Mapping Soil Trace Metals Using VIS–NIR–SWIR Spectroscopy and Machine Learning in Aligudarz District, Western Iran.
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| Title: | Mapping Soil Trace Metals Using VIS–NIR–SWIR Spectroscopy and Machine Learning in Aligudarz District, Western Iran. |
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| Authors: | Pourmorad, Saeid1 (AUTHOR), Abbasi, Samira2 (AUTHOR), Dimuccio, Luca Antonio1 (AUTHOR) luca@ci.uc.pt |
| Source: | Remote Sensing. Feb2026, Vol. 18 Issue 3, p465. 31p. |
| Subjects: | Reflectance spectroscopy, Machine learning, Artificial neural networks, Analytical geochemistry, Regression analysis, Metal content of soils |
| Geographic Terms: | Iran |
| Abstract: | Highlights: What are the main findings? VIS–NIR–SWIR spectroscopy combined with ML (ANNs, SVR) accurately predicts soil Cr, As, Cu, and Cd. Predicted spatial patterns match key geological features of the Sanandaj–Sirjan Zone (western Iran). What are the implications of the main findings? Provides a fast, non-destructive, and scalable alternative to laboratory geochemical assays. The workflow can be transferable to airborne and satellite hyperspectral missions for large-area soil metal mapping. Detecting trace metals in soil across geologically diverse terrains remains challenging due to complex mineral–metal interactions and the limited spatial coverage of traditional geochemical tests. This study develops a scalable VIS–NIR–SWIR spectroscopy and machine learning (ML) framework to predict and map soil concentrations of Cr, As, Cu, and Cd in the Aligudarz District, located within the geotectonically complex Sanandaj–Sirjan Zone of western Iran. Laboratory reflectance spectra (~350–2500 nm) obtained from 110 soil samples were pre-processed using derivative filtering, scatter-correction techniques, and genetic algorithm (GA)-based wavelength optimisation to enhance diagnostic absorption features linked to Fe-oxides, clay minerals, and carbonates. Multiple ML-based approaches, including artificial neural networks (ANNs), support vector regression (SVR), and partial least squares regression (PLSR), as well as stepwise multiple linear regression (SMLR), were compared using nested, spatial, and external validation. Nonlinear models, particularly ANNs, exhibited the highest predictive accuracy, with strong generalisation confirmed via an independent test set. GA-selected wavelengths and derivative-enhanced spectra revealed mineralogical controls on metal retention, confirming that spectral predictions reflect underlying geological processes. Ordinary kriging of spectral-ML residuals generated spatially consistent metal-distribution maps that aligned well with local and regional geological features. The integrated framework demonstrates high predictive accuracy and operational scalability, providing a reproducible, field-ready method for rapid geochemical assessment. The findings highlight the potential of VIS–NIR–SWIR spectroscopy, combined with advanced modelling and geostatistics, to support environmental monitoring, mineral exploration, and risk assessment in geologically complex terrains. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? VIS–NIR–SWIR spectroscopy combined with ML (ANNs, SVR) accurately predicts soil Cr, As, Cu, and Cd. Predicted spatial patterns match key geological features of the Sanandaj–Sirjan Zone (western Iran). What are the implications of the main findings? Provides a fast, non-destructive, and scalable alternative to laboratory geochemical assays. The workflow can be transferable to airborne and satellite hyperspectral missions for large-area soil metal mapping. Detecting trace metals in soil across geologically diverse terrains remains challenging due to complex mineral–metal interactions and the limited spatial coverage of traditional geochemical tests. This study develops a scalable VIS–NIR–SWIR spectroscopy and machine learning (ML) framework to predict and map soil concentrations of Cr, As, Cu, and Cd in the Aligudarz District, located within the geotectonically complex Sanandaj–Sirjan Zone of western Iran. Laboratory reflectance spectra (~350–2500 nm) obtained from 110 soil samples were pre-processed using derivative filtering, scatter-correction techniques, and genetic algorithm (GA)-based wavelength optimisation to enhance diagnostic absorption features linked to Fe-oxides, clay minerals, and carbonates. Multiple ML-based approaches, including artificial neural networks (ANNs), support vector regression (SVR), and partial least squares regression (PLSR), as well as stepwise multiple linear regression (SMLR), were compared using nested, spatial, and external validation. Nonlinear models, particularly ANNs, exhibited the highest predictive accuracy, with strong generalisation confirmed via an independent test set. GA-selected wavelengths and derivative-enhanced spectra revealed mineralogical controls on metal retention, confirming that spectral predictions reflect underlying geological processes. Ordinary kriging of spectral-ML residuals generated spatially consistent metal-distribution maps that aligned well with local and regional geological features. The integrated framework demonstrates high predictive accuracy and operational scalability, providing a reproducible, field-ready method for rapid geochemical assessment. The findings highlight the potential of VIS–NIR–SWIR spectroscopy, combined with advanced modelling and geostatistics, to support environmental monitoring, mineral exploration, and risk assessment in geologically complex terrains. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18030465 |
