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
Parents
6
1–5 of 6
23
Commonwealth Scientific and Industrial Research Organisation, Australia
2 publications · 63 citations
2
h-index
Gorgan University of Agricultural Sciences and Natural Resources, Iran
1 publications · 37 citations
1
h-index
University of Engineering and Technology Lahore, Pakistan
1 publications · 26 citations
1
h-index
1
h-index
Commonwealth Scientific and Industrial Research Organisation, Australia
1 publications · 32 citations
1
h-index
1–5 of 23
12

Commonwealth Scientific and Industrial Research Organisation
Canberra, Australia
2 h-index
4 publications
126 citations
CSIRO Land and Water
Canberra, Australia
1 h-index
1 publications
32 citations
Oregon State University
Corvallis, United States
1 h-index
2 publications
304 citations

Mansoura University
Al Mansurah, Egypt
1 h-index
1 publications
152 citations
Gorgan University of Agricultural Sciences and Natural Resources
Gorgan, Iran
1 h-index
2 publications
74 citations
K.N.Toosi University of Technology
Tehran, Iran
1 h-index
1 publications
37 citations
University of California, Davis
Davis, United States
1 h-index
1 publications
37 citations
Agriculture and Food
Canberra, Australia
1 h-index
1 publications
32 citations
1–8 of 12
1 publications · 152 citations
1
h-index
1 publications · 19 citations
1
h-index
JEQ · 1 publications · 32 citations
1
h-index
1 publications · 37 citations
1
h-index
1 publications · 26 citations
1
h-index
1–5