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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, Tapoglou Evdokia, Vozinaki Anthi-Eirini, Tsanis Ioannis

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/1E698660-5AF5-4525-BDFD-8F3DA28C0CD7-
Αναγνωριστικόhttps://doi.org/10.3390/rs11091106-
Αναγνωριστικόhttps://www.mdpi.com/2072-4292/11/9/1106-
Γλώσσαen-
Μέγεθος21 pagesen
ΤίτλοςIntegrated use of satellite remote sensing, artificial neural networks, field spectroscopy, and GIS in estimating crucial soil parameters in terms of soil erosionen
ΔημιουργόςAlexakis Dimitriosen
ΔημιουργόςΑλεξακης Δημητριοςel
ΔημιουργόςTapoglou Evdokiaen
ΔημιουργόςΤαπογλου Ευδοκιαel
ΔημιουργόςVozinaki Anthi-Eirinien
ΔημιουργόςΒοζινακη Ανθη-Ειρηνηel
ΔημιουργόςTsanis Ioannisen
ΔημιουργόςΤσανης Ιωαννηςel
ΕκδότηςMDPIen
ΠερίληψηSoil 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.en
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2020-06-15-
Ημερομηνία Δημοσίευσης2019-
Θεματική ΚατηγορίαANNen
Θεματική ΚατηγορίαField spectroscopyen
Θεματική ΚατηγορίαGWRen
Θεματική ΚατηγορίαLandsat 8en
Θεματική ΚατηγορίαOLSRen
Θεματική ΚατηγορίαRemote sensingen
Θεματική ΚατηγορίαRUSLEen
Θεματική ΚατηγορίαSentinel-2en
Θεματική ΚατηγορίαSoil erosionen
Βιβλιογραφική ΑναφοράD.D. Alexakis, E. Tapoglou, A.K. Vozinaki and I.K. Tsanis "Integrated use of satellite remote sensing, artificial neural networks, field spectroscopy, and GIS in estimating crucial soil parameters in terms of soil erosion," Remote Sens., vol. 11, no. 9, May 2019. doi: 10.3390/rs11091106en

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