A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes

Abstract

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.

Sentinel-2 and Sentinel-1 Bare Soil Temporal Mosaics of 6-year Periods for Soil Organic Carbon Content Mapping in Central France

“…A strong limitation is that gamma-ray data are far from covering the whole world (and even the whole of France) because of their cost and because they have been mainly used for mining purposes. Although the use of airborne gamma-ray data in soil science has been mainly explored to assist digital soil mapping and to understand landscape processes, lithology and mineralogy [54,77,78], there are still a few papers that relate soil attributes to gamma radiation emissions at a rather broad scale [75,[79][80][81]. Thorium is rather stable under conditions of soil at about or higher than pH seven, where it can be adsorbed to the surface of clay minerals and organic matter [82].…”

Could Airborne Geophysical Data Be Used to Improve Predictive Modeling of Agronomic Soil Properties in Tropical Hillslope Area?

“…The covariate with the highest sum of correlation coefficients with the other covariates is eliminated [61]. In this study, the findCorrelation function was applied with Spearman's correlation method with a critical value of 95% (as adopted by [10,62]).…”

“…In addressing this issue, gamma-ray spectrometry data have increased as a covariate in DSM to represent parental material information (e.g., [7][8][9][10][11]). It is possible to make associations between relief denudation processes and the relative rates of soil formation and erosion [12,13], as [14] also pointed out in a critical review of gamma-ray spectrometry as a tool in soil science.…”

“…As well as gamma-ray spectrometry, the magnetic method can be measured in situ or based on aerial surveys. Studies involving magnetic data and soils are usually in situ [10,[18][19][20]. However, its applicability may be valid due to the possibility of contrasting with gammaray data in the type of parent material highlighted by these methods in DSM.…”

Comparative Analysis of Soil Salinity under Flat and Ridge Cultivation Methods in Citrus Orchards

“…In this regard, the EM38 methodology is extensively used for a variety of aims, namely for vertical and horizontal salinity in the soil as well as leaching, salt buildup, and water content changes versus crop yields [9,[12][13][14][15][16][17][18][19]. Nevertheless, sodicity [20,21], acidity [22], moisture monitoring [23], variations of texture and compaction along a soil pedon [24][25][26][27], and finally improving soil maps [28] are the other relevant fields of application for the EM38 methodology. In conclusion, regional studies of particular significance with environmental implications and ecosystem services are needed in order to improve the implementation of the EM38 methodology and adapt to the above-mentioned conditions.…”