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Soil characteristics, especially clay content and clay type, have significant impacts on splash erosion. This investigation was conducted to determine the effects of clay content and clay type (zeolite, phlogopite, bentonite, and kaolinite) on the shear strength and splash erosion of clay–sand mixtures compared with a clay soil under controlled conditions. Clay–sand mixtures were prepared by mixing 15, 30, and 45 kg 100 kg−1 of the selected clays with pure sand and a clay soil; these mixtures were pre-treated with three levels of wetting and drying (W&D) cycles, and then shear strength was measured. The splash erosion rate was measured at three levels of water status: air-dry, plastic limit (PL), and liquid limit (LL). The highest values of splash erosion were observed in the samples without the W&D cycle, and after applying the W&D cycles, splash erosion decreased. Moreover, splash erosion was higher in the air-dry and LL groups. Splash erosion decreased with increased clay content because of the structure development in the mixtures. In general, as clay content increased, splash erosion was reduced and shear strength was significantly increased. From the highest to lowest mean of cohesion, the results showed the following order: bentonite > kaolinite > phlogopite > clay soil > zeolite. However, splash erosion showed the following order for the studied clays: zeolite > clay soil > phlogopite > kaolinite > bentonite. Nonlinear power models, best-fitted relations between splash erosion and shear strength, and the shear strength could explain about 30–33% of the splash erosion variability in this study.

This study was conducted to examine the capability of topographic features and remote sensing data in combination with other auxiliary environmental variables (geology and geomorphology) to predict CEC by using different machine learning models ((random forest (RF), k-nearest neighbors (kNNs), Cubist model (Cu), and support vector machines (SVMs)) in the west of Iran. Accordingly, the collection of ninety-seven soil samples was performed from the surface layer (0–20 cm), and a number of soil properties and X-ray analyses, as well as CEC, were determined in the laboratory. The X-ray analysis showed that the clay types as the main dominant factor on CEC varied from illite to smectite. The results of modeling also displayed that in the training dataset based on 10-fold cross-validation, RF was identified as the best model for predicting CEC (R2 = 0.86; root mean square error: RMSE = 2.76; ratio of performance to deviation: RPD = 2.67), whereas the Cu model outperformed in the validation dataset (R2 = 0.49; RMSE = 4.51; RPD = 1.43)). RF, the best and most accurate model, was thus used to prepare the CEC map. The results confirm higher CEC in the early Quaternary deposits along with higher soil development and enrichment with smectite and vermiculite. On the other hand, lower CEC was observed in mountainous and coarse-textured soils (silt loam and sandy loam). The important variable analysis also showed that some topographic attributes (valley depth, elevation, slope, terrain ruggedness index—TRI) and remotely sensed data (ferric oxides, normalized difference moisture index—NDMI, and salinity index) could be considered as the most imperative variables explaining the variability of CEC by the best model in the study area.

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Although many Soil Spectral Libraries (SSLs) have been created globally, these libraries still have not been operationalized for end-users. To address this limitation, this study created an online Brazilian Soil Spectral Service (BraSpecS). The system was based on the Brazilian Soil Spectral Library (BSSL) with samples collected in the Visible–Near–Short-wave infrared (vis–NIR–SWIR) and Mid-infrared (MIR) ranges. The interactive platform allows users to find spectra, act as custodians of the data, and estimate several soil properties and classification. The system was tested by 500 Brazilian and 65 international users. Users accessed the platform (besbbr.com.br), uploaded their spectra, and received soil organic carbon (SOC) and clay content prediction results via email. The BraSpecS prediction provided good results for Brazilian data, but performed variably for other countries. Prediction for countries outside of Brazil using local spectra (External Country Soil Spectral Libraries, ExCSSL) mostly showed greater performance than BraSpecS. Clay R2 ranged from 0.5 (BraSpecS) to 0.8 (ExCSSL) in vis–NIR–SWIR, but BraSpecS MIR models were more accurate in most situations. The development of external models based on the fusion of local samples with BSSL formed the Global Soil Spectral Library (GSSL). The GSSL models improved soil properties prediction for different countries. Nevertheless, the proposed system needs to be continually updated with new spectra so they can be applied broadly. Accordingly, the online system is dynamic, users can contribute their data and the models will adapt to local information. Our community-driven web platform allows users to predict soil attributes without learning soil spectral modeling, which will invite end-users to utilize this powerful technique.

