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Unstable soil aggregates can easily be dispersed when mechanical energy is applied, denominated as readily dispersible clay (RDC) which is usually measured by turbidimetry. Faster methods that require minimum preparation at reduced costs are needed, such as the spectroscopy technique. Nevertheless, the technique has not yet been tested on soils. Electromagnetic radiation has strong interaction with soil physical elements (sand, silt, and clay) and with selective elements of the clay fraction, such as mineralogy. Thus, this study assessed the potential of 400–16,680 nm range (visible – Vis; near-infrared – NIR; short-wave infrared – SWIR; and medium infrared – MIR) spectroscopy to estimate RDC in some tropical soils. We also evaluated the wavelengths and regions that contribute to this prediction. We collected 68 samples from the underlying horizon of three different soils from three important states in Brazil The soil analyses were performed (physical and chemical) in the laboratory. RDC quantification was compared with the Vis-NIR-SWIR and MIR wavelengths models. The random forest (RF) algorithm was used to generate the prediction in the soil database. For RDC, the Vis-NIR-SWIR and MIR configuration presented a better relationship (coefficient of correlation (r) = 0.659 and root of the mean square error (RMSE) = 0.469%), although the results were similar when the regions were evaluated separately (Vis-NIR-SWIR – 350 to 2500 nm and MIR 2500 to 16,680 nm). RDC determination is more related to bands from 1980 to 2040, 6840 to 7360, and 7750 to 9820 nm. Spectroscopy showed its importance in RDC quantification, as it is fast and does not require soil preparations. The results show that spectral wavelengths are appropriate to assist on the estimative of RDC in the studied soils and may provide a basis for future research. [ABSTRACT FROM AUTHOR]

• Soil elemental composition is key to infer other soil attributes. • DSM and remote sensing can take punctual XRF information to maps. • We mapped the main chemical elements using punctual XRF information and DSM. • XRF allied with DSM can be a new and low-cost way to large areas soil mapping. • Spatial XRF information can be used to access soil variations in the landscape. 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. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon sequestration potential of the clay fraction. • Determination of the carbon saturation deficit of each mineral of the clay fraction. • Identification of the equation for the prediction of carbon sequestration potential. • Identification of land uses with the greatest carbon sequestration potential. The capacity of soil to sequester carbon (C) is a key process that promotes the reduction of CO 2 in the atmosphere. Soils can absorb as much as 20% of anthropogenic carbon emissions, which can contribute to mitigate climate change. This capacity relies on the organo-mineral association, which includes different minerals, Fe and Al oxides, which have a critical soil organic carbon (SOC) sorption surface. Based on an equation of the potential C saturation deficit of fine soil particles (<20 μm/silt and clay fractions) for tropical regions, this study investigated the SOC sequestration potential of the clay fraction for soils in Piracicaba region, São Paulo State, Brazil as influenced by the clay minerals. This potential was fitted to a spatial regression model for soil depths 0–20 cm and 80 to 100 cm. In the surface layer, the sequestration potential was mostly explained by the relative abundance of soil minerals (Kaolinite, Hematite, Goethite and Gibbsite) determined using vis-NIR-SWIR spectroscopy. A direct relationship was observed with Goethite and Gibbsite, indicating that low concentrations would reduce the sequestration potential. At 80 to 100 cm depth, Kaolinite and Hematite explained most variation in SOC sequestration potential. Additionally, the C associated with the mineral fraction and the C saturation potential as a function of minerals were modeled and a strong importance of hematite in the C sequestration and stabilization cycle was identified at both depths. The individual mineral contribution to SOC sequestration potential was also mapped, which identified high contributions of goethite and gibbsite for deep soil layers. The influence of land use on the carbon sequestration potential of minerals was observed, with the greatest potential being found in areas with pasture and cropping mosaics and grassland and forest mosaics, with a high presence of kaolinite and hematite. These minerals have a greater potential for carbon sequestration at greater depths and, therefore, could be critical in climate change mitigation strategies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Goethite, haematite, kaolinite and gibbsite abundances releveled by Vis-NIR-SWIR data; • Novelty framework to reach a continuums bare soil reflectance image; • Major soil mineralogical components mapping for the whole Brazilian territory; • Soil mineralogy mapping for large area by spectroscopy and Digital Soil Mapping; Minerals control many soil functions and play a crucial role in addressing global existential issues. Measuring the abundance of soil minerals is a laborious, costly, and time-consuming task; however, soil spectroscopy can be a useful tool to overcome this issue. This work aimed to map the abundance of major mineralogical components of soils in Brazil from surface to 1 m deep and at a spatial resolution of 30 m. Spectral data of the Brazilian Soil Spectral Library with Vis-NIR-SWIR was used to estimate the abundance of haematite, goethite, kaolinite, and gibbsite. These minerals were spatialized using digital soil mapping techniques. We also developed a novel framework to obtain bare soil reflectance for areas without natural or anthropic soil exposure (continuous image) and used it as covariate. Soil minerals and their abundances were successfully estimated by Vis-NIR-SWIR reflectance. Haematite predictions presented the most accurate results with Random Forest models, followed by gibbsite, kaolinite, and goethite. The spatial validation with reference mineralogical data found R2 of 0.64 (haematite), 0.40 (goethite), 0.20 (kaolinite/Kt), 0.29 (gibbsite/Gbs), and 0.40 (Kt/Kt + Gbs). The resulting maps of soil minerals were in accordance with the geology, pedology, climate, and relief of Brazil and revealed the spatial distribution of mineral abundances at a finer resolution than existing geological and pedological maps, reaching a farm level detail. [ABSTRACT FROM AUTHOR]

