## 5 Conclusions  
Mapping intertidal areas using satellite imagery is a challenging task, particularly due to the spectral homogeneity of different types of substrate and to the interference of surface water with reflectance of different wavelengths. Nonetheless, tidal areas represent very important environments for a suite of bird species, including migratory shorebirds, many of which are in decline worldwide. By presenting a detailed workflow that allows the successful mapping of a considerable number of habitat classes that are potentially important for migratory shorebirds and their benthic invertebrate prey, this study contributes to the field of ecological applications of remote sensing within intertidal ecosystems. Among the relevant contributions in this study is the production of the first distribution map of fiddler crabs, an ecosystem engineer known to play an important role in shaping benthic food webs in intertidal landscapes. Using an extensive testing and validation procedure, we showed that the application of both active and passive sensors from freely available sources (ESA's Sentinel program) and a DEM can improve the classification accuracy in intertidal environments. This may serve as a basis for similar applications elsewhere, and we argue that the proposed workflow may be replicated in other intertidal areas, with the required methodological adjustments to the particular conditions in each area. Nevertheless, we faced challenges when trying to identify more than two sediment types, and further improvements, e.g., considering more complex, textural, and spatial-dependent sediment descriptors (e.g., [103]) and other thresholding techniques, should now be explored. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs14143260/s1](https://www.mdpi.com/article/10.3390/rs14143260/s1)]([https://www.mdpi.com/article/10.3390/rs14143260/s1](https://www.mdpi.com/article/10.3390/rs14143260/s1)), Table S1. Sentinel-2 satellite image time-series selected to produce the water and land mask, and the digital elevation model. For each date (n = 29), two adjacent scenes were downloaded (T28 PCT and T28 PDT), totalling 58 images. Tidal heights (water height at the exact time of image acquisition) for each image were calculated following the methods described in [69]. Table S2: Performance statistics and respective equations. TP: true positives (fraction of pixels that are correctly classified as belonging to their true class). TN: true negatives (fraction of pixels correctly classified as not belonging to a class). FP: false positives (fraction of pixels wrongly classified as not belonging to a class). An: observed Accuracy (true proportion of correctly classified pixels in relation to all pixels). Ae: expected Accuracy (the accuracy that would be expected to be achieved by the classifier purely by chance). i: any given class. Table S3. Results of the stepwise backward variable selection, with mean, min and max validation statistics (n = 25 random forests for each predictors set). Each row represents one step of the backward selection. Overall Accuracy and Kappa Coefficient values range between 0 and 100%. Least important variable shows the predictors with the highest % of votes as the least important throughout the 25 random forests, in each step (and therefore, the next variable to remove). The last variable kept was nNDMI. N/A: not applicable. Table S4. By-class permutated variable importance measure (PVIM) for each predictor used in the final model (with 14 predictors). Values presented were rescaled in relation to the maximum value, to range between 0 and 1. Predictor acronyms are described in Table 2 of the main article. BS: Bare sediment areas. FCB: Fiddler crab burrow areas. Figure S1. Visual representation of two merged Sentinel-2 scenes before (A) and after (B) scene intercalibration.