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.
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The dataset includes 583 spectra collected by nine EJP Soil Probefield\u2019s partners in eight countries in 214 sampling points. Nine different VNIR spectroradiometers equipped with contact probes were used to collect the spectra (350 \u2013 2500 nm). Each spectrum corresponds to the average reflectance of five repetitions . Soil samples were collected from each sampling point and brought to the laboratory for determining the organic content (TOC). The raw spectra were reduced in the range between 400 and 2450 to remove the spectrum tails usually containing noisy signal and the following spectral pre-treatments and transformation were applied: 1.\u00a0\u00a0\u00a0\u00a0\u00a0 Spline interpolation of the values of the edges of the middle sensor to correct steps 2.\u00a0\u00a0\u00a0\u00a0\u00a0 Savitzky-Golay smoothing filter using a second polynomial order and a window of 11 nm 3.\u00a0\u00a0\u00a0\u00a0\u00a0 Detrending spectral data by applying a SNV transformation 4.\u00a0\u00a0\u00a0\u00a0\u00a0 Spectral alignment applying a different correction factor for each spectroradiometer using the Lucky Bay sands as internal soil standard 5.\u00a0 \u00a0The spectra were further transformed using the External Parameter Orthogonalization (EPO) matrix obtained from\u00a0DT_EPO.csv to\u00a0remove the effects of soil moisture Therefore, three different datasets were extracted: a \u00a0 \u00a0 \u00a0D3.2_20240117_ProbeField_Preprosessed_Spectra_DT_raw.csv: data extracted at the end of point 3 b\u00a0 \u00a0 \u00a0D3.2_20240117_ProbeField_Preprosessed_Spectra_DT_ISS.csv: data extracted at the end of point 4 c\u00a0 \u00a0 \u00a0 D3.2_20240117_ProbeField_Preprosessed_Spectra_DT_EPO.csv: data extracted at the end of point 5", "keywords": ["Soil sciences", "Soil", "ProbeField", "EJP SOIL", "Soil Organic Carbon", "Soil spectroscopy", "EJPSOIL", "SOC", "Proximal sensing", "soil spectral library", "Field Spectroscopy"], "contacts": [{"organization": "Castaldi, Fabio", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13753159"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13753159", "name": "item", "description": "10.5281/zenodo.13753159", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13753159"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-09-12T00:00:00Z"}}, {"id": "10.5281/zenodo.13757028", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:23:14Z", "type": "Dataset", "title": "D3.3_20230919_ProbeField_Aligned_Spectra_V1", "description": "Open AccessThe reference data (SOC) of the Swedish soil samples are part of the Swedish national soil monitoring programme for agricultural soils, Soil and crop inventory and are owned by The Swedish Environmental Protection Agency. 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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.
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