Assessing toxic metal chromium in the soil in coal mining areas via proximal sensing: Prerequisites for land rehabilitation and sustainable development

The rapid and accurate determination of soil chromium (Cr) is crucial for preventing toxic element pollution in soils and ensuring ecological security. Proximal sensing technology uses visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy, which has been demonstrated to be a viable approach for monitoring soil Cr concentrations. However, at trace levels, soil Cr is not especially spectrally active, thus limiting the practical application of using corresponding spectral data for quantifying soil Cr concentrations. In this study, we hypothesized that fused proximal sensing and soil auxiliary attributes (including organic matter (OM) and pH) could improve estimation of Cr concentrations in the soil. Additionally, the introduction of best-fit variogram models was theoretically possible to improve spatial visualization. To address these hypotheses, we collected 168 soil samples from the open coal mine area in the Eastern Junggar Basin, China. Fractional-order derivative (FOD) pretreatment and optimal band combination methods were implemented for spectral data mining and the derivation of spectral parameters, respectively. Soil Cr estimation models were calibrated with a partial least squares (PLS) approach through four designed strategies with different predictors: (I) full Vis-NIR variables, (II) effective three-band spectral indices (TBIs), (III) the effective TBIs and OM, and (IV) the effective TBIs, OM, and the pH. The results suggest that FOD could identify abundant spectral variability. Compared with full Vis-NIR variables, the effective TBIs can effectively magnify the subtle spectral signals concerning soil Cr. The optimal estimation model was determined as Strategy IV, indicating that the introduction of soil auxiliary attributes (pH and OM) can improve the estimation performance of the model; notably, the coefficient of determination (R²) and ratio of performance to interquartile distance (RPIQ) were 0.87 and 2.68, respectively. Based on the optimal semivariance model, we used kriging interpolation to map regional soil Cr. In the study area, the soil Cr distribution features strong spatial dependence and strong associations. Our study might inspire further research on soil contamination mapping based on proximal Vis-NIR sensors.