Seasonally Dry Forests, or 'Dry Forests,' are characterized by deciduous vegetation that sheds leaf during the dry season due to strong soil moisture stress. Despite their ecological significance, as part of the Atlantic Forest realm, research on the diversity, genesis, and soil-vegetation interactions in these ecosystems remains limited. This study investigated the soil attributes of Dry Forests of an ecotonal zone, comparing with the neighboring biomes, the Caatinga (Steppe Savanna) and Cerrado (Brazilian Savanna). We analyzed 16 soil profiles, evaluating their physical, chemical, and mineralogical properties using Principal Component Analysis (PCA). Dry Forest soils (DFS) showed higher amounts of exchangeable cations such as Ca2+, Mg2+, and K+ (Sum of Bases: 2.84-14.79 cmolc dm- 3 in the surface horizons) and no detectable Al3+. The clay fraction of DFS is mainly illite and kaolinite. The PCA results (PC1: 29.4 %) revealed that Dry Forest soils, particularly on limestone, are more fertile than adjacent Caatinga and Cerrado soils. In contrast, according to the interpretation of PC2 (19.5 %) Cerrado soils are much more weathered and nutrient-depleted (Sum of Bases: 0.37-1.29 cmolc dm-3 in the surface horizons), while Caatinga soils exhibit an intermediate fertility (Sum of Bases: 1.48-21.81 cmolc dm- 3 in the surface horizons), and less weathered. The lithologies under DFS at northern Minas Gerais are limestones of the Bambui Group, resistant sedimentary rocks of the Maca & uacute;bas Group, and Granite/gneisses of the Crystalline Basement, which strongly influence soil fertility, mineralogy, and weathering degree. The higher nutrient levels and organic matter contents (2.05-12.03 % in the surface horizons) in DFS support greater productivity and biomass accumulation. These findings highlight the ecological significance of DFS on diverse geological substrates, offering insights for conservation, sustainable management, and ecological restoration in the face of increasing deforestation and land degradation threats.

Soils provide a range of essential ecosystem services for sustaining life, including climate regulation. Advanced technologies support the protection and restoration of this natural resource. We developed the first fine-resolution spectral grid of bare soils by processing a spatiotemporal satellite data cube spanning the globe. Landsat imagery provided a 30 m composite soil image using the Geospatial Soil Sensing System (GEOS3), which calculates the median of pixels from the 40-year time series (1984-2022). The map of the Earth's bare soil covers nearly 90 % of the world's drylands. The modeling resulted in 10 spectral patterns of soils worldwide. Results indicate that plant residue and unknown soil patterns are the main factors that affect soil reflectance. Elevation and the shortwave infrared (SWIR2) band show the highest importance, with 78 and 80 %, respectively, suggesting that spectral and geospatial proxies provide inference on soils. We showcase that spectral groups are associated with environmental factors (climate, land use and land cover, geology, landforms, and soil). These outcomes represent an unprecedented information source capable of unveiling nuances on global soil conditions. Information derived from reflectance data supports the modeling of several soil properties with applications in soil-geological surveying, smart agriculture, soil tillage optimization, erosion monitoring, soil health, and climate change studies. Our comprehensive spectrally-based soil grid can address global needs by informing stakeholders and supporting policy, mitigation planning, soil management strategy, and soil, food, and climate security interventions.

Geophysical methods support soil security by providing non-invasive tools to assess soil properties, monitor degradation, and guide sustainable management strategies. However, studies focusing the spatial prediction of geophysical data remain limited. In this research, we aimed to model and predict the spatial distribution of soil geophysical properties using parent material and terrain attributes with machine learning algorithms. In addition, we tested the nested leave-one-out cross validation (nested-LOOCV) method to deal with datasets with limited size. We performed a geophysical survey using three types of sensors (radiometric, magnetic and electric methods). The random forest (RF) and support vector machine (SVM) algorithms presented the best results, with RF showing higher performance for K40 and magnetic susceptibility, and SVM had higher performance for eU, eTh and apparent electrical conductivity. Parent materials and digital elevation model were the most significant variables for the modelling. The nested-LOOCV method proved to be adequate for small soil dataset. Machine learning techniques are potential tools for modelling soil geophysical variables. The combination with computational techniques shows the great relevance of geophysical measurements for the estimation of soil properties related to fertility and soil genesis.

