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Assessments of soils by reflectance spectroscopy have potential to facilitate and optimize soil survey, classification, and mapping of large areas. Visible and near-infrared (vis–NIR) spectra have been moderately used in pedological studies regarding characterization of soil samples and profiles, weathering and pedogenetic alterations along toposequences and landscapes, and soil classification. Therefore, there still is a lack of information about vis–NIR spectral pedology. For soil samples, our aims were to characterize the effects of physical, chemical, and mineralogical properties on vis–NIR soil spectral behavior, and to evaluate its potentiality in clustering 1259 soil samples according to weathering levels. For soil profiles, our aims were to evaluate the influence of pedogenesis in spectral behavior of some typical Brazilian soil classes, and to discriminate them by integrating proximal sensing and distance metrics. Continuum removed spectral data were transformed by Principal Component Analysis (PCA), and Fuzzy K-means algorithm and taxonomic distance were applied to cluster soil samples and profiles, respectively. Differences in reflectance intensity and absorption features caused by weathering intensification enabled to distinguish soil samples regarding similarity of particle size distribution, mineralogy, and some chemical properties. Soil formation processes, in particular, lessivage, dessilication, and ferralization similarly affected the spectral behavior soil profiles considering changes in depth caused by re-distribution of soil properties by horizons. A coherent discrimination of soil profiles was possible by combining spectral data and pedological distance metrics where 30% of the soil classes could be individually clustered as follow: Ferralsol, Nitisol, Acrisol, Lixisol, Arenosol, Gleysol, Cambisol, and Leptsol. vis–NIR spectral data enabled a coherent grouping of Ferralsol profiles with different contents of clay and iron oxides. vis–NIR reflectance spectroscopy is presented here as a useful and reliable tool for direct applications in pedological assessments, in particular, soil survey and classification. [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 minerals are usually quantified by the conventional laboratory soil analyses. However, developments in interpretations and analyses of the visible, near-infrared, and short-infrared (Vis-NIR-SWIR) diffuse reflectance have allowed the quantification of some soil minerals. In this study, we aimed to implement a novel framework using Vis-NIR-SWIR spectroscopy to quantify the main soil minerals. We also assessed the application of this framework to create new environmental variables for digital soil mapping (DSM). The soil spectra database comprised 2701 samples from 1008 sites in the spectral range of 350–2500 nm at 0–20, 40–60, and 80–100 cm depths. The specific bands in the Vis-NIR-SWIR spectra that identify the presence of soil mineral were selected based on the literature with the United States Geological Survey Spectral Library Version 7 and in the strong maxima and minima of the second-derivative curves of the soil mineral standards using the Savitzky-Golay method. We proposed an estimation and conversion of the measurement unit of soil minerals in amplitude to g kg−1 using a small dataset of mineral content quantified via X-Ray Powder Diffraction. We selected randomly 85 samples out of 2701 available at 0–20 cm depth and sent to conventional laboratory analyses to calibrate the final estimation, using the kaolinite soil mineral as an example. Therefore, a constant factor was determined to estimate mineral content in soils displaying RMSE, R2 adj , the Lin's concordance coefficient (CCC), Bias, and RPIQ values of 7612 g kg−1, 0.28, 0.50, 13.09 g kg−1, and 0.56, respectively. This evaluation was assessed by splitting 85 samples into 80% to determine and 20% to validate the constant factor. For the DSM procedure, we used 2701 samples split into 80% and 20% for calibration and validation, respectively, of the models for each of the nine minerals. This study showed that the proposed framework using Vis-NIR-SWIR spectroscopy to estimate soil minerals is promising due to higher CCC and lower RMSE values obtained. Furthermore, the spectral amplitude for each mineral provides important information to be used as environmental variables for the prediction of soil attributes, soil types, and soil properties. • Soil mineral content estimate was suggested based on XRD and spectroscopy. • Diffuse reflectance spectroscopy quantified the soil minerals. • Integrating remote and proximal sensing helped to characterise soil mineralogy. [ABSTRACT FROM AUTHOR]

