Remote sensing-based soil organic carbon monitoring using advanced machine learning techniques under conservation agriculture systems
Accurate soil organic carbon (SOC) monitoring is essential for sustainable agriculture and climate change mitigation. This study integrates remote sensing and machine learning to improve SOC estimation in agricultural soils across two contrasting sites: Niigata, Japan (temperate, sandy soils) and Ag...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525002692 |
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| author | Nail Beisekenov Wiyao Banakinaou Ayomikun David Ajayi Hideo Hasegawa Aoda Tadao |
| author_facet | Nail Beisekenov Wiyao Banakinaou Ayomikun David Ajayi Hideo Hasegawa Aoda Tadao |
| author_sort | Nail Beisekenov |
| collection | DOAJ |
| description | Accurate soil organic carbon (SOC) monitoring is essential for sustainable agriculture and climate change mitigation. This study integrates remote sensing and machine learning to improve SOC estimation in agricultural soils across two contrasting sites: Niigata, Japan (temperate, sandy soils) and Agbelouve, Togo (tropical, clayey soils). Conservation agriculture (CA) practices, including no-tillage and mulching, were assessed for their role in carbon sequestration. Using freely available satellite data from Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery (MSI), machine learning models were trained and validated. The eXtreme Gradient Boosting (XGBoost) model achieved the highest accuracy, with a cross-validation coefficient of determination (R²) of 0.88, a test R² of 0.91, and a root mean square error (RMSE) of 0.17 t of carbon per hectare (t C ha⁻¹). Other models, including Random Forest (RF) and Support Vector Machine (SVM), showed competitive but slightly lower performance. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Soil-Adjusted Vegetation Index (SAVI) were identified as key predictors of SOC variation. SOC maps revealed significant spatial variability, ranging from 1.2 to 3.8 t C ha⁻¹ in Niigata and 0.9 to 3.2 t C ha⁻¹ in Togo, reflecting land use and climate differences. The results demonstrate the potential of integrating satellite-based observations with machine learning for cost-effective, high-resolution SOC assessment. This approach provides a scalable solution for site-specific land management, carbon market verification, and sustainable farming practices. |
| format | Article |
| id | doaj-art-d80e5cf79ce5419f8312c9628e2623d3 |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-d80e5cf79ce5419f8312c9628e2623d32025-08-20T02:34:12ZengElsevierSmart Agricultural Technology2772-37552025-08-011110103610.1016/j.atech.2025.101036Remote sensing-based soil organic carbon monitoring using advanced machine learning techniques under conservation agriculture systemsNail Beisekenov0Wiyao Banakinaou1Ayomikun David Ajayi2Hideo Hasegawa3Aoda Tadao4Graduate School of Science and Technology, Niigata University, Niigata, 950-2181, Japan; Corresponding author at: Graduate School of Science and Technology, Niigata University, Niigata, 950-2181, Japan.Graduate School of Science and Technology, Niigata University, Niigata, 950-2181, JapanGraduate School of Science and Technology, Niigata University, Niigata, 950-2181, JapanInstitute of Science and Technology, Niigata University, Niigata, 950-2181, JapanInstitute of Science and Technology, Niigata University, Niigata, 950-2181, JapanAccurate soil organic carbon (SOC) monitoring is essential for sustainable agriculture and climate change mitigation. This study integrates remote sensing and machine learning to improve SOC estimation in agricultural soils across two contrasting sites: Niigata, Japan (temperate, sandy soils) and Agbelouve, Togo (tropical, clayey soils). Conservation agriculture (CA) practices, including no-tillage and mulching, were assessed for their role in carbon sequestration. Using freely available satellite data from Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery (MSI), machine learning models were trained and validated. The eXtreme Gradient Boosting (XGBoost) model achieved the highest accuracy, with a cross-validation coefficient of determination (R²) of 0.88, a test R² of 0.91, and a root mean square error (RMSE) of 0.17 t of carbon per hectare (t C ha⁻¹). Other models, including Random Forest (RF) and Support Vector Machine (SVM), showed competitive but slightly lower performance. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Soil-Adjusted Vegetation Index (SAVI) were identified as key predictors of SOC variation. SOC maps revealed significant spatial variability, ranging from 1.2 to 3.8 t C ha⁻¹ in Niigata and 0.9 to 3.2 t C ha⁻¹ in Togo, reflecting land use and climate differences. The results demonstrate the potential of integrating satellite-based observations with machine learning for cost-effective, high-resolution SOC assessment. This approach provides a scalable solution for site-specific land management, carbon market verification, and sustainable farming practices.http://www.sciencedirect.com/science/article/pii/S2772375525002692Soil organic carbonRemote sensingPrecision agricultureMachine learningCarbon neutralitySustainable agriculture |
| spellingShingle | Nail Beisekenov Wiyao Banakinaou Ayomikun David Ajayi Hideo Hasegawa Aoda Tadao Remote sensing-based soil organic carbon monitoring using advanced machine learning techniques under conservation agriculture systems Smart Agricultural Technology Soil organic carbon Remote sensing Precision agriculture Machine learning Carbon neutrality Sustainable agriculture |
| title | Remote sensing-based soil organic carbon monitoring using advanced machine learning techniques under conservation agriculture systems |
| title_full | Remote sensing-based soil organic carbon monitoring using advanced machine learning techniques under conservation agriculture systems |
| title_fullStr | Remote sensing-based soil organic carbon monitoring using advanced machine learning techniques under conservation agriculture systems |
| title_full_unstemmed | Remote sensing-based soil organic carbon monitoring using advanced machine learning techniques under conservation agriculture systems |
| title_short | Remote sensing-based soil organic carbon monitoring using advanced machine learning techniques under conservation agriculture systems |
| title_sort | remote sensing based soil organic carbon monitoring using advanced machine learning techniques under conservation agriculture systems |
| topic | Soil organic carbon Remote sensing Precision agriculture Machine learning Carbon neutrality Sustainable agriculture |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525002692 |
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