Soil organic carbon (SOC) is an important indicator of soil quality and directly determines soil fertility. Hence, understanding its spatial distribution and controlling factors is necessary for efficient and sustainable soil nutrient management. In this study, machine learning algorithms including artificial neural network (ANN), support vector machine (SVM), cubist regression, random forests (RF), and multiple linear regression (MLR) were chosen for advancing the prediction of SOC. A total of sixty (n = 60) soil samples were collected within the research area at 30 cm soil depth and measured for SOC content using the Walkley–Black method. From these samples, 80% were used for model training and 21 auxiliary data were included as predictors. The predictors include effective cation exchange capacity (ECEC), base saturation (BS), calcium to magnesium ratio (Ca_Mg), potassium to magnesium ratio (K_Mg), potassium to calcium ratio (K_Ca), elevation, plan curvature, total catchment area, channel network base level, topographic wetness index, clay index, iron index, normalized difference build-up index (NDBI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI) and land surface temperature (LST). Mean absolute error (MAE), root-mean-square error (RMSE) and R² were used to determine the model performance. The result showed the mean SOC to be 1.62% with a coefficient of variation (CV) of 47%. The best performing model was RF (R² = 0.68) followed by the cubist model (R² = 0.51), SVM (R² = 0.36), ANN (R² = 0.36) and MLR (R² = 0.17). The soil nutrient indicators, topographic wetness index and total catchment area were considered an indicator for spatial prediction of SOC in flat homogenous topography. Future studies should include other auxiliary predictors (e.g., soil physical and chemical properties, and lithological data) as well as cover a broader range of soil types to improve model performance.

Background: Human activities considerably contribute to polluting potentially toxic element (PTEs) levels in soils, especially agricultural soils. The consistent introduction of PTEs in the environment and the soil pose health-related risks to humans, flora and fauna. One hundred and fifteen samples were collected in the district of Frydek Mistek (Czech Republic) in a regular grid form. The soil samples were air-dried, and the concentrations of PTEs (i.e. lead, arsenic, chromium, nickel, manganese, cadmium, copper, and zinc) were determined by ICP-OES (inductively coupled plasma optical emission spectrometry). The purpose of this study is to create digitized soil maps that expose the human-related health risks posed by PTEs, estimate pollution indices, ascertain the spatially distributed patterns of PTEs, source apportionment and quantify carcinogenic and non-carcinogenic health risks using the sample location approach.Results: The results revealed that the pollution assessment of the soils in the study area using diverse pollution assessment indexes (pollution index, pollution load index, ecological risk and risk index), based on the application of the local background value and the European average value, displayed a range of pollution levels due to differences in the threshold limits from differing geochemical background levels. The principal components analysis and positive matrix factorization, respectively, identified the sources of pollution and the distribution of PTE sources. Mapping the health index and total carcinogenic risk highlighted hotspots of areas within the study area that require immediate remediation. The self-organizing map (SeOM) revealed a diversified colour pattern for the factor scores. A single neuron exhibited a high hotspot in all factor loadings on different blocks of neurons. Children’s CDItotal (Chronic Daily Intake total) values for non-carcinogenic risk and carcinogenic risk were found to be greater than adults’, as were their HQ (hazard quotients) and CR (carcinogenic risk) values. According to the health index of non-carcinogenic risk, 6.1% of the study area sampled posed a potential risk to children rather than adults. Corresponding to the sampled pointwise health risk assessment, 13.05% of the sampled locations are carcinogenic to children. The estimated health risk in the agricultural soil was high, with both carcinogenic and non-carcinogenic risks that could threaten persons living in the study area, particularly children.Conclusion: In general, the continuous application of agriculturally related inputs such as phosphate fertilizers and other anthropogenic activities (e.g., steel industry) can increase the level of PTEs in soils. The use of mean, maximum, and minimum values in health risk estimation does not provide a comprehensive picture of a research area’s health state. This study recommends using a sampled pointwise or location health risks assessment approach, which allows researchers to identify high-risk environments that exceeds the recommended threshold as well as areas on the verge of becoming high risk, allowing for rapid remedial action.

