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In deeply weathered terrains, with high geological diversity and scarce outcrops, the lateritic regolith features support mineral research. Predictive regolith mapping uses spatial modeling and airborne gamma-ray spectrometric data to locate weathered residual products, such as the ferruginous lateritic duricrusts found in tropical regions. Although the regolith materials exhibit qualitative similarities in their gamma-ray spectrometric responses in RGB (KThU) images, the Boolean and fuzzy approaches highlighted differences that allows for mapping the regolith weathering residual products on a wide geodiversity protoliths. Two inference systems were used, one identified the ferruginous lateritic duricrusts and oxisols derived from the felsic/pelitc and mafic rocks types (TP test procedures), and the other identified solely those derived exclusively from mafic rocks (TPM test procedures). Cutoff values for the conjunction eTh/K ∩ eU/K ∩ eTh ∩ K (TP-4) using Boolean logic, provided the most accurate regolith map (accuracy = 95%) among the various tests carried out and indicate that in 28.6% of the total study area, there are lateritic duricrust and oxisols relative to rocks, saprolites, mottled horizons and other soils type. The eTh cutoff value > 4.4 ppm was used to exclude mafic rocks and associated soils, and the K value < 1.3% to exclude rocks with eTh/K and eU/K values similar to the lateritic duricrusts and oxisols. For mapping the lateritic duricrusts derived exclusively from the mafic rocks the conjunction of eTh ∩ eU/K ∩ eTh/K ∩ K ∩ eU (TPM-3) fuzzified by the large membership function was the most efficient. The eU variable with values < 1.21 ppm helps improve the effectiveness of this procedure. The generated predictive map indicated that in about ∼12% (657.9 km2) of the total study area, there are lateritic duricrusts and oxisols derived from mafic rocks. Therefore, a successful method of mapping vast areas from complicated lateritic regolith mosaic derived from a large geodiversity is to apply specific plugins for data fuzzification, supported by observations from field outcrops. • Boolean and fuzzy techniques in airborne gamma-ray spectrometric data. • Regolith with extensive lateritic residual cover. • The gamma spectrometric data reflects the chemical composition of the regolith. • Prediction of lateritic duricrusts derived from mafic rocks. • Prediction of regolith derived from metasedimentary, felsic metaplutonic and metavolcanosedimentary rocks. [ABSTRACT FROM AUTHOR]

Recent studies have proposed several possibilities of combining K, Th and U airborne gamma-ray spectrometry channels to generate predictive algorithms maps. These algorithms can be used for mapping regolith in deeply weathered terrains with residual, erosion, and deposition surfaces helping to developed strategies to better understand the regolith landscape and to improve geomorphology interpretation, and to identify mineral exploration target sites for primary (bedrock or saprolite) or supergene (hosted in lateritic duricrust) ore deposits. With the goal to easily map the regolith, two mathematical procedures were used on airborne gamma-ray spectrometry data in GIS software, validated by fieldwork on granite-greenstone belts in Midwest Brazil: 1. airborne gamma-ray spectrometry and altimetric data integrated in Boolean and fuzzy logic allowed segregating the areas with ferruginous and manganese residual lateritic duricrusts from erosional surface with rocks and saprolite with 90% of accuracy (κ Boolean = 0.69 and κ FAPO = 0.66) and 2. airborne gamma-ray spectrometry and altimetric data integrated with weathering stages in multivariate linear regression (basic statistic) helped establish the regional weathering intensity index, with acceptable error (r2 adjusted >0.6 and p-value < 5%). These two modeling techniques provide useful, accurate, rapidly and complementary regolith maps and can be applied in large regions for preliminary interpretations. [Display omitted] • Characterization of regolith in deeply weathered terrains. • Airborne gamma-ray spectrometry is efficient in mapping lateritic duricrusts even in areas with vegetation. • (eTh + eU)/K ratio allows discriminating the ferruginous and manganese lateritic duricrust. • Predictive algorithms help in the selection of faster target sites for mineral exploration in tropical regolith. [ABSTRACT FROM AUTHOR]

