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[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]

The weathering degree (WD) of soils is usually based on the presence of weatherable primary minerals and the content of chemical elements along the soil profile, which traditionally demand expensive, time-consuming and environmentally unfriendly laboratory analyses (e.g. wet chemistry). To address this issue, proximal sensors often operating in only a single spectral range have been used in soil WD assessments. In this sense, we analyzed in detail the performance of proximal sensors operating at several spectral ranges to assess the soil mineralogy and WD. We selected three soil profiles developed from sedimentary rocks and analyzed samples from parent rock to topsoil. Samples from each horizon of the soil profiles and from the parent rock were collected and submitted to chemical, physical and mineralogical laboratory analyses. These samples were also analyzed using several sensors operating within different spectral ranges, i.e., gamma-ray, X-ray fluorescence (XRF), visible (VIS), infrared (IR) (near infrared, NIR, shortwave infrared, SWIR, and middle infrared, Mid-IR), and the AISA Fenix hyperspectral imaging systems (VIS-NIR-SWIR). Several WD indices were calculated using laboratory (reference) and multi-range sensor data for comparison. The spectral differences in the VIS-IR range were evaluated according to the soil mineralogy and WD. Gamma-ray and XRF sensors presented high correlation with the main soil elements in oxide minerals used in the WD quantification (R ranging from 0.70 to 0.99). We detected differences associated with the WD of each horizon of all profiles using data from VIS to Mid-IR ranges. The application of linear models to AISA images enabled the mapping and quantification of main elements along one soil profile (R2 ranging from 0.48 to 0.8) using a pixel-to-pixel approach, which facilitated the interpretation of the WD. Due to the specificity of each spectral region, the use of several sensors at different wavelengths provided a holistic overview of mineralogy and WD of the analyzed soil profiles, using a quick and non-destructive approach. This technique produces overlapping and/or complementary information of WD and mineralogy from all spectral ranges and can significantly assist traditional laboratory methods. • Gamma spectroscopy elements were efficient to evaluate the soil weathering degree. • The weathering indices detected by the XRF sensor were similar to those ICP-AES. • Oxides quantified by the AISA sensor models were analyzed over the entire profile. • Spectral pattern changed according to mineralogical weathering. • Horizons weathering were better distinguished by XRF-Vis-NIR-SWIR-Mid-IR group. [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]

• Soil mineralogy plays a fundamental role in pXRF patterns. • Selective dissolution of SOM and iron forms can be detected by pXRF. • Low contents of SOM did not interfere in pXRF analyses. • Soil moisture must be considered for pXRF analyses in field. • Soil fertility and soil particle size distribution are related to pXRF data. Portable X-ray fluorescence (pXRF) has great potential for numerous applications in soil science. However, the basic knowledge about the effects of soil properties on pXRF spectra are still poorly studied, which may lead users to biased interpretations of mathematical models. The present study aimed to evaluate the outcomes of moisture, soil organic matter content (SOM), and iron forms on pXRF data. The work was conducted with seventeen soil samples from the central region of São Paulo state (Brazil). Three selective dissolution treatments were applied to remove: (i) soil organic matter (−SOM), ii) SOM and poorly crystalline iron forms (−o), iii) SOM and poorly crystalline plus well crystalline iron forms (−d). One additional treatment iv) including water addition (+W) was also carried out. The effects of treatments were evaluated for sandy and clayey samples. Soil particle size distribution and elemental content affected the bremsstrahlung and characteristic peaks counts. In +W, there was a generalized decrease in counts mainly for the light elements (magnesium, aluminum and silicon). Regarding the selective dissolution procedures, alterations were verified, reflecting mainly the removal power of reagents. Generally, the most pronounced alterations occurred for −d and moderate alterations for −SOM and −o. The pXRF data showed high correlation with particle size distribution and mineralogy attributes. The kaolinite, gibbsite, Fe 2 O 3 , Al 2 O 3 , SiO 2 , TiO 2 and MnO contents were quantified with satisfactory accuracy (0.61 < R2 < 0.97). The pXRF was able to detect changes caused by the selective dissolution treatments and soil particle size distribution. Sources of uncertainty, mainly soil moisture, must be considered. The understanding of the fundamentals of energy interaction with the sample matrix in the X-ray range is the starting point for characterizing the soil through pXRF. [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]

