Coastal wetlands are critical from an ecological, hydrological, and biodiversity point of view [1]. Today, these areas are under threat and have been declining every year since they were first studied [2]. Satellite images are an invaluable tool for understanding wetlands. Thanks to the revisit capability of satellites, coastal wetlands can be mapped in real time, allowing us to understand how they are changing. This work aims to map these ecosystems as accurately as possible. In order to achieve this, two types of sensors were used: a Sentinel-2 time series, a SPOT6/7 image, and altimetric data (RGE Alti©). Two automated learning algorithms were used: random forest (RF) and convolutional neural networks (CNN). Two methods were also used: a pixel approach and an object approach. The results show that the synergy of Sentinel-2 and SPOT with the contribution of indices and an object method works best, with an overall accuracy of 0.90 compared to 0.78 for a SPOT-6 pixel-by-pixel approach