Inversion model of soil salinity in alfalfa covered farmland based on sensitive variable selection and machine learning algorithms.

Journal: PeerJ
PMID: 39346075

Abstract

PURPOSE: Timely and accurate monitoring of soil salinity content (SSC) is essential for precise irrigation management of large-scale farmland. Uncrewed aerial vehicle (UAV) low-altitude remote sensing with high spatial and temporal resolution provides a scientific and effective technical means for SSC monitoring. Many existing soil salinity inversion models have only been tested by a single variable selection method or machine learning algorithm, and the influence of variable selection method combined with machine learning algorithm on the accuracy of soil salinity inversion remain further studied.

Authors

  • Hong Ma
    College of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou, China.
  • Wenju Zhao
    College of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou, China.
  • Weicheng Duan
    College of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou, China.
  • Fangfang Ma
    College of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou, China.
  • Congcong Li
    College of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou, China.
  • Zongli Li
    General Institute for Water Resources and Hydropower Planning and Design, Ministry of Water Resources, Beijing, China.

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