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• Pioneer proximal radiometric and magnetic susceptibility survey in Antarctica. • Geophysical variables related to periglacial processes and pedogenesis. • Soil geophysical data helped explain pedogenesis and periglacial processes. • Geophysical data affected by cryoturbation in Turbic Cryosols. • Qualitative analysis of geophysical variables identified soil types in depth. Maritime Antarctica is still pedologically unexplored by proximal geophysical sensors. In-depth soil geophysical characterizations in depth can provide comprehensive information regarding how weathering, pedogenesis, and periglacial processes operate in periglacial environments, with advantages like non-invasive methodology, fast and precise data acquisition, practical equipment operation, and cost-effectiveness. This research aimed to carry out the first proximal gamma-spectrometric and magnetic geophysical characterization of soil profiles in maritime Antarctica and to relate the geophysical variables analyzed with periglacial processes, weathering, pedogenesis, geology, and landscape dynamics. The study was carried out in the Keller Peninsula (King George Island, Maritime Antarctica) with the pedological characterization of soil profiles, using two proximal geophysical sensors (gamma spectrometer and susceptibility meter) that quantify, respectively, soil radionuclides uranium, thorium and potassium as well as soil magnetic susceptibility. For that, twenty soil profiles were described and characterized, and soil samples were collected for physico-chemical analysis. In addition, readings were carried out with the two geophysical sensors in each soil profile, through soil horizons up to the lithic contact and/or permafrost top. We performed exploratory statistical analyses in R software as follows: visualizing the geophysical variables in soil depth for each profile and soil class, obtaining descriptive statistics among soil classes, analyzing geophysical variables by geology and soil classes, performing Spearman correlation between geophysical variables and soil physico-chemical attributes, and applying principal component analysis to pedological classes, geophysical variables, and soil physico-chemical attributes. The results showed that radionuclide contents increased with soil profile depth in all profiles and soil classes analyzed, except for Cryosols where permafrost is present, and cryoturbation was more pronounced. The potassium content was high in all soil profiles, evidencing the low degree of chemical weathering and leaching. The soil magnetic susceptibility values tended to decrease in depth, mainly where permafrost was present. The gravel content contributed more to the levels of radionuclide content. The clay content in some soil classes present contributed secondarily to uranium and thorium contents. Soil profiles located on marine terraces were influenced by periglacial reworking and deposition of different materials, showing increases in magnetic susceptibility values with soil depth by the absolute accumulation of ferrimagnetic minerals. Principal component analyses efficiently separated Regosols from other soil classes by soil κ related to lithology, whereas Cambisols and other soil classes were separated by radionuclide contents related to incipient pedogenesis. These findings may give new insights into understand this inhospitable continent. [ABSTRACT FROM AUTHOR]

Understanding the weathering intensities can provide answers for environmental issues, soil, and geoscience studies. Recently, geophysical approaches and machine learning techniques have been applied in soil science to access weathering. This study aimed to model weathering intensity using combined data from geophysical sensors, satellite images, and morphometry associated to machine learning algorithms. We also we evaluated the efficiency of nested-leave one out cross-validation applicability in a small geodata set evaluated the importance of covariates and the resulting weathering intensity map in relation to pedogeomorphological processes. Our study focused on a 184-ha area in southwest Brazil, where we conducted soil analysis at 71 sites. We applied principal component analysis and determined the ideal number of clusters to determine the classes of weathering intensity. We used six geophysical sensor parameters, including equivalent uranium, equivalent thorium, potassium 40, magnetic susceptibility, and soil apparent electrical conductivity, along with the weathering index to create the clusters. Then, four machine learning algorithms were used to infer different weathering intensities in soils formed from the same parent materials. To validate our results, we used the nested-leave-one-out-cross-validation ("nested-LOOCV") method, which is suitable for small datasets. Our findings showed that the random forest model performed the best with three clusters as the ideal number. We also found that the geophysical data, clusterization, and machine learning algorithm contributed significantly to identifying different weathering intensities. The results indicated that weathering operated at different intensities on both the diabase/Rhodic-Nitisols and the siltite/metasiltite Rhodic and Xanthic Lixisols areas, with the highest intensities occurring in the west Xanthic Lixisols and the lowest intensities occurring in the Rhodic and Lixisols in the east area. The siltite/metamorphosed siltite and Lixisols areas presented moderate weathering rates. We found that the all-geophysical variables used were related and affected by weathering intensity, which contributed to the modeling and clusterization processes. • Combined use of geophysical sensors in modeling soil weathering intensity. • Nested Leave One Out Cross-Validation for external validation in small dataset. • Use pedoenvironmental covariates and satellite image to determine ideal number of clusters. • Geophysical data, machine learning and clustering identified different weathering intensities. • Use of clusters in modeling processes. [ABSTRACT FROM AUTHOR]

