Climate Data Fusion

Climate Data Fusion is a systematic computational approach that integrates heterogeneous observational, model-generated, and remote sensing climate data streams into a unified, spatially and temporally coherent representation to enhance the accuracy, resolution, and completeness of climate information beyond the capabilities of individual data sources.

20

Publications

926

Citations

120

Authors

68

Institutions

Publications per year

2017–2026

15

Publications

20

1–5 of 20

Authors

120

  1. AP
    André Paul

    University of Bremen, Germany

    1 publications · 21 citations

    1

    h-index

  2. AM
    Alistair McConnell

    Aarhus University, Denmark

    1 publications · 42 citations

    1

    h-index

  3. RD
    Robert Dunn

    Met Office, United Kingdom

    1 publications · 37 citations

    1

    h-index

  4. SY
    Shanshan Yang

    Chinese Academy of Sciences, China

    1 publications · 22 citations

    1

    h-index

  5. PM
    Parmutia Makui

    World Agroforestry Centre, Kenya

    1 publications · 41 citations

    1

    h-index

1–5 of 120

Venues

  1. Agricultural and Forest Meteorology

    3 publications · 130 citations

    3

    h-index

  2. Applied Soft Computing

    1 publications · 25 citations

    1

    h-index

  3. Remote Sensing of Environment

    1 publications · 236 citations

    1

    h-index

  4. Procedia Computer Science

    1 publications · 16 citations

    1

    h-index

  5. Journal of Environmental Management

    1 publications · 32 citations

    1

    h-index

1–5