Hyperspectral Soil Heavy Metal Prediction via Privileged-Informed Residual Correction
This study integrates hyperspectral remote sensing with chemical and pedological data to estimate Zn, Pb, and Cd concentrations in the upper soil layers. Conducted in agricultural fields east and northeast of Celje, Slovenia, an area impacted by past industrial activities such as zinc ore smelting,...
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/12/1987 |
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| author | Alen Mangafić Krištof Oštir Mitja Kolar Marko Zupan |
| author_facet | Alen Mangafić Krištof Oštir Mitja Kolar Marko Zupan |
| author_sort | Alen Mangafić |
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| description | This study integrates hyperspectral remote sensing with chemical and pedological data to estimate Zn, Pb, and Cd concentrations in the upper soil layers. Conducted in agricultural fields east and northeast of Celje, Slovenia, an area impacted by past industrial activities such as zinc ore smelting, the research integrates remote sensing and soil sampling to rapidly identify and map soil pollution over large surfaces. A multi-sensor approach was employed, combining two hyperspectral cameras (VNIR and SWIR, aerial), laboratory spectrometry, soil parameters, and content of chemical covariates measured with portable XRF and ICP-OES with a direct comparison of both techniques for this specific purpose. Accurate atmospheric and signal transformations were performed to improve modeling. The importance of covariates was thoroughly evaluated using conditional permutations to assess their contribution to the prediction of metal concentrations. The proposed framework utilizes spectral data and privileged information during training, improving prediction accuracy through a multi-stage model architecture. Here, a base model trained on spectral data is corrected using privileged information. During inference, the model functions without relying on privileged data providing a scalable and cost-effective solution for large-scale environmental monitoring. Our model achieved a reduction of predicted RMSE for Zn and Cd maps in comparison to the baseline models, translating to more precise identification of possibly polluted zones. However, for Pb, no improvements were observed, potentially due to variability in the data, including spectral issues or imbalances in the training and test datasets. |
| format | Article |
| id | doaj-art-d1daed9ef6914513a6c1d2dd758773fe |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-d1daed9ef6914513a6c1d2dd758773fe2025-08-20T02:21:57ZengMDPI AGRemote Sensing2072-42922025-06-011712198710.3390/rs17121987Hyperspectral Soil Heavy Metal Prediction via Privileged-Informed Residual CorrectionAlen Mangafić0Krištof Oštir1Mitja Kolar2Marko Zupan3Geodetic Institute of Slovenia, Jamova cesta 2, 1000 Ljubljana, SloveniaFaculty of Civil and Geodetic Engineering, University of Ljubljana, Jamova cesta 2, 1000 Ljubljana, SloveniaFaculty of Chemistry and Chemical Technology, University of Ljubljana, Večna pot 113, 1000 Ljubljana, SloveniaBiotechnical Faculty, University of Ljubljana, Jamnikarjeva ulica 101, 1000 Ljubljana, SloveniaThis study integrates hyperspectral remote sensing with chemical and pedological data to estimate Zn, Pb, and Cd concentrations in the upper soil layers. Conducted in agricultural fields east and northeast of Celje, Slovenia, an area impacted by past industrial activities such as zinc ore smelting, the research integrates remote sensing and soil sampling to rapidly identify and map soil pollution over large surfaces. A multi-sensor approach was employed, combining two hyperspectral cameras (VNIR and SWIR, aerial), laboratory spectrometry, soil parameters, and content of chemical covariates measured with portable XRF and ICP-OES with a direct comparison of both techniques for this specific purpose. Accurate atmospheric and signal transformations were performed to improve modeling. The importance of covariates was thoroughly evaluated using conditional permutations to assess their contribution to the prediction of metal concentrations. The proposed framework utilizes spectral data and privileged information during training, improving prediction accuracy through a multi-stage model architecture. Here, a base model trained on spectral data is corrected using privileged information. During inference, the model functions without relying on privileged data providing a scalable and cost-effective solution for large-scale environmental monitoring. Our model achieved a reduction of predicted RMSE for Zn and Cd maps in comparison to the baseline models, translating to more precise identification of possibly polluted zones. However, for Pb, no improvements were observed, potentially due to variability in the data, including spectral issues or imbalances in the training and test datasets.https://www.mdpi.com/2072-4292/17/12/1987hyperspectral imageryaerial remote sensingimaging spectroscopydigital soil mappingpedometric mappingsoil contamination |
| spellingShingle | Alen Mangafić Krištof Oštir Mitja Kolar Marko Zupan Hyperspectral Soil Heavy Metal Prediction via Privileged-Informed Residual Correction Remote Sensing hyperspectral imagery aerial remote sensing imaging spectroscopy digital soil mapping pedometric mapping soil contamination |
| title | Hyperspectral Soil Heavy Metal Prediction via Privileged-Informed Residual Correction |
| title_full | Hyperspectral Soil Heavy Metal Prediction via Privileged-Informed Residual Correction |
| title_fullStr | Hyperspectral Soil Heavy Metal Prediction via Privileged-Informed Residual Correction |
| title_full_unstemmed | Hyperspectral Soil Heavy Metal Prediction via Privileged-Informed Residual Correction |
| title_short | Hyperspectral Soil Heavy Metal Prediction via Privileged-Informed Residual Correction |
| title_sort | hyperspectral soil heavy metal prediction via privileged informed residual correction |
| topic | hyperspectral imagery aerial remote sensing imaging spectroscopy digital soil mapping pedometric mapping soil contamination |
| url | https://www.mdpi.com/2072-4292/17/12/1987 |
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