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Soil minerals are usually quantified by the conventional laboratory soil analyses. However, developments in interpretations and analyses of the visible, near-infrared, and short-infrared (Vis-NIR-SWIR) diffuse reflectance have allowed the quantification of some soil minerals. In this study, we aimed to implement a novel framework using Vis-NIR-SWIR spectroscopy to quantify the main soil minerals. We also assessed the application of this framework to create new environmental variables for digital soil mapping (DSM). The soil spectra database comprised 2701 samples from 1008 sites in the spectral range of 350–2500 nm at 0–20, 40–60, and 80–100 cm depths. The specific bands in the Vis-NIR-SWIR spectra that identify the presence of soil mineral were selected based on the literature with the United States Geological Survey Spectral Library Version 7 and in the strong maxima and minima of the second-derivative curves of the soil mineral standards using the Savitzky-Golay method. We proposed an estimation and conversion of the measurement unit of soil minerals in amplitude to g kg−1 using a small dataset of mineral content quantified via X-Ray Powder Diffraction. We selected randomly 85 samples out of 2701 available at 0–20 cm depth and sent to conventional laboratory analyses to calibrate the final estimation, using the kaolinite soil mineral as an example. Therefore, a constant factor was determined to estimate mineral content in soils displaying RMSE, R2 adj , the Lin's concordance coefficient (CCC), Bias, and RPIQ values of 7612 g kg−1, 0.28, 0.50, 13.09 g kg−1, and 0.56, respectively. This evaluation was assessed by splitting 85 samples into 80% to determine and 20% to validate the constant factor. For the DSM procedure, we used 2701 samples split into 80% and 20% for calibration and validation, respectively, of the models for each of the nine minerals. This study showed that the proposed framework using Vis-NIR-SWIR spectroscopy to estimate soil minerals is promising due to higher CCC and lower RMSE values obtained. Furthermore, the spectral amplitude for each mineral provides important information to be used as environmental variables for the prediction of soil attributes, soil types, and soil properties. • Soil mineral content estimate was suggested based on XRD and spectroscopy. • Diffuse reflectance spectroscopy quantified the soil minerals. • Integrating remote and proximal sensing helped to characterise soil mineralogy. [ABSTRACT FROM AUTHOR]

Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0–20 cm depth, 919 points) from an area of 14,614 km2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R2 = 0.83; RMSE = 65.0 g kg−1) and sand (R2 = 0.86; RMSE = 79.9 g kg−1). Multispectral satellite images were more stable for the identification of soil properties than relief parameters. [ABSTRACT FROM AUTHOR]