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Novel technologies help to monitor the environmental impact of human activities, but tests involving datasets from several countries, encompassing a large variability of soil properties, are still scarce. This study utilized proximal sensors to predict soil organic carbon (OC) and soil texture of samples from Brazil, France, India, Mozambique, and United States. A total of 1749 samples were analyzed by portable X‐ray fluorescence (pXRF) spectrometry and visible near‐infrared diffuse reflectance spectroscopy. Sand (R2 = 0.89), silt (0.87), and clay (0.84) predictions were very accurate, despite contrasting climates, soil parent materials, and weathering degrees. Soil OC predictions were similarly successful (0.74) using samples from five countries. pXRF was the optimal sensor for soil texture predictions. The addition of international data may improve local models. Proximal soil sensing can be successfully used with a multinational soil database offering a clean, rapid, and accurate alternative to estimate soil texture and OC with international datasets. Core Ideas: Soil properties can be predicted via proximal sensors using multinational datasets.This study encompassed soil samples from Brazil, France, Mozambique, India, and United States.Soil organic carbon and texture were accurately predicted via proximal sensors.The broad application of proximal sensors to aid soil characterization is encouraged. [ABSTRACT FROM AUTHOR]

Quartz is one of the most common minerals in soils, mostly present in sand and silt fractions. Although quartz is basically formed of SiO2, other elements can be easily detected and assessed nowadays using a portable X-ray fluorescence (pXRF) spectrometer. Our study aims to evaluate the chemical composition of different quartz varieties, identifying their main elements, and relating them to soil attributes. Six quartz varieties (hyaline, amethyst, milky, rose, smoky, and ferriferous) were analyzed via pXRF and 13 oxides/trace elements were identified and used for quartz discrimination (Al2O3, CaO, P2O5, SiO2, Cl, Cr, Fe, K2O, Mn, Rb, S, Ti, and V). Hyaline quartz was characterized by the highest SiO2 and the lowest contents of other elements. Al2O3 was the second-highest compound present in all varieties of quartz, reaching 21,547 mg kg−1 in the smoky variety. S, P2O5, Cl, SiO2, and K2O were the main components determined by Random Forest algorithm for discriminating quartz varieties. Some elements detected may serve as a reserve of nutrients to plants to be released in soils along weathering, depending on quartz particle size, soil texture, leaching, and associated attributes. pXRF enhanced the information on chemical characterization of quartz varieties, without the generation of chemical pollutants. [ABSTRACT FROM AUTHOR]

Despite the increasing adoption of proximal sensors worldwide, rare works have coupled proximal with remotely sensed data to spatially predict soil properties. This study evaluated the contribution of proximal and remotely sensed data to predict soil texture and available contents of micronutrients using portable X-ray fluorescence (pXRF) spectrometry, magnetic susceptibility (MS), and terrain attributes (TA) via random forest algorithm. Samples were collected in Brazil from soils with high, moderate, and low weathering degrees (Oxisols, Ultisols, Inceptisols, respectively), and analyzed by pXRF and MS and for texture and available micronutrients. Seventeen TA were generated from a digital elevation model of 12.5 m spatial resolution. Predictions were made via: (i) TA; (ii) TA + pXRF; (iii) TA + MS; (iv) TA + MS + pXRF; (v) MS + pXRF; and (vi) pXRF; and validated via root mean square error (RMSE) and coefficient of determination (R2). The best predictions were achieved by: pXRF dataset alone for available Cu (R² = 0.80) and clay (R2 = 0.67) content; MS + pXRF dataset for available Fe (R2 = 0.68) and sand (R2 = 0.69) content; TA + pXRF + MS dataset for available Mn (R2 = 0.87) content. PXRF data were key to the best predictions. Soil property maps created from these predictions supported the adoption of sustainable soil management practices. [ABSTRACT FROM AUTHOR]

Precision agriculture provides detailed information on the spatial variability of soil properties, including nutrient content, allowing for local-specific decision making. Recently, proximal sensors have been used to accurately predict soil properties, contributing to reduce costs of conventional wet-chemistry analyses for soil characterization. However, further investigations on this approach in tropical soils are needed. This work aimed to use portable X-ray fluorescence (pXRF) spectrometry data for prediction of exchangeable Ca2+ and available K+ and P contents in soils of a highly heterogeneous tropical area and evaluating its practical applications. 90 samples from soil A horizon were collected in a regular grid design, and analyzed through pXRF and for nutrient contents. Such data were split into modeling (63 samples) and validation (27 samples) datasets. Linear regression (LR), polynomial regression (PR), power regression (PwR) and stepwise multiple linear regression (SMLR) were tested for predictions. The models were used to spatially represent nutrient contents across the area and to compare the practical effects of varying regression models. PXRF elemental data provided reliable predictions of exchangeable Ca2+ and available P via SMLR and PwR, respectively, reaching root mean square errors (RMSE) of 5.66 cmolc dm−3 for Ca2+ and 9.13 mg dm−3 for P. Available K+ predictions were not successful. Different models yielded contrasting maps showing the classes of soil fertility across the area, drawing attention to the importance of testing multiple prediction models and using the best one for precision agriculture. Fusion of data from different proximal sensors may enhance available K+ predictions. [ABSTRACT FROM AUTHOR]

