PSO-Transformer for mapping soil heavy metals using UAV hyperspectral data with spectral calibration and indices.

Xiaohan Zhang, Yulan Tang, Qing-Wei Wang, Diannan Huang, Yue Feng, Jingli Wang, Zhao Wang, Yulong Cong

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In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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8 authors.

Xiaohan ZhangSchool of Municipal and Environmental Engineering, Shenyang Jianzhu University, Shenyang 110168, China.

Yulan TangSchool of Municipal and Environmental Engineering, Shenyang Jianzhu University, Shenyang 110168, China.

Qing-Wei WangCAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China.

Diannan HuangSchool of Municipal and Environmental Engineering, Shenyang Jianzhu University, Shenyang 110168, China.

Yue FengSchool of Transportation and Surveying Engineering, Shenyang Jianzhu University, Shenyang 110168, China.

Jingli WangSchool of Transportation and Surveying Engineering, Shenyang Jianzhu University, Shenyang 110168, China.

Zhao WangSchool of Municipal and Environmental Engineering, Shenyang Jianzhu University, Shenyang 110168, China.

Yulong CongLiaoning Inspection, Examination and Certification Centre, Shenyang 110032, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, UAV hyperspectral imagery and 52 topsoil samples were collected from a representative industrial legacy site in Tieling City, Liaoning Province. UAV spectra were calibrated to lab spectra using piecewise direct standardization (PDS). Feature bands were selected using the competitive adaptive reweighted sampling (CARS) method; feature band combinations were created using the dual-band spectral indices (DBSIs) and three-band spectral indices (TBSIs). A Transformer algorithm was optimized by particle swarm optimization (PSO) to predict soil copper (Cu) and arsenic (As) and to generate distribution maps. The results indicated that the PDS substantially reduced environmental effects in the UAV data. Spectral indices improved prediction accuracy, and TBSIs consistently outperformed DBSIs. The PSO-Transformer with TBSI-3 achieved the best performance, with validation R

Indexed as

applied sciencesmodeling in soil sciencesoil science

Identifiers

PMID42389585

DOI10.1016/j.isci.2026.116455

PMCPMC13320560

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.