Reflectance spectroscopy has great potential to monitor and evaluate soils at large scale; however, its effectiveness in predicting properties from tropical soils still needs to be tested since their mineralogy, organic matter levels, and charge and ion adsorption dynamics are different from temperate soils. Also, it is important to assess the most appropriate spectral range for quantification of specific soil property. Therefore, this study aimed to predict physical, chemical, and mineralogical soil properties using vis–NIR (350–2500 nm) and mid-IR (4000–400 cm − 1 ) spectral libraries and statistically compare their modeling performances. We used 1259 soil samples distributed along four Brazilian States. Soil particle size, chemical analyses including macro and micronutrients, and oxides from sulfuric acid digestion were performed. Vis–NIR reflectance data were obtained by the FieldSpec Pro sensor while mid-IR data were collected using the Nicolet 6700 FT-IR sensor. Support Vector Machine was used as multiple regression algorithm and modeling performance was evaluated by R 2 , RMSE and RPIQ. This research presented a complete prediction analysis of soil properties important for survey, classification, and fertility management. Models fit very well (0.76 ≤ R 2 ≤ 0.90 and 2.81 ≤ RPIQ ≤ 5.62) for sand, clay, Al 3+ , H + Al 3+ , CEC, clay activity, Fe 2 O 3 , and TiO 2 predictions, and showed reasonable performance (0.50 ≤ R 2 ≤ 0.73 and 1.83 ≤ RPIQ ≤ 3.78) for OC, Ca, Mg, SB, V%, m%, pH in H 2 O, oxides (Si, Al, and Mn), and Cu and Mn (micronutrients). Phosphorus, potassium and some micronutrients (Fe and B) were not reliably quantified (R 2 ≤ 0.47 and RPIQ ≤ 1.83). For both spectral ranges, performance indices were kept in testing steps, and no atypical distribution pattern was identified by residual analysis. Statistically, mid-IR spectral models showed better performance for 60% of the studied properties. For some oxides (Al, Fe, Ti, and Mn), vis–NIR models were better. Models developed from vis–NIR and mid-IR spectral libraries are effective and useful to quantify properties suggesting soil mineralogy, reactivity, fertility and acidity of tropical Brazilian soils; however, mid-IR is the greatest potential spectral range. The excellent results of clay (0.85 ≤ R 2 ≤ 0.88 and 3.88 ≤ RPIQ ≤ 5.56) and sand (0.85 ≤ R 2 ≤ 0.90 and 4.85 ≤ RPIQ ≤ 5.62) modeling prove that at least soil particle size analyses can be efficiently replaced by the reflectance spectroscopy methods. [ABSTRACT FROM AUTHOR]

Soil maps at regional and farm levels are vital for the best management agricultural practices (BMAP). The soil is the substrate for plant growth and essential to ensure food security. In this context, soil maps require a detailed cartographic scale for BMAP. This study (i) investigated the use of digital soil mapping (DSM) products, such as soil chemical and physical attributes, indices, mineralogy, and properties to extrapolate late soil survey maps at 1:20,000 scale; (ii) created the digital yield environment for sugarcane based on the DSM products; and (iii) evaluated qualitatively the predict soil maps and relationship with previous studies and the predicted yield environment. The region of interest covers eight municipalities and almost 2598 km2 in São Paulo State, Brazil. The soil survey at farm level conducted covered almost 86.52 km2, ∼3.33% of the total area (96.67% of the unmapped area). We created a point grid (centroid) with the same spatial resolution (30 m) of the rasters used as covariates for soil mapping unit (SMU) predictions. This grid intended to retrieve the representative soil mapping unit of each geometric polygon. It was retrieved 117,413 points representing 27 SMU of seven soil orders at a first categorical level, according to the Brazilian Classification System, and seven yield environment for sugarcane production. SMU predictions and their respective soil orders were performed using the random forest machine learning regression method. The level of association between SMU and yield environments was 0.34 (α = 0.01) by the Cramer's V coefficient with a very strong relationship. Our approach could provide the first digital yield environment for sugarcane based on the DSM products. Furthermore, a qualitative evaluation of our framework was substantiated with previous research in the same study site. This framework could be replicated and fulfil the need for DSM at regional and farm levels for policy-makers and farmers. [Display omitted] • Soil mapping units (SMU) and yield environments had very strong association. • Digital soil mapping (DSM) products extrapolated late soil survey maps. • DSM products were used to create digital yield environment for sugarcane. • DSM products worked effectively as predictors of the SMU and yield environment. [ABSTRACT FROM AUTHOR]

• The first spectral library in MIR range for Brazil. • Prediction of soil attributes in national and regional scales. • It is possible to classify the soil by MIR spectra. • The data are available online (http://www.besbbr.com.br/). Brazil plays an important role in global agricultural production and environmental politics, and thus a national spectral library can bring light to new initiatives on soil monitoring. The middle infrared spectral range (MIR, 4000–600 cm−1) includes the fundamental reflectance of soil organic and mineral components and thus can bring important information on soil characterization. This work aimed to build a national soil spectral library from the MIR, relate its descriptive and quantitative potential to soil assessment. We characterized up to 4309 soil samples from different depths (0–20, 40–60, 80–100, and 100–120 cm) including particle size distribution and chemical analyses (2963–4309 samples), and mineralogical analyses (278–861 samples). Unsupervised classification distinguished five soil spectra classes with distinct absorption shapes and features. The modeling results showed that MIR can accurately predict soil physicochemical attributes and revealed a strong association with environmental and geographical variables. There were no significant differences between raw and preprocessed MIR spectral data in predicting soil attributes. In general, local spectral models presented better results than national ones, as expected. Consistent results were achieved for several soil attributes, such as mineralogy (R2 = 0.68–0.87), clay fraction (R2 = 0.55–0.91), organic carbon (R2 = 0.77–0.94), and base saturation (R2 = 0.68–0.87). It was possible to create a MIR spectral classification based only on the spectra and assess its relation with environmental and geographical variables. The information generated in this work may be the basis for future research as MIR spectra contain fundamental soil signatures that can be practically used for agronomic and environmental decisions. [ABSTRACT FROM AUTHOR]