Soil organic carbon (C) is an important indicator of agricultural and environmental quality. It improves soil fertility and helps to mitigate greenhouse gas emissions. Soil spectroscopy with either vis–NIR (350–2500 nm) or mid-IR (4000–400 cm −1 ) spectra have been used successfully to predict organic C concentrations in soil. However, research to improve predictions of soil organic C by simply combining vis–NIR and mid-IR spectra to model them together has been unsuccessful. Here we use the Outer Product Analysis (OPA) to fuse vis–NIR and mid-IR spectra by bringing them into a common spectral domain. Using the fused data, we derived models to predict soil organic C and compared its predictions to those derived with vis–NIR and mid-IR models separately. We analyzed 1259 tropical soil samples from surface and subsurface layers across agricultural areas in Central Brazil. Soil organic C contents were determined by a modified Walkley-Black method, and vis–NIR and mid-IR reflectance spectra were obtained with a FieldSpec Pro and a Nicolet 6700 Fourier Transformed Infrared (FT-IR), respectively. Reflectances were log-transformed into absorbances. The mean content of soil organic C was 9.14 g kg −1 (SD = 5.64 g kg −1 ). The OPA algorithm was used to emphasize co-evolutions of each spectral domain into the same one by multiplying the absorbances from both sets of spectra to produce a matrix with all possible products between them. Support Vector Machine with linear kernel function was used for the spectroscopic modeling. Predictions of soil organic C using vis–NIR, mid-IR, and fused spectra were statistically compared by the Tukey's test using the coefficient of determination (R 2 ), root mean squared error (RMSE), and ratio of performance to interquartile distance (RPIQ). Absorbances in vis–NIR and mid-IR were emphasized in the common spectral domain presenting stronger correlations with soil organic C than individual ranges. Soil organic C predictions with the OPA fused spectra were significantly better (R 2  = 0.81, RMSE = 2.42 g kg −1 , and RPIQ = 2.87) than those with vis–NIR (R 2  = 0.69, RMSE = 3.38 g kg −1 , and RPIQ = 2.08) or mid-IR spectra (R 2  = 0.77, RMSE = 2.90 g kg −1 , and RPIQ = 2.43). Fusing vis–NIR and mid-IR spectra by OPA improves predictions of soil organic C. [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]

• The combination of soil depths led to a more accurate prediction of soil texture. • The MIR region has developed more robust models for predicting clay and sand content. • MIR region has more highlighted and intense peaks related to soils. • The most important bands for texture prediction were related to mineralogy. The demand for quality and low-cost soil information is growing due to the demands of land use planning and precision agriculture. Soil texture is one of the key soil properties, as it determines other vital soil characteristics such as soil structure, water and thermal regime, diversity of living organisms, plant growth, as well as the soil quality in general. It is usually not constant over an area, varying in space and with soil depth. Routine soil texture analysis is, however, time consuming and expensive. Because of this, the success of proximal soil sensing techniques in estimate soil properties using the VIS-NIR-SWIR and MIR regions is increasing. Advantages of soil spectroscopy include time efficiency, economic convenience, non-destructive application and freeing of chemical agents involved. Therefore, the objectives of this study were: (a) to explore the potential of clay, sand and silt prediction using reflectance spectroscopy; (b) assess the performance of predictive models in different spectral regions, i.e. VIS-NIR-SWIR and MIR; (c) assess the effect of different soil depths on predictive models; and finally (d) explain the differences in prediction accuracy in the means of the input data structure. Soil samples were collected at three depths (0–20, 20–40 and 40–60 cm) at 70 sampling sites over a study area located in the State of Rio Grande do Sul (Brazil). The content of soil texture was determined by Pipette method, and soil spectra were obtained with FieldSpec Pro (VIS-NIR-SWIR) and by Alpha Sample Compartment RT (MIR). Cubist regression algorithm was applied to train predictive models in three separate modeling modes differing in spectral region: (i) VIS-NIR-SWIR, (ii) MIR and (iii) VIS-NIR-SWIR plus MIR. The results showed that the combination of all three soil depths led to a more accurate prediction of soil texture compared to subdivided soil depths. This was explained by variability of the data, which was larger for the total dataset than for the depth-specific data. Consequently, we suggested that no precise comparison between different studies can be made without a proper description of the input data. For all-depths models, the MIR calibration obtained the best accuracy, which was explained due to more information comprised in the MIR region against the VIS-NIR-SWIR. The bands that were more important in predicting soil texture in MIR are related to mineralogy, specifically to kaolinite. This study demonstrated that the MIR spectroscopy technique is capable to complement the standard soil particle size analysis, specially where a large number of soil samples need to be treated in a short period of time. [ABSTRACT FROM AUTHOR]

