Enhancing Soil Mapping with Hyperspectral Subsurface Images generated from soil lab Vis-SWIR spectra tested in southern Brazil

The Remote Sensing (RS) products involving the visible, near-infrared and short-wave infrared (Vis-NIR-SWIR, simply Vis-SWIR) wavelengths are applicable for spatial prediction of soil properties, usually in the process called Digital Soil Mapping (DSM). The multispectral optical orbital sensors lack spectral resolution, while the Proximal Soil Sensing (PSS) offers many application possibilities. The limited spectral resolution and soil cover are the main challenging aspects for DSM. The objective of the study was to combine the soil lab Vis-SWIR spectra within DSM technique to create new hyperspectral images to use as covariates in a novel method of soil mapping. The advantages of the method were demonstrated throughout the spatial prediction of the Total Carbon (TC) content on soils of the upper part of Itatiaia National Park (INP), (Rio de Janeiro and Minas Gerais States, southeastern Brazil). The covariates applied were multispectral orbital bands from Sentinel 2, relief with its terrain derivations, geology and geomorphology. Using uppermost soil horizon lab Vis-SWIR spectra information from 72 points in the upper part of INP, 130 hyperspectral images were generated, in other words, Hyperspectral Subsurface Image. The validation methods were 8-fold cross-validation (CV) and External Validation (EV) running alongside within Random Forest algorithm, where randomness was locked. The DSM reaches an average CV coefficient of determination (R2) of 0.39 and Root Mean Square Error (RMSE) of 4.6, while creating a new model with a set of hyperspectral images gave a CV R2 of 0.60 and RMSE of 4.06. The EV also presented an advantage for the second approach. The results support the method as the first strong integration between PSS and DSM, named by the authors as Hyperspectral Soil Mapping (HSM), which proved to be more efficient for mapping TC due to the pure soil lab Vis-SWIR spectra signal (free of atmospheric influence and vegetation cover). The method can be applied in dense or sparsely vegetated areas, for agricultural or conservation purposes, for various soil properties, with each and every wavelength.