## 1 Introduction
The joint use of multispectral imagery and synthetic aperture radar (SAR) products has shown a great potential to advance our knowledge on the structure of ecosystems at large scales, to increase the opportunities to acquire spatially explicit data on species composition and distribution, and to map key habitat features [41]. Among several satellite constellations, the imagery made freely available by the Copernicus Program through Sentinel-1 and Sentinel-2 missions [42] has significantly increased the potential to monitor ecosystems and detect and predict changes by featuring a higher spatial resolution and shorter revisitation cycle, in comparison to Landsat satellites, including multispectral and SAR products. The mapping of intertidal flats can be very challenging due to the similarities between the spectral signatures of different habitat types, especially among areas with distinct sediment properties, which occur along smooth gradients and present subtle differences in their surface characteristics. Furthermore, as intertidal areas are subject to cyclical inundation periods, the spectral characteristics of the sediments can change substantially due to the presence of surface water [43; 44]. Therefore, land cover classification exercises in intertidal areas require constant updates, allowing for the application of the latest advances in machine learning to deliver the best possible predictions. Among the most used machine learning algorithms, random forests [45] have been favoured by many recent studies focusing on supervised land cover classifications because of their flexibility, high accuracy, and computation efficiency in large datasets [46; 47]. This has been further improved with the introduction of several variable selection algorithms, which reduce the number of variables (and therefore, reduce redundancy), and increase computation efficiency, classification accuracy, and the interpretability of the results [48]. Despite the multiple refined remote sensing methods currently available, there is still a lack of applied studies gathering the most suitable techniques in automated/semi-automated workflows aiming at classifying intertidal habitats. By using well-established remote sensing and machine learning techniques, we aim to map, for the first time, the intertidal habitats of relevance to shorebirds and their benthic invertebrate prey in a key non-breeding area of the EAF of the Bijagos Archipelago. We combined data acquired by active (SAR) and passive (multispectral) sensors and a digital elevation model (DEM). We base this exercise on a thorough collection of the information in the field, which was used independently to train and validate classification algorithms. The workflow proposed includes a field reference data collection, satellite data collection and processing, computation of a DEM, variable selection, model training with random forest, and the application of a post-classification filter to produce the final output. Additionally, we assessed the benefits of combining multispectral, SAR, and DEM predictors to map intertidal habitat types by comparing our best model against alternative models excluding radar and DEM layers. With this study, we aim to contribute to filling the current knowledge gap on the application of remote sensing and machine learning methods in habitat classification in intertidal flats, one of the most challenging environments to map using remote sensing techniques, by proposing a generalizable workflow. It is also our objective to contribute with a baseline knowledge that is critical for further studies focused on shorebird ecology, distribution, and habitat use at a landscape scale in this key intertidal area.