ResHAN-GAM: A novel model for the inversion and prediction of soil organic matter content
Soil organic matter (SOM) is crucial in determining soil health, improving crop production, and enabling sustainability in agriculture. Precise determination of SOM content is thus crucial for land management as well as for maintaining ecological equilibrium. This research introduces a new hierarchi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125002018 |
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| author | Liying Cao Dongjie Yin Miao Sun Yuzhu Yang Musharaf Hassan Yunpeng Duan |
| author_facet | Liying Cao Dongjie Yin Miao Sun Yuzhu Yang Musharaf Hassan Yunpeng Duan |
| author_sort | Liying Cao |
| collection | DOAJ |
| description | Soil organic matter (SOM) is crucial in determining soil health, improving crop production, and enabling sustainability in agriculture. Precise determination of SOM content is thus crucial for land management as well as for maintaining ecological equilibrium. This research introduces a new hierarchical attention mechanism that unifies residual networks with GAM attention. Through data smoothing and discretization in terms of fractions, the model is equipped to effectively repress noise as it enhances primary spectral features related to SOM, thus enhancing the robustness as well as explainability of the model. Hyperspectral reflectance data were recorded in the visible to near-infrared (Vis-NIR) range (350–2500 nm) with a high spatial-resolution sensor. The dataset is made available with samples from lands under cultivation for soybean as well as corn in the fertile black soil region. Experimental results indicate that the proposed method achieves an R2 value of 0.945, an RMSE of 0.117% and RPD of 4.26 on the validation set. Furthermore, the model’s generalization ability was validated using the Land Use/Cover Area Frame Survey (LUCAS) dataset, a large-scale European soil database, where similarly high performance was achieved. These results highlight the effectiveness and transferability of the proposed method in estimating SOM content, offering a reliable, non-destructive tool for large-scale soil monitoring and environmental protection applications. |
| format | Article |
| id | doaj-art-8ab70cf3192f4d5c8e9f5da1eb9c126d |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-8ab70cf3192f4d5c8e9f5da1eb9c126d2025-08-20T05:05:01ZengElsevierEcological Informatics1574-95412025-12-019010319210.1016/j.ecoinf.2025.103192ResHAN-GAM: A novel model for the inversion and prediction of soil organic matter contentLiying Cao0Dongjie Yin1Miao Sun2Yuzhu Yang3Musharaf Hassan4Yunpeng Duan5Department of Information Technology, Jilin Agricultural University, 2888 Xincheng Street, Changchun, Jilin, ChinaDepartment of Information Technology, Jilin Agricultural University, 2888 Xincheng Street, Changchun, Jilin, ChinaDepartment of Information Technology, Jilin Agricultural University, 2888 Xincheng Street, Changchun, Jilin, ChinaDepartment of Information Technology, Jilin Agricultural University, 2888 Xincheng Street, Changchun, Jilin, ChinaDepartment of Information Technology, Jilin Agricultural University, 2888 Xincheng Street, Changchun, Jilin, ChinaCorrespondence to: Department of Information Technology, Jilin Agricultural University, China.; Department of Information Technology, Jilin Agricultural University, 2888 Xincheng Street, Changchun, Jilin, ChinaSoil organic matter (SOM) is crucial in determining soil health, improving crop production, and enabling sustainability in agriculture. Precise determination of SOM content is thus crucial for land management as well as for maintaining ecological equilibrium. This research introduces a new hierarchical attention mechanism that unifies residual networks with GAM attention. Through data smoothing and discretization in terms of fractions, the model is equipped to effectively repress noise as it enhances primary spectral features related to SOM, thus enhancing the robustness as well as explainability of the model. Hyperspectral reflectance data were recorded in the visible to near-infrared (Vis-NIR) range (350–2500 nm) with a high spatial-resolution sensor. The dataset is made available with samples from lands under cultivation for soybean as well as corn in the fertile black soil region. Experimental results indicate that the proposed method achieves an R2 value of 0.945, an RMSE of 0.117% and RPD of 4.26 on the validation set. Furthermore, the model’s generalization ability was validated using the Land Use/Cover Area Frame Survey (LUCAS) dataset, a large-scale European soil database, where similarly high performance was achieved. These results highlight the effectiveness and transferability of the proposed method in estimating SOM content, offering a reliable, non-destructive tool for large-scale soil monitoring and environmental protection applications.http://www.sciencedirect.com/science/article/pii/S1574954125002018SOMResNetDeepLearnSoilHyperspectralHAN |
| spellingShingle | Liying Cao Dongjie Yin Miao Sun Yuzhu Yang Musharaf Hassan Yunpeng Duan ResHAN-GAM: A novel model for the inversion and prediction of soil organic matter content Ecological Informatics SOM ResNet DeepLearn Soil Hyperspectral HAN |
| title | ResHAN-GAM: A novel model for the inversion and prediction of soil organic matter content |
| title_full | ResHAN-GAM: A novel model for the inversion and prediction of soil organic matter content |
| title_fullStr | ResHAN-GAM: A novel model for the inversion and prediction of soil organic matter content |
| title_full_unstemmed | ResHAN-GAM: A novel model for the inversion and prediction of soil organic matter content |
| title_short | ResHAN-GAM: A novel model for the inversion and prediction of soil organic matter content |
| title_sort | reshan gam a novel model for the inversion and prediction of soil organic matter content |
| topic | SOM ResNet DeepLearn Soil Hyperspectral HAN |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125002018 |
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