Abstract The impact of intensified climate change driven by global warming on the stocks and dynamics of soil organic carbon in Antarctica is currently uncertain. Our objective with this was evaluate the potential repercussions of global warming on soil organic carbon under three Shared Socioeconomic Pathways. Employing a methodology that integrates soil field data, machine learning, and projections of future climate change scenarios for the Maritime and Peninsular Antarctic ice-free areas, we focus on predicting the soil organic carbon within the 0–30 cm soil layer. To achieve this, we utilized one of the largest soil databases of Antarctica, which contains data from 2800 observation sites. In our predictive modeling of SOC stocks, we used relief data and, bioclimatic variables (from Chelsa database) as predictor variables, primarily focusing on temperature, precipitation, and net primary production. The prediction performance of the soil organic carbon stocks model, as measured by concordance correlation coefficient, was 0.52 for the 0-5 cm soil depth, 0.56 for the 5-15 cm depth, and 0.46 for the 15-30 cm depth. Our model reveal that the effects of climate change, primarily changes in temperature and precipitation, are going to increase in soil organic carbon stock (359 ± 146 Mg to 686 ± 197 Mg), indicating that ice-free regions of Maritime and Peninsular Antarctica will tend to function as a carbon sink. However, the magnitude of the soil carbon sink is contingent upon the existing soil organic carbon content and soil depth. The estimated soil organic carbon stocks are controlled mainly by temperature and precipitation, which are interconnected with net primary productivity.

Soil thickness is an important property as it influences the landscape dynamics, partakes part in hydrologic and geomorphologic processes, and controls water saturation and soil moisture, which are directly related to agricultural production. However, soil thickness data are difficult to obtain in situ, especially in areas with deep soils (>2 m). In this study, we developed and compared three models to predict soil thickness. First, we developed a mechanistic model which uses physical equations from a landscape evolution model applied to a digital elevation model (DEM) (30 m spatial resolution). We evaluated the inclusion of parameters derived from the soil parent material, including erosion and sediment deposition. Second, we developed an empirical model using terrain derivatives obtained from a 30-m DEM, using a Random Forest algorithm. This model was calibrated using 1,362 soil thickness data collected in field as right censored data. We implemented a hybrid model using the residual from the mechanistic model as a dependent variable in the empirical model. The models were validated with 214 soil observation points collected in field as right censored data and 12 data points with real data. The result was added back to predictions of the mechanistic model. For all models, we verified coherence with a soil map at 1:100,000. The models were also evaluated considering changes in the spatial resolution. The mechanistic model was improved when parent material parameters were added. The mechanistic models performed better in areas with shallow soils (<1 m), whereas the empirical model was better in predicting deeper soils and was more coherent with a soil class map. Model performance could be further improved when updating DEM data to a 5-m resolution. As expected, the hybrid model could combine the model performances and improve the predictions. However, the predictions remained poor for shallow soils.

