The Impact of Salinization and Wind Erosion on the Texture of Surface Soils: An Investigation of Paired Samples from Soils with and without Salt Crust

Wind erosion removes fine soil particles and thus affects surface soil properties, but the existence of a salt crust could prevent wind erosion and protect fine soil particles. Such results referring to wind erosion affecting soil surface textural properties have been reported by many studies. However, it is still not clear whether soil properties differ between salt-crusted soils and adjacent soils without a salt crust in areas experiencing serious wind erosion. Therefore, the objective of this study was to investigate paired samples from salt-crusted and non-crusted surface soils at 23 sites in the Tarim River Basin. The particle size distribution, salt content and composition, and crust thickness and strength were determined. The results of the pooled t-test reveal that, compared with soils without a salt crust, the salt-crusted soils had finer particles (silt + clay), but this difference only occurred in paired soils from the same site, and the silt content showed the largest difference between the paired soils. The salt content and salt crust strength showed great variability, from 88.52 to 603 g·kg−1 and from 0.30 to 5.96 kg·cm−1, respectively, at all sites, but only a weak relationship (R2 = 0.396) between the salt content and crust strength was found, indicating that the salt content was not the only factor affecting crust strength. Our results suggest that wind erosion and salinization cause great soil texture spatial heterogeneity, especially for silt particles in the Tarim River Basin. Variation in salt crust strength can influence dust emissions and must be considered in future management.

Long-Term Impact of Wind Erosion on the Particle Size Distribution of Soils in the Eastern Part of the European Union

Wind erosion is the leading cause of soil degradation and air pollution in many regions of the world. As wind erosion is controlled by climatic factors, research on this phenomenon is urgently needed in soil and land management in order to better adapt to climate change. In this paper, the impact of wind erosion on the soil surface in relation to particle size distribution was investigated. Changes in percentage of sand, silt and clay fractions based on historical KPP data (1961–1970), LUCAS data base (2009), and field measurements (2016) were analysed in five cadastral areas impacted by wind erosion (Záhorie Lowlands, Slovakia). With the use of GIS tools, models of spatial distribution of sand, silt, clay and erodible fraction (EF) content were developed based on those measurements. Our findings proved that soil texture change driven by wind erosion could happen relatively quickly, and a significant proportion of soil fine particles may be carried away within a few years. The results indicate that the soil surface became much rougher over the period of more than 50 years, but also that the accumulation of fraction of the silt particles occurred in most of the areas affected by the erosive effect.

Improved digital soil mapping with multitemporal remotely sensed satellite data fusion: A case study in Iran

Modeling and mapping of soil properties are critical in many environmental, climatic, ecological and hydrological applications. Digital soil mapping (DSM) techniques are now commonly applied to predict soil properties with limited data by developing predictive relationships with environmental covariates. Most studies derive covariates from a digital elevation model (named static covariates). Many works also include single-day remotely sensed satellite imagery. However, multitemporal satellite images can capture information about soil properties over time and bring additional information in predicting soil properties in DSM. We refer to covariates derived from multitemporal satellite images as dynamic covariates. The objective of this study was to assess the performance of DSM when using terrain derivatives (static covariates), single-date remotely sensed satellite indices (limited dynamic covariates), multitemporal satellite indices (dynamic covariates), and combinations of terrain derivatives and satellite indices (covariate fusion) as covariates in predicting soil properties and estimating uncertainty. Three soil properties are considered in this study: organic carbon (OC), sand content, and calcium carbonate equivalent (CCE). Inclusion of single and/or multitemporal remotely sensed satellite indices improved the prediction of soil properties over traditionally used terrain indices. Significant improvements were observed in the prediction of soil properties using two models, Cubist and random forest (RF). The increase in the R² values for Cubist and RF were 126% and 78% for OC, 110% and 54% for sand, and 87% and 32% for CCE. The RMSE decreased by 34% and 27% for OC, 25% and 12% for sand, and 39% and 19% for CCE, when compared to the terrain indices only model. This also reduced the uncertainty of estimation and mapping. These clearly showed the advantage of using multitemporal satellite data fusion rather than simply using static terrain indices for DSM of soil properties to deliver a great potential in improving soil modeling and mapping for many applications.