Soil type spatial prediction from Random Forest: different training datasets, transferability, accuracy and uncertainty assessment

Different uses of soil legacy data such as training dataset as well as the selection of soil environmental covariables could drive the accuracy of machine learning techniques. Thus, this study evaluated the ability of the Random Forest algorithm to predict soil classes from different training datasets and extrapolate such information to a similar area. The following training datasets were extracted from legacy data: a) point data composed of 53 soil samples; b) 30 m buffer around the soil samples, and soil map polygons excluding: c) 20 m; and d) 30 m from the boundaries of polygons. These four datasets were submitted to principal component analysis (PCA) to reduce multidimensionality. Each dataset derived a new one. Different combinations of predictor variables were tested. A total of 52 models were evaluated by means of error of models, prediction uncertainty and external validation for overall accuracy and Kappa index. The best result was obtained by reducing the number of predictors with the PCA along with information from the buffer around the points. Although Random Forest has been considered a robust spatial predictor model, it was clear it is sensitive to different strategies of selecting training dataset. Effort was necessary to find the best training dataset for achieving a suitable level of accuracy of spatial prediction. To identify a specific dataset seems to be better than using a great number of variables or a large volume of training data. The efforts made allowed for the accurate acquisition of a mapped area 15.5 times larger than the reference area

Spatial prediction of soil properties in two contrasting physiographic regions in Brazil

This study compared the performance of ordinary kriging (OK) and regression kriging (RK) to predict soil physical-chemical properties in topsoil (0-15 cm). Mean prediction of error and root mean square of prediction error were used to assess the prediction methods. Two watersheds with contrasting soil-landscape features were studied, for which the prediction methods were performed differently. A multiple linear stepwise regression model was performed with RK using digital terrain models (DTMs) and remote sensing images in order to choose the best auxiliary covariates. Different pedogenic factors and land uses control soil property distributions in each watershed, and soil properties often display contrasting scales of variability. Environmental covariables and predictive methods can be useful in one site study, but inappropriate in another one. A better linear correlation was found at Lavrinha Creek Watershed, suggesting a relationship between contemporaneous landforms and soil properties, and RK outperformed OK. In most cases, RK did not outperform OK at the Marcela Creek Watershed due to lack of linear correlation between covariates and soil properties. Since alternatives of simple OK have been sought, other prediction methods should also be tested, considering not only the linear relationships between covariate and soil properties, but also the systematic pattern of soil property distributions over that landscape

Hybrid kriging methods for interpolating sparse river bathymetry point data

Terrain models that represent riverbed topography are used for analyzing geomorphologic changes, calculating water storage capacity, and making hydrologic simulations. These models are generated by interpolating bathymetry points. River bathymetry is usually surveyed through cross-sections, which may lead to a sparse sampling pattern. Hybrid kriging methods, such as regression kriging (RK) and co-kriging (CK) employ the correlation with auxiliary predictors, as well as inter-variable correlation, to improve the predictions of the target variable. In this study, we use the orthogonal distance of a (x, y) point to the river centerline as a covariate for RK and CK. Given that riverbed elevation variability is abrupt transversely to the flow direction, it is expected that the greater the Euclidean distance of a point to the thalweg, the greater the bed elevation will be. The aim of this study was to evaluate if the use of the proposed covariate improves the spatial prediction of riverbed topography. In order to asses such premise, we perform an external validation. Transversal cross-sections are used to make the spatial predictions, and the point data surveyed between sections are used for testing. We compare the results from CK and RK to the ones obtained from ordinary kriging (OK). The validation indicates that RK yields the lowest RMSE among the interpolators. RK predictions represent the thalweg between cross-sections, whereas the other methods under-predict the river thalweg depth. Therefore, we conclude that RK provides a simple approach for enhancing the quality of the spatial prediction from sparse bathymetry data

Natural variation of arsenic fractions in soils of the Brazilian Amazon

Arsenic (As) in native soils of the Amazon rainforest is a concern due to its likely origin from the Andean rivers, which transport loads of sediments containing substantial amounts of trace elements coming from the cordilleras. Yet, unveiling soil As baseline concentrations in the Amazon basin is still a need because most studies in Brazil have been performed in areas with predominantly high concentrations and cannot express a real baseline value for the region. In this study, 414 soil samples (0–20, 20–40 and 40–60 cm layers) were collected from different sites throughout the Amazon basin - including native Amazon rainforest and minimally disturbed areas - and used to determine total and extractable (soluble + available) As concentrations along with relevant soil physicochemical properties. Descriptive statistics of the data was performed and Pearson correlation supported by a Principal Component Analysis (PCA) provided an improved understanding of where and how As concentrations are influenced by soil attributes. Total As concentration ranged from 0.98 to 41.71 mg kg−1 with values usually increasing from the topsoil (0–20 cm) to the deepest layer (40–60 cm) in all sites studied. Considering the proportional contribution given by each fraction (soluble and available) on extractable As concentration, it is noticeable that KH2PO4-extractable As represents the most important fraction, with >70% of the As extracted on average in all the sites studied. Still, the extractable fractions (soluble + available) correspond to ~0.24% of the total As, on average. Total, available, and soluble As fractions were strongly and positively correlated with soil Al3+. The PCA indicated that soil pH in combination with CEC might be the key factors controlling soil As concentrations and the occurrence of each arsenic fraction in the soil layers

