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Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion

Alexakis, Dimitrios D.; Tapoglou, Evdokia; Vozinaki, Anthi-Eirini K.; Tsanis, Ioannis K.

Authors

Dimitrios D. Alexakis

Anthi-Eirini K. Vozinaki

Ioannis K. Tsanis

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

Journal Article Type Article
Publication Date May 9, 2019
Journal Remote Sensing
Print ISSN 2072-4292
Electronic ISSN 2072-4292
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 11
Issue 9
Article Number 1106
Institution Citation Alexakis, D. D., Tapoglou, E., Vozinaki, A. K., & Tsanis, I. K. (2019). Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion. Remote Sensing, 11(9), https://doi.org/10.3390/rs11091106
DOI https://doi.org/10.3390/rs11091106
Keywords Soil erosion; Remote sensing; Sentinel-2; Landsat 8; ANN; RUSLE; Field spectroscopy; OLSR; GWR
Publisher URL https://www.mdpi.com/2072-4292/11/9/1106

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Copyright Statement
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).




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