•The chemical weathering intensity in Maritime Antarctica is modeled.•A geophysical survey and lithological characteristics as input data.•Proximal geophysical sensors and terrain attributes are ...combined for modeling.•Use of leave-one-out-cross-validation method in modeling.•Periglacial processes control the distribution of geophysical variables.
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, gamma-spectrometric 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.
•Lithology, pedogenesis and relief affect soil magnetic susceptibility.•Pedogenesis strongly affect magnetic susceptibility on more evolved soil classes.•Lithology contribute more to magnetic ...susceptibility in less evolved soil classes.•Magnetic susceptibility had good correlation with soil texture, CEC and iron content.•Field sensor can be used in delimitation of soil classes and digital soil mapping.
Soil magnetic susceptibility (κ) has potential to be used as a pedoenvironmental indicator from which mineralogy, pedogeochemical, pedogeomorphological and pedogenic processes can be inferred. It can be used in pedosphere studies, as an auxiliary information for appropriate and sustainable soil use and management. This research aimed to analyze how pedogenesis and geochemical processes affect the κ and some of its attributes, as well as its potential use in discriminating soil great groups, following the digital soil mapping approach. The study area is located in São Paulo State - Brazil. Soil samples were collected for physical–chemical analysis from 79 locations (0–20 cm depth). At these sites, magnetic susceptibility was measured with a portable field instrument and analyzed in terms of geology, relief and soil class. The results showed that geology strongly affects κ, mainly in diabase derived soils, followed by metamorphosed siltstone and siltstone. In fluvial sediments, the κ exhibits different behaviors due to different sediments deposited by the Capivari River. In less evolved soils, such as Cambisols, lithology is a more important contributor to κ than pedogenesis. In more evolved soils, pedogenesis increases κ, whereas argilluviation/ferralitization reduces it. The κ values did not decrease significantly or even increase downslope, due to the presence of diabase on the lower parts. Differences in κ where observed between diabase bedrock located in different parts of the study area, indicating more of an influence by geomorphic processes rather than lithology. With respect to soil attributes, positive correlations between κ and base saturation, cation exchange capacity, organic matter, and iron and clay content were found, whereas a negative correlation was found between κ and sand content. The κ correlates with changes in lithology and soil class demonstrating its application as a potential tool for the discrimination of soil great groups and digital soil mapping.
The Earth's surface dynamics provide essential information for guiding environmental and agricultural policies. Uncovered and unprotected surfaces experience several undesirable effects, which can ...affect soil ecosystem functions. We developed a technique to identify global bare surface areas and their dynamics based on multitemporal remote sensing images to aid the spatiotemporal evaluation of anthropic and natural phenomena. The bare Earth's surface and its changes were recognized by Landsat image processing over a time range of 30 years using the Google Earth Engine platform. Two additional products were obtained with a similar technique: a) Earth's bare surface frequency, which represents where and how many times a single pixel was detected as bare surface, based on Landsat series, and b) Earth's bare soil tendency, which represents the tendency of bare surface to increase or decrease. This technique enabled the retrieval of bare surfaces on 32% of Earth's total land area and on 95% of land when considering only agricultural areas. From a multitemporal perspective, the technique found a 2.8% increase in bare surfaces during the period on a global scale. However, the rate of soil exposure decreased by ~4.8% in the same period. The increase in bare surfaces shows that agricultural areas are increasing worldwide. The decreasing rate of soil exposure indicates that, unlike popular opinion, more soils have been covered due to the adoption of conservation agriculture practices, which may reduce soil degradation.
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.
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•Digital soil mapping techniques have overcome the challenges associated with phosphorus mapping•Fertilizers have significantly increased Available P stocks in agricultural areas.•Available P stocks strongly influence Brazil's agricultural production, especially soybeans.•The phosphorus stock map provides insights for the sustainable management ofthis nutrient.