• Hydrological data contributed more than relief for prediction of sand, clay, and OM. • Confluence angle and channel sinuosity have distinct patterns over different soil types. • Hydrology, relief, and SySI data resulted in R2 of 0.71 for clay prediction. • Drainage morphometry changes across the landscape according to the soil class. Drainage network (DN) is the representation of all the stream channels developed over the landscape. The morphometry of DN describes the relationship between channel characteristics and basin geometry, which is regulated by a series of processes, such as weathering, geomorphology, sediment erosion/deposition. The interaction between these factors impacts soil formation, resulting in a relationship between DN morphometry and soil characteristics. Unstable surfaces produced shallow soils, enhancing surface runoff and high drainage density, while stable landscapes favor vertical infiltration through old and weathered soils. Digital soil mapping has benefited from multiple environmental variables, such as relief and satellite data, but DN information can offer great contributions for the prediction of soil attributes. In this work, we applied a set of complex DN variables to perform digital maps of clay, sand, and soil organic carbon (SOC), for a 1378 km2 site in the São Paulo state, Brazil. We analyzed the relationship between the drainage density (DD), drainage frequency (DF), channel sinuosity, and confluence angle with soil classes and attributes. The Cubist and Random Forest algorithms were tested to predict the soil information, and to evaluate the impact of the new hydrological variables. The results showed that landscapes predominated by clayey soils favor surface runoff and increase channel formation, with higher channel sinuosity and acute confluence angles. These new drainage variables contributed 35 to 55% for SOC prediction. DD and DF were the most important drainage variables on the models, ranging from 65 to 70%. The external validation reached R2 of 0.72 and 0.56 for the prediction of clay and sand, respectively. The impact of DN information on the model performance suggests that more work is needed to better explore and understand the relationship between DN and soil information. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • 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. [ABSTRACT FROM AUTHOR]

• 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. [ABSTRACT FROM AUTHOR]

• Multi-temporal SYSI S2-MSI image provided the best model performances. • PlanetScope image did not provide any improvements in model performances. • Satellite images in different resolutions affect soil property maps and strategies related to classification and management. Soil maps at appropriate scales can aid decision-making in agriculture and the environment. In this sense, remote sensing products have shown their power to investigate soil properties, but the assessment of its spatial information can be hampered by the presence of other objects than soil. To overcome this issue, soil scientists have been studying spatial patterns from multi-temporal satellite images aiming at at improving soil property maps. In this work, we applied the cubist algorithm to predict topsoil properties (clay, sand, organic matter and iron contents, and soil color components) in south-eastern Brazil using multi-temporal (Landsat8-OLI, and Sentinel2-MSI) and single-date images (PlanetScope, Landsat8-OLI, and Sentinel2-MSI). We aimed to evaluate the influence of satellite's spatial, spectral and temporal resolutions on soil mapping. Predictive models were constructed with 120 soil samples and using four (vis-NIR) and six (vis-NIR-SWIR) spectral bands as predictors in a 10-fold cross-validation procedure. The multi-temporal image obtained from the Sentinel2-MSI satellite (with 10 m pixel size and six spectral bands), showed the best model performances in cross-validation (R2 between 0.48 and 0.78). The PlanetScope image, which has only four bands in the vis-NIR region and spatial resolution of 3 m did not improve model performances, although the R2 values were higher for soil color components (R2 > 0.5). Satellite images in different spatial, spectral and temporal resolution provides slightly different soil property maps which may promote different strategies regarding soil classification and management. However, satellite images should be used with caution, as they provide only surficial information about the soil variability and confirmation with field surveys is required. [ABSTRACT FROM AUTHOR]

• Spectral and terrain features for mapping cropland soils in Brazil. • Detailed soil spatial distribution and SOC stock was estimated in croplands. • Predicted maps revealed a high variability of topsoil among the cropped areas. • Earth Observation data is a valuable source for mapping and monitoring cropland soils. Despite the natural spatial variability, cropland soils are subject to many interventions that can lead to alterations of soil functioning. As the cropland expansion took place in Brazil the last decades, leading to significant land-use change and environmental impacts, detailed information about soils is fundamental for sustainable development. Thus, considering the lack of spatially explicit information about cropland soils in Brazil, we aimed at performing high-resolution mapping of key topsoil attributes using spectral and terrain features extracted from Earth observation data (EOD). With the resulting information, we also aimed at performing a general examination of the main agricultural regions and estimate the total organic carbon stocks on croplands soils. For this, we obtained environmental predictors from the historical collection of Landsat data and the digital elevation model from Shuttle Radar Topographic Mission at the cloud-based platform of Google Earth Engine. The environmental predictors (30 m spatial resolution) with georeferenced soil samples (n = 5097) were used for predicting the topsoil content (0–20 cm) of clay, sand, silt, cation exchange capacity, pH, soil organic carbon (SOC) and SOC stock. Prediction models of clay, sand, SOC content, and SOC stocks had the best performance metrics, achieving a R2 ranging from 0.44 to 0.74 and ratio of performance to the interquartile range higher than 1.5. The predicted maps revealed the variability of topsoil among the cropped areas, indicating that the agricultural expansion took place on sandy soils. The SOC stock map provided consistent estimates compared to previous datasets but revealed additional information at the local and regional scales. Thus, this study supports the proposition that EOD is a valuable source for extracting environmental features for mapping and monitoring cropland soils at finer resolutions, assisting the evaluation of soil spatial distribution and the historical agriculture expansion over large geographical areas. [ABSTRACT FROM AUTHOR]

