In order to monitor Potentially Toxic Elements (PTEs) in anthropogenic soils on brown coal mining dumpsites, a large number of samples and cumbersome, time-consuming laboratory measurements are required. Due to its rapidity, convenience and accuracy, reflectance spectroscopy within the Visible-Near Infrared (Vis-NIR) region has been used to predict soil constituents. This study evaluated the suitability of Vis-NIR (350–2500 nm) reflectance spectroscopy for predicting PTEs concentration, using samples collected on large brown coal mining dumpsites in the Czech Republic. Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR) with cross-validation were used to relate PTEs data to the reflectance spectral data by applying different preprocessing strategies. According to the criteria of minimal Root Mean Square Error of Prediction of Cross Validation (RMSEPcv) and maximal coefficient of determination (R2 cv) and Residual Prediction Deviation (RPD), the SVMR models with the first derivative pretreatment provided the most accurate prediction for As (R2 cv) = 0.89, RMSEPcv = 1.89, RPD = 2.63). Less accurate, but acceptable prediction for screening purposes for Cd and Cu (0.66 ˂ R2 cv) ˂ 0.81, RMSEPcv = 0.0.8 and 4.08 respectively, 2.0 ˂ RPD ˂ 2.5) were obtained. The PLSR model for predicting Mn (R2 cv) = 0.44, RMSEPcv = 116.43, RPD = 1.45) presented an inadequate model. Overall, SVMR models for the Vis-NIR spectra could be used indirectly for an accurate assessment of PTEs’ concentrations.

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Any strategy to change the Carbon (C) pool has a substantial effect on the functionality of numerous ecosystem functions, the detachment of Soil Organic Carbon (SOC), the atmospheric carbon dioxide (CO2) concentration, and climate change mitigation. As the largest amount of the world's C is stored in forests soils, the importance of forest SOC management is highlighted. The total SOC in a forest varies not only laterally, but also vertically (i.e., with depth). However, the SOC storage of different forest soil horizons has not been investigated in a national scale thoroughly, despite their potential to frame our understanding of soil function. Visible--Near Infrared (vis--NIR) reflectance spectroscopy enables rapid examination of the horizontal distribution of forest SOC, overcoming the limitations of traditional soil assessment methods. This study aims to evaluate the potential of vis--NIR spectroscopy in characterizing and predicting the SOC content of organic and mineral horizons in forests. We investigate 1080 forested sites across the Czech Republic at five individual soil layers, representing the Litter (L), Fragmented (F), and Humus (H) organic horizons, as well as the A1 (depth of 2--10 cm) and A2 (depth of 10--40 cm) mineral horizons (for a total of 5400 samples). We, then, use Support Vector Machines (SVMs) to classify the soil horizons based on their spectra and also to predict the SOC content of (i) the profile (all organic and mineral horizons together), (ii) the combined organic horizons, (iii) the combined mineral horizons, and (iv) each individual horizon separately. The models are validated using 10-repeated 10-fold cross validation. The results show that there is at least more than seven times as much SOC in the combined organic horizons, compared to the combined mineral horizons, with more variation in the deeper layers. The SVM with radial based kernel is a reliable classifier for classification of soil horizons, with Correct Classification Rate (CCR) of 70% and Kappa coefficient of 0.63. All individual horizon SOCs are successfully predicted with low error and with R2 values higher than 0.63. However, the prediction accuracies of the F and A1 models are greater, compared to others (R2~0.70 and very low-biased spatial estimates). We conclude that the modelling of SOC with vis--NIR spectra in different soil horizons of highly heterogeneous forests in the Czech Republic is practical. This study provides an example of how general pedological knowledge can be used to define depth functions of SOC for forested sites.

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