The District of Mariana in Minas Gerais, Brazil, witnessed one of the largest natural disasters in history. It involved the deposition of thousands of cubic meters of mining waste on soils near tributaries of the Doce River, resulting in degraded areas and a new environmental landscape. Numerous studies have focused on changes in soil microbiota and physical-chemical attributes. However, studies quantifying and analyzing carbon flux (FCO 2) dynamics in the field using proximal sensors under varying temperatures, humidity conditions, and vegetation cover are scarce and nearly nonexistent. These studies could aid in monitoring the recovery of degraded areas and in understanding FCO 2 dynamics in such environments. The aim of this study was to quantify and assess FCO 2 dynamics using combined sensing techniques in diverse mining-affected areas, comparing different stages of recovery and their vegetation impact, and linking the results to pedoenvironmental factors. To achieve this, four areas were carefully selected and evaluated: affected pasture (AP), pasture (P) (non-affected), mix (MIX), and native trees (NT). In each area, readings were taken using an Infrared gas analyzer (IRGA) sensor, and temperature and soil moisture were measured. Additionally, soil samples were collected for chemical, physical, and biological characterization. Normalized Difference Vegetation Index (NDVI) assessments were conducted using satellites to evaluate vegetation cover. Non-parametric tests (Kruskal Wallis and Principal Component Analyses - PCA) were employed to evaluate differences between the areas and to assess the correlation among the environmental variables. The P area had the highest FCO 2 and total organic carbon (TOC), differing significantly from the other site areas. Conversely, AP had the lowest FCO 2 and was distinct from other sites. The MIX and NT areas had intermediate FCO 2 and TOC values, which were statistically similar to each other. PCA identified distinct patterns in FCO 2 , soil temperature, and moisture. FCO 2 was positively correlated with soil temperature and negatively correlated with moisture. There was a relationship between the biological variables microbial quotient (qMic), metabolic quotient (qCO 2), COT, and FCO 2. qMic reached the highest values in the MIX area, decreasing linearly from NT to P. Conversely, AP had the lowest qMic. qCO 2 had the highest value in AP and NT. The proximal IRGA sensor effectively quantified FCO 2 and differentiated mining tailing-affected areas. This assessment incorporated soil temperature, moisture sensors, vegetation index, and soil attribute data. The FCO 2 levels were higher in P areas and lower in AP areas. Elevated FCO2 levels were correlated with a high soil temperature and low humidity. In AP, qMic was low, and qCO 2 was high, indicating lower FCO 2 levels. Conversely, P exhibited a higher FCO2. Higher NDVI values were correlated with elevated FCO 2 in areas with specific vegetation cover during environmental recovery, while lower NDVI areas had lower FCO2 levels. • Application of IRGA to monitor environmental changes in tailing-affected areas. • Soil CO 2 flux dynamics and emissions were evaluated by sensing techniques. • Integration of pedoenvironmental variables with proximal and remote sensing techniques to enhance environmental monitoring. • Representative data on carbon flux dynamics in tailing-affected areas. • Technonosol CO 2 flux varies in tailing-affected areas by land cover. [ABSTRACT FROM AUTHOR]

• Orbital and proximal sensors predict sand subfractions proportions in sandy soils. • Particle sizes content were identified by proximal and orbital sensing. • SWIR and MIR regions are important for sandy soils study and their subfractions. • Bare topsoil reflectance, by orbital level, can identify sandy subfractions. • Orbital sensing could define potential uses for sandy soils. Sandy soils, which expressly cover 7% of the Earth's land surface, are known for their management complexity and their significant influence on the proportion of sand subfractions in terms of physical, chemical, and physical-hydric properties. Proximal and remote sensing techniques offer cost-effective ways to improve soil evaluation. To address this gap, this study evaluates the potential of different sensing techniques, including laboratory-based analysis using the FieldSpec 3 spectroradiometer equipment and satellite imagery, to characterize and estimate sandy soil texture fractions and subfractions. We first defined 216 samplings in Mato Grosso State, Brazil considering the pedology, geology, synthetic soil image (exposed soil), curvature and slope of the terrain. This sampling location include sandy soils with different proportions of sand subfractions and mineralogy within a region of agricultural expansion. The sensing data used in the study were composed of total element concentrations obtained by pXRF, Vis-NIR-SWIR, and MIR spectra. The satellite data were obtained from exposed soil images of Landsat 5 and Sentinel 2, and also from simulations of Landsat 5, Sentinel 2, and Terra (ASTER). The proximal and satellite data were descriptively analysed and used to estimate the levels of clay, total sand (TS), very coarse sand (VCS), coarse sand (CS), medium sand (MS), fine sand (FS) and very fine sand (VFS). It was observed, at both the proximal and the satellite levels, that the reflectance intensity of sandy soils is inversely proportional to the particle diameter of the predominant sand subfraction. Proximal level models were slightly more accurate in predicting the texture fractions of sandy soils compared to models based on satellite data (mean validation R2 0,45 and 0,40, respectively). In both models, SWIR and MIR stand out as key predictor variables. The results obtained in this study can be implemented to optimise the expansion of agricultural frontiers in sandy soils in other areas. [ABSTRACT FROM AUTHOR]