Thermodegradation is an analytical tool that quantifies the loss of organic carbon under increasing temperatures. The degradation process is related to chemical functions and provides data on the stability of soil organic matter (SOM). There needs to be more information about SOM stability in different Brazilian ecosystems. This study aimed to evaluate SOM stability under three tropical environments: Dry Forest, Brazilian Savanna, and Atlantic Forest. Thus, we related thermal stability to soil mineral composition and SOM quality and quantity. We analyzed samples of the surface horizon of four illitic soil profiles sampled in Dry Forest (P1 to P4), three soil profiles in Cerrado (Brazilian Savanna), with predominant 2:1 clay (P5), iron oxide (P6), and gibbsite (P7), and a kaolinite profile from Atlantic Forest (Rainforest) (P8). Mineralogical, chemical, and physical analyses of the soils were studied to better comprehend soils' behavior under thermodegradation. The thermic treatment was performed by heating air-dried fine earth (ADFE) samples at 100, 200, 300, 400, and 500 degrees C for 2 h. The results were submitted to sigmoidal regression with four points and presented a good curve fit (r(2) > 0.99). Dry Forest soils with the most humified SOM presented greater resistance to loss of SOM by the thermic process. However, P7 (gibbsitic clayey soil) stood out and was the most resistant to thermodegradation. P4 was the sandiest soil and showed the curve most affected by the thermal process, indicating the importance of clay content to SOM stability. The results suggest that the TOC (content and quality), calcium content, and clay (content and type) play essential roles in determining the thermostability of SOM in different ecosystems.

Phosphorus (P) is a critical nutrient for primary production in terrestrial and aquatic ecosystems. As P mineral reserves are finite and non-renewable, there is an increasing discussion on its sustainable utilization to safeguard food security for future generations. Understanding the spatial distribution of soil P is central in advancing effective phosphorus management and fostering sustainable agricultural practices. This study aims to digitally map the stocks of available P (AP) and total P (TP) in Brazil at a fine resolution (30 m). Using the Random Forest machine learning algorithm and a database of topsoil (0-20 cm) with 28,572 samples for AP and 3154 for TP, we predicted P stocks based on environmental covariates related to soil formation processes. By dividing Brazil into two sub-regions, representing areas with native coverage and anthropogenic ones, we built independent predictive models for each sub-region. Our results show that Brazil has a TP stock of 531 Tg and an AP stock of 17.4 Tg. The largest soil TP stocks are in the Atlantic Forest biome (73.8 g.m2), likely due to higher organic carbon stocks in this biome. The largest AP stocks were in the Caatinga biome (2.51 g.m2) because of younger soils with low P adsorption capacity. We also found that fertilizer use significantly increased AP stocks in agricultural areas compared to native ones. Our results indicated that AP stocks strongly influenced Brazil's agricultural production, with a correlation coefficient ranging from 0.20 for coffee crops to 0.46 for soybean. The maps generated in this study are expected to contribute to the sustainable use of P in agriculture and environmental systems.

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.

This study analyzed the role of soil health (SH) and ecosystem services (ESs) in global mangrove research articles from 1958 to 2024. The SH approach is vital for evaluating mangroves’ ability to provide ES. However, most studies made no reference to these topics, an important gap that must be addressed. We performed a systematic literature review of the Scopus database using the following prompts: Level 1: “mangrove*” and “soil” or “sediment”; Level 2: “mangrove*” and “soil health” or “soil quality”; and Level 3: “mangrove*” and “soil health” or “soil quality” and “ecosystem service*” or “ecologic* service*”. A total of 8289 scientific articles were published that explored mangrove soils or sediments, of which 321 included a discussion of SH, and 39 discussed SH and ES. There is a historical preference for the term “sediment” in marine sciences. Carbon is the most studied topic. Six of the fifteen most productive countries are also among the fifteen with the largest mangrove areas. There is a scientific gap regarding studies that link mangrove soil studies with SH and ES. We recommend the development of a soil health index fully adapted to mangroves, considering their physical and geochemical dynamics, climate conditions, and anthropic relevance.

Soil drainage is an essential factor that influences plant growth and various biophysical processes, such as nutrient cycling and greenhouse gas fluxes. Therefore, soil drainage maps are fundamental tools for managing crops, forests, and the environment. This study compared two approaches to mapping soil drainage classes in the state of São Paulo, Brazil, using geographic information systems (GIS). The first approach employed expert knowledge (EK) to develop a simple model based on soil color and texture, while the second used machine learning (ML) with an extensive set of covariates and a decision tree algorithm. To evaluate the full, operational implementation of soil mapping, this study assessed the two approaches in terms of accuracy, labor efficiency, transferability, interpretability, and agreement/disagreement statistical methods. In terms of accuracy, the ML-based strategy showed greater agreement with the reference map (53%) compared to the EK approach (50%). However, the EK strategy was more time- and resource-efficient, as well as being more transferable and interpretable due to the simplicity of its rules based on soil properties. Given its higher interpretability and ease of application, the EK approach was recommended as the most suitable for operational soil drainage mapping in tropical environments.