Parent material is one of the five factors in soil formation. Studies on parent material allow interpreting soil genesis processes and improve our knowledge of specific soil attributes. However, soil parent material maps at detailed cartographic scale (finer than 1:100,000) are rare in tropical areas and it is usually inferred from poorly detailed geological data, which generally group different lithologies into single units. Thus, we propose a methodology to map soil parent material based on remote sensing and machine learning in a geologically very complex area. The study site covers 1378 km2 in São Paulo State, Brazil. Prediction models used data from 280 geological observation points, a digital elevation model (spatial resolution of 5 m, upscale to 30 m) and multitemporal Landsat images in a range of 30 years. We evaluated six classification algorithms, namely random forest, decision tree, support vector machine, multinomial logistic regression, K-means (unsupervised classification), and object-based image analysis with maximum likelihood classification. Environmental covariates were grouped to create different scenarios combining terrain derivatives, hydrologic covariates, topsoil spectral reflectance, and spatial coordinates. A bare soil image, elaborated using 30 years of Landsat data, was evaluated as a covariate to predict soil parent material. Predictions were validated using three different strategies: cross-validation, separate validation dataset (20%), and comparison with legacy geological maps (information from two areas with geological maps at fine scale). We also assessed the correspondence between the map of predicted soil parent material and data of soil particle size from 571 soil sampling points. Random forest algorithm presented the best validation performance, whereas the group of terrain derivatives and hydrologic covariates explained most of model variation. The produced parent material map was coherent with the spatial distribution of soil particle size across the study area. Unlabelled Image • A soil parent predicted showed details not present in an available geological map. • Random Forest showed the highest accuracy and Kappa coefficient. • Spatial coordinates were important covariates. However, may cause artifacts. • The predicted map showed high correspondence with soil particle size distribution. [ABSTRACT FROM AUTHOR]

• 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. [ABSTRACT FROM AUTHOR]

• Soil texture can be detected through its emissivity spectra. • Soil emissivity decreases as sand content of the samples increases. • Laboratory and satellite sensors have distinct accuracy in soil attributes modeling. • Qualitative analyses of soil spectra reveal relevant information of its composition. Soil texture and organic carbon (OC) content influence the spectral response. These attributes are relevant for the preservation and proper management of land use in the pursuit of a sustainable agriculture. Laboratory and satellite sensors have been applied as a powerful tool for studying so is, but their analysis using these sensors has mainly focused on the visible (Vis), near infrared (NIR) and shortwave infrared (SWIR) regions of the electromagnetic spectrum, with few studies in the Medium Infrared (MIR). The aim of this study was to identify the spectral pattern of soils with different granulometry (sand and clay) and OC content using laboratory and satellite sensors in the MIR region, specifically in the Thermal Infrared (TIR) range (ASTER, Landsat satellites). The study performed qualitative and quantitative analyses of clay, OC and sand fractions (fine and coarse). The study area is located in the region of Piracicaba, São Paulo, Brazil, where collected 150 soil samples (0–20 cm depth). Soil texture was determined by the pipette method and OC via dry combustion. Reflectance and emissivity (Ɛ) spectral data were obtained with the Fourier Transform Infrared (FT-IR) Alpha sensor (Bruker Optics Corporation). An image "ASTER_05" from July 15, 2017 was acquired with values of Ɛ. Samples were separated by textural classes and the spectral behavior in the TIR region was described. The data obtained by the laboratory sensor were resampled to the satellite sensor bands. The behavior between spectra of both sensors was similar and had significant correlation with the studied attributes, mainly sand. For the partial least squares regression (PLSR) models, six strategies were used (MIR, MIR_ASTER, ASTER, TIR, TIR Correlation Index (TIR_CID), and MIR Correlation Index (MIR_CID)), which consisted in the use of all sensors bands, or by the selection of bands that presented the most significant correlations with each one of the attributes. Models presented a good performance in the prediction of all attributes using the whole MIR. In the TIR region, the models for total sand content and for fine and coarse fractions were good. Models created with ASTER sensor data were not as promising as those with laboratory ones. The use of specific bands was useful in estimating some attributes in the MIR and TIR, improving the predictive performance and validation of models. Therefore, the discrimination of soil attributes with satellite sensors can be improved with the identification of specific bands, as observed in the results with laboratory sensors. [ABSTRACT FROM AUTHOR]

Textural differentiation between soil horizons occurs due to the soil formation processes and is one of the main characteristics used for soil classification and erosion risk analysis. The aim of this work was to develop a Ratio of Clay Spectroscopic Index (RCSI) and establish a degree of granulometric differentiation between soil horizons using VIS-NIR-SWIR (350–2500 nm) and MIR (2500–25,000 nm) data. We described morphologically and collected 150 soil profile samples of five representative soils from the southeastern and central-western Brazil. Three spectral models (VIS-NIR-SWIR, MIR and VIS-NIR-SWIR-MIR) were classified in terms of analytical organic matter contents from A and B horizons. We constructed 15 RCSI and considered reflectance difference for specific bands or band combinations, which were selected based on the qualitative and quantitative spectral signatures assessment. The soil horizons were differentiated through their spectral regions associated to iron oxides, clay minerals and quartz. The use of RCSI-15 resulted in a R2 of 0.79 and a RPD of 2.21. The indices reduce the dependence on advanced statistical methods and support the development of optical equipment to work with specific spectral bands. • Soil horizon contrast observations are greatly enhanced by reflectance difference analyses. • Spectral indices are accurate on Ratio of Clay prediction (R2 = 0.79; RPD = 2.21). • The present technique is robust to measure ratio of clay, a traditional requisite for soil classification and erosion risk. • The results encourage the creation of field equipments for a quick Ratio of Clay evaluation. [ABSTRACT FROM AUTHOR]

