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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]

Diverse projects are being carried out worldwide focusing on development of more accurate soil maps and one of the most valuable sources of data are the existing soil maps. This work aimed to (i) compare two data mining tools, KnowledgeMiner and decision trees, to retrieve legacy soil data from a detailed soil map, (ii) to create and validate the predicted soil maps in the field with the objective to identify the best method for modeling and refining soil maps, (iii) extrapolating soils information to the surrounding similar areas and (iv) to assess the accuracy of this soil map. The study was carried out in Minas Gerais state, Southeastern Brazil. From a detailed soil map, information of 12 terrain attributes was retrieved from the entire polygon of each mapping unit of the map (MUP) and from a circular buffer around the sampled points (CBP). KnowledgeMiner and decision trees were employed to retrieve information per soil class and soil maps were created per method. A field validation of 20 samples was chosen by a cost-constrained conditioned Latin hypercube sampling scheme and the accuracy of all maps was assessed using a global index, Kappa index, and errors of omission and commission. The KnowledgeMiner MUP map had a greater accuracy than the other methods, being even more accurate than the original map, accounting for 80% of global index and a Kappa index of 0.6524. The information extracted by KnowledgeMiner provided rules for mapping the watershed surroundings with 70.97% of global index and a kappa index of 0.5586. Legacy soil data extracted by KnowledgeMiner from a detailed soil map and used to model soil class distribution outperformed decision trees, promoted improvements on the existing soil map, and allows for the creation of a low cost soil map for the surroundings of the study area. [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]

Soil legacy data are important sources of soil information, especially when dealing with limited resources. In countries with high geographical diversity and few financial resources, such as Brazil, they represent an economical alternative to obtaining soil spatial information in higher resolution. By retrieving the soil scientist's knowledge, it can be used as guidance for knowledge-based digital soil mapping approaches. In this sense, this work aimed to evaluate Rule-Based Reasoning and Case-Based Reasoning knowledge-based approaches to predict soil types up to the third categorical level (U.S Soil Taxonomy) in a non-sampled area, by retrieving and then extrapolating the information of a detailed soil legacy map, from a reference area. The study was carried out in Minas Gerais state, Southeastern Brazil. The methodology includes three main steps: i) knowledge acquisition; ii) soil inference; and iii) accuracy and uncertainty assessment. For the validation, 23 independent samples were chosen by means of the Regional Random method, and the accuracy was assessed by Kappa index, Overall Accuracy, Users', and Producers' Accuracy. The uncertainty was evaluated through entropy and exaggeration. A total of 24 inference models were obtained with the Case-Based Reasoning approach, in which the best model had an overall accuracy of 61% and a Kappa index of 0.52. The Rule-based reasoning approach performed better, with an overall accuracy of 82% and 0.75 for Kappa index. These approaches generated a higher accuracy soil map for an unmapped area that was 15 times larger than the reference area and at lower cost. • The terrain surface texture was useful to discriminate Oxisols from Inceptisols. • Hapludoxes and Acrudoxes are strongly related within similar morphometric features. • The greater uncertainty was located between morphological transitions. • To reduce the polygon area increased the uncertainty of predictions for CBR. • The extrapolation of soil type information was successfully achieved by using the RBR. [ABSTRACT FROM AUTHOR]

This study aimed to i) explore the land-use effect (former lakebed, pasture, and agricultural areas) on elemental concentrations, enrichment factors (EF s), and geo-accumulation indices (Igeo s), ii) provide X-ray diffractograms of the primary minerals and identify the clay minerals dominant in each land-use scenario, iii) map the spatial distribution of elemental concentration, EF s , and Igeo s using Digital Soil Mapping (DSM) approaches. We collected a total of 240 soil samples. Our findings highlight that vermiculite was a common clay-sized mineral, and aragonite, gypsum, and calcite were dominant primary minerals in each land-use. The remote sensing data (Sentinel-2 MSI and Landsat-8 OLI) were used to calculate some environmental covariates useful for digital mapping of elemental concentrations, EF s , and Igeo s. The highest concordance correlation coefficient (CCC) was found in the prediction of Hg (CCC = 0.79) using remote sensing data based on Sentinel-2 with the Random forest (RF) model. There is neither threat in terms of Cu, Zn, and Pb nor the impact of anthropogenic activities in their distribution under each land-use scenario. The study area was enriched with Hg, Cd, and Ni (very high, EF = 20–40), followed by Cr (significant, EF = 5.88) and As (moderate, EF = 2.08). A similar trend was also found for Igeo s across the study area. We conclude that the distribution of elemental concentration, EF s , and Igeo s were significantly affected by the shrinking of the Burdur Lake and the different land-use scenarios in this semi-arid region. • XRD, pXRF, SEM, RS, and DSM were simultaneously used. • Former lakebeds, pasture, and agricultural areas were the land-use scenarios for investigation. • Land-use changes and agricultural activities impacted EF s and Igeo s. • Sentinel-2 MSI data delivered better prediction than Landsat-8 OLI data. • Random forest outperformed cubists in predictions. [ABSTRACT FROM AUTHOR]