Determining the spatial distribution of soil properties using the environmental covariates and multivariate statistical analysis: a case study in semi-arid regions of Iran

Natural soil-forming factors such as landforms, parent materials or biota lead to high variability in soil properties. However, there is not enough research quantifying which environmental factor(s) can be the most relevant to predicting soil properties at the catchment scale in semi-arid areas. Thus, this research aims to investigate the ability of multivariate statistical analyses to distinguish which soil properties follow a clear spatial pattern conditioned by specific environmental characteristics in a semi-arid region of Iran. To achieve this goal, we digitized parent materials and landforms by recent orthophotography. Also, we extracted ten topographical attributes and five remote sensing variables from a digital elevation model(DEM) and the Landsat Enhanced Thematic Mapper(ETM), respectively. These factors were contrasted for 334 soil samples(depth of 0–30 cm). Cluster analysis and soil maps reveal that Cluster 1 comprises of limestones, massive limestones and mixed deposits of conglomerates with low soil organic carbon(SOC) and clay contents, and Cluster 2 is composed of soils that originated from quaternary and early quaternary parent materials such as terraces, alluvial fans, lake deposits, and marls or conglomerates that register the highest SOC content and the lowest sand and silt contents. Further, it is confirmed that soils with the highest SOC and clay contents are located in wetlands, lagoons, alluvial fans and piedmonts, while soils with the lowest SOC and clay contents are located in dissected alluvial fans, eroded hills, rock outcrops and steep hills. The results of principal component analysis using the remote sensing data and topographical attributes identify five main components, which explain 73.3% of the total variability of soil properties. Environmental factors such as hillslope morphology and all of the remote sensing variables can largely explain SOC variability, but no significant correlation is found for soil texture and calcium carbonate equivalent contents. Therefore, we conclude that SOC can be considered as the best-predicted soil property in semi-arid regions.

Impacts of oak deforestation and rainfed cultivation on soil redistribution processes across hillslopes using ^(137)Cs techniques

Background:As one of the main components of land-use change,deforestation is considered the greatest threat to global environmental diversity with possible irreversible environmental consequences.Specifically,one example could be the impacts of land-use changes from oak forests into agricultural ecosystems,which may have detrimental impacts on soil mobilization across hillslopes.However,to date,scarce studies are assessing these impacts at different slope positions and soil depths,shedding light on key geomorphological processes.Methods:In this research,the Caesium-137(^(137)Cs)technique was applied to evaluate soil redistribution and soil erosion rates due to the effects of these above-mentioned land-use changes.To achieve this goal,we select a representative area in the Lordegan district,central Iran.^(137)Cs depth distribution profiles were established in four different hillslope positions after converting natural oak forests to rainfed farming.In each hillslope,soil samples from three depths(0–10,10–20,and 20–50 cm)and in four different slope positions(summit,shoulder,backslope,and footslope)were taken in three transects of about 20m away from each other.The activity of ^(137)Cs was determined in all the soil samples(72 soil samples)by a gamma spectrometer.In addition,some physicochemical properties and the magnetic susceptibility(MS)of soil samples were measured.Results:Erosion rates reached 51.1 t·ha^(−1)·yr^(−1) in rainfed farming,whereas in the natural forest,the erosion rate was 9.3 t·ha^(−1)·yr^(−1).Magnetic susceptibility was considerably lower in the cultivated land(χhf=43.5×10^(−8)m^(3)·kg^(−1))than in the natural forest(χhf=55.1×10^(−8)m^(3)·kg^(−1)).The lower soil erosion rate in the natural forest land indicated significantly higher MS in all landform positions except at the summit one,compared to that in the rainfed farming land.The shoulder and summit positions were the most erodible hillslope positions in the natural forest and rainfed farming,respectively.Conclusions:We concluded that land-use change and hillslope positions played a key role in eroding the surface soils in this area.Moreover,land management can influence soil erosion intensity and may both mitigate and amplify soil loss.

Remote sensing of soil degradation:Pro gress andperspective

Soils constitute one of the most critical natural resources and maintaining their health is vital for agricultural development and ecological sustainability,providing many essential ecosystem services.Driven by climatic variations and anthropogenic activities,soil degradation has become a global issue that seriously threatens the ecological environment and food security.Remote sensing(RS)technologies have been widely used to investigate soil degradation as it is highly efficient,time-saving,and broad-scope.This review encompasses recent advances and the state-of-the-art of ground,proximal,and novel Rs techniques in soil degradation-related studies.We reviewed the RS-related indicators that could be used for monitoring soil degradation-related properties.The direct indicators(mineral composition,organic matter,surface roughness,and moisture content of soil)and indirect proxies(vegetation condition and land use/land cover change)for evaluating soil degradation were comprehensively summarized.The results suggest that these above indicators are effective for monitoring soil degradation,however,no indicators system has been established for soil degradation monitoring to date.We also discussed the RS's mechanisms,data,and methods for identifying specific soil degradation-related phenomena(e.g.,soil erosion,salinization,desertification,and contamination).We investigated the potential relations between soil degradation and Sustainable Development Goals(SDGs)and also discussed the challenges and prospective use of RS for assessing soil degradation.To further advance and optimize technology,analysis and retrieval methods,we identify critical future research needs and directions:(1)multi-scale analysis of soil degradation;(2)availability of RS data;(3)soil degradation process modelling and prediction;(4)shared soil degradation dataset;(5)decision support systems;and(6)rehabilitation of degraded soil resource and the contribution of RS technology.Because it is difficult to monitor or measure all soil properties in the large scale,remotely sensed characterization of soil properties related to soil degradation is particularly important.Although it is not a silver bullet,RS provides unique benefits for soil degradation-related studies from regional to global scales.