The present study was developed in a joint partnership with the Brazilian pedometrics community to standardize and evaluate spectra within the 350–2500 nm range of Brazilian soils. The Brazilian Soil Spectral Library (BSSL) began in 1995, creating a protocol to gather soil samples from different locations in Brazil. The BSSL reached 39,284 soil samples from 65 contributors representing 41 institutions from all 26 states. Through the BSSL spectra database, it was possible to estimate important soil attributes, such as clay, sand, soil organic carbon, cation exchange capacity, pH and base saturation, resulting in differences among the multi-scale models taking Brazil (overall), regional and state scale. In general, spectral descriptive and quantitative behavior indicated important relationship with physical, chemical and mineralogical properties. Statistical analyses showed that six basic patterns of spectral signatures represent the Brazilian soils types and that environmental conditions explain the differences in spectra. This study demonstrates that spectroscopy analyses along with the establishment of soil spectral libraries are a powerful technique for providing information on a national and regional levels. We also developed an interactive online platform showing soil sample locations and their contributors. As soil spectroscopy is considered a fast, simple, accurate and nondestructive analytical procedure, its application may be integrated with wet analysis as an alternative to support the sustainable management of soils. Unlabelled Image • The first Brazilian Soil Spectral Library (BSSL) • With the BSSL it was possible to estimate important soil attributes. • Prediction of soil attributes was different for national, regional and state scales. • The Brazilian soils types can be represented with 6 patterns of spectral signatures. • The results endorse the relevance of spectral libraries on soil evaluation. [ABSTRACT FROM AUTHOR]

Pedological assessment and mapping by hyperspectral proximal and multispectral remote sensing comes up as an important alternative for large extent areas with high soil variability. Strategies indicating how to integrate these multi-source data for digital soil mapping are emerging. In this paper, we used proximal and remote sensed data to perform pedological assessments and to support a detailed soil class mapping by MESMA. The study area is located in Southeast of Federal District, Brazil. Six toposequences were defined according to pedomorphogeological assessments and 34 sites were collected for laboratory analysis (texture and chemical) and VIS-NIR-SWIR (350–2500 nm) spectroscopy reflectance analysis. We assessed soil mineralogy based on derivative analysis of topsoil reflectance and we grouped observations to recognize topsoil spectral patterns by clustering method. Topsoil patterns (mean spectral curve for each cluster) were convolved using a Gaussian function of Landsat 5-TM spectral bands to obtain endmembers. Then, we used a Landsat 5 TM time series to produce a bare soil composite denominated Synthetic Soil Image (SYSI). Endmembers and SYSI were used as input data for Multiple Endmember Spectral Mixture Analysis (MESMA) to map the soil classes. Topsoil spectra clustered soil samples that were similar in texture, mineralogy and color, and identified 13 topsoil patterns (endmembers). SYSI retrieved 74% of bare soil area coverage and presented very similar spectral patterns to endmembers. The RGB 543 composite highlighted the mineralogy (i.g. sesquioxides, kaolinite and quartz), texture and color of the soils. MESMA modeled almost 100% of SYSI from endmembers, with low global RMSE of 0.86% and high global fraction of 62%. Soil classes were mapped using topsoil reflectance patterns and satellite images by MESMA method. We validated the digital soil map by using independent field-visited sites, which reached a Kappa coefficient of 0.73. We efficiently assessed soil mineralogy and recognized patterns from laboratory topsoil spectra. We successfully mapped soil classes using topsoil reflectance patterns and a bare soil composite image by MESMA method. Flowchart showing the procedure applied to produce the digital soil map where the soils are classified up to the second categorical level (subgroup) of the Brazilian Soil Classifications System (SiBCS). Unlabelled Image • Pedological mapping based on topsoil spectra is feasible. • We assessed soil mineralogy and recognized patterns from laboratory topsoil spectra. • We mapped soil classes using topsoil spectra and a bare soil composite by MESMA. • The non-detection of subsurface reflectance was not impeditive for soil mapping. • MESMA-derived digital soil map reached Kappa of 0.73. [ABSTRACT FROM AUTHOR]

Abstract It is impossible to make pedological maps without understanding subsurface attributes. Several strategies can be used for soil mapping, from a tacit knowledge to mathematical modeling. However, there are still gaps in knowledge regarding how to optimize subsurface mapping. This work aimed to quantify subsurface soil attributes using satellite spectral reflectance and geographically weighted regression (GWR) techniques. The study was carried out in São Paulo, Brazil, in an area spanning 47,882 ha. Multitemporal satellite images (Landsat-5) were initially processed in order to retrieve spectral reflectance from the bare soil surface. Based on a toposequence method, 328 points were then distributed across the area (at depths between 0 and 20 cm and 80 and 100 cm) and analyzed for their soil chemical and physical attributes (including the reflectance spectra (400 to 2500 nm)) in the laboratory. We achieved 67.72% of bare soil for the whole study area, with the remaining 32.28% of the unmapped surface being filled by kriging interpolation. All 328 samples were modeled using surface (Landsat-5 TM spectral reflectance) and subsurface (acquired in the laboratory) data, reaching up to 0.72 R2 adj. The correlation between the spectra of both depths was significant and the soil attributes prediction reached an R2 adj of validation above 0.6 for clay, hue, value, and chroma at 0–20 and 80–100 cm depths. The satellite soil surface reflectance allowed the estimation of soil subsurface attributes. These results demonstrate that diagnostic soil attributes can be quantified based on spectral pedotransfer (SPEDO) functions to assist digital soil mapping and soil monitoring. Despite our efforts to determine soil subsurface properties using digital soil mapping approach, this task still need considerable refinement. Thus, research must continue to aggregate outcomes from other techniques. Highlights • Subsurface soil attributes can be estimated by spectral pedotransfer functions • Bare soil composites support spectral pedotransfer functions • Bare soil composites assist in digital soil mapping [ABSTRACT FROM AUTHOR]

