Machine Learning Estimation

Machine Learning Estimation is a computational methodology within remote sensing and vegetation modeling that employs algorithms capable of learning patterns from empirical data to predict Leaf Area Index, distinguished from traditional statistical or physical inversion techniques by its reliance on data-driven, non-parametric models that improve through experience without explicit programming for specific biophysical assumptions.

576

Publications

30.4K

Citations

2.7K

Authors

1.1K

Institutions

Publications per year

2017–2026

505

Authors

2.7K

  1. SL
    Shunlin Liang

    University of Maryland, College Park, United States

    6 publications · 471 citations

    6

    h-index

  2. GC
    Gustau Camps‐Valls

    Universitat de València, Spain

    5 publications · 737 citations

    5

    h-index

  3. SK
    Sina Keller

    Karlsruhe Institute of Technology, Germany

    5 publications · 194 citations

    5

    h-index

  4. KJ
    Kun Jia

    State Key Laboratory of Remote Sensing Science, China

    5 publications · 182 citations

    5

    h-index

  5. YY
    Yunjun Yao

    Beijing Normal University, China

    5 publications · 314 citations

    5

    h-index

1–5 of 2.7K

Venues

  1. Remote Sensing

    52 publications · 3K citations

    33

    h-index

  2. Computers and Electronics in Agriculture

    20 publications · 728 citations

    18

    h-index

  3. Water

    13 publications · 557 citations

    13

    h-index

  4. 10

    h-index

  5. Remote Sensing of Environment

    10 publications · 1.9K citations

    10

    h-index

1–5