This study was executed to explore soil redistribution and soil quality changes induced by land degradation and then rehabilitation by orchard plantation in different slope positions in a semi-arid region in central Iran. A total of 72 surface soil samples (0–30 cm) were collected from three land uses (natural rangelands, dryland farming, and apple orchards) in four slope positions (shoulder, backslope, footslope, and toeslope). The soil physicochemical properties and magnetic parameters were measured, and soil redistribution was determined in the selected soil samples using the 137Cs technique. The results showed that rangeland degradation and, subsequently, rainfed cultivation, led to a significant decline in the soil quality indicators, such as soil organic matter (SOM), total nitrogen (TN), available potassium (Kava), and available phosphorous (Pava), thus incurring further soil loss, as determined by the 137Cs technique. Conversely, the conversion and rehabilitation of drylands to apple orchards cultivated on the contour terraces improved soil quality significantly and decreased soil loss (p < 0.05) and soil quality grade (p < 0.01). Additionally, the findings indicated that slope positions relative to land use change had a reasonable impact on the variability of soil properties and soil loss and deposition. The results of 137Cs analysis showed that the drylands had the highest soil loss (185.3 t ha−1 yr−1) and maximum sedimentation (182. 5 t ha−1 yr−1) in the shoulder and footslope positions, respectively. The random forest model applied between 137Cs inventory and soil properties indicated that calcium carbonate equivalent (CCE), TN, Pava, Kava, and bulk density (ρb) could explain 75% of the total variability in 137Cs inventory with high R2 (0.94) and low RMSE (111.29). Magnetic measurements have shown great potential as a cost-effective and fast method for assessing soil redistribution in hilly regions, as confirmed by the findings of the 137Cs analysis, which agreed well with the magnetic susceptibility at low frequency (χlf). Overall, the results confirmed that restoring abandoned dryland by orchard cultivation may improve soil quality and diminish soil loss in the semi-arid region of Iran. However, further research is required to assess other aspects of the ecosystem affected by this restoration.

Soil salinization is a severe danger to agricultural activity in arid and semi-arid areas, reducing crop production and contributing to land destruction. This investigation aimed to utilize machine learning algorithms to predict spatial soil salinity (dS m−1) by combining environmental covariates derived from remotely sensed (RS) data, a digital elevation model (DEM), and proximal sensing (PS). The study is located in an arid region, southern Iran (52°51′–53°02′E; 28°16′–28°29′N), in which we collected 300 surface soil samples and acquired the spectral data with RS (Sentinel-2) and PS (electromagnetic induction instrument (EMI) and portable X-ray fluorescence (pXRF)). Afterward, we analyzed the data using five machine learning methods as follows: random forest—RF, k-nearest neighbors—kNN, support vector machines—SVM, partial least squares regression—PLSR, artificial neural networks—ANN, and the ensemble of individual models. To estimate the electrical conductivity of the saturated paste extract (ECe), we built three scenarios, including Scenario (1): Synthetic Soil Image (SySI) bands and salinity indices derived from it; Scenario (2): RS data, PS data, topographic attributes, and geology and geomorphology maps; and Scenario (3): the combination of Scenarios (1) and (2). The best prediction accuracy was obtained for the RF model in Scenario (3) (R2 = 0.48 and RMSE = 2.49), followed by Scenario (2) (RF model, R2 = 0.47 and RMSE = 2.50) and Scenario (1) for the SVM model (R2 = 0.26 and RMSE = 2.97). According to ensemble modeling, a combined strategy with the five models exceeded the performance of all the single ones and predicted soil salinity in all scenarios. The results revealed that the ensemble modeling method had higher reliability and more accurate predictive soil salinity than the individual approach. Relative improvement (RI%) showed that the R2 index in the ensemble model improved compared to the most precise prediction for the Scenarios (1), (2), and (3) with 120.95%, 56.82%, and 66.71%, respectively. We applied the best model in each scenario for mapping the soil salinity in the selected area, which indicated that ECe tended to increase from the northwestern to south and southeastern regions. The area with high ECe was located in the regions that mainly had low elevations and playa. The areas with low ECe were located in the higher elevations with steeper slopes and alluvial fans, and thus, relief had great importance. This study provides a precise, cost-effective, and scientific base prediction for decision-making purposes to map soil salinity in arid regions.