Soil total nitrogen inversion and interpretability analysis using vis-NIR spectroscopy and transfer learning
The use of Vis-NIR Spectroscopy for soil component inversion has increased, driven by its advantages in non-destructive, large-scale monitoring. However, it often faces challenges in model generalization. Transfer learning, leveraging large existing soil sample datasets, is considered an effective s...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2528621 |
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| author | Ping He Yu Chen Xingping Wen Xiaohua Zhou Zailin Chen Zhongchang Sun Xianfeng Cheng |
| author_facet | Ping He Yu Chen Xingping Wen Xiaohua Zhou Zailin Chen Zhongchang Sun Xianfeng Cheng |
| author_sort | Ping He |
| collection | DOAJ |
| description | The use of Vis-NIR Spectroscopy for soil component inversion has increased, driven by its advantages in non-destructive, large-scale monitoring. However, it often faces challenges in model generalization. Transfer learning, leveraging large existing soil sample datasets, is considered an effective solution to overcome the mentioned limitations. This study explores the use of transfer learning, utilizing the LUCAS database, to improve the accuracy of estimating soil total nitrogen (STN) with Vis-NIR spectroscopy in Gejiu, Yunnan, China, addressing challenges in model generalization. It compares spectral preprocessing methods (logR, SNV, MSC) and models (PLS, RF, ResNet) to assess their impact on inversion performance. SHapley Additive exPlanations (SHAP) is employed for model interpretability. Results show that transfer learning with the ResNet model significantly enhances STN inversion, particularly with MSC preprocessing, where the average R2 improves from 0.51 to 0.70. Among the models tested, ResNet with transfer learning outperforms others in accuracy. SHAP analysis identifies key wavelengths −2050, 2459, 2149, 2109, 2410, and 1470 nm – as crucial for predicting STN, which aligns with the observed correlations. This research validates the effectiveness of transfer learning on small datasets, offering a robust solution for STN inversion using Vis-NIR spectroscopy. |
| format | Article |
| id | doaj-art-b5bf1d3de37e4d8dbda9755c768b5a99 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-b5bf1d3de37e4d8dbda9755c768b5a992025-08-25T11:28:44ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2528621Soil total nitrogen inversion and interpretability analysis using vis-NIR spectroscopy and transfer learningPing He0Yu Chen1Xingping Wen2Xiaohua Zhou3Zailin Chen4Zhongchang Sun5Xianfeng Cheng6School of Fine Art and Design, Kunming University, Kunming, Yunnan, People’s Republic of ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing, People’s Republic of ChinaFaculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, People’s Republic of ChinaYunnan Provincial Bureau of Geology and Mineral Exploration and Development Center Laboratory, Kunming, People’s Republic of ChinaSchool of Earth and Environmental Sciences, Yunnan Land and Resources Vocational College, Kunming, Yunnan, People’s Republic of ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing, People’s Republic of ChinaSchool of Earth and Environmental Sciences, Yunnan Land and Resources Vocational College, Kunming, Yunnan, People’s Republic of ChinaThe use of Vis-NIR Spectroscopy for soil component inversion has increased, driven by its advantages in non-destructive, large-scale monitoring. However, it often faces challenges in model generalization. Transfer learning, leveraging large existing soil sample datasets, is considered an effective solution to overcome the mentioned limitations. This study explores the use of transfer learning, utilizing the LUCAS database, to improve the accuracy of estimating soil total nitrogen (STN) with Vis-NIR spectroscopy in Gejiu, Yunnan, China, addressing challenges in model generalization. It compares spectral preprocessing methods (logR, SNV, MSC) and models (PLS, RF, ResNet) to assess their impact on inversion performance. SHapley Additive exPlanations (SHAP) is employed for model interpretability. Results show that transfer learning with the ResNet model significantly enhances STN inversion, particularly with MSC preprocessing, where the average R2 improves from 0.51 to 0.70. Among the models tested, ResNet with transfer learning outperforms others in accuracy. SHAP analysis identifies key wavelengths −2050, 2459, 2149, 2109, 2410, and 1470 nm – as crucial for predicting STN, which aligns with the observed correlations. This research validates the effectiveness of transfer learning on small datasets, offering a robust solution for STN inversion using Vis-NIR spectroscopy.https://www.tandfonline.com/doi/10.1080/17538947.2025.2528621Soil total nitrogenVis-NIR spectroscopytransfer learningspectral preprocessingResNet model |
| spellingShingle | Ping He Yu Chen Xingping Wen Xiaohua Zhou Zailin Chen Zhongchang Sun Xianfeng Cheng Soil total nitrogen inversion and interpretability analysis using vis-NIR spectroscopy and transfer learning International Journal of Digital Earth Soil total nitrogen Vis-NIR spectroscopy transfer learning spectral preprocessing ResNet model |
| title | Soil total nitrogen inversion and interpretability analysis using vis-NIR spectroscopy and transfer learning |
| title_full | Soil total nitrogen inversion and interpretability analysis using vis-NIR spectroscopy and transfer learning |
| title_fullStr | Soil total nitrogen inversion and interpretability analysis using vis-NIR spectroscopy and transfer learning |
| title_full_unstemmed | Soil total nitrogen inversion and interpretability analysis using vis-NIR spectroscopy and transfer learning |
| title_short | Soil total nitrogen inversion and interpretability analysis using vis-NIR spectroscopy and transfer learning |
| title_sort | soil total nitrogen inversion and interpretability analysis using vis nir spectroscopy and transfer learning |
| topic | Soil total nitrogen Vis-NIR spectroscopy transfer learning spectral preprocessing ResNet model |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2528621 |
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