Surface Formations Salinity Survey in an Estuarine Area of Northern Morocco, by Crossing Satellite Imagery, Discriminant Analysis, and Machine Learning

Author:

El Jarjini Youssouf1,Morarech Moad1ORCID,Valles Vincent23,Touiouine Abdessamad4ORCID,Touzani Meryem5,Arjdal Youssef6ORCID,Barry Abdoul Azize7,Barbiero Laurent8ORCID

Affiliation:

1. Laboratory in Applied and Marine Geosciences, Geotechnics and Georisk (LR3G) Faculty of Science Te-touan, Abdelmalek Essaâdi University, Tetouan 93002, Morocco

2. Laboratoire Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes, Université Avignon, Avignon 84000, France

3. Faculty of Sciences and Technics (FSTBM), Beni Mellal 523 000, Morocco

4. Geosciences Laboratory, Faculty of Sciences, Ibn Tofaïl University, BP 133, Kénitra 14000, Morocco

5. National Institute of Agronomic Research, Rabat 10060, Morocco

6. Natural Resources and Sustainable Development Laboratory, Faculty of Sciences, Ibn Tofail University, BP 133, Kenitra 14000, Morocco

7. Geoscience and Environment Laboratory, (LaGE), Department of Earth Sciences, Joseph KI-ZERBO University, Ouagadougou 7021, Burkina Faso

8. Géoscience Environnement Toulouse, IRD, CNRS, UPS, OMP, Mixed Research Unit UMR5563, 14 Av. E. Belin, 31400 Toulouse, France

Abstract

The salinity of estuarine areas in arid or semi-arid environments can reach high values, conditioning the distribution of vegetation and soil surface characteristics. While many studies focused on the prediction of soil salinity as a function of numerous parameters, few attempted to explain the role of salinity and its distribution within the soil profile in the pattern of landscape units. In a wadi estuary in northern Morocco, landscape units derived from satellite imagery and naturalistic environmental analysis are compared with a systematic survey of salinity by means of apparent electrical conductivity (Eca) measurements. The comparison is based on the allocation of measurement points to an area of the estuary from Eca measurements alone, using linear discriminant analysis and four machine learning methods. The results show that between 57 and 66% of the points are well-classified, highlighting that salinity is a major factor in the discrimination of estuary zones. The distribution of salinity is mainly the result of the interaction between capillary rise and flooding by the tides and the wadi. The location of the misclassified points is analysed and discussed, as well as the possible causes of the confusions.

Publisher

MDPI AG

Subject

Earth-Surface Processes,Soil Science

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