• A Paleosol was investigated in detail using digital morphometrics. • pXRF revealed different parent materials without apparent morphological differences. • Ancient redoximorphism contrasts with current oxidation processes in the profile. • A topographical evolution of the Paleosol region is proposed. Although soil formation results from the interactions of five classical factors and various pedogenic processes, such interactions can drastically change over time, which may be recorded in the so-called Paleosols. These soils may be formed under contrasting environmental conditions. Given such soils are rare in Brazil, this pilot study aimed to detail the morphological, physical, chemical (including elemental content and magnetic susceptibility (MS)), and mineralogical properties of a Paleosol with contrasting paleo- and current-drainage conditions, derived from sandstone having pellitic (fine) sediments intermixed, with support via digital morphometrics. Samples were collected in a regular grid of 15x15 cm along the soil profile down to 1.8 m depth. The soil samples were analyzed via portable X-ray fluorescence (pXRF) spectrometry and for assessment of magnetic susceptibility (MS), texture, fertility, and organic matter content. The mineralogy of the clay, silt, and sand fractions were determined via X-ray diffraction analyses. The pXRF and MS results were spatialized to the Paleosol profile. The first 108 cm of the soil profile are well-drained having reddish colors, contrasting with the underlying poorly-drained unity having greyish colors. The MS values were higher in the first 108 cm of the soil profile, reaching the lowest values in the 2Cg horizon at 142–180 cm depth. Quartz was the main mineral in sand and silt fractions, while kaolinite largely dominates the clay fraction in association with hematite. In the 2Cg horizon, the absence of hematite is related with the paleoredoximorphic conditions there. The Ti/Zr ratio indicated differences in parent materials of the reddish and greyish units of the Paleosol. The midslope position of the Paleosol makes colluvial deposition unlikely. The combined use of pXRF, MS, morphological, physical, chemical, and mineralogical analyses provided valuable details on the variation of the contrasting formation processes of the Paleosol studied. [ABSTRACT FROM AUTHOR]

Farms use large quantities of fertilizers from many sources, making quality control a challenging task, as the traditional wet-chemistry analyses are expensive, time consuming and not environmentally-friendly. As an alternative, this work proposes the use of portable X-ray fluorescence (pXRF) spectrometry and machine learning algorithms for rapid and low-cost estimation of macro and micronutrient contents in mineral and organic fertilizers. Four machine learning algorithms were tested. Whole (i.e., as delivered by the manufacturer) (CP) and ground (AQ) samples (429 in total) were analyzed to test the effect of fertilizer granulometry in prediction performance. Model validation indicated highly accurate predictions of macro (N: R2 = 0.92; P: 0.97; K: 0.99; Ca: 0.94, Mg: 0.98; S: 0.96) and micronutrients (B: 0.99; Cu: 0.99; Fe: 0.98; Mn: 0.91; Zn: 0.94) for both organic and mineral fertilizers. RPD values ranged from 2.31 to 9.23 for AQ samples, and Random Forest and Cubist Regression were the algorithms with the best performances. Even samples analyzed as they were received from the manufacturer (i.e., no grinding) provided accurate predictions, which accelerate the confirmation of nutrient contents contained in fertilizers. Results demonstrated the potential of pXRF data coupled with machine learning algorithms to assess nutrient composition in both mineral and organic fertilizers with high accuracy, allowing for clean, fast and accurate quality control. Sensor-driven quality assessment of fertilizers improves soil and plant health, crop management efficiency and food security with a reduced environmental footprint. [Display omitted] • Quality control of organic and mineral fertilizers can be accurately done via pXRF. • Sample preprocessing did not affect results significantly. • Random Forest and Cubist Regression achieved the best prediction performances. • Nutrient content assessment in fertilizers is possible via a green method. [ABSTRACT FROM AUTHOR]