Soil and climate effects on winter wine produced under the tropical environmental conditions of southeastern Brazil

Southeastern Brazil is an emergent region in terms of the production of high-quality fine wines. To contribute to typicity assessment, the soils (morphology, mineralogy, chemical and physical analyses), parent material (geologic maps and portable X-ray fluorescence spectrometry) and climate (temperature and precipitation) were characterized in seven vineyards located in the state of Minas Gerais and São Paulo, Brazil, by carrying out state-of-the-art terroir analysis and assessing the environmental variations of the study sites. A soil profile was described and sampled in the central part of each vineyard. Principal Component Analysis (PCA) biplots were used to analyze the relationships between these factors and the composition of wines (2016, 2017 and 2018 harvests) produced from Syrah in commercial vineyards in different municipalities of Três Corações (TC), Cordislândia (COR), Andradas (AND), São Sebastião do Paraíso (SSP), Três Pontas (TP), Espírito Santo do Pinhal (PIN) and Itobí (ITO). The vineyards were grouped according to soil and climate characteristics. Group A was composed of COR, AND and PIN vineyards, which exhibited the highest correlation with soil Al3+ content and accumulated rainfall. The group’s wines had the lowest ash alkalinity, total polyphenol index (TPI) and pH values and the highest fixed acidity. Group B consisted of the TP and TC vineyards, which had the highest soil organic matter and boron contents and the highest thermal amplitude with similar values (15.4 °C in TC and 15.2 °C in TP); their wines showed average composition. Group C comprised ITO alone, which was characterized by the shallowest and least developed soils. Its wine had the highest flavonol content and high dry extract, color intensity, TPI, alcohol content and sugar values. Group D contained the SSP vineyard, in which the soil subsurface horizons were correlated with the highest wine pH. Late harvest in this vineyard caused the most dehydration of grapes and consequent concentration of most wine compounds (human effect on terroir). The terroir information produced in this study adds substantial value to the wines produced under the tropical environmental conditions of southeastern Brazil, for which such studies are very rare. By characterizing the natural factors (soil, soil parent material and climate) and human factors (vineyard management and wine characteristics) related to terroir, this study can also provide historical information about the wine from this emergent region (the historical factors). In addition, its results can be used to guide producers in their choice of vineyard cultivation sites according to preference in wine composition

Solum depth spatial prediction comparing conventional with knowledge-based digital soil mapping approaches

Solum depth and its spatial distribution play an important role in different types of environmental studies. Several approaches have been used for fitting quantitative relationships between soil properties and their environment in order to predict them spatially. This work aimed to present the steps required for solum depth spatial prediction from knowledge-based digital soil mapping, comparing the prediction to the conventional soil mapping approach through field validation, in a watershed located at Mantiqueira Range region, in the state of Minas Gerais, Brazil. Conventional soil mapping had aerial photo-interpretation as a basis. The knowledge-based digital soil mapping applied fuzzy logic and similarity vectors in an expert system. The knowledge-based digital soil mapping approach showed the advantages over the conventional soil mapping approach by applying the field expert-knowledge in order to enhance the quality of final results, predicting solum depth with suited accuracy in a continuous way, making the soil-landscape relationship explicit

Natural variation of arsenic fractions in soils of the Brazilian Amazon

Arsenic (As) in native soils of the Amazon rainforest is a concern due to its likely origin from the Andean rivers, which transport loads of sediments containing substantial amounts of trace elements coming from the cordilleras. Yet, unveiling soil As baseline concentrations in the Amazon basin is still a need because most studies in Brazil have been performed in areas with predominantly high concentrations and cannot express a real baseline value for the region. In this study, 414 soil samples (0–20, 20–40 and 40–60 cm layers) were collected from different sites throughout the Amazon basin - including native Amazon rainforest and minimally disturbed areas - and used to determine total and extractable (soluble + available) As concentrations along with relevant soil physicochemical properties. Descriptive statistics of the data was performed and Pearson correlation supported by a Principal Component Analysis (PCA) provided an improved understanding of where and how As concentrations are influenced by soil attributes. Total As concentration ranged from 0.98 to 41.71 mg kg−1 with values usually increasing from the topsoil (0–20 cm) to the deepest layer (40–60 cm) in all sites studied. Considering the proportional contribution given by each fraction (soluble and available) on extractable As concentration, it is noticeable that KH2PO4-extractable As represents the most important fraction, with >70% of the As extracted on average in all the sites studied. Still, the extractable fractions (soluble + available) correspond to ~0.24% of the total As, on average. Total, available, and soluble As fractions were strongly and positively correlated with soil Al3+. The PCA indicated that soil pH in combination with CEC might be the key factors controlling soil As concentrations and the occurrence of each arsenic fraction in the soil layers