Digital mapping of soil physical and mechanical properties using machine learning at the watershed scale

Ghavami, Mohammad Sajjad; Ayoubi, Shamsollah; Mosaddeghi, Mohammad Reza; Naimi, Salman

doi:10.1007/s11629-023-8056-z

J. Mt. Sci.

2023

DOI: 10.1007/s11629-023-8056-z

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Digital mapping of soil physical and mechanical properties using machine learning at the watershed scale

Mohammad Sajjad Ghavami,

Shamsollah Ayoubi,

Mohammad Reza Mosaddeghi

et al.

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2024

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Cited by 1 publication

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Assessing the Role of Environmental Covariates and Pixel Size in Soil Property Prediction: A Comparative Study of Various Areas in Southwest Iran

Khosravani,

Baghernejad,

Taghizadeh-Mehrjardi

et al. 2024

Land

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(1) Background: The use of multiscale prediction or the optimal scaling of predictors can enhance soil maps by applying pixel size in digital soil mapping (DSM). (2) Methods: A total of 200, 50, and 129 surface soil samples (0–30 cm) were collected by the CLHS method in three different areas, namely, the Marvdasht, Bandamir, and Lapuee plains in southwest Iran. Then, four soil properties—soil organic matter (SOM), bulk density (BD), soil shear strength (SS), and mean weighted diameter (MWD)—were measured at each sampling point as representative attributes of soil physical and chemical quality. This study examined different-scale scenarios ranging from resampling the original 30 m digital elevation model and remote sensing indices to various pixel sizes, including 60 × 60, 90 × 90, 120 × 120, and up to 2100 × 2100 m. (3) Results: After evaluating 22 environmental covariates, 11 of them were identified as the most suitable candidates for predicting soil properties based on recursive feature elimination (RFE) and expert opinion methods. Furthermore, among different pixel size scenarios for SOM, BD, SS, and MWD, the highest accuracy was achieved at 1200 × 1200 m (R2 = 0.35), 180 × 180 m (R2 = 0.67), 1200 × 1200 m (R2 = 0.42), and 2100 × 2100 m (R2 = 0.34), respectively, in Marvdasht plain. (4) Conclusions: Adjusting the pixel size improves the capture of soil property variability, enhancing mapping precision and supporting effective decision making for crop management, irrigation, and land use planning.

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Assessing the Role of Environmental Covariates and Pixel Size in Soil Property Prediction: A Comparative Study of Various Areas in Southwest Iran

Khosravani,

Baghernejad,

Taghizadeh-Mehrjardi

et al. 2024

Land

View full text Add to dashboard Cite

show abstract

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Digital mapping of soil physical and mechanical properties using machine learning at the watershed scale

Cited by 1 publication

References 58 publications

Assessing the Role of Environmental Covariates and Pixel Size in Soil Property Prediction: A Comparative Study of Various Areas in Southwest Iran

Assessing the Role of Environmental Covariates and Pixel Size in Soil Property Prediction: A Comparative Study of Various Areas in Southwest Iran

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