Although parent material (PM) is one of the five soil formation factors providing key information on soil variability, the complexity of PM distributions and the difficulty of reaching PM in deep soils prevent its detailed assessment. Proximal sensors such as portable X-ray fluorescence (pXRF) spectrometer and magnetic susceptibility (MS) may be helpful in predicting soil PM in a more practical and accessible way. This pilot study aimed to create spatial PM predictive models for three distinct PMs (charnockite, mudstone, and alluvial sediments) of an experimental farm (Brazil) through random forest (RF) algorithm based on soil samples analyzed via pXRF and MS. Soils were sampled in A and B horizons following a regular-grid design covering the whole study area. The RF algorithm was calibrated to predict PMs using samples from the B horizon of soils with known PM. The prediction model was applied to the area for mapping PM across the whole farm. For validation, PM was identified at 15 different sites and compared with the predicted PM shown on the maps via overall accuracy, Kappa coefficient, producer's and user's accuracies. Al, Fe, Si, Ti, and MS proximal sensor data discriminated well among soils derived from charnockite, mudstone, and alluvial sediments. The map built based on B horizon data showed greater accuracy (overall accuracy = 0.93, Kappa coefficient = 0.85, user's accuracy = 0.92, and producer's accuracy = 0.97) than the map built from the model using A horizon samples (0.73, 0.48, 0.48, and 0.58). These results could represent alternative methods for reducing costs and accelerating the assessment of soil PM spatial variability, supporting soil mapping, and optimized agronomic and environmental decision-making. • Proximal sensors were used for soil parent material (PM) prediction. • pXRF and MS differentiated soil types and their respective parent materials. • Different parent materials were predicted using random forest algorithm. • pXRF and MS combined with RF were successful for PM prediction. [ABSTRACT FROM AUTHOR]

Portable X-ray fluorescence (pXRF) spectrometers allow rapid detection of soil elements and owing to several advantages have been increasingly used in many environmental samples, soils included. This study aimed to: i) evaluate the potential of pXRF for soil profile characterization; ii) use pXRF for investigating soil weathering through elemental contents quantification in sand, silt, and clay fractions; and iii) spatially evaluate elemental composition in the soil profile to assess variation within and between horizons. An Inceptisol from the Brazilian Cerrado was morphologically described and composite samples from the A, B, C, and Cr horizons were collected for physical/chemical characterization. Additional samples were collected from a regular grid (12.5 × 12.5 cm, n  = 24) in the soil profile for further analyses with pXRF in the air-dried fine earth (<2 mm), as well as in the sand, silt, and clay fractions, which were also mineralogically characterized by X-ray diffractometry (XRD). Contents of Ca, K, Al, Si, Zn, and Cu decreased with depth. Calcium, K, Ba, Cr, and Rb contents were less abundant in the sand fraction of the Cr horizon, when compared to the A horizon, whereas Si increased from the Cr to A horizon, probably due to the relative residual accumulation of quartz in this fraction in the A horizon, which was supported by XRD analyses. There was a considerable variation of elemental concentrations with depth and within horizons. Elemental composition obtained by pXRF can help in soil genesis studies indicating weathering pathways and is a powerful tool for complementing XRD data. Future studies can evaluate correlations between soil properties and contents of elements that are now easily obtained by pXRF, such as V, Sr, Rb, and Ba. [ABSTRACT FROM AUTHOR]

Ca and Mg are the most important chemical elements in lime. Properly measuring Ca and Mg contents is essential to assess the quality of lime products. Quality control guarantees the adequate use of lime in industrial processes, in soils, and helps avoiding adulteration. Proximal sensors can aid in this process by determining Ca and Mg contents easily, rapidly and without producing chemical waste. The objective of this study was to evaluate the use an environmentally-friendly method of analyzing the quality of lime. We studied 1) the use of portable X-ray fluorescence (pXRF) to predict concentrations of Ca and Mg in lime, 2) tested if NixPro™ sensor can improve prediction accuracy and 3) tested if sample preparation methods (grinding) affect analyses. 74 samples of lime were analyzed by two different laboratories (lab. 1 = 38, lab. 2 = 36). All samples submitted to pXRF and NixPro™ analyses. Sensor analyses were done in whole (CP) and ground (AQ) samples to test the effect of sample preparation in prediction performance. High correlation was found between Ca and Mg contents measured via pXRF and laboratory analyses. Mg-CP presented the highest correlation coefficient (r = 0.81); Mg-AQ, the lowest (0.57). Predictions presented good performance (R2 > 0.68); Mg had the best results (0.86). Separating models per laboratory showed that some datasets are harder to model, probably due to variability in the source material (limestone). The addition of NixPro™ data contributed to improve prediction accuracy, although slightly. Predictions using CP samples presented the best results, especially for Mg, indicating that grinding is not necessary. This pioneer study demonstrated that fused proximal sensors can be used to rapidly and easily determine contents of Ca and Mg in soil amendments without producing chemical waste. [Display omitted] • Highly accurate predictions of Ca and Mg contents in lime were possible. • Quality control of lime can be done without producing chemical waste. • Grinding samples did not affect results significantly. • PXRF alone was capable of achieving the best prediction performances. • NixPro™ did not contribute significantly to prediction accuracy. [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 variability of mineralogical and chemical characteristics of soils reflects their parent material, the different weathering degrees and soil formation processes. Through the association of portable X-ray fluorescence (pXRF) spectrometry and X-ray diffraction (XRD), this work aimed to evaluate the concentration of major and minor elements and their relationship with the mineralogy of the sand, silt and clay fractions in a catena of soils derived from quartzite with different weathering degrees in Southern Minas Gerais State, Brazil. Five soil profiles were described and sampled: Lithic Ustorthent, Typic Ustorthent, Typic Dystrustept, Xanthic Haplustox, and Typic Ustifolist. Sand, silt and clay fractions were separated and analyzed with pXRF and XRD for characterization of elemental contents and mineralogy, respectively. For all soils, Si content was higher in the sand fraction, corresponding to quartz, confirmed by XRD, a predominant mineral in quartzite and in the sand fraction of tropical soils. Al content was always higher in the clay fraction, reflecting the presence of kaolinite, gibbsite and mica. Quartz, mica and kaolinite were the dominant minerals in the silt fraction. Si in the sand fraction showed a strong and negative correlation with K and Ca, while Al in the sand fraction showed high and positive correlations with Ca and Fe. Fe, K, Ca and Al were released from the most weathered soils, while Si remained almost constant due to its large presence in quartz. The pXRF in tandem with XRD provided in-depth clarifications on the chemical and mineralogical differentiation of the sand, silt and clay fractions of soils along weathering. • A sequence of quartzite-derived soils was investigated via pXRF and DRX. • Elemental contents and mineralogy of the textural fractions were correlated. • Detailed chemical and mineralogical differences along weathering were detected. • Investigations of processes driving soil variation were improved with pXRF and DRX. [ABSTRACT FROM AUTHOR]