The orbital LANDSAT-8/OLI sensor and SRTM altimetric images combined with Boolean algebra and complemented by fieldwork and XRD, chemical and spectroradiometric analysis, allowed to develop a mathematical model for mapping the tropical regolith in the Midwest of Brazil. It was used four mapping classes following the arithmetic band operations of 4/2 and (4+6)/5 to identify lateritic duricrusts, loosed iron concretions, nodules and pisoliths, (6/7)/(5/4) to parent rock, saprolite, mottled horizon and soil, and (5–4)/(5+4) to vegetation. Hyperspectral Hyperion/EO-1 image endorse the mathematical model identifying the pure pixels of kaolinite, goethite, and hematite in the lateritic duricrusts, loosed iron concretions, nodules and pisoliths occurrence pixels. Narrow and low reflectance spectroscopy concavity between 400 and 600 nm and a broad medium concavity between 800 and 1100 nm are the typical spectral features of the study lateritic duricrusts, highlighting the strong influence of the goethite and hematite contents. The development of these LANDSAT-8/OLI and Hyperion/EO-1 classification models and the identification of the specific reflectance spectroscopy features for the tropical lateritic terrains, provide a more precise regolith-geological architecture. It is very helpful for mineral exploration, for developed strategies for understanding the regolith landscape better and how surficial geochemical anomalies are formed, and for geomorphology interpretation in deeply weathered regions. • Orbital LANDSAT-8/OLI sensor and SRTM images combined with Boolean algebra allowed developed the regolith mathematical model. • Hyperspectral Hyperion/EO-1 image endorse the mathematical model. • Medium concavity between 800 and 1100 nm are the typical spectral features of the lateritic duricrusts. • It was provided a more precise regolith-geological map. • Useful for mineral exploration and regolith and geomorphology interpretation in deeply weathered regions. [ABSTRACT FROM AUTHOR]

• Soil variability was correctly described by the bare surface composite image. • Depth-transfer functions were combined to remote sensing to map subsoil reflectance. • Prediction models had good performance for clay content, color and iron concentration. • Soil classes were mapped using attribute predictions and Boolean classification key. • Uncertainties in clay and CEC prediction contributed to soil misclassifications. Detailed soil maps are essential for agricultural management, but they are scarce in many regions. Even with the recent development of digital soil mapping (DSM) strategies, providing an adequate spatial representation of soils is still a challenging task. Therefore, this work aims to define a DSM approach, which combines proximal and remote sensing data to describe the spatial variation of soil attributes and types. The study was carried out in a site at southeastern Brazil, where 326 sampling points were defined and collected at two depths. Soil Vis-NIR spectra and physico-chemical attributes were measured in laboratory. A bare surface synthetic image (SYSI) was created from multi-temporal Landsat images and later validated with lab. spectra. Geographically weighted regression was used to calibrate depth transfer functions, which were applied to SYSI, generating a subsurface soil synthetic image (SYSI sub). Soil attributes at both depths were mapped with SYSI, SYSI sub and terrain derivatives. A soil classification key was designed following the Brazilian Soil Classification System and boolean logic. Soils were classified based on the soil attributes maps and boolean key. Hence, Monte-Carlo simulation (MCS) was performed to evaluate the error propagation from predicted attribute maps to soil types map. Correlations between satellite and lab. spectra varied from 0.68 to 0.8, indicating good capacity of SYSI in retrieving bare soil reflectance. Depth transfer functions also had good performance, with R2 ranging from 0.62 to 0.72. The soil attribute maps with best performance were clay content (R2 = 0.63), iron concentration (R2 = 0.72) and soil color (hue R2 = 0.57; value R2 = 0.73; chroma R2 = 0.63). Soil organic matter and chemical attributes were poorly predicted, with R2 between 0.12 and 0.38. MCS indicated that uncertainties in attributes maps might result in confusion between Ferralsols and Acrisols, Regosols and Luvisols, as well as Luvisols and Acrisols. Comparison between digital and conventional maps of soil classes, presented satisfactory kappa (34.65%) and global accuracy (54.46%). The technique presents an improvement to DSM, as it integrates soil sensing and depth transfer functions into DSM. [ABSTRACT FROM AUTHOR]