The mapping of hydrothermal alteration zones associated with mineralization is of paramount importance in searching for metal deposits. For this purpose, targeting alteration zones by analyzing airborne geophysical and satellite imagery provides accurate and reliable results. In the Kelâat M’Gouna inlier, located in the Saghro Massif of the Moroccan Anti Atlas, natural gamma-ray spectrometry and ASTER satellite data were used to map hydrothermal alteration zones. Natural gamma-ray spectrometry data were processed to produce maps of Potassium (K in %), Uranium (eU in ppm), Thorium (eTh in ppm) and ratios of K/eTh and K/eU. In addition, four-band ratios were computed, on ASTER data, to map the distribution of clay minerals, phyllitic minerals, propylitic minerals, and iron oxides. The combined results obtained from geophysical and satellite data were further exploited by fuzzy logic modelling using the Geographic Information System (GIS) to generate a mineral prospectivity map. Seven hydrothermal alteration zones likely to be favorable for mineralization have been identified. They show a spatial correlation with (i) known surface prospects and mineral occurrences, (ii) the granite-encasing contact zone, and (iii) the fault zones (Sidi Flah and Tagmout faults). This research therefore provides important information on the prospecting of mineral potential in the study area.

Soil aggregate stability (SAS) is a critical parameter of soil quality and its mapping can help determine erosion hotspots. Despite this importance, SAS is less documented in available literature due to limited number of analyzes besides being a time consuming. For this reason, many researchers have turned to alternative methods that often use readily available variables such as soil parameters or remote sensing indices to estimate this variable. In that framework, the aim of the present study focused on the investigation of the feasibile use of adapted Leo Breiman’s random forest algorithm (RF) to mapping different mean weight diameter (MWD) tests as an index of SAS (mechanical breakdown (MWDmb), slow wetting (MWDsw), fast wetting (MWDfw) and the mean of the three tests (MWDmean)). The model was built with 77 samples distributed in the three watersheds of the study area located at Settat Ben-Ahmed, in Morocco and with the use of several environmental variables such as soil parameters (organic matter and clay), remote sensing indices (band 2, band 3, band 4, band 5, normalized difference vegetation index (NDVI) and transformed normalized difference vegetation index (TNDVI)), topography (elevation, slope, curvature plane and the topographic wetness index (TWI)) along with additional categorical variables as geological maps, land use and soil classes. The results showed a good level of accuracy for the training phase (75% of samples) for the different tests (R² > 0.92, RMSE and MAE < 0.15) and were satisfactory for the testing phase (25% of samples, R² > 0.65, RMSE and MAE < 0.31). Also, organic matter, topography and geology were the most important parameters in the spatial prediction of SAS. Finally, the maps build during this study could be of great use to identify areas of less stable soils in the perspective for taking the necessary measures to improve their quality.