• Soil fertility properties were more important for predicting β-glucosidase. • PXRF variables were more important to predict acid phosphatase and urease. • Spatial analysis allows better distinction of soil enzymes activity between seasons. Soil enzymes act in biogeochemical cycles of elements and are indicators of soil quality since they rapidly reflect changes of the environmental conditions. Moreover, enzymes are related to soil physicochemical properties, but their spatial distribution has been rarely evaluated. The hypothesis of this work is that soil properties related to fertility and texture (F), total contents of chemical elements obtained by portable X-ray fluorescence (pX) spectrometry and terrain attributes (T) can be used as predictor variables to soil enzyme activity, along with phytophysiognomy and season information. The objective of this work was to predict soil enzymes activity and assess its spatial variability in the most common phytophysiognomies of the Quadrilátero Ferrífero mineral province, in Brazil. Soil samples were collected in four phytophysiognomies during both dry and humid seasons. Activity of β-glucosidase, acid phosphatase, alkaline phosphatase, urease, and hydrolysis of fluorescein diacetate (FDA) was determined. Phytophysiognomy, season, F, T, and pX, were used together or separately to predict the enzymes activity through conditional random forest algorithm and the accuracy was assessed via leave-one-out cross validation. The generated models were accurate, with coefficient of determination (R2) varying from 0.63 (FDA by pX) to 0.82 (β-glucosidase by F). F variables were more important for the predictions, while pX variables were more important for predicting acid phosphatase and urease. The accurate models using T variables allowed the generation of maps showing the enzymes variability along the phytophysiognomies. This approach can accelerate the determination of soil enzymes activity across the landscape. [ABSTRACT FROM AUTHOR]

• Soil texture of 1565 samples was determined and analyzed with pXRF in Brazil. • pXRF data was successfully predicted soil texture through SVM and RF algorithms. • Predictions were accurate independently of soil class, parent material, and horizon. • Fe and Al positively correlated to clay content due to Fe- and Al-oxides in soils. • Si positively correlated to sand, due to dominance of quartz in this fraction. Soil texture is an important feature in soil characterization, although its laboratory determination is costly and time-consuming. As an alternative, this study aimed at predicting soil texture from portable X-ray fluorescence (pXRF) spectrometry data in Brazilian soils. 1565 soil samples (503 from superficial and 1062 from subsuperficial horizons) were analyzed in the laboratory for soil texture and scanned with the pXRF. Elemental contents determined by pXRF were correlated with soil texture and used to calibrate regression models through the generalized linear model (GLM), support vector machine (SVM), and random forest (RF) algorithm. Models were created with 70% of the data using three datasets: i) only superficial horizon data; ii) only subsuperficial horizon data; and iii) data from both horizons. Validation was performed with 30% of the data. Clay content was positively correlated with Fe (0.79) and Al 2 O 3 (0.41) reflecting the great residual concentration of Fe- and Al-oxides in this fraction. This same fraction correlated negatively with SiO 2 (-0.75), while the sand fraction correlated positively with SiO 2 corresponding to quartz dominance in the sand fraction of Brazilian soils. For the separated superficial and subsuperficial horizon datasets, SVM promoted the best predictions of clay (R2 0.83; RMSE = 7.04%) and sand contents (R2 0.87; RMSE = 9.11%), while RF provided the best results for silt (R2 0.60; RMSE = 6.33%). When combining both datasets, RF was better for sand prediction (R2 0.73; RMSE = 5.79%), while SVM promoted better predictions for silt (R2 0.72; RMSE = 5.77%) and clay (R2 0.84; RMSE = 7.08%). Elemental contents obtained by pXRF are capable of accurately predicting soil texture for a great variety of Brazilian soils. [ABSTRACT FROM AUTHOR]