There are several methods to extract soil information by spectral sensing. For this reason, the database should be built including standards and protocols both in the lab and in field acquisitions. If we does not align the measurement one to each other, the models will have no merit in term of large-scale application and stay as an academic exercise only. The use of standard samples with known spectra is expected to allow parameterizing mathematically soil spectra collected by different equipment and with distinct geometries. This study was proposed for the reason that to date, the new methodology proposed has not been evaluated in Brazilian tropical soils (Oxisols) in visible, near and short infrared (VIS-NIR-SWIR – 350 to 2500 nm) spectral regions from three spectrometers (FieldSpec 3) in three distinct protocols (Long Light, Near Light and Contact Probe). The current study compared the spectral intensity of each combination between spectrometers and protocols by ANOVA module and the clay prediction capacity by PLSR with cross-validation, before and after the internal soil standard (ISS) method application. The results showed that visual spectral variation is minimized by the ISS method and its correction enables better proxy modeling of clay content especially if the data are mixed from different protocols. The model for clay prediction was improved showing a favorable case to use the ISS technique in any soil spectral measurement in a better way to merge spectral libraries from different sources. [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]

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 Amazon rainforest is one of the most important ecosystems on the planet; however, the environmental pressure created by anthropic activities require monitoring of this critical biome. In this study, we assessed the evolution of deforestation in an open mine pit and its impact on surface environment (i.e., temperature and carbon stocks) using remote sensing techniques. The study was carried out on an area of 11,283.3 ha in the municipality of Marabá, Pará State, Brazil, where the "Salobo" copper mine is located. A temporal analysis was conducted, using Landsat satellite images (2005–2020). Subsequently, the Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI) of the deforested area were determined. Mining-induced deforestation has expanded from 0.9 ha in 2005 to 2214 ha in 2020 with an increase in surface temperature of 10 °C in the period. The temperature difference between the pit and the adjacent forest ranged from 30 to 40 °C over the 15 years, while the temperature at the forest edges rose by 4 °C. The correlation coefficient between exposed soil temperatures and mining deforestation was 0.66. CO 2 emissions, increasing from 0.005 Tg CO 2 in 2005 to 1.82 Tg CO 2 in 2020 due to mining deforestation. The findings demonstrate the significant environmental impact of deforestation, which may also have an influence on local climate. The results can provide scientific support for public policies aimed at mitigating the issue. • A site specific mining deforestation in amazonia increased from 0.9 to 2214 ha in 15 years. • Deforestation impacted on an increase in surface temperature of 10 °C. • The temperature difference between the bare soil and the adjacent forest varied on 10 °C, as in the edges only 4 °C. • CO 2 emissions increased from 0.005 to 1.82 Tg. • Scientific support for public policies on how to mitigate the increased local temperature due to deforestation. [ABSTRACT FROM AUTHOR]

[Display omitted] • Proximal sensing is a useful tool be part of a wet laboratory dynamics. • Using vis-NIR-SWIR most laboratories reached R2 of 0.5 for clay, silt, sand and OM. • The models for sand and clay were better for the three sensors. • The hybrid laboratory is the key to the future of soil analysis. • The data are available online (https://www.besbbr.com.br/). Spectroscopy has been extensively used in soil analysis. However, users, such as commercial laboratories, still do not know the application, potential, and limitations of this technique. This paper aims to present a given course going from theory to practice for end-users regarding spectroscopy technique. The paper combines the use of soil spectroscopy and wet laboratories to perform a scientific-teaching dynamic using their own data, allowing for a better understanding of the technique. The course is denominated ProBASE (The Brazilian Program of Soil Analysis via Spectroscopy). Soil samples from 35 laboratories were sent (34 from Brazil and 1 from Paraguay) to a central spectroscopy laboratory. Samples were measured for visible-near-short-wave-infrared, Vis-NIR (400–2500 nm), and mid-infrared, MIR (3000–25000 nm) ranges and by portable X-ray Fluorescence (pXRF) sensor. We also used the Brazilian Soil Spectral Library (BSSL) dataset (Vis-NIR). We performed three different population models with Vis-NIR as follows: a) using the dataset of each laboratory (Local), b) using the entire ProBASE-dataset (Regional), and c) using the BSSL (Country). Afterward, we developed spectral models using the other spectral ranges for comparison. We also used a qualitative approach to detect errors from the wet laboratory analyses using Vis-NIR data and evaluated their impact on spectral modeling. The models that used Local samples had the best performance, with R2 in validation reaching up to 0.93 for clay, 0.92 for sand, 0.86 for P, 0.82 for pH, 0.81 for organic matter (OM), 0.75 for Ca2+, 0.72 for cation exchange capacity (CEC), 0.71 for aluminum saturation, 0.71 for Al3+, 0.70 for Mg2+, 0.64 for base saturation (V%), and 0.56 for K+. However, the base saturation presented greater variation from good to poor results. For the comparison dataset, the results can be summarized as follows: a) pXRF was better for P, Ca2+ B, V% and Mn; b) MIR was better for clay, sand, OM, pH, Mg2+, CEC and Mn; c) Vis-NIR was better for H + Al; d) the three spectral ranges had good performance for OM, sand, silt and clay. In addition, our findings indicate that all spectral ranges are useful for a wet laboratory, where each model has advantages and limitations, but they can be used complementary to each other. Spectroscopy can detect inconsistencies of the wet laboratory analyses, affecting thus the quality of the results. The commercial laboratory community viewed the techniques positively. The results indicate the viability to create a hybrid laboratory , combining both wet and dry (soil spectroscopy) chemistry. [ABSTRACT FROM AUTHOR]

