Automated Quality Flagging

Automated Quality Flagging is a computational methodology that systematically identifies and annotates anomalies or inaccuracies in elevation or yield data through algorithmic rules or statistical thresholds, distinguishing it from manual inspection by enabling scalable, consistent, and real-time data integrity assessment.

16

Publications

1.9K

Citations

134

Authors

81

Institutions

Publications per year

2017–2026

2

Authors

134

  1. JY
    Jun Yu

    Umeå University, Sweden

    2 publications · 48 citations

    2

    h-index

  2. JG
    Jeff Gerbracht

    Cornell University, United States

    2 publications · 48 citations

    2

    h-index

  3. WW
    Weng‐Keen Wong

    Oregon State University, United States

    2 publications · 48 citations

    2

    h-index

  4. SK
    Steve Kelling

    Cornell University, United States

    2 publications · 48 citations

    2

    h-index

  5. DS
    Daniel Schläpfer

    University of Zurich, Switzerland

    1 publications · 121 citations

    1

    h-index

1–5 of 134

Venues

  1. Meteorological Applications

    1 publications · 21 citations

    1

    h-index

  2. Vadose Zone Journal

    VZJ · 1 publications · 633 citations

    1

    h-index

  3. Journal of Applied Meteorology and Climatology

    JAMC · 1 publications · 113 citations

    1

    h-index

  4. Journal of Applied Meteorology

    1 publications · 19 citations

    1

    h-index

  5. The Open Atmospheric Science Journal

    TOASCJ · 1 publications · 249 citations

    1

    h-index

1–5