THE CONTRIBUTION OF RADAR REMOTE SENSING VIA SENTINEL-1 DATA AND PHOTO-INTERPRETATION BY GOOGLE EARTH IMAGES FOR WETLAND MAPPING

Abstract. Wetlands are considered as sensitive ecosystems exposed and threatened by climate change and the urbanization of natural environments. In the purpose of managing these sensitive areas and conservatizing their biodiversity, remote sensing is an efficient way to track environmental variables over large areas as wetlands. However, when it comes to the study of hydrologic dynamics, high temporal and spatial resolutions are essential. Since the access to optical satellite imagery is restrictive because of the large cloud cover that masks the ground, radar sensors that are working in the microwave field, are particularly suited to the characterization of hydrological dynamics due to the sensitivity of their measurements in the presence of water, regardless of the vegetation in place. Recently, radar remote sensing has experienced a real revolution with the launch of the Sentinel-1A satellite in 2014, followed by its twin Sentinel-1B two years later by the European Space Agency as part of the Copernicus program. These sensors acquire C-band data (λ = 5.6 cm) with a temporal resolution of 12 days by satellite and their distribution is open and free. This article aims to assess the potential of Sentinel A1 SAR data for wetland mapping in the city of Benslimane (Central Morocco). The first part is explaining the methodology for mapping water surfaces. We identified a confusion of the C-band radar response of water surfaces and that of certain bare soils. We then showed that the VH polarization is the most suitable for the mapping of water surfaces, comparing four methods of detecting areas in water. It. The second part is discussing the use of unsupervised methods without a priori data demonstrating that the methods taking into account the spatial neighborhood give better results. Temporal filtering has been developed and has made it possible to improve detection and to overcome confusion between bare soil and permanent water surfaces. Water surfaces larger than 0.5 ha are at 80% detected. Classification was performed using the SVM (Support Vector Machine) algorithm. This latter information was then implemented into the thematic map derived from SPOT-4 images to obtain the final weltands map.