Portable X-ray fluorescence (pXRF) spectrometry is becoming increasingly popular for predicting soil properties worldwide. However, there are still very few works on this subject under tropical conditions. Therefore, the objectives of this study were to use pXRF data to characterize the Brazilian Coastal Plains (BCP) soils and assess four machine learning algorithms [ordinary least squares regression (OLS), cubist regression (CR), XGBoost (XGB), and random forest (RF)] for prediction of total nitrogen (TN), cation exchange capacity (CEC), and soil organic matter (SOM) using pXRF data. A total of 285 soil samples were collected from the A and B horizons representing Ultisols, Oxisols, Spodosols, and Entisols. The pXRF reported elements helped in the characterization of the BCP soils. In general, the RF model achieved the best performances for TN (R2 = 0.50), CEC (0.75), and SOM (0.56) when A and B horizons were combined, although better results have been reported in the literature for soils from other regions of the world. The results reported here for the BCP soils represent alternatives for reducing costs and time needed for assessing such data, supporting agronomic and environmental strategies. • The elemental data provided by pXRF were used to characterize the BCP soils • Four different prediction algorithms were used to predict SOM, TN, and CEC • In general, RF algorithm outperformed OLS, XGBoost, and CR algorithms [ABSTRACT FROM AUTHOR]

• Prediction of soil texture via pXRF and Vis-NIR DRS data was evaluated. • Combination of A and B horizons data resulted in predictions with R2 above 0.80. • In general, the best predictions were achieved using pXRF data. • RF algorithm outperformed other algorithms for soil texture prediction. • The best prediction models were obtained with pXRF + RF using B horizon data. Portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared diffuse reflectance spectroscopy (Vis-NIR DRS), used separately or in tandem, have become important techniques for determination and prediction of soil attributes worldwide. However, there is little information available regarding the effectiveness of their combined use in tropical soils. This study aimed to predict soil texture using pXRF and Vis-NIR DRS, evaluating the efficiency of using these proximal sensors separately and in tandem. A total of 315 soil samples were collected from A and B horizons in Brazil. Soil samples were submitted to analyses of texture, pXRF and Vis-NIR DRS. Vis-NIR DRS spectral data pre-processing was evaluated by comparing results delivered by the derivative smoothing methods Savitzky-Golay (WT), Savitzky-Golay with Binning (WB), and data without the pre-processing treatment (WOT). Four algorithms were utilized for predictions: Gaussian Process (Gaussian), Support Vector Machine with linear (SVM-L) and radial (SVM-R) kernels, and Random Forest (RF). In general, models using only pXRF data slightly outperformed models using Vis-NIR DRS (WT, WB, WOT) data alone. Models combining data from both sensors achieved similar results to those obtained by pXRF alone. The best predictions of sand, silt, and clay contents were obtained via pXRF + RF using B horizon data, reaching R2 values of 0.91, 0.81, and 0.83, respectively. Although pXRF alone provided slightly better results, soil texture can be accurately predicted via both pXRF and Vis-NIR DRS data, separately and in tandem. These sensors can contribute to reduce costs and time required for tropical soil texture determination. [ABSTRACT FROM AUTHOR]

• First time pXRF and Vis-NIR data were combined to predict tropical soil types. • Vis-NIR spectral data preprocessing is relevant to increase its predictive efficiency. • SVM-L, SVM-R, C5.0, and RF algorithms provided efficient predictions. • RF model using pXRF and soil B horizon data outperformed other models. • Tropical soil type predictions using only pXRF data provided accurate results. Recently, portable X-ray fluorescence (pXRF) spectrometer and visible near-infrared (Vis-NIR) spectroscopy are increasingly being applied for soil types and attributes prediction, but a few works have used them combined in tropical regions. Thus, this work aimed at analyzing models' performance when predicting soil types at subgroup taxonomic level via pXRF and Vis-NIR separately and together. 315 soil samples were collected in both A and B horizons in three important Brazilian states. Samples undergone laboratorial analyses for soil classification and were submitted to pXRF and Vis-NIR (350–2500 nm) analyses. Vis-NIR spectral data preprocessing was evaluated utilizing Savitzky-Golay (WT) and Savitzky-Golay with Binning (WB) methods. Four classification algorithms were employed in modeling: Support Vector Machine with Linear (SVM-L) and Radial (SVM-R) kernel, C5.0, and Random Forest (RF). Predictions were made using only B horizon and using A + B horizon data. Overall accuracy and Cohen's Kappa index evaluated model quality. Both sensors displayed efficacy in soil types prediction. A + B horizons data combined using pXRF + Vis-NIR via SVM-R (WT and WB) delivered accurate predictions (89.32% overall accuracy and 0.75 Kappa index), but the best predictions were achieved using only B horizon data via pXRF with RF, pXRF + Vis-NIR (WT) with RF, pXRF + Vis-NIR (WB) with C5.0, and pXRF + Vis-NIR (WB) with RF (89.23% overall accuracy and 0.80 Kappa index). For tropical soils, soil subgroup prediction using only B horizon data obtained by pXRF in tandem with RF algorithm may be a viable alternative to assist in soil classification, especially when the acquisition of Vis-NIR is not possible. [ABSTRACT FROM AUTHOR]