Soil organic carbon (SOC) is a variable of vital environmental significance in terms of soil quality and function, global food security, and climate change mitigation. Estimation of its content and prediction accuracy on a broader scale remain crucial. Although, spectroscopy under proximal sensing remains one of the best approaches to accurately predict SOC, however, spectroscopy limitation to estimate SOC on a larger spatial scale remains a concern. Therefore, for an efficient quantification of SOC content, faster and less costly techniques are needed, recent studies have suggested the use of remote sensing approaches. The primary aim of this research was to evaluate and compare the capabilities of small Unmanned Aircraft Systems (UAS) for monitoring and estimation of SOC with those obtained from spaceborne (Sentinel-2) and proximal soil sensing (field spectroscopy measurements) on an agricultural field low in SOC content. Nine calculated spectral indices were added to the remote sensing approaches (UAS and Sentinel-2) to enhance their predictive accuracy. Modeling was carried out using various bands/wavelength (UAS (6), Sentinel-2 (9)) and the calculated spectral indices were used as independent variables to generate soil prediction models using five-fold cross-validation built using random forest (RF) and support vector machine regression (SVMR). The correlation regarding SOC and the selected indices and bands/wavelengths was determined prior to the prediction. Our results revealed that the selected spectral indices slightly influenced the output of UAS compared to Sentinel-2 dataset as the latter had only one index correlated with SOC. For prediction, the models built on UAS data had a better accuracy with RF than the two other data used. However, using SVMR, the field spectral prediction models achieved a better overall result for the entire study (log(1/R), RPD = 1.40; R²_CV = 0.48; RPIQ = 1.65; RMSEPCV = 0.24), followed by UAS and then Sentinel-2, respectively. This study has shown that UAS imagery can be exploited efficiently using spectral indices.

Spectroscopy has demonstrated the ability to predict specific soil properties. Consequently, it is a promising avenue to complement the traditional methods that are costly and time-consuming. In the visible-near infrared (Vis-NIR) region, spectroscopy has been widely used for the rapid determination of organic components, especially soil organic carbon (SOC) using laboratory dry (lab-dry) measurement. However, steps such as collecting, grinding, sieving and soil drying at ambient (room) temperature and humidity for several days, which is a vital process, make the lab-dry preparation a bit slow compared to the field or laboratory wet (lab-wet) measurement. The use of soil spectra measured directly in the field or on a wet sample remains challenging due to uncontrolled soil moisture variations and other environmental conditions. However, for direct and timely prediction and mapping of soil properties, especially SOC, the field or lab-wet measurement could be an option in place of the lab-dry measurement. This study focuses on comparison of field and naturally acquired laboratory measurement of wet samples in Visible (VIS), Near-Infrared (NIR) and Vis-NIR range using several pretreatment approaches including orthogonal signal correction (OSC). The comparison was concluded with the development of validation models for SOC prediction based on partial least squares regression (PLSR) and support vector machine (SVMR). Nonetheless, for the OSC implementation, we use principal component regression (PCR) together with PLSR as SVMR is not appropriate under OSC. For SOC prediction, the field measurement was better in the VIS range with R²_CV = 0.47 and RMSEPcv = 0.24, while in Vis-NIR range the lab-wet measurement was better with R²_CV = 0.44 and RMSEPcv = 0.25, both using the SVMR algorithm. However, the prediction accuracy improves with the introduction of OSC on both samples. The highest prediction was obtained with the lab-wet dataset (using PLSR) in the NIR and Vis-NIR range with R²_CV = 0.54/0.55 and RMSEPcv = 0.24. This result indicates that the field and, in particular, lab-wet measurements, which are not commonly used, can also be useful for SOC prediction, just as the lab-dry method, with some adjustments.