In the study of the inversion of soil multi-species heavy metal element concentrations using hyperspectral techniques, the selection of feature bands is very important. However, interactions among soil elements can lead to redundancy and instability of spectral features. In this study, heavy metal elements (Pb, Zn, Mn, and As) in entisols around a mining area in Harbin, Heilongjiang Province, China, were studied. To optimise the combination of spectral indices and their weights, radar plots of characteristic-band Pearson coefficients (RCBP) were used to screen three-band spectral index combinations of Pb, Zn, Mn, and As elements, while the Catboost algorithm was used to invert the concentrations of each element. The correlations of Fe with the four heavy metals were analysed from both concentration and characteristic band perspectives, while the effect of spectral inversion was further evaluated via spatial analysis. It was found that the regression model for the inversion of the Zn elemental concentration based on the optimised spectral index combinations had the best fit, with R² = 0.8786 for the test set, followed by Mn (R² = 0.8576), As (R² = 0.7916), and Pb (R² = 0.6022). As far as the characteristic bands are concerned, the best correlations of Fe with the Pb, Zn, Mn and As elements were 0.837, 0.711, 0.542 and 0.303, respectively. The spatial distribution and correlation of the spectral inversion concentrations of the As and Mn elements with the measured concentrations were consistent, and there were some differences in the results for Zn and Pb. Therefore, hyperspectral techniques and analysis of Fe elements have potential applications in the inversion of entisols heavy metal concentrations and can improve the quality monitoring efficiency of these soils.

Quantitative studies on surface elements require satellite hyperspectral images with high spatial resolution. The identification of different surface elements requires different characteristic bands and their corresponding optimal spatial–spectral fusion methods. To address these problems, the harmonic analysis (HA), guided filtering, and Gram–Schmidt (GS) algorithms were integrated to propose a spatial–spectral fusion method called HGF. The fusion experiment and validation of the hyperspectral images of GaoFen-5 (GF-5) and ZY1-02D were conducted separately using the HGF method, and the fusion effect was evaluated in three band intervals according to the spectral response of the ground class. First, HGF was used to fuse the GF-5 and GaoFen-1 (GF-1) images, and the fusion effect was evaluated both qualitatively and quantitatively. Second, the optimal fusion method was selected for the corresponding characteristic bands of the different surface elements. Finally, the hyperspectral image obtained by ZY1-02D and multispectral image of Sentinel-2B were used for validation to improve the accuracy and efficiency of satellite hyperspectral images in quantitative studies. The results show that for further studies on soil, vegetation, and water bodies, the best fusion methods in the 390–730, 730–1400, and 1400–2260 nm intervals are the GS, HGF, and HGF algorithms, respectively. Further analysis showed that the HGF or GS methods can be selected for quantitative studies on vegetation and water bodies and that the HGF method exhibits outstanding advantages for quantitative analysis of each soil element.