Traditional methods of soil chemical analysis are time consuming, costly, and generate chemical waste. Proximal sensors, such as portable X-ray fluorescence (pXRF) spectrometry, may help to overcome these issues since they have been shown to produce accurate predictions of many soil properties. However, such processes need to be further investigated in Brazilian soils. This work aimed to assess the influence of soil management and mineralogy on elemental composition of soils and predict exchangeable Al3+, Ca2+, Mg2+, and available K+, and P contents from pXRF data alone and associated with soil texture through machine learning algorithms [stepwise generalized linear models (SGLM), and random forest (RF)] in soils of the Brazilian Coastal Plains (BCP). A total of 285 soil samples were collected from the A (n = 123) and B (n = 162) horizons and subjected to laboratory analyses and pXRF scans. Samples were randomly separated into 70% for modeling and 30% for validation. Soil mineralogy and management mainly influenced Al, and Ca and K total content, respectively. In general, the inclusion of the auxiliary input data of soil texture did not change the predictive power of the models. The best results highlight a considerable promise of pXRF technique for rapidly assessing exchangeable Ca2+ (RMSE = 176.3 mg kg−1, R2 = 0.71), Mg2+ (37.7 mg kg−1, 0.60), and available K+ (27.46 mg kg−1, 0.67). The algorithms could not generate reliable models to predict exchangeable Al3+ (30.6 mg kg−1, 0.47) and available P (19.9 mg kg−1, 0.14). In sum, pXRF can be used to reasonably predict soil fertility properties in the BCP soils. Further studies may extend predictions to other soil properties. • pXRF elemental data was used to available/exchangeable soil nutrients. • Soil textural data as auxiliary input data minimally improved predictive algorithms. • Random forest algorithm provided optimal prediction of soil parameters. [ABSTRACT FROM AUTHOR]

Knowledge about parent material (PM) is crucial to understand the properties of overlying soils. Assessing PM of very deep soils, however, is not easy. Previous studies have predicted PM via proximal sensors and machine learning algorithms, but within small areas and with low soil variety. This study evaluates the efficiency of using portable X-ray fluorescence (pXRF) spectrometry together with machine learning algorithms in a large area populated with a wide variety of soil classes and land uses to build accurate PM distribution maps from soil data. Samples from A and B horizons were collected from 117 sites, totaling 234 samples, along with representative PM samples of the predominant PMs in the region. Elemental contents of PM and soil samples were obtained using pXRF. Random Forest (RF), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) models built with and without Principal Component Analysis (PCA) were used to predict PMs from A and B horizon samples separately. For validation, PM was identified in 23 different spots and compared with the predicted PM via overall accuracy and Kappa coefficient. Maps built with models excluding PCA had an overall accuracy ranging from 0.87 to 0.96 and kappa coefficient ranging from 0.74 to 0.91. Maps generated with PCA based models reached 100% overall accuracy. Prediction of PM using pXRF with samples from either A or B horizon and machine learning algorithms offers solid results even when applied in large areas with high land use and soil class variability. • Soil parent material can be accurately predicted via pXRF analysis of soils. • Maps from B horizon samples were slightly superior to A horizon samples. • Overall map predictive accuracy was strong. [ABSTRACT FROM AUTHOR]