In countries with extensive agricultural practices, there is a significant risk of soil degradation, making it essential to develop techniques for understanding and detecting these changes. In this study, we used an earth observation system to identify the temporal bare soil frequency and thus, relate it with soil tillage and its impact on soil carbon degradation. The work was performed in two important agricultural states of Brazil, São Paulo and Paraná. For that, historical field and Remote Sensing (RS) data were analyzed to identify the relation between bare soil areas and their degradation. The frequency of bare soil was detected by Landsat images, in the last 36 years using the Geospatial Soil Sensing System (GEOS3). Historical soil surface temperature data was produced using the same images. In addition, legacy pedological and crops (i.e., soil cover) maps were used. Finally, soil texture information was spatialized based on a synthetic soil image (SISY). A total of 28,000 sites with topsoil organic carbon (SOC) were used as the reference for degradation. The soils of the state of Paraná presented significantly lower bare soil areas when compared to the state of São Paulo, mainly due to the wide use of the No-Tillage system. The advancement of sugarcane harvesting technologies together with the "boom" of the commodities after 2000s was responsible for the considerable increases in soil cover conservation. It was noticed that the more exposed the soil remains, the less carbon it has, having a negative correlation (r≈ -0.5). Sandy soils in both states proved to be the ones that were subject to the highest exposure rates and thus, more degraded. This fact is of concern, given that sandy soils are more susceptible to degradation factors, such as erosion. We observed important historical public policies related to the temporal tillage systems adopted by agro-community, which had a significant impact on carbon dynamics. The remote technique was able to infer how the soil has been managed. This information is crucial as it provides a solid basis for developing future public policies aimed at sustainable production.

Spatial soil information has significantly contributed to public policy and planning. The use of X-ray fluorescence spectrometry (XRF) in soil science, primarily in laboratory settings or for isolated point analyses, has been well documented. This study broadens the application of XRF data by integrating it with remote sensing data for a comprehensive understanding of soil patterns and processes. Focusing on a 2574 km2 area in Brazil, we utilized a lab-based XRF instrument to measure the total concentrations of key chemical elements in the soil, such as Al, Si, Ti, and Fe. These geochemical data were then spatialized using the digital soil mapping (DSM) framework. We employed a synthetic bare soil image and elevation data as covariates in the analysis. The spatial accuracy assessment yielded R2 values ranging from 0.65 to 0.81 for Fe, 0.62 to 0.78 for Ti, 0.52 to 0.63 for Si, and 0.48 to 0.63 for Al. The digital soil maps of geochemical elements aligned well with existing pedological, and geological maps as evaluated using multiple correspondence analysis. The integration of spatialized XRF data with DSM and remote sensing techniques shows significant promise in assessing soil variations across landscapes based on their chemical composition.

