Hyperspectral data, which have fine continuous spectrum, have been recognized to be more suitable for the detailed identification and classification of land surface, especially for minerals. The combining of the hyperspectral visible/ near-infrared (VNIR) and shortwave infrared (SWIR) data with the hyperspectral thermal infrared (TIR) data is proven to be an effective way. However, what are the effects of introduction of multispectral TIR data on the minerals identification and classification and how those effects are, are not well studied. To fully evaluate those effects, this paper tries to use both simulated data and real data to testify the practicability of introduction of multispectral TIR data for the accuracies of mineral identification and classification. Four classifiers, i.e. spectral angle mapping (SAM), spectral feature fitting (SFF), orthogonal subspace projection (OSP), and adaptive coherence/cosine estimator (ACE), are selected in the experiment. Compared with the results using hyperspectral data alone, the introducing of multispectral TIR data in identification and classification has improved accuracies for both the simulated and real data. The overall accuracies are improved about 4-13% for the simulated data and about 1-5% for the real data by using different classifiers. Those improvements prove that the spectral diagnosed characteristics in TIR region even for multispectral data help identify and classify minerals. Although the improvements for real data are not well obvious due to the low spatial resolution, the multispectral TIR data are still effective supplements for hyperspectral VNIR and SWIR data in mineral identification and classification.

Yuexi County, a key tea-producing area in eastern Dabie Mountain, may face potential heavy metal(oid) (HM) contamination risks due to nearby mining and intensive agricultural activities. This study investigated seven HMs (As, Cd, Cr, Hg, Ni, Pb, and Zn) in paired soil–tea samples using multiple analytical approaches, including the geoaccumulation index (I_geo), the potential ecological risk index (RI), bioconcentration factor (BCF), and positive matrix factorization (PMF) with Monte Carlo simulation for health risk assessment. Results showed that Zn (82.65 mg/kg) and Cd (0.15 mg/kg) were the most enriched HMs in soils with higher I_geo values than other HMs. PMF analysis identified four major HM sources: mining and transportation (27.75%), agricultural activities (26.90%), natural soil parent material (26.17%), and industrial emissions (19.18%). Tea plants exhibited selective HM absorption, with Hg showing the highest bioaccumulation (BCF = 0.45), while As, Cr, and Pb had minimal uptake (BCF < 0.05). Although health risk assessments confirmed that both non-carcinogenic and carcinogenic risks from soil and tea consumption were within safe limits for adults and children, Cr and Ni required special attention due to their risk contributions. Overall, ecological and health risks in the region were found to be low. These findings provide important scientific support for pollution monitoring, risk management, and overcoming trade barriers in tea-growing regions with acidic soils. Future research should integrate HM speciation analysis with seasonal monitoring to further optimize tea plantation management strategies.