Predicting soil chemical characteristics in the arid region of central Iran using remote sensing and machine learning models
Abstract Digital Soil Mapping (DSM) techniques have advanced significantly in recent decades, helping to close critical gaps in soil data and knowledge. This study was conducted in the arid Gavkhouni sub-basin of Isfahan Province, central Iran, where environmental stresses such as salinity and water...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-04554-8 |
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| author | Azita Molaeinasab Hossein Bashari Mostafa Tarkesh Esfahani Saeid Pourmanafi Norair Toomanian Bahareh Aghasi Ahmad Jalalian |
| author_facet | Azita Molaeinasab Hossein Bashari Mostafa Tarkesh Esfahani Saeid Pourmanafi Norair Toomanian Bahareh Aghasi Ahmad Jalalian |
| author_sort | Azita Molaeinasab |
| collection | DOAJ |
| description | Abstract Digital Soil Mapping (DSM) techniques have advanced significantly in recent decades, helping to close critical gaps in soil data and knowledge. This study was conducted in the arid Gavkhouni sub-basin of Isfahan Province, central Iran, where environmental stresses such as salinity and water scarcity challenge sustainable land management. We employed 34 environmental covariates derived from Landsat 8 imagery and a digital elevation model, combined with 96 surface soil samples (0 to 20 cm depth), to assess the performance of six machine-learning models: Random Forest (RF), Classification and Regression Tree (CART), Support Vector Regression (SVR), Generalized Additive Model (GAM), Generalized Linear Model (GLM), and an ensemble approach. Unlike many previous studies that have focused on a single soil attribute with a limited set of predictors, our work adopts an integrated approach to map four salinity-related soil properties: Ca, CaCO3, CaSO4, and SO4. Predictor selection involved multicollinearity testing using the Variance Inflation Factor (VIF) and the Boruta algorithm. Model performance was assessed using tenfold cross-validation. The ensemble model performed best, achieving R2 values of 0.89 for Ca, 0.84 for CaCO3, 0.79 for SO4, and 0.73 for CaSO4. Elevation and the Temperature-Vegetation Dryness Index (TVDI) were the most influential predictors for Ca, while the Tasseled Cap Brightness (TCB) and Tasseled Cap Wetness (TCW) indices were most important for CaCO3. For CaSO4, Band 5 (B5) and TCB were the most effective, whereas SO4 predictions were driven by TCB along with Bands 5 and 7. These findings highlight the potential of remote sensing-based DSM to enhance soil monitoring in data-scarce, arid environments. The growing availability of free satellite data, such as Landsat, offers valuable opportunities to improve soil assessment and promote sustainable land management in resource-limited regions like Iran. |
| format | Article |
| id | doaj-art-4303644456ad4ceeb6f1afa4a7dcebd5 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-4303644456ad4ceeb6f1afa4a7dcebd52025-08-20T03:38:12ZengNature PortfolioScientific Reports2045-23222025-07-0115111910.1038/s41598-025-04554-8Predicting soil chemical characteristics in the arid region of central Iran using remote sensing and machine learning modelsAzita Molaeinasab0Hossein Bashari1Mostafa Tarkesh Esfahani2Saeid Pourmanafi3Norair Toomanian4Bahareh Aghasi5Ahmad Jalalian6Department of Natural Resources, Isfahan University of TechnologyDepartment of Natural Resources, Isfahan University of TechnologyDepartment of Natural Resources, Isfahan University of TechnologyDepartment of Natural Resources, Isfahan University of TechnologySoil and Water Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEODepartment of Natural Resources and Watershed Management of Isfahan ProvinceDepartment of Natural Resources, Isfahan University of TechnologyAbstract Digital Soil Mapping (DSM) techniques have advanced significantly in recent decades, helping to close critical gaps in soil data and knowledge. This study was conducted in the arid Gavkhouni sub-basin of Isfahan Province, central Iran, where environmental stresses such as salinity and water scarcity challenge sustainable land management. We employed 34 environmental covariates derived from Landsat 8 imagery and a digital elevation model, combined with 96 surface soil samples (0 to 20 cm depth), to assess the performance of six machine-learning models: Random Forest (RF), Classification and Regression Tree (CART), Support Vector Regression (SVR), Generalized Additive Model (GAM), Generalized Linear Model (GLM), and an ensemble approach. Unlike many previous studies that have focused on a single soil attribute with a limited set of predictors, our work adopts an integrated approach to map four salinity-related soil properties: Ca, CaCO3, CaSO4, and SO4. Predictor selection involved multicollinearity testing using the Variance Inflation Factor (VIF) and the Boruta algorithm. Model performance was assessed using tenfold cross-validation. The ensemble model performed best, achieving R2 values of 0.89 for Ca, 0.84 for CaCO3, 0.79 for SO4, and 0.73 for CaSO4. Elevation and the Temperature-Vegetation Dryness Index (TVDI) were the most influential predictors for Ca, while the Tasseled Cap Brightness (TCB) and Tasseled Cap Wetness (TCW) indices were most important for CaCO3. For CaSO4, Band 5 (B5) and TCB were the most effective, whereas SO4 predictions were driven by TCB along with Bands 5 and 7. These findings highlight the potential of remote sensing-based DSM to enhance soil monitoring in data-scarce, arid environments. The growing availability of free satellite data, such as Landsat, offers valuable opportunities to improve soil assessment and promote sustainable land management in resource-limited regions like Iran.https://doi.org/10.1038/s41598-025-04554-8Digital soil mappingEnsemble modelingSpectral indicesVariable selection (Boruta)Environmental covariatesCross-validation |
| spellingShingle | Azita Molaeinasab Hossein Bashari Mostafa Tarkesh Esfahani Saeid Pourmanafi Norair Toomanian Bahareh Aghasi Ahmad Jalalian Predicting soil chemical characteristics in the arid region of central Iran using remote sensing and machine learning models Scientific Reports Digital soil mapping Ensemble modeling Spectral indices Variable selection (Boruta) Environmental covariates Cross-validation |
| title | Predicting soil chemical characteristics in the arid region of central Iran using remote sensing and machine learning models |
| title_full | Predicting soil chemical characteristics in the arid region of central Iran using remote sensing and machine learning models |
| title_fullStr | Predicting soil chemical characteristics in the arid region of central Iran using remote sensing and machine learning models |
| title_full_unstemmed | Predicting soil chemical characteristics in the arid region of central Iran using remote sensing and machine learning models |
| title_short | Predicting soil chemical characteristics in the arid region of central Iran using remote sensing and machine learning models |
| title_sort | predicting soil chemical characteristics in the arid region of central iran using remote sensing and machine learning models |
| topic | Digital soil mapping Ensemble modeling Spectral indices Variable selection (Boruta) Environmental covariates Cross-validation |
| url | https://doi.org/10.1038/s41598-025-04554-8 |
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