Decision Tree Salinity Mapping

Decision Tree Salinity Mapping is a machine learning–based remote sensing methodology that employs a hierarchical decision rule structure to classify and map soil or water salinity levels by differentiating spectral or geospatial features according to thresholds derived from training data.

6

Publications

297

Citations

23

Authors

12

Institutions

Publications per year

2017–2026

3

Authors

23

  1. PC
    Peter Caccetta

    Commonwealth Scientific and Industrial Research Organisation, Australia

    2 publications · 63 citations

    2

    h-index

  2. SB
    Soraya Bandak

    Gorgan University of Agricultural Sciences and Natural Resources, Iran

    1 publications · 37 citations

    1

    h-index

  3. HM
    Hafiz Muhammad Shahzad Asif

    University of Engineering and Technology Lahore, Pakistan

    1 publications · 26 citations

    1

    h-index

  4. JS
    Jay S. Noller

    Oregon State University, United States

    1 publications · 152 citations

    1

    h-index

  5. RD
    Robert Dunne

    Commonwealth Scientific and Industrial Research Organisation, Australia

    1 publications · 32 citations

    1

    h-index

1–5 of 23

Venues

  1. Remote Sensing

    1 publications · 152 citations

    1

    h-index

  2. CATENA

    1 publications · 19 citations

    1

    h-index

  3. Journal of Environmental Quality

    JEQ · 1 publications · 32 citations

    1

    h-index

  4. Scientific Reports

    1 publications · 37 citations

    1

    h-index

  5. Sustainability

    1 publications · 26 citations

    1

    h-index

1–5