Mining activities significantly impact the environment, calling for environmental restoration efforts. In this study, we quantified and mapped areas affected by seasonal floods by using a combination of geophysical, geotechnological, and digital tools for data acquisition. The objective was to assess the hydrological and pedological processes resulting from changes in water table dynamics. The study was conducted in the Brazilian Amazon Forest within the Jamari National Forest (FLONA), analyzing four mines: Serra da Onça, Santa Maria, Potosí, and 14 de Abril. We employed satellite imagery for acquiring hyperspectral bands in the Vis-NIR-SWIR range, georadar surveys for subsurface analysis, and digital field sensors for monitoring soil moisture, temperature, and fertility. Based on Normalized Difference Water Index (NDWI) and Normalized Difference Moisture Index (NDMI) data, we applied Sentinel-2 images using ESRI ArcGIS 10.4 to quantify flooded areas. Additionally, the soil profiles were examined, temperature and humidity sensors were installed for monitoring purposes, and we determined subsurface water dynamics, alterations in soil attributes, and limitations in soil fertility affecting plant growth in mined areas. The combined use of field data, georadar surveys, and satellite-derived indices is a valid approach to effectively quantify areas affected by seasonal floods and indicated maximum flooding levels across all mines, evidenced by radargrams displaying saturated soil areas at varying depths. Using remote sensing data and indices such as NDVI and NDMI facilitated the identification of areas affected by seasonal flooding. The volumetric water content significantly influenced soil temperature based on moisture levels and depth. Low soil fertility, identified through laboratory analysis and water saturation data, impeded vegetation establishment and development and favored redoximorphic soil processes. The integration of remote (satellite) and proximal (GPR) sensing techniques proved efficient for accurately quantifying changes in soil water dynamics.

Abstract. Maritime Antarctica (M.A.) contains the most extensive and diverse lithological exposure compared to the entire continent. This lithological substrate reveals a rich history encompassing lithological, pedogeomorphological, and glaciological aspects of M.A., all influenced by periglacial processes. Although geophysical surveys can detect and provide valuable information to understand Antarctic lithologies and their history, such surveys are scarce on this continent and, in practice, almost non-existent. In this sense, we conducted a pioneering and comprehensive gamma-spectrometric (natural radioactivity) and magnetic susceptibility (κ) survey on various igneous rocks. The main objective was to create ternary gamma-ray and κ maps using machine learning algorithms, terrain attributes, and a nested-leave-one-out cross-validation method. Additionally, we investigated the relationship between the distribution of natural radioactivity and κ to gain insights into pedogeomorphological and periglacial processes and dynamics. For that, we used proximal gamma-spectrometric and κ data in different lithological substrates associated to terrain attributes. The geophysical variables were collected in the field from various lithological substrates, by use field portable equipment. The geophysical variables were collected in the field from various lithological substrates using portable equipment. These variables, combined with relief data and lithology, served as input data for modeling to predict and spatially map the content of radionuclides and κ by random forest algorithm (RF). In addition, we use nested-LOOCV as a form of external validation in a geophysical data with a small number of samples, and the error maps as evaluation of results. The RF algorithm successfully generated detailed maps of gamma-spectrometric and κ variables. The distribution of radionuclides and ferrimagnetic minerals was influenced by morphometric variables. Nested-LOOCV method evaluated algorithm performance accurately with limited samples, generating robust mean maps. The highest thorium levels were observed in elevated, flat, and west beach areas, where detrital materials from periglacial erosion came through fluvioglacial channels. Lithology and pedogeomorphological processes-controlled thorium contents. Steeper areas formed a ring with the highest uranium contents, influenced by lithology and geomorphological-periglacial processes (rock cryoclasty, periglacial erosion, and heterogeneous deposition). Felsic rocks and areas less affected by periglacial erosion had the highest potassium levels, while regions with sulfurization-affected pyritized-andesites near fluvioglacial channels showed the lowest potassium contents. Lithology and pedogeochemical processes governed potassium levels. The κ values showed no distinct distribution pattern. Pyritized-andesite areas had the highest levels due to sulfurization and associated pyrrhotite, promoting iron release. Conversely, Cryosol areas, experiencing freezing and thawing activity, had the lowest κ values due to limited ferrimagnetic mineral formation. Lithology and pedological-periglacial processes in Cryosols played a significant role in controlling κ values. In regions characterized by diverse terrain attributes and abundant active and intense periglacial processes, the spatial distribution of geophysical variables does not reliably reflect the actual lithological composition of the substrate. The complex interplay of various periglacial processes in the area, along with the morphometric features of the landscape, leads to the redistribution, mixing, and homogenization of surface materials, contributing to the inaccuracies in the predicted-spatialized geophysical variables.