The use of satellite images is a widespread technique for estimating soil attributes. However, what is the potential of satellite data on the prediction of soil weathering indices? How much in their performance is affected by spectral mixing and data resolution? There is no consensus for answering these questions. Hence, the main objectives of this work were: i) to show the difference of spectral signature between prepared and field-condition samples; ii) to compare the convoluted data from laboratory spectroradiometer with the spectral signatures from Sentinel-2 and Landsat-8 satellites; and iii) to build and evaluate predictive models for soil weathering indices using diffuse reflectance spectroscopy from samples under natural conditions, air-dried-sieved and from satellite images. Soil samples were collected from the first 1-cm depth in 27 locations along seven catenas located in a 163-ha area in São Paulo State, Brazil. The samples were analyzed by a Vis-NIR-SWIR spectrometer without sample preparation (field conditions) and after being air-dried and sieved (prepared). The air-dried-sieved samples were also analyzed by an X-ray fluorescence equipment. Reflectance data were obtained from Sentinel and Landsat for the same 27 locations. The spectral data from the lab were convoluted to respective bands of the real satellite data. Predictive models for soil weathering indices using the convoluted laboratory data and the satellite data were also built and compared. The reflectance of samples under field conditions contrasted significantly between soils derived from basalt and sedimentary rocks. In general, the reflectance intensity of prepared samples was 80% higher than that observed for field-conditions, which can be explained by the roughness and the structure effect. However, some cases showed an opposite trend, with a smaller relative reflectance from the air-dried-sieved samples. These trends support the hypothesis of aggregates heterogeneity and clay occlusion. The sample preparation can increase or decrease the reflectance of the soil surface material, depending, mainly, on roughness, soil structure and particles occlusion by the structure. The Landsat spectral signatures were more similar to those from convoluted laboratory than to the Sentinel ones. The satellite performance on the estimation of the soil weathering indices is comparable to those from convoluted laboratory data, but it is limited by spectral resolution. Sentinel and Landsat achieved ratio of performance to interquartile distance (RPIQ) from 1.53 to 2.38 and from 1.56 to 2.42 on soil weathering indices estimation, respectively. The models for Plagioclase Index of Alteration (PIA) and Si/Ti [Si and (Ti and Al) ratio] indices were considered the best ones (RPIQ from 2.17 to 3.6; and RPIQ from 1.90 to 5.56; respectively) for both laboratory and satellite data; in turn, Si/Al, Si (Al + Fe) and Chemical Index of Alteration (CIA) indices performed poorly/reasonably (RPIQ <1.92). • The satellite performance was similar to those from convoluted laboratory data. • Sample preparation can change the reflectance of the soil surface. • The L samples showed a higher reflectance. • Landsat curves are more similar to those obtained in laboratory. • Spectral curves from Sentinel showed pronounced bands on the visible range. [ABSTRACT FROM AUTHOR]

[Display omitted] • Land-use change impacts on WEOM dynamics were assessed down to 1-m depth. • Agriculture under NT affected more WEOM composition than its concentration. • WEOM molecular weight and aromaticity decreased after LUC to agriculture under NT. • WEOM became N- and polysaccharide-rich after LUC. Most studies assessing land-use change (LUC) and management impacts on the soil organic carbon (SOC) budget mainly focused on total soil organic matter (SOM), neglecting its functional fractions such as water-extractable organic matter (WEOM). This may limit a better understanding of SOC dynamics along the soil profile as WEOM plays a key role in several soil and microbial processes. Here, we aimed to obtain a quantitative and qualitative assessment of WEOM dynamics to 1 m depth in areas after native vegetation conversion to long-term agriculture in Brazil. For this purpose, cropland areas under no-tillage (NT; ∼ 23 years old) and native vegetation (NV; i.e., Cerrado vegetation) were evaluated across a transect of 1000 km within Brazil's new agricultural frontier – the so-called MATOPIBA region. We combined spectroscopic analyses (UV–Vis and DRIFT spectroscopies), C and nitrogen (N) determination in different SOM pools (i.e., bulk SOM, WEOM, and microbial biomass), and microbial measurements [e.g., microbial biomass C (MBC) and N (MBN), and β-glucosidase activity] to better understand changes in WEOM dynamics induced by LUC. We observed that although SOC decreased along the soil profile after NV (4.2–20.7 g kg−1) conversion to NT (3.8–14.2 g kg−1), water-extractable organic C levels (3.6–79.3 mg L−1) were similar between land uses. On the other hand, an increase in water-extractable total N was observed after NV (0.4–4.6 mg L−1) conversion to NT (0.5–19.3 mg L−1). Although MBC and MBN levels decreased for most study sites, β-glucosidase activity increased after LUC indicating intensive microbial processing of SOM. As a result, WEOM had lower aromaticity and molecular weight (i.e., low values of SUVA254 and A2/A3) in NT than NV; and a relative decomposition of aliphatic and polysaccharides over aromatic and amine/amide was observed after LUC through DRIFT spectroscopy. Our study indicates that LUC had stronger impacts on WEOM quality than its quantity, with agricultural areas having more biochemical changes in WEOM than NV. [ABSTRACT FROM AUTHOR]

• Genesis of an apparently homogenous and 4.5 m deep Oxisol profile was investigated. • Proximal sensors data brought to light variations within and between horizons. • Very low fertility even in C horizon was found. • X-ray diffraction correlated well with Vis-NIR data of sand, silt and clay fractions. • Maps of elemental contents determined by pXRF showed high variation in the profile. This study combined proximal sensors and soil analyses for the assessment of a 4.5-m deep Oxisol representative of southeastern Brazil. The soil was derived from gneiss. Soil samples were collected in a 20 × 20 cm grid design across the profile wall, analyzed for texture and chemical properties, and scanned by visible near infrared diffuse reflectance spectroscopy (Vis-NIR DRS), portable X-ray fluorescence spectrometry (pXRF), and X-ray diffraction (XRD). Total elemental contents by pXRF analyses were mapped to provide 2D elemental content maps. The elemental contents revealed alternating patterns in the C horizon attributed to the mafic and felsic bands of the gneiss. The extremely low exchangeable/available nutrient contents in the C horizon are remarkable. Profile maps showed Si accumulation downwards and relative Fe and Al accumulation in the upper horizons indicating desilication and intense weathering. Vis-NIR DRS spectra differentiated horizons and detected mineralogy traits. Vis-NIR DRS data correlated well with XRD data, strengthening its potential for assessing soil mineralogy. Proximal sensors can detect variations of soil properties in apparently homogeneous soil profiles, and improve studies of soil genesis. [ABSTRACT FROM AUTHOR]

