A Review of Machine Learning Approaches to Soil Temperature Estimation

Mercedeh Taheri (), Helene Katherine Schreiner, Abdolmajid Mohammadian, Hamidreza Shirkhani, Pierre Payeur, Hanifeh Imanian and Juan Hiedra Cobo
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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

Sustainability, 2023, vol. 15, issue 9, 1-26

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.

Keywords: soil temperature; direct measurement; empirical methods; physical models; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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