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 (FCO2) 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 FCO2 2 dynamics in such environments. The aim of this study was to quantify and assess FCO2 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 FCO2 2 and total organic carbon (TOC), differing significantly from the other site areas. Conversely, AP had the lowest FCO2 2 and was distinct from other sites. The MIX and NT areas had intermediate FCO2 2 and TOC values, which were statistically similar to each other. PCA identified distinct patterns in FCO2, 2 , soil temperature, and moisture. FCO2 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 (qCO2), 2 ), COT, and FCO2. 2 . qMic reached the highest values in the MIX area, decreasing linearly from NT to P. Conversely, AP had the lowest qMic. qCO2 2 had the highest value in AP and NT. The proximal IRGA sensor effectively quantified FCO2 2 and differentiated mining tailing-affected areas. This assessment incorporated soil temperature, moisture sensors, vegetation index, and soil attribute data. The FCO2 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 qCO2 2 was high, indicating lower FCO2 2 levels. Conversely, P exhibited a higher FCO2. Higher NDVI values were correlated with elevated FCO2 2 in areas with specific vegetation cover during environmental recovery, while lower NDVI areas had lower FCO2 levels.

The chemical weathering intensity in Antarctica is underestimated. As the chemical weathering intensity increases, hydrological, geochemical and geophysical changes occur in the different environmental spheres and at their interfaces through reactions and energy flows. Thus, once chemical weathering rates are understood and estimated, they can be used to predict and assess changes and trends in different environmental spheres. Few studies on the chemical weathering intensity have been performed in Antarctica. We used radiometric and magnetic properties associated with terrain attributes and the chemical degree of alteration of the igneous rock to model the chemical weathering intensity in Maritime Antarctica by using machine learning. Then, we related the chemical weathering intensity and geophysical variables with periglacial processes. To do this, gammaspectrometric and magnetic readings were carried out using proximal-field sensors at 91 points located on different lithologies in a representative area of Maritime Antarctica. A qualitative analysis of chemical alteration for the different lithologies was carried out based on field observations and rock properties, and the levels of the chemical weathering degree were established. The geophysical data associated with terrain attributes were used as input data in the modeling of the weathering intensity. Then, the levels of the rock weathering degree were used as the "y" variable in the models. The results indicated that the C5.0 algorithm had the best performance in predicting the weathering intensity, and the most important variables were eTh, 40K, 40K/eTh, 40K/eU, the magnetic susceptibility and terrain attributes. The contents of radionuclides and ferrimagnetic minerals in different lithologies, concomitantly with the intensity at which chemical weathering occurs, determine the contents of these elements. However, the stability and distribution of these elements in a cold periglacial environment are controlled by periglacial processes. The chemical weathering intensity prediction model using gamma-spectrometric and magnetic data matched the in situ estimate of the chemical degree of alteration of the rock. The pyritized andesites showed the highest intensities of weathering, followed by tuffites, diorites, andesitic basalts and basaltic andesites, and the lowest weathering intensity was shown by undifferentiated marine sediments. This work highlighted the suitability of using machine learning techniques and proximal-field sensor data to study the chemical weathering process on different rocks in these important and inhospitable areas of the cryosphere system.

Context Soil maps are a fundamental tool for agriculture development and for land management planning. Digital soil mapping (DSM) consists of a group of techniques based on geotechnologies and statistics/geostatistics that helps soil specialists to map soil types and properties. Aims Four DSM strategies were applied in south-east Brazil. The goal was to visually delineate soil polygons with support of different strategies. Methods The delineation started with aerial photographs, followed by a bare soil image composition. Afterwards, it was added layers with landscape characterisation derived from digital terrain covariates and clustering analysis. Finally, digital clay content map from A and B horizons were used to produce a soil texture gradient raster (clay content increasing in depth). Key results The increasing number of polygons proved that the addition of covariates increased the detail level of the soil map, enhancing visualisation of the landscape variation, resulting on a map that substantially improved both national and state soil inventories. Conclusions We concluded that combining simple geotechnological tools might be of great utility for increasing detailed soil information proper for farmers and decision making. Implications Therefore, new soil information will be available for end users, supporting land management, food production sustainability, and soil conservation.