Intensive cropland expansion for an increasing population has driven soil degradation worldwide. Modeling how agroecosystems respond to variations in soil attributes, relief and crop management ...dynamics can guide soil conservation. This research presents a new approach to evaluate soil loss by water erosion in cropland using the RUSLE model and Synthetic Soil Image (spectroscopy technique), which uses time series remotely sensed environmental, agricultural and anthropic variables, in the southeast region of São Paulo State, Brazil. The availability of the open-access satellite images of Tropical Rainfall Measuring Mission (TRMM) and Landsat satellite images provided ten years of rainfall data and 35 years of exposed soil surface. The bare soil surface and agricultural land use were extracted, and the multi-temporal rainfall erosivity was assessed. We predict soil maps’ attributes (texture and organic matter) through innovative soil spectroscopy techniques to assess the soil erodibility and soil loss tolerance. The erosivity, erodibility, and topography obtained by the Earth observations were adopted to estimate soil erosion in four scenarios of sugarcane (Saccharum spp.) residue coverage (0%, 50%, 75%, and 100%) in five years of the sugarcane cycle: the first year of sugarcane harvest and four subsequent harvesting years from 2013 to 2017. Soil loss tolerance means 4.3 Mg ha−1 exceeds the minimum rate in 40% of the region, resulting in a total soil loss of ~6 million Mg yr−1 under total coverage management (7 Mg ha−1). Our findings suggest that sugarcane straw production has not been sufficient to protect the soil loss against water erosion. Thus, straw removal is unfeasible unless alternative conservation practices are adopted, such as minimum soil tillage, contour lines, terracing and other techniques that favor increases in organic matter content and soil flocculating cations. This research also identifies a spatiotemporal erosion-prone area that requests an immediately sustainable land development guide to restore and rehabilitate the vulnerable ecosystem service. The high-resolution spatially distribution method provided can identify soil degradation-prone areas and the cropland expansion frequency. This information may guide farms and the policymakers for a better request of conservation practices according to site-specific management variation.
•Weathering and pedogenesis on landscape affect distribution radionuclides.•Lithology strongly affect radionuclides content on less evolved soil classes.•Landscape evolution can affect distribution ...of radionuclides.•Soil texture and other attributes can be inferred by radionuclides content.•Gamma-ray spectrometry can be used in digital soil mapping.
Geophysical methods, such as gamma-ray spectrometry, have great potential to enhance knowledge of the pedosphere (pedogenesis, pedogeochemistry and pedogeomorphology), helping to predict tropical soil attributes. We applied proximal gamma-ray spectrometry to evaluate tropical landscape dynamics, pedogenesis and spatial distribution of radionuclides and selected soil attributes. This study was carried out in southeast Brazil, where 79 soil samples (0–20 cm) along transects were collected to perform physical-chemical analysis coupled with collection of surface gamma-ray spectrometric data, allowing the detection of the radionuclides uranium (U238), thorium (Th232), and potassium (K40). Additionally, we analyzed soils in four toposequences, with varying lithology, relief, and hydrology. The radionuclide concentrations in soils showed a direct relationship with the parent material composition, either rocks or sediments. Weathering degree and the geochemical behavior of each radionuclide determines its permanence or removal in soils. Denudation processes along the toposequences also influence the distribution of radionuclides. On one hand, the radionuclide contents of mature, weathered soils are closely associated with advanced pedogenesis, with a higher clay contents and argic horizon formation, whereas in younger, less weathered soils the parent material exerts a greater influence than pedogenesis. Uranium decreased with altitude, and showed greater mobility compared with thorium. Thorium presented a higher correlation with clay content, and the opposite with sand content. Potassium detection increased with soil cation exchange capacity (CEC) and clay content. Gamma-spectrometry detected significant variations in some segments along the toposequences, undetectable by conventional soil survey techniques. This may indicate changes from one soil class to another or the continuity of a particular soil class, demonstrating the potential of this tool in digital soil mapping, pedogeochemical, pedogeomorphological and pedogenesis processes studies.
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•Combined use of geophysical sensors in modeling pedogenetic processes.•Use of Nested Cross Validation as a form of external validation.•Use soil, lithology, relief and satellite ...image to determine ideal number of clusters.•Geophysical data, machine learning and clustering identified different pedogenesis rates.•Use of clusters in modeling processes.
Pedogenetic processes such as ferralitization and argilluviation control various soil attributes. Understanding the intensities of pedogenesis can provide answers for several fields of environmental studies, including soil science and the geosciences. Recently, new geotechnologies such as geophysics applied to soil science and machine learning algorithms have proven to be a potential tool in pedosphere studies. In this research, we performed component principal analyses and determined the ideal number of clusters based on geophysical soil data and satellite images. Then, we used the ideal number of clusters, and ferralitization and argilluviation indices, as input data in five modeling (prediction and spatialization) algorithms to infer different ferralitization and argilluviation intensities in soils formed from the same soil parent material. The results showed that avNNet had the best model performance for modeling the clusters showing that the ideal number of clusters was three. The variables which contributed the most to the modeling were the solar diffuse radiation, topographic wetness index, and digital elevation model. The model’s specificity was greater than its sensitivity. The areas over diabase and Nitisols in the east of the study area presented greater ferralitization rates than diabase and Nitisols over western areas. On the other hand, the areas over siltite and Lixisols in the east presented greater argilluviation rates than metamorphosed siltite/siltite and Lixisols over western areas. The relief and topographic position strongly affected the evaluated pedogenetic processes, since they controlled the hydric dynamics in the area. The geophysical variables were related to soil attributes and pedogenesis, which contributed to modeling and clusterization processes.