• Large differences in relative absorbances were found across twenty instruments. • All instruments delivered good predictions when calibrated internally. • Predictive performance varied when a single existing library was used for calibration. • Standard Normal Variate helps reduce dissimilarities across instruments. • However, spectral standardization may be necessary in contrasting situations. Diffuse reflectance spectroscopy has been extensively employed to deliver timely and cost-effective predictions of a number of soil properties. However, although several soil spectral laboratories have been established worldwide, the distinct characteristics of instruments and operations still hamper further integration and interoperability across mid-infrared (MIR) soil spectral libraries. In this study, we conducted a large-scale ring trial experiment to understand the lab-to-lab variability of multiple MIR instruments. By developing a systematic evaluation of different mathematical treatments with modeling algorithms, including regular preprocessing and spectral standardization, we quantified and evaluated instruments' dissimilarity and how this impacts internal and shared model performance. We found that all instruments delivered good predictions when calibrated internally using the same instruments' characteristics and standard operating procedures by solely relying on regular spectral preprocessing that accounts for light scattering and multiplicative/additive effects, e.g., using standard normal variate (SNV). When performing model transfer from a large public library (the USDA NSSC-KSSL MIR library) to secondary instruments, good performance was also achieved by regular preprocessing (e.g., SNV) if both instruments shared the same manufacturer. However, significant differences between the KSSL MIR library and contrasting ring trial instruments responses were evident and confirmed by a semi-unsupervised spectral clustering. For heavily contrasting setups, spectral standardization was necessary before transferring prediction models. Non-linear model types like Cubist and memory-based learning delivered more precise estimates because they seemed to be less sensitive to spectral variations than global partial least square regression. In summary, the results from this study can assist new laboratories in building spectroscopy capacity utilizing existing MIR spectral libraries and support the recent global efforts to make soil spectroscopy universally accessible with centralized or shared operating procedures. [ABSTRACT FROM AUTHOR]

Oxisols are generally deep, weathered-leached soils that present morphologically homogeneous profiles. Despite their apparent homogeneity, different parent materials and pedogenetic processes grant these soils diverse chemical and mineralogical attributes. Proximal sensors might help to detect the variability of soil attributes along such old and deep profiles. The objective of this study was to investigate the physical, chemical and mineralogical variability of Oxisol profiles originated from different parent materials with aid of visible and near-infrared diffuse reflectance spectroscopy (Vis-NIR DRS) and portable X-ray fluorescence (pXRF) spectrometry, in order to evaluate if these proximal sensors can complement the analysis of these deep and weathered tropical soils. Two Oxisol profiles were selected: Anionic Acrudox (AA) and Typic Hapludox (TH). One hundred samples were collected in each profile down to 2 m of depth, following a grid of 10 × 10 cm, for pXRF, Vis-NIR and XRD analyses. Vis-NIR DRS spectra provided key mineralogical information in agreement with X-ray diffraction results, showing differential occurrence of kaolinite, hematite, maghemite and gibbsite/kaolinite ratio in these profiles. PXRF detected high chemical variability across the profiles and allowed to map the spatial chemical distribution of both soils. Proximal sensors provided an inexpensive and efficient way to help complement the identification of meaningful variability across Oxisol profiles that remained even after the long formation process of these very weathered tropical soils. • Very weathered and apparently homogeneous Oxisols were detailed investigated. • Anionic Acrudox (AA) and Typic Hapludox (TH) were analyzed via proximal sensing. • Proximal sensors detected detailed variations across horizons of the studied profiles. • AA showed higher contents of metals, while TH presented more Si and Al. • TH presented greater variation of properties between horizons than AA. [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]