Assessing the Role of Environmental Covariates and Pixel Size in Soil Property Prediction: A Comparative Study of Various Areas in Southwest Iran
Pegah Khosravani,
Majid Baghernejad (),
Ruhollah Taghizadeh-Mehrjardi (),
Seyed Roohollah Mousavi,
Ali Akbar Moosavi,
Seyed Rashid Fallah Shamsi,
Hadi Shokati,
Ndiye M. Kebonye and
Thomas Scholten
Additional contact information
Pegah Khosravani: Department of Soil Science, Faculty of Agriculture, Shiraz University, Shiraz 7194684471, Iran
Majid Baghernejad: Department of Soil Science, Faculty of Agriculture, Shiraz University, Shiraz 7194684471, Iran
Ruhollah Taghizadeh-Mehrjardi: Department of Geosciences, Soil Science and Geomorphology, University of Tübingen, 72076 Tübingen, Germany
Seyed Roohollah Mousavi: Department of Soil Science, Faculty of Agriculture, University of Tehran, Karaj 7787131587, Iran
Ali Akbar Moosavi: Department of Soil Science, Faculty of Agriculture, Shiraz University, Shiraz 7194684471, Iran
Seyed Rashid Fallah Shamsi: Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz 7194684471, Iran
Hadi Shokati: Department of Geosciences, Soil Science and Geomorphology, University of Tübingen, 72076 Tübingen, Germany
Ndiye M. Kebonye: Department of Geosciences, Soil Science and Geomorphology, University of Tübingen, 72076 Tübingen, Germany
Thomas Scholten: Department of Geosciences, Soil Science and Geomorphology, University of Tübingen, 72076 Tübingen, Germany
Land, 2024, vol. 13, issue 8, 1-23
Abstract:
(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 (R 2 = 0.35), 180 × 180 m (R 2 = 0.67), 1200 × 1200 m (R 2 = 0.42), and 2100 × 2100 m (R 2 = 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.
Keywords: model extrapolation; environmental covariates; reference area; receptor areas; similarity index; XGBoost tree (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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