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A Review of Machine Learning Approaches to Soil Temperature Estimation

Author

Listed:
  • Mercedeh Taheri

    (Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

  • Helene Katherine Schreiner

    (Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

  • Abdolmajid Mohammadian

    (Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

  • Hamidreza Shirkhani

    (National Research Council Canada, Ottawa, ON K1A 0R6, Canada)

  • Pierre Payeur

    (School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

  • Hanifeh Imanian

    (Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

  • Juan Hiedra Cobo

    (National Research Council Canada, Ottawa, ON K1A 0R6, Canada)

Abstract

Soil temperature is an essential factor for agricultural, meteorological, and hydrological applications. Direct measurement, despite its high accuracy, is impractical on a large spatial scale due to the expensive and time-consuming process. On the other hand, the complex interaction between variables affecting soil temperature, such as topography and soil properties, leads to challenging estimation processes by empirical methods and physical models. Machine learning (ML) approaches gained considerable attention due to their ability to address the limitations of empirical and physical methods. These approaches are capable of estimating the variables of interest using complex nonlinear relationships with no assumptions about data distribution. However, their sensitivity to input data as well as the need for a large amount of training ground truth data limits the application of machine learning approaches. The current paper aimed to provide a review of ML techniques implemented for soil temperature modeling, their challenges, and milestones achieved in this domain.

Suggested Citation

  • Mercedeh Taheri & Helene Katherine Schreiner & Abdolmajid Mohammadian & Hamidreza Shirkhani & Pierre Payeur & Hanifeh Imanian & Juan Hiedra Cobo, 2023. "A Review of Machine Learning Approaches to Soil Temperature Estimation," Sustainability, MDPI, vol. 15(9), pages 1-26, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7677-:d:1141319
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    References listed on IDEAS

    as
    1. Xing, Lu & Li, Liheng & Gong, Jiakang & Ren, Chen & Liu, Jiangyan & Chen, Huanxin, 2018. "Daily soil temperatures predictions for various climates in United States using data-driven model," Energy, Elsevier, vol. 160(C), pages 430-440.
    2. Hanifeh Imanian & Juan Hiedra Cobo & Pierre Payeur & Hamidreza Shirkhani & Abdolmajid Mohammadian, 2022. "A Comprehensive Study of Artificial Intelligence Applications for Soil Temperature Prediction in Ordinary Climate Conditions and Extremely Hot Events," Sustainability, MDPI, vol. 14(13), pages 1-25, July.
    3. Fanhua Yu & Huibowen Hao & Qingliang Li, 2021. "An Ensemble 3D Convolutional Neural Network for Spatiotemporal Soil Temperature Forecasting," Sustainability, MDPI, vol. 13(16), pages 1-16, August.
    Full references (including those not matched with items on IDEAS)

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