Predictive Soil Modeling

Predictive Soil Modeling is a geostatistical methodology that employs environmental covariates and machine learning algorithms to infer and map soil properties or classes across spatial domains based on sparse observational data.

716

Publications

45.5K

Citations

2.3K

Authors

1K

Institutions

Publications per year

2017–2026

499

Variants

Digital Soil Modeling

Authors

2.3K

  1. BM
    Budiman Minasny

    The University of Sydney, Australia

    14 publications · 2K citations

    15

    h-index

  2. GH
    G.B.M. Heuvelink

    Wageningen University & Research, Netherlands

    13 publications · 2.8K citations

    13

    h-index

  3. RT
    Ruhollah Taghizadeh‐Mehrjardi

    University of Tübingen, Germany

    14 publications · 1K citations

    13

    h-index

  4. AB
    Alex B. McBratney

    The University of Sydney, Australia

    13 publications · 3.1K citations

    13

    h-index

  5. TS
    Thomas Scholten

    University of Tübingen, Germany

    10 publications · 992 citations

    11

    h-index

1–5 of 2.3K

Venues

  1. Geoderma

    84 publications · 9.8K citations

    56

    h-index

  2. CATENA

    24 publications · 2.2K citations

    21

    h-index

  3. The Science of The Total Environment

    22 publications · 1.3K citations

    20

    h-index

  4. Soil Science Society of America Journal

    SSSAJ · 20 publications · 1.7K citations

    19

    h-index

  5. Geoderma Regional

    17 publications · 1.3K citations

    14

    h-index

1–5