A soil organic carbon mapping method based on transfer learning without the use of exogenous data
Accurate and cost-effective mapping of soil organic carbon (SOC) is critical for understanding carbon dynamics and informing sustainable land management. Although deep learning-based methods have demonstrated strong potential in digital soil mapping, they typically require large amounts of data. How...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Environmental Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2025.1580085/full |
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| author | Jingfeng Han Mujie Wu Yanlong Qi Xiaoning Li Xiao Chen Jing Wang Jinlong Zhu Qingliang Li |
| author_facet | Jingfeng Han Mujie Wu Yanlong Qi Xiaoning Li Xiao Chen Jing Wang Jinlong Zhu Qingliang Li |
| author_sort | Jingfeng Han |
| collection | DOAJ |
| description | Accurate and cost-effective mapping of soil organic carbon (SOC) is critical for understanding carbon dynamics and informing sustainable land management. Although deep learning-based methods have demonstrated strong potential in digital soil mapping, they typically require large amounts of data. However, the availability of site-level SOC observations is often limited, which poses a challenge for model performance. To address this, we propose a novel transfer learning approach based on a Convolutional Neural Network (CNN) model that does not rely on exogenous data. Specifically, when predicting SOC for a given soil layer, the model is first pre-trained on data from all layers and then fine-tuned using data from the target layer. This design enables more efficient use of limited site data. Experimental results show that the proposed transfer model consistently outperforms other machine learning models, including the Random Forest (RF), standard CNN, and Multi-Task CNN (MTCNN) models. The transfer model achieves a coefficient of determination (R2) of 0.374 and a root mean square error (RMSE) of 2.937%, indicating superior performance. These findings highlight the effectiveness of the proposed approach for digital soil mapping under data-scarce conditions and underscore its potential as a robust tool for accurate SOC estimation. |
| format | Article |
| id | doaj-art-56181cda2b464bde9bbe2ffab073afd7 |
| institution | OA Journals |
| issn | 2296-665X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj-art-56181cda2b464bde9bbe2ffab073afd72025-08-20T02:31:03ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2025-05-011310.3389/fenvs.2025.15800851580085A soil organic carbon mapping method based on transfer learning without the use of exogenous dataJingfeng HanMujie WuYanlong QiXiaoning LiXiao ChenJing WangJinlong ZhuQingliang LiAccurate and cost-effective mapping of soil organic carbon (SOC) is critical for understanding carbon dynamics and informing sustainable land management. Although deep learning-based methods have demonstrated strong potential in digital soil mapping, they typically require large amounts of data. However, the availability of site-level SOC observations is often limited, which poses a challenge for model performance. To address this, we propose a novel transfer learning approach based on a Convolutional Neural Network (CNN) model that does not rely on exogenous data. Specifically, when predicting SOC for a given soil layer, the model is first pre-trained on data from all layers and then fine-tuned using data from the target layer. This design enables more efficient use of limited site data. Experimental results show that the proposed transfer model consistently outperforms other machine learning models, including the Random Forest (RF), standard CNN, and Multi-Task CNN (MTCNN) models. The transfer model achieves a coefficient of determination (R2) of 0.374 and a root mean square error (RMSE) of 2.937%, indicating superior performance. These findings highlight the effectiveness of the proposed approach for digital soil mapping under data-scarce conditions and underscore its potential as a robust tool for accurate SOC estimation.https://www.frontiersin.org/articles/10.3389/fenvs.2025.1580085/fulltransfer learningsoil organic carbondigital soil mappingdeep learningsoil depth correlation |
| spellingShingle | Jingfeng Han Mujie Wu Yanlong Qi Xiaoning Li Xiao Chen Jing Wang Jinlong Zhu Qingliang Li A soil organic carbon mapping method based on transfer learning without the use of exogenous data Frontiers in Environmental Science transfer learning soil organic carbon digital soil mapping deep learning soil depth correlation |
| title | A soil organic carbon mapping method based on transfer learning without the use of exogenous data |
| title_full | A soil organic carbon mapping method based on transfer learning without the use of exogenous data |
| title_fullStr | A soil organic carbon mapping method based on transfer learning without the use of exogenous data |
| title_full_unstemmed | A soil organic carbon mapping method based on transfer learning without the use of exogenous data |
| title_short | A soil organic carbon mapping method based on transfer learning without the use of exogenous data |
| title_sort | soil organic carbon mapping method based on transfer learning without the use of exogenous data |
| topic | transfer learning soil organic carbon digital soil mapping deep learning soil depth correlation |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2025.1580085/full |
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