Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses
Abstract Improper management of soil resources leads to the destruction of soil organic carbon (SOC) stock and, as a result, the reduction of soil quality, as well as accelerating the process of climate change through the release of SOC into the atmosphere. This study was conducted to evaluate poten...
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Nature Portfolio
2025-02-01
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| author | Yahya Parvizi Shahrokh Fatehi |
| author_facet | Yahya Parvizi Shahrokh Fatehi |
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| description | Abstract Improper management of soil resources leads to the destruction of soil organic carbon (SOC) stock and, as a result, the reduction of soil quality, as well as accelerating the process of climate change through the release of SOC into the atmosphere. This study was conducted to evaluate potential of different simulation models to map the spatial variability of SOC as affected by land use in a study area in Qarasu watershed in Kermanshah province, west of Iran. Map of sampling points was prepared using the Latin hypercube sampling method. A total of 168 observation points were selected and the soil profile was dug and described in these points. The soil samples were taken in different horizon to determine the SOC content in laboratory. SOC was mapped using kriging geostatistical method in the study area. The SOC changes were simulated using multivariate analysis and machine learning methods including generalized linear model (GLM), linear additive model (LAM), cubist, random forest (RF), and support vector machine (SVM) models. Comprehensive measurement data is utilized to develop and validate the predictive models. Predictor variables included 16 topographic variables as well as two vegetation, six parent material, and four climatic variables. In-depth statistical analyses are proposed to evaluate the proposed models performance. The results showed that the SOC content ranged from 0.19 to 8.44 percent in different land uses. The spherical variogram model with MAE = 0.41 best fits to interpolate and map SOC using ordinary kriging in different land uses. The LAM method was estimated wider range of the SOC change (SOC = 0.18–4.82%) among different simulation model. However, the RF model (R2 = 0.64 and RMSE = 0.58%) was the most accurate in predicting SOC quantity comparing the other simulation models. It can be used as a reliable simulation model to predict SOC variability in the study area and other similar semiarid regions of West Asia in different land uses. Among the different predictor variables, the parent material’s intrinsic soil properties and topography variables had the greatest effect in predicting soil organic carbon variability. |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-4fa6c4f45a2a43f2bbc42cb4861174962025-02-09T12:31:54ZengNature PortfolioScientific Reports2045-23222025-02-0115111610.1038/s41598-025-88062-9Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land usesYahya Parvizi0Shahrokh Fatehi1Soil Conservation and Watershed Management Research Institute, AREEOAgriculture and Natural Resource Research and Education Research Center of Kermanshah, AREEOAbstract Improper management of soil resources leads to the destruction of soil organic carbon (SOC) stock and, as a result, the reduction of soil quality, as well as accelerating the process of climate change through the release of SOC into the atmosphere. This study was conducted to evaluate potential of different simulation models to map the spatial variability of SOC as affected by land use in a study area in Qarasu watershed in Kermanshah province, west of Iran. Map of sampling points was prepared using the Latin hypercube sampling method. A total of 168 observation points were selected and the soil profile was dug and described in these points. The soil samples were taken in different horizon to determine the SOC content in laboratory. SOC was mapped using kriging geostatistical method in the study area. The SOC changes were simulated using multivariate analysis and machine learning methods including generalized linear model (GLM), linear additive model (LAM), cubist, random forest (RF), and support vector machine (SVM) models. Comprehensive measurement data is utilized to develop and validate the predictive models. Predictor variables included 16 topographic variables as well as two vegetation, six parent material, and four climatic variables. In-depth statistical analyses are proposed to evaluate the proposed models performance. The results showed that the SOC content ranged from 0.19 to 8.44 percent in different land uses. The spherical variogram model with MAE = 0.41 best fits to interpolate and map SOC using ordinary kriging in different land uses. The LAM method was estimated wider range of the SOC change (SOC = 0.18–4.82%) among different simulation model. However, the RF model (R2 = 0.64 and RMSE = 0.58%) was the most accurate in predicting SOC quantity comparing the other simulation models. It can be used as a reliable simulation model to predict SOC variability in the study area and other similar semiarid regions of West Asia in different land uses. Among the different predictor variables, the parent material’s intrinsic soil properties and topography variables had the greatest effect in predicting soil organic carbon variability.https://doi.org/10.1038/s41598-025-88062-9Soil organic carbon stockRandom forestSupport vector machineOrdinary krigingLand use changeKermanshah province |
| spellingShingle | Yahya Parvizi Shahrokh Fatehi Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses Scientific Reports Soil organic carbon stock Random forest Support vector machine Ordinary kriging Land use change Kermanshah province |
| title | Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses |
| title_full | Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses |
| title_fullStr | Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses |
| title_full_unstemmed | Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses |
| title_short | Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses |
| title_sort | geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses |
| topic | Soil organic carbon stock Random forest Support vector machine Ordinary kriging Land use change Kermanshah province |
| url | https://doi.org/10.1038/s41598-025-88062-9 |
| work_keys_str_mv | AT yahyaparvizi geospatialdigitalmappingofsoilorganiccarbonusingmachinelearningandgeostatisticalmethodsindifferentlanduses AT shahrokhfatehi geospatialdigitalmappingofsoilorganiccarbonusingmachinelearningandgeostatisticalmethodsindifferentlanduses |