Abstract
Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), soil structure, particle size, permeability, and Calcium Carbonate equivalent (CaCO3), can be the key properties for the evaluation of soil erosion. In this work, several innovative methods (satellite remote sensing, field spectroscopy, soil chemical analysis, and GIS) were investigated for their potential in monitoring SOM, CaCO3, and soil erodibility (K-factor) of the Akrotiri cape in Crete, Greece. Laboratory analysis and soil spectral reflectance in the VIS-NIR (using either Landsat 8, Sentinel-2, or field spectroscopy data) range combined with machine learning and geostatistics permitted the spatial mapping of SOM, CaCO3, and K-factor. Synergistic use of geospatial modeling based on the aforementioned soil properties and the Revised Universal Soil Loss Equation (RUSLE) erosion assessment model enabled the estimation of soil loss risk. Finally, ordinary least square regression (OLSR) and geographical weighted regression (GWR) methodologies were employed in order to assess the potential contribution of different approaches in estimating soil erosion rates. The derived maps captured successfully the SOM, the CaCO3, and the K-factor spatial distribution in the GIS environment. The results may contribute to the design of erosion best management measures and wise land use planning in the study region.
Highlights
Soil erosion is a complex four-stage dynamic process involving soil detachment, breakdown, transport, and subsequent deposition of sediments [1]
Soil erosion may pose a threat to food security if we consider that about nine billion people should be fed by 2050 [4]
Satellite remote sensing is virtually the only data source that permits a repeated monitoring of land degradation dynamics [18]. Both [10] and [13] applied integrated use of geostatistics, geoinformatics, and field spectroscopy to study the correlation between soil erosion and various soil parameters such as soil organic matter (SOM), CaCO3, and K-factor
Summary
Soil erosion is a complex four-stage dynamic process involving soil detachment, breakdown, transport, and subsequent deposition of sediments [1]. Satellite remote sensing is virtually the only data source that permits a repeated monitoring of land degradation dynamics [18] In this context, both [10] and [13] applied integrated use of geostatistics, geoinformatics, and field spectroscopy to study the correlation between soil erosion and various soil parameters such as SOM, CaCO3, and K-factor. Machine learning approaches such as artificial neural networks (ANN) have been successfully used in the recent past to simulate soil erosion processes [27,28,29] In those cases, ANNs have been utilized to describe the nonlinear relationships between eroded soils and relevant soil parameters such as SOM and CaCO3. Soil samples spectral data as derived from field spectroscopy campaigns were used in order to assess and correlate soil properties with specific spectral bands
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