• The higher the clay content was, the higher was the amplitude of the mineral bands. • VIS–NIR–SWIR spectroscopy successfully identified the mineral composition of soils. • The separation of samples by textural class facilitated mineralogical identification. The physical and chemical attributes of soils are strongly influenced by the nature of the minerals they contain and their concentration. Thus, soil texture is directly dependent on the content in clayey minerals, which influences a number of characteristics such as water dynamics. Although the mineralogical composition of soil is usually determined by X-ray diffraction spectroscopy, this technique is expensive and time-consuming, and uses toxic materials, all of which makes it impractical for obtaining large data sets. Also, available methods for acquiring, interpreting and examining visible–near infrared–shortwave infrared (VIS–NIR–SWIR) spectra are largely ineffective with tropical soils. The aim of this work was to ascertain whether VIS–NIR–SWIR reflectance spectroscopy (350–2500 nm) is useful for identifying minerals in subtropical soils as classified by textural class. For this purpose, soil samples were collected at 66 points at three different soil depths (0–20, 20–40 and 40–60 cm) over a study area located in the State of Rio Grande do Sul (Brazil). The soil texture were determined with the pipette method, and soil spectra were recorded on a FieldSpec Pro VIS-NIR-SWIR laboratory spectrophotometer. Soil minerals were identified, and their proportions determined, from the second-derivative of the Kubelka–Munk (KM) function for the spectra. Five main minerals were thus identified from their spectral signatures, namely: hematite, goethite, kaolinite, chlorite and illite. Identification of the minerals was facilitated by classifying the samples according to texture. The higher the clay content was, the higher was the spectral amplitude of the minerals identified. Those textural classes with the highest clay contents exhibited the greatest proportions of iron oxides and of clay minerals such as kaolinite. These relationships allowed more comprehensive analysis of the soils and expeditious characterization of the study area in terms of texture and mineralogy with a view to facilitating decision-making agricultural support policies. [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]

• The mapping of minerals for large areas by conventional techniques is costly. • The diffuse reflectance spectroscopy is an efficient indirect technique. • Spectroscopy has been widely used for quantification of soil minerals. • The technique allowed the quantification of the minerals kaolinite and gibbsite. • Maps of the minerals provide information to define management zones. The use of diffuse reflectance spectroscopy (DRS) has gained prominence in the quantification of soil attributes due to its ease and practicality of obtaining data. This study aimed to evaluate the potential of different methodologies applied to spectral curves given by DRS to estimate kaolinite (Kt) and gibbsite (Gb), and their spatial variability characterization for the Western Plateau of São Paulo. The Western Plateau of São Paulo has 13 million hectares, 2 million of them covered by basalt and 11 million by sandstones. A total of 600 samples were collected at a depth of 0.0–0.20 m. Calibration curves were constructed with pure minerals for x-ray diffraction (XRD) and DRS techniques. The Kt/(Kt + Gb) ratio and the percentages of Kt and Gb were determined by XRD and using the following three methodologies applied to spectral curves: continuum removal technique (CR), direct ratio of the valley (DRV), and multivariate analysis by partial least squares regression (PLSR). The CR procedure had means similar to those observed by XRD, i.e., 0.90 and 0.92, respectively, while DRV overestimated the ratio, with a mean of 1.32. DRS allowed the estimation of the Kt/(Kt + Gb) ratio for the different geological and landscape compartments of the Western Plateau of São Paulo for the CR and DRV procedures. CR procedure allowed constructing models to be more efficient compared to those obtained by DRV and PLSR. The use of geostatistics to interpolate the data of the ratio Kt/(Kt + Gb) by DRS provided important information to define specific management zones accurately and more economically. [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]