Hyperspectral soil heavy metal prediction via privileged-informed residual correction

Mangafić, Alen (avtor), Oštir, Krištof (avtor), Kolar, Mitja (avtor), Zupan, Marko (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: hyperspectral imagery, aerial remote sensing, imaging spectroscopy, digital soil mapping, pedometric mapping, onesnaženost tal, environmental monitoring, residual modeling
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FGG - Fakulteta za gradbeništvo in geodezijo ; FKKT - Fakulteta za kemijo in kemijsko tehnologijo ; BF - Biotehniška fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: 32 str.
• Številčenje: Vol. 17, iss. 12, art. 1987
• PID: 20.500.12556/RUL-169951
• UDK: 504
• ISSN pri članku: 2072-4292
• DOI: 10.3390/rs17121987
• COBISS.SI-ID: 240480259
• Datum objave v RUL: 26.06.2025

Gradivo je del revije

• Naslov: Remote sensing
• Skrajšan naslov: Remote sens.
• Založnik: MDPI
• ISSN: 2072-4292
• COBISS.SI-ID: 32345133

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: hiperspektralni posnetki, daljinsko zaznavanje iz zraka, slikovna spektroskopija, digitalno kartiranje tal, pedometrično kartiranje, onesnaženost tal, okoljsko spremljanje, modeliranje ostankov

Projekti

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: P1-0153
• Naslov: Raziskave in razvoj analiznih metod in postopkov

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: P2-0406
• Naslov: Opazovanje Zemlje in geoinformatika