The continental margin of Northeast Brazil (NE Brazil) is the most seismogenic of the passive margin of South America and has several evidence of neotectonics. However, the duration and magnitude of neotectonic events cannot be uniformly replicated for a scale of ∼3000 km of extension. Therefore, this study aimed to understand the tectonic dynamics, especially from Neogene to recent, for a region little studied in this scope. We conducted multiscale analysis: landforms, sub-basins, drainage systems, and sedimentary outcrops. Applying a multi-technical approach: geomorphic indices (Stream Length-Gradient-Sl, Channel Steepness k sn , Basin Asymmetry Factor); use of Machine Learning Algorithms (ML) for spatialization of Sl and k sn indices; and OSL-palynology chronology. The results indicate multiphase tectonic control occurring by reactivation of geological faults from the Cretaceous to the recent, affecting shear zones, Cretaceous Basins, and Miocene deposits (Barreiras Formation). In these areas, predictions by ML (Sl and k sn indices) showed high values associated with fault zones, which confine and direct drainage, and landforms conditioning ungraded river long-profiles and tilted sub-basins. Tectonic deformations also manifest at the outcrop scale, with strata showing deformations (folds, joints, and atypical dips). Furthermore, strata with marine palynomorphs (Miocene) and Pleistocene-Holocene sediments (Post-Barreiras) in an altitudinal position above ∼38 m above sea level suggest post-depositional movement. The multiscale and multi-technical approaches showed that tectonic activity acted in different periods. Notably, the spatial prediction of geomorphic indices with ML is potentially competent in regional analyzes of neotectonics. [Display omitted] • Geomorphic indices and sediment analysis explain passive margin neotectonics. • Machine learning algorithms enable spatialization of Sl and k sn geomorphic indices. • Neotectonics affected Miocene and Pleistocene sediments in northeastern Brazil. • Marine palynomorphs occur in sediments tens of meters above the present sea level. [ABSTRACT FROM AUTHOR]

• We apply the Random Forest algorithm to predict regolith thickness. • The most important covariates were the drainage density and east–west direction. • The average regolith thickness for the Quadrilátero Ferrífero was 125 m. • The model explained only about 40% of the regolith thickness variation. The Quadrilátero Ferrífero (QF) in Brazil is a region of great economic, social and environmental importance, involving conflicts of interest due to land use and heavily pressured by iron mining and urban sprawl. This is an area of environmental importance due to supporting rupestrian vegetation areas on ferruginous substrates and springs from important watersheds. Thus, studies that can bring more knowledge about this region becomes important to support future decisions based on technical information. We used Random Forests algorithm and several databases to model the regolith thickness of the entire QF region and we also created an individual model to predict regolith thickness in the lithostratigraphic unit Minas Supergroup that contains most of the drillhole samples. The regolith thickness modeled for the QF region presented an average of 125.32 m and R2 of 0.38, and the specific model for the Minas Supergroup also predicted the average regolith thickness of 125.39 m and R2 of 0.39. These values are in accordance with the average regolith thickness (124 m) data from the drillhole database obtained from exploratory programs for iron ore in the QF region. The most important predictive covariates included drainage density, east–west direction, terrain texture and vertical distance from drainage. This study is the first attempt to model the regolith thickness in this important region and the analysis of model uncertainty can orient future studies. [ABSTRACT FROM AUTHOR]

Focused on supporting the planning and management of water resources in a watershed domain, this study aimed to assess and compare the performance of multiple approaches for streamflow regionalization. The applied methods were based on linear interpolation, regional regressions (RR), and kriging. The dependent variables used were low-flows of reference (Q 7.10 , Q 95 , and Q 90) and the long-term mean streamflow (Q mld). The independent variables used by the RR method were drainage area, mean annual precipitation, mean precipitation in the dry and rainy seasons, besides the flow equivalent to the precipitated volume minus the 750 mm abstraction (Peq750). The RR's equations were best fitted using the potential model, and three hydrologically homogeneous regions (RHH) were identified. Meanwhile, the exponential model provided the best fit to the experimental semivariograms in the geostatistical analysis. The methodologies' performance was estimated by the following metrics: mean relative error, percent bias, root mean square error, Nash-Sutcliffe coefficient, and coefficient of determination. The evaluated methodologies had similar performance for the low-flows and long-term mean streamflow, despite the lower accuracy by using the kriging methods for the Q 7.10 at the RHH III. All approaches performed better for Q mld , since it is not an extreme variable. Although all methods have shown good results, the adoption of kriging and linear interpolation must always be linked with a careful analysis of factors such as the density and spatial distribution of stream gauge stations in the watershed. • Very similar performance between the traditional and geostatistical approaches. • The methodologies showed better prediction for the long-term mean streamflow. • Specific flow approach was mostly affected by differences in areas size involved. • The specific flow and geostatistical approaches need careful analysis beforehand. • The geostatistical methods showed to be promising for poorly gauged basins. [ABSTRACT FROM AUTHOR]