Soil contamination by potentially toxic elements (PTEs) is one of the greatest threats to environmental degradation. Knowing where PTEs accumulated in soil can mitigate their adverse effects on ...plants, animals, and human health. We evaluated the potential of using long-term remote sensing images that reveal the bare soils, to detect and map PTEs in agricultural fields. In this study, 360 soil samples were collected at the superficial layer (0–20 cm) in a 2574 km2 agricultural area located in São Paulo State, Brazil. We tested the Soil Synthetic Image (SYSI) using Landsat TM/ETM/ETM+, Landsat OLI, and Sentinel 2 images. The three products have different spectral, temporal, and spatial resolutions. The time series multispectral images were used to reveal areas with bare soil and their spectra were used as predictors of soil chromium, iron, nickel, and zinc contents. We observed a strong linear relationship (−0.26 > r > −0.62) between the selected PTEs and the near infrared (NIR) and shortwave infrared (SWIR) bands of Sentinel (ensemble of 4 years of data), Landsat TM (35 years data), and Landsat OLI (4 years data). The clearest discrimination of soil PTEs was obtained from SYSI using a long term Landsat 5 collection over 35 years. Satellite data could efficiently detect the contents of PTEs in soils due to their relation with soil attributes and parent materials. Therefore, distinct satellite sensors could map the PTEs on tropics and assist in understanding their spatial dynamics and environmental effects.
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•Bare soil pixels were able to retrieve potential toxic elements from space.•Landsat and Sentinel presented important data to detect topsoil toxic elements on soils.•Long term images generated better spectra to detect toxic element.•Spatial dynamics of toxic elements in agriculture areas are presented.
Geophysical sensors combined with machine learning
algorithms were used to understand the pedosphere system and landscape
processes and to model soil attributes. In this research, we used parent
...material, terrain attributes, and data from geophysical sensors in different
combinations to test and compare different and novel machine learning
algorithms to model soil attributes. We also analyzed the importance of
pedoenvironmental variables in predictive models. For that, we collected
soil physicochemical and geophysical data (gamma-ray emission from uranium,
thorium, and potassium; magnetic susceptibility and apparent electric
conductivity) by three sensors (gamma-ray spectrometer, RS 230;
susceptibilimeter KT10, Terraplus; and conductivimeter, EM38 Geonics) at
75 points and analyzed the data. The models with the best performance
(R2 0.48, 0.36, 0.44, 0.36, 0.25, and 0.31) varied for clay, sand,
Fe2O3, TiO2, SiO2, and cation exchange capacity
prediction, respectively. Modeling with the selection of covariates at three
phases (variance close to zero, removal by correction, and removal by
importance) was adequate to increase the parsimony. The results were
validated using the method “nested leave-one-out cross-validation”. The
prediction of soil attributes by machine learning algorithms yielded
adequate values for field-collected data, without any sample preparation,
for most of the tested predictors (R2 values ranging from 0.20 to
0.50). Also, the use of four regression algorithms proved to be important
since at least one of the predictors used one of the tested algorithms. The
performance values of the best algorithms for each predictor were higher
than those obtained with the use of a mean value for the entire area
comparing the values of root mean square error (RMSE) and mean absolute
error (MAE). The best combination of sensors that reached the highest model
performance was that of the gamma-ray spectrometer and the
susceptibilimeter. The most important variables for most
predictions were parent material,
digital elevation, standardized height, and magnetic susceptibility. We concluded that soil attributes can be efficiently modeled
by geophysical data using machine learning techniques and geophysical sensor
combinations. This approach can facilitate future soil mapping in a more
time-efficient and environmentally friendly manner.
•The sensors provide complementary and useful information about the soil profile horizonation.•PHI values are affected by soil-forming factors and processes.•The higher PHI values were mainly related ...to the erosion and redeposition processes.•The PHI was useful to study soil genesis and layers differentiation.
The technological and computational development has raised new possibilities for soil scientists to describe and infer about soil-forming processes and functions. Thus, why not to insert the contrasts between soil layers in a profile revealed by proximal sensing? We proposed to study the soil profile heterogeneity by reflectance (Vis-NIR-SWIR), X-ray fluorescence and magnetic susceptibility aiming to quantify it with the proposed “Profile Heterogeneity Index” (PHI) through seven soil sequences in twenty-seven locations over different hillslope positions and parent materials. The study was conducted in a 165-ha farm, at Rio das Pedras municipality, São Paulo State, Brazil. The differences between two consecutive soil layers were calculated by subtracting the data to the bottom and the immediately above for each soil profile. The results are depicted as a depth function with proportional colors to highlight the heterogeneity in the soil profiles. This approach provided a new method to identify where the transitions of soil attributes occur exactly and improve the understanding about soil-formation factors and processes. In this study, greater PHI values were mainly related to erosion and redeposition process through the landscape, differences in soil parent materials and also soil-forming processes. The sensors can furnish important and complementary information to the characterization of soil profile heterogeneity.
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