Abstract Parent material (PM) type is crucial for understanding the distribution of soils across the landscape. However, such information is not available at a detailed scale in Brazil. Thus, portable X-ray fluorescence (pXRF) spectrometry can aid in PM characterization by measuring elemental concentrations. This work focused on mapping soil PM (specifically variations of phyllite) using pXRF data and evaluating which soil horizon (A, B, or C) provides optimal PM identification in the Brazilian Cerrado. A total of 120 soil samples were collected from A, B, and C horizons across the study area as well as associated PMs; all were subjected to pXRF analysis. Artificial neural network, support vector machine, and random forest were used to model and predict PMs through pXRF data to the entire area. The nine maps (3 soil horizons data × 3 algorithms) generated for PM prediction were validated through overall accuracy, Kappa coefficient, producer's, and user's accuracy. The most accurate PM maps were obtained by using C horizon information (overall accuracy of 0.87 and Kappa coefficient of 0.79) via support vector machine algorithm. Land use dramatically influenced the results. In sum, pXRF data can be successfully used to predict soil PMs by robust algorithms. Specifically, V, Ni, Sr, and Pb were optimal for predicting PM regardless of land use. Highlights • Soil parent material can be accurately predicted via pXRF analysis of soils. • First time detecting parent material variability in area with unique parent rock. • Land use affects PM predictions, with less effect when using soil C horizon data. • V, Ni, Sr, and Pb produced good PM predictions due to their high stability in soils. • SVM and RF algorithms outperformed ANN in most predictions. [ABSTRACT FROM AUTHOR]

Erosion is a major cause of soil degradation. Understanding the sources of sediment and the processes that exacerbate this problem is crucial for soil conservation. Remote and proximal sensing technologies have contributed to advance our understanding of soil erosion processes and support soil conservation research. The objective of this study is to use two proximal sensors, a portable X-ray fluorescence spectrometer (pXRF) and a magnetic susceptibilimeter, to identify the origin of sediments deposited in the lower portion of a drainage subbasin by using a fingerprinting approach. Five sediment tracing elements were selected: Fe, Si, Al, Ti, and Zr. The sediment source area is composed of gabbro, granite, and felsic orthogneiss, upon which two soil classes were formed: Rhodic Ferralsol (Dystric) (Latossolo Vermelho Distroférrico típico, LV) and Haplic Acrisol (Argissolo Vermelho-Amarelo Distrófico típico, PVA). In the source area, 35 samples were collected from the topsoil layer (0–2 cm of depth) using a regular grid of 50 × 50 m2. In the deposition area, 24 samples were collected in a regular grid of 7 × 7 m2. The source of the sediments was determined using cluster analysis with data provided by the proximal sensors and topographic attributes. Results indicated that the Haplic Acrisol, which is more susceptible to erosion, contributed most to sediment deposition. Analysis of pXRF data showed its potential to identify and differentiate sediments and their sources. Cluster models based on the abundance of Fe, Si and Al, as well as the Ti/Zr ratio, demonstrated the best results with a global accuracy of 88% and a Kappa coefficient of 0.74. The use of the susceptibilimeter data alone did not yield satisfactory results. Results indicate the usefulness of pXRF in identifying the source of sediments in drainage basins, making it a valuable tool in soil conservation and erosion assessments. • pXRF was capable of identifying the origin of sediments. • Fe, Si, Al and the Ti/Zr ratio were efficient in identifying sediment sources. • The Chromic Acrisol contributed most with sediment deposition. • Magnetic susceptibility contributed little to identify sediment sources. [ABSTRACT FROM AUTHOR]

• Does combining proximal sensor always bring synergy for predicting soil texture? • Vis-NIR, NixPro TM , and pXRF data were tested for soil texture predictions. • PXRF provided key information to create accurate prediction models. • Smoothed Vis-NIR data preprocessing and dry NixPro TM condition positively influenced the results. • Soil texture and textural classes were accurately predicted by at least one approach. Soil texture is a primary variable influencing many soil chemical-physical-biological processes, providing important information for decision-making regarding sustainable soil management. The standard traditional methods for determining soil texture, however, are performed manually and are time-consuming, costly, and generate chemical wastes. As an alternative, portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared spectroscopy (Vis-NIR) have been increasingly used worldwide to predict soil attributes. Other sensors (e.g., NixPro TM color sensor) are also promising, but less evaluated to date. Thus, investigations towards proximal sensor data fusion for prediction of soil textural separates (clay, silt, and total, coarse, and fine sand contents) and soil textural classes (loam, loamy sand, etc) in tropical soils are rare. Therefore, this study aimed to evaluate proximal sensor data for predicting soil particle size fractions and soil textural classes (both Family particle size classes and USDA soil texture triangle) via random forest algorithm in tropical regions. A total of 464 soil samples were collected from A (n = 208) and B (n = 256) horizons in Brazil. Soil samples were submitted to laboratory analyses for soil texture and proximal sensor (pXRF, Vis-NIR, and NixPro TM) scanning. Samples were randomly split into 70% for modeling and 30% for validation. The best approach varied according to the predicted attribute; however, pXRF data were key information for soil texture prediction accuracy. The best results delivered highly accurate predictions via the aforementioned proximal sensors for rapid assessment of soil texture (total sand R2 = 0.84, RMSE = 7.60%; silt 0.83, 6.11%; clay 0.90, 5.64%; coarse sand 0.87, 6.30%; fine sand 0.82, 5.27%). Categorical prediction accuracy for soil textural classes (Family particle size classes, overall accuracy = 0.97, Kappa index = 0.95; USDA soil texture triangle, 0.83, 0.73) was enhanced when the predictions were made by soil order sub-datasets. Smoothed Vis-NIR preprocessing and dry NixPro TM color data positively influenced the results. The results reported here represent alternatives for reducing costs and time needed for evaluating soil texture, supporting agronomic and environmental strategies in Brazilian conditions. Further works should extend the results of this study to temperate regions to corroborate the conclusions presented herein regarding the fusion of these three proximal sensors. [ABSTRACT FROM AUTHOR]

