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Research on the Prediction of Several Soil Properties in Heihe River Basin Based on Remote Sensing Images

Author

Listed:
  • Zhihui Li

    (College of Computer Science and Technology, Harbin Engineering University, Harbin 150000, China)

  • Yang Yang

    (College of Computer Science and Technology, Harbin Engineering University, Harbin 150000, China)

  • Siyu Gu

    (College of Resources and Environment, Northeast Agricultural University, Harbin 150000, China)

  • Boyu Tang

    (College of Computer Science and Technology, Harbin Engineering University, Harbin 150000, China)

  • Jing Zhang

    (School of Information Science and Engineering, University of Jinan, Jinan 250000, China)

Abstract

Soil property monitoring is useful for sustainable agricultural production and environmental modeling. It is possible to automatically predict soil properties in a wide range based on remote sensing images. Heihe River Basin was chosen as the research area. Measurements on three soil properties, which were pH, organic carbon, and bulk density, were available there. Two kinds of attributes were extracted, which were the remote sensing index and terrain attributes. The prediction models were constructed by random forest algorithms. The features were determined by combining correlation statistics with prediction error, and different features were selected for each of the three properties. The validation experimental results are presented. The error results were as follows: pH (MAE = 0.28, RMSE = 0.39, R 2 = 0.41), organic carbon (MAE = 4.75, RMSE = 8.26, R 2 = 0.75), and bulk density (MAE = 0.11, RMSE = 0.13, R 2 = 0.70). Through the analysis and comparison of the experimental results, it was proven that the algorithm in this paper had a good performance in the prediction of organic carbon and bulk density.

Suggested Citation

  • Zhihui Li & Yang Yang & Siyu Gu & Boyu Tang & Jing Zhang, 2021. "Research on the Prediction of Several Soil Properties in Heihe River Basin Based on Remote Sensing Images," Sustainability, MDPI, vol. 13(24), pages 1-14, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13930-:d:704212
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    References listed on IDEAS

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    1. Gerald Forkuor & Ozias K L Hounkpatin & Gerhard Welp & Michael Thiel, 2017. "High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-21, January.
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