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 & kappa; 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.

Thermodegradation is an analytical tool that quantifies the loss of organic carbon when under increasing temperature. The degradation process is related to chemical functions and is a way to provide data on the stability of soil organic matter (SOM). There needs to be more information about the SOM stability in the different Brazilian ecosystems. This study aimed to evaluate the SOM stability under three different tropical environments: Dry Forest, Brazilian Savannah, and Atlantic Forest. Thus, we related the thermic stability with soil mineralogy, SOM quality, and quantity. The objective of this work is to observe whether soils with different mineralogy and ecosystems present divergent behavior regarding SOM thermal degradation. We analyzed samples of the surface horizon of four soil profiles sampled in Dry Forest (P1 to P4), three soil profiles in Cerrado (Brazilian Savannah), with predominant 2:1 clay (P5), iron oxides (P6), and gibbsite (P7) and the last one profile was kaolinite from Atlantic Forest (Rainy Forest) (P8). It was performed a mineralogical, chemical, and physical analyses of the soils studied to a better comprehension of soils behavior under Thermodegradation. The thermic treatment was performed by heating air-dried fine earth (ADFE) samples at temperatures 100, 200, 300, 400, and 500 ºC for two hours. The results are submitted to sigmoidal regression with four points for presenting a good curve fit (r2>0.99). Dry Forest soils with the most humified SOM presented a greater resistance to loss SOM by the thermic process. However, the P7 (gibbisite clayey soil) stood out and was the most resistant to degradation. The P4 was the sandiest soil and showed the curve most affected by the thermal process, indicating the importance of clay content to SOM stability. The results suggest that the parent material that TOC (content and quality), calcium content, and clay (content and type) play an essential role in determining the stability of SOM in different ecosystems.

Although limited alternatives have been proposed to describe soils by using proximal sensors under natural conditions, efforts must be made to apply the newest technologies to assist in soil characterization and mapping. Our main objective was: to create a protocol incorporating technology that assists pedologists: (i) to compare soil horizonation by using visible, near, and shortwave infrared (Vis-NIR-SWIR) spectroscopy, mid-infrared spectroscopy (MIR), portable X-ray fluorescence (pXRF) indices, and conventional field observations; (ii) to identify mineralogical and physico-chemical variations between soil horizons by using proximal sensors, with inferences about pedogenic processes; and (iii) to expand punctual information to spatial dimensions with RGB images and terrain attributes. Seven soil profiles located through a toposequence evaluation were analyzed on a farm in the São Paulo State, Brazil. First, when the area consisted of exposed soils, images were obtained by Landsat and an unmanned aerial vehicle (UAV), and a digital surface model (DSM) was then built by stereoscopy, which was subsequently used to calculate thirteen terrain attributes. Each soil profile was evaluated with devices directly in the field (except for the MIR analyses, for which soil samples were brought to the laboratory and dried). Several indices were calculated from the pXRF data during the profile analysis, while principal component analyses (PCA) were performed to reduce the dimensions of the spectral data. Four datasets for each soil profile (VIS-NIR-SWIR, pXRF, MIR, and a combination of all the data) were submitted for k-means clustering analysis and the similarities among the layers were evaluated. Following the profile evaluation, spatialization was performed based on single bands from the RGB image and PCA using terrain attributes. The data were sampled to a punctual view of 370 points, classified, and then interpolated with the spline method. The Vis-NIR-SWIR spectral range, together with soil color, provided better correspondence with conventional field observations. We observed that the combined use of Vis-NIR-SWIR, MIR, and pXRF helped to identify and describe the soil profiles appropriately. Proximal sensors also allowed us to make more robust inferences regarding pedogenesis. We delineated soil information profiles in soil units by using the spatial dimension's terrain attributes and, RGB images from a UAV.