Proximal sensors have proven capable of predicting multiple soil properties under different conditions. However, doubts remain about which sensor is preferable for delivering optimal prediction models and which preprocessing methods produce the most accurate results. Portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared (Vis-NIR) diffuse reflectance spectroscopy have been widely used, while the NixPro TM color sensor has been explored more recently. This study evaluated the use of pXRF, Vis-NIR, and NixPro TM data to predict soil organic matter content (SOM), pH, base saturation (BS), the sum of bases (SB), cation exchange capacity (CEC) at pH = 7 and effective CEC (eCEC), via each sensor in isolation, and via combined sensors data. Moreover, factors interfering in the prediction models' accuracy (data preprocessing methods, soil horizon, soil class, parent material) were used as auxiliary variables. 604 soil samples were collected in Brazil, encompassing ten soil orders and 19 parent materials. Numerical and categorical prediction models (7,980) were created for six soil properties using a random forest algorithm, totaling 7980 models, delivering almost 24,000 results., Coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and residual prediction deviation (RPD) were used for validation of numerical predictions., Overall accuracy and kappa coefficient were calcualted for categorical predictions. Although the combination of sensors provided most of the best predictions, pXRF in isolation achieved accuracies close to the three sensors combined. NixPro TM offered superior contributions to SOM and CEC predictions, but pXRF and Vis-NIR were responsible for the best results of most studied variables. On average, by adding pXRF to Vis-NIR data, predictive accuracy improved 32%; while adding Vis-NIR to pXRF data increased accuracy by ca. 6%. Soil-order-specific models improved predictions for Ultisols compared to general models (without soil order distinction), reaching R2 > 0.90. Soil parent material and horizon did not improve models significantly. Categorical predictions improved the accuracy for some properties, reaching an overall accuracy of 100% and kappa index of 1.0 for pH in A horizons of Ultisols via pXRF + Vis-NIR data. Proximal sensor data with no auxiliary variables provided almost all the best results. The fusion of proximal sensors can provide better predictions, but pXRF alone can deliver satisfactory results in most cases for the six soil properties. • Soil pH, SOM, BS, SB, eCEC, CEC can be predicted by pXRF sensor alone. • PXRF provided results similar to the best ones provided by both Vis-NIR and NixPro TM . • Soil horizon, parent material, or class did not significantly improve predictions. • PXRF alone delivers satisfactory results for all the six soil properties evaluated. [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]

• 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]

• 1975 samples of varied soils were analyzed for fertility and elemental contents. • pXRF data was successfully used to rapidly predict soil fertility properties. • Random Forest outperformed other machine learning algorithms. • Prediction models allowed spatial distribution of soil fertility properties. • Inexpensive and environment-friendly soil fertility prediction was achieved by pXRF. Conventional soil fertility analyses are laborious, costly, time-consuming, and produce hazardous waste. The high demand of these laboratory-based analyses prompted us to investigate an environment-friendly, rapid, and inexpensive methodology for soil fertility assessment. Portable X-ray fluorescence (pXRF) spectrometry allows the determination of total elemental concentration in soils quickly, simply and without hazardous waste production. However, incipient usage of this technology for the prediction of soil fertility properties has been reported for tropical conditions. Soil samples were collected from seven Brazilian states (n = 1975) aiming to use pXRF data to predict contents of available or exchangeable Ca2+, Mg2+, Al3+, K+ and P by testing different algorithms using 70% of the samples for model training, and the remaining 30% for model validation. In addition to point data predictions, the best performing models were applied to data obtained from a farm within the studied regions with a known cropping history to create soil fertility maps and illustrate another applicability of this approach. The attested use of pXRF data and machine learning algorithms stepwise Generalized Linear Model (GLM) and Random Forest (RF) to predict the contents of relevant soil fertility properties exhibited great potential. Validation of the models corroborated that RF resulted in more accurate predictions than GLM. Validation R2 values ranged from 0.59 to 0.82. Maps created were coherent with expected distributions of soil fertility attributes. This environment-friendly methodology may be used for the assessment of soil fertility properties in a wide range of tropical and subtropical soils with minimum waste generation and reduced costs. [ABSTRACT FROM AUTHOR]