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Abstract. Weathering is widely used for pedogenesis and soil fertility studies, once it affects several soil attributes. Understanding the intensities of weathering can provide answers for environmental issues, soil and geosciences studies. Recently, there are available geotechnologies (such as geophysics and machine learning algorithms) that can be applied in soil science to provide pedosphere information. In this research, we performed a method to evaluate weathering intensity in a heterogeneous tropical area by proximal remote sensing data acquired by geophysical and satellite images respectively. The area is located in southwest Brazil, with 184ha and we sampled 79 sites (all with soil analysis) using toposequence knowledge. Afterwards, the principal component analysis and the ideal number of clusters was determined. Then, we determine and used the ideal number of clusters, weathering index, as input data in four modelling (prediction and spatialization) algorithms to infer different weathering intensities in soils formed from the same soil parent material. The results showed that the best model performance was for the random forest reaching 3 clusters as the ideal number. The surface pixel reflectance acquired from a Synthetic Soil Image, the terrain surface convexity and digital elevation model were the covariates that most contributed to modelling processes. The model’ specificity was greater than sensitivity. The East areas over diabase such as the Nitisol presented greater weathering intensity than the Nitisol over West diabase areas. The areas over siltite/metamorphosed siltite and Lixisols presented moderate weathering rates. The relief and topographic position strongly affected the weathering, once they controlled the hydric dynamics. The geophysical variables were related to soil attributes and weathering, which contributed to modelling and clusterization processes. The different weathering rates are mainly modulated by geomorphic processes that relief, topographic position, and the associated soil types control water dynamic at the landscape and directly affect the weathering intensities.

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Abstract. Geophysical sensors combined with machine learning algorithms have been used to understand the pedosphere system, landscape processes and to model soil attributes. In this research, we used parent material, terrain attributes and data from geophysical sensors in different combinations, to test and compare different and novel machine learning algorithms to model soil attributes. Also, we analyzed the importance of pedoenvironmental variables in predictive models. For that, we collected soil physico-chemical and geophysical data (gamma-ray emission from uranium, thorium and potassium, magnetic susceptibility and apparent electric conductivity) by three sensors, gamma-ray spectrometer – RS 230, susceptibilimeter KT10 – Terraplus and Conductivimeter – EM38 Geonics) at 75 points and, we performed soil analysis afterwards. The results showed varying models with the best performance (R2 > 0.2) for clay, sand, Fe2O3, TiO2, SiO2 and Cation Exchange Capacity prediction. Modeling with selection of covariates at three phases (variance close to zero, removal by correction and removal by importance), demonstrated to be adequate to increase the parsimony. The prediction of soil attributes by machine learning algorithms demonstrated adequate values for field collected data, without any sample preparation, for most of the tested predictors (R2 ranging from 0.20 to 0.50). Also, the use of four regression algorithms proved important, since at least one of the predictors used one of the tested algorithms. The performances of the best algorithms for each predictor were higher than the use of a mean value for the entire area comparing the values of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The best combination of sensors that reached the best model performance to predict soil attributes were gamma-ray spectrometer and susceptibilimeter. The most important variables were parent material, digital elevation model, standardized height and magnetic susceptibility for most predictions. We concluded that soil attributes can be efficiently modelled by geophysical data using machine learning techniques and geophysical sensors combinations. The technique can bring light for future soil mapping with gain of time and environment friendly.

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Abstract. Geophysical sensors combined with machine learning algorithms were used to understand the pedosphere system and landscape processes and to model soil attributes. In this research, we used parent material, terrain attributes, and data from geophysical sensors in different combinations to test and compare different and novel machine learning algorithms to model soil attributes. We also analyzed the importance of pedoenvironmental variables in predictive models. For that, we collected soil physicochemical and geophysical data (gamma-ray emission from uranium, thorium, and potassium; magnetic susceptibility and apparent electric conductivity) by three sensors (gamma-ray spectrometer, RS 230; susceptibilimeter KT10, Terraplus; and conductivimeter, EM38 Geonics) at 75 points and analyzed the data. The models with the best performance (R2 0.48, 0.36, 0.44, 0.36, 0.25, and 0.31) varied for clay, sand, Fe2O3, TiO2, SiO2, and cation exchange capacity prediction, respectively. Modeling with the selection of covariates at three phases (variance close to zero, removal by correction, and removal by importance) was adequate to increase the parsimony. The results were validated using the method “nested leave-one-out cross-validation”. The prediction of soil attributes by machine learning algorithms yielded adequate values for field-collected data, without any sample preparation, for most of the tested predictors (R2 values ranging from 0.20 to 0.50). Also, the use of four regression algorithms proved to be important since at least one of the predictors used one of the tested algorithms. The performance values of the best algorithms for each predictor were higher than those obtained with the use of a mean value for the entire area comparing the values of root mean square error (RMSE) and mean absolute error (MAE). The best combination of sensors that reached the highest model performance was that of the gamma-ray spectrometer and the susceptibilimeter. The most important variables for most predictions were parent material, digital elevation, standardized height, and magnetic susceptibility. We concluded that soil attributes can be efficiently modeled by geophysical data using machine learning techniques and geophysical sensor combinations. This approach can facilitate future soil mapping in a more time-efficient and environmentally friendly manner.

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