Yue‐Ping Xu

    Zhejiang University, China

    2 publications · 42 citations

    2

    h-index

  5. QY
    Qinli Yang

    University of Electronic Science and Technology of China, China

    2 publications · 288 citations

    2

    h-index

1–5 of 138

Venues

  1. Journal of Hydrology

    7 publications · 786 citations

    7

    h-index

  2. Journal of Hydrometeorology

    2 publications · 249 citations

    2

    h-index

  3. Remote Sensing

    2 publications · 69 citations

    2

    h-index

  4. Journal of Geophysical Research Atmospheres

    JGR · 2 publications · 273 citations

    2

    h-index

  5. 2

    h-index

1–5