• We used three different satellite multi-temporal bare soil images. • Clay prediction models were generated from five different sensors data. • Best prediction models were fitted using hyperspectral and ultraspectral data. • Regional clay prediction models were applied in study area data. • Only the ultraspectral regional model obtained a good prediction result. Proximal and remote sensors are emerging as powerful sources of soil spectral information at an array of temporal and spatial resolutions. This study investigated clay content prediction at three spectral acquisition levels: laboratory, airborne, and spaceborne. Two approaches were tested, the use of prediction models developed with local and regional spectral libraries (52 samples for local scale and 950, 200 e 224 samples for regional scale), termed internal and external models respectively. Local soil samples (52), were collected in a smallholder area, 83 ha, located in southeastern Brazil. Spectral data in the visible (Vis), near-infrared (NIR), and shortwave infrared (SWIR) regions were acquired in the laboratory using FieldSpec 3 sensor, and the clay content was determined by sedimentation technique. Afterward, bare soil images from AISA-FENIX, Planetscope, Sentinel-2 MultiSpectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) were obtained. The clay content determined in the laboratory was related to the soil spectra acquired by each of the sensors and was predicted using the Cubist regression tree algorithm. The results obtained from local spectral libraries showed good predictions using FieldSpec 3 and AISA-FENIX sensors. Landsat-8 OLI and Sentinel-2 MSI provided satisfactory results, while PlanetScope gave poor results. For the prediction using regional spectral libraries, only lab-based FieldSpec 3 sensor provided a fair prediction, while other sensors gave poor results. This study demonstrated that soil sensing is possible at any level taking into account its advantages and limitations. This approach paves the way for acquiring soil spectra for smallholder farms. [ABSTRACT FROM AUTHOR]

Studies on soil degradation are essential for environmental preservation. Since almost 30% of the global soils are degraded, it is important to study and map them for improving their management and use. We aimed to obtain a Soil Degradation Index (SDI) based on multi-temporal satellite images associated with climate variables, land use, terrain and soil attributes. The study was conducted in a 2598 km2 area in São Paulo State, Brazil, where 1562 soil samples (0–20 cm) were collected and analyzed by conventional methods. Spatial predictions of soil attributes such as clay, cation exchange capacity (CEC) and soil organic matter (OM) were performed using machine learning algorithms. A collection of 35-year Landsat images was used to obtain a multi-temporal bare soil image, whose spectral bands were used as soil attributes predictors. The maps of clay, CEC, climate variables, terrain attributes and land use were overlaid and the K-means clustering algorithm was applied to obtain five groups, which represented levels of soil degradation (classes from 1 to 5 representing very low to very high soil degradation). The SDI was validated using the predicted map of OM. The highest degradation level obtained in 15% of the area had the lowest OM content. Levels 1 and 4 of SDI were the most representative covering 24% and 23% of the area, respectively. Therefore, satellite images combined with environmental information significantly contributed to the SDI development, which supports decision-making on land use planning and management. • Areas with higher amount of Clay, OM and CEC showed lower levels of degradation risk. • Higher frequency of bare soil showed higher susceptibility to degradation. • Regions with lower terrain elevation were more susceptible to degradation. • The SDI could be generated from environmental variables and remote sensing techniques. [ABSTRACT FROM AUTHOR]

• Removals of iron forms and organic matter alter absorption features in the mid-IR. • The addition of water have stronger influence in sandy than in clay soil. • High correlation values were observed for Kt, Gb, SiO 2 and Fe 2 O 3 with ASTER bands. • New mid-IR regions have been proposed for satellite remote sensing. The soil mineralogical constitution directly influences its chemical, physical and hydraulic characteristics. Although very important, it is still rarely used for decision-making in agriculture, mainly due to the complexity and cost of standard analyzes. In this sense, the middle infrared spectroscopy (mid-IR, 4000 to 400 cm−1) has great potential to obtain soil mineralogical information quickly and accurately. Nevertheless, some soil constituents can severely influence the spectra and produce misinterpretations. In this research, we aim to detect changes in the mid-IR spectra caused by water, iron forms and organic matter (OM), and to relate soil attributes to laboratory spectra and remote sensing simulated spectral bands. The research area is located in São Paulo State, Brazil, where seventeen soil samples were collected. The reflectance intensities, shapes and absorption features of the mid-IR spectra before and after the removal of OM and iron forms and the addition of water were described. Soil attributes, such as kaolinite, gibbsite, 2:1 minerals among others were correlated with the mid-IR spectra and simulated ASTER spectral bands by Pearson's analysis, to verify its potential on mineralogical evaluation. The description of mid-IR revealed that the removal of the OM from the soil samples decreased the reflectance intensities between 4000 and 2000 cm−1. Iron forms mainly influence the 3250 – 1200 cm−1 spectral range and mask the spectral features of other minerals as well. The addition of water masked several absorption features and decreased the reflectance intensities from 3700 to 2700 cm−1. High correlation coefficients were obtained between soil attributes and ASTER simulated spectral bands, which allowed the selection of potential spectral regions for future satellite sensors: 2760 – 2500 cm−1 (3600 – 4000 nm), 2150 – 1875 cm−1 (4600 – 5300 nm), and 840 – 740 cm−1 (11900 – 3500 nm). [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]