## 4 Discussion
### Classification Performance  
Using a stringent validation procedure (based only on polygons that were not used for classification), we achieved an Overall Accuracy of ca. 82% and a Kappa Coefficient of ca. 73% when classifying seven distinct intertidal habitat types. These indicators suggest a high quality classification, comparable to other similar studies for intertidal habitat mapping (e.g., [29; 43; 103; 104; 105; 106]). Remotely sensed mapping of intertidal areas is particularly challenging, especially when distinguishing different sediment types and can be hindered by the spectral similarities between sediment types with different grain sizes and by the presence of water. For example, by combining RapidEye (a multispectral instrument with 5 m spatial resolution) and TerraSAR-X (a SAR instrument with 1 m spatial resolution) [PERSON] et al. (2015) justified achieving lower classification accuracies for intertidal sediment areas than for intertidal vegetation and shellfish beds with the presence of water on the sediment. Likewise, [PERSON] et al. (2019) achieved accuracies limited to ca. 73% when attempting to classify six types of sediments using a very high spatial resolution (0.26 m) RGB orthoimage (from a CMOS sensor of a DSLR camera mounted on an unmanned aerial vehicle), due to the spectral similarities between the different sediment classes. Ref. [107] attained only 65% when using Sentinel-1 and Sentinel-2 to map three classes of sediment clay content. In our study, we successfully mapped two sediment types (sandy and mixed) for both the bare sediment and the FCB areas, with User's and Producer's Accuracies above 72% for all sediment types, except for bare sediment mixed, for which we only achieved 37%. Most of the pixels of this habitat type in the validation dataset were incorrectly classified as bare sediment sandy. Both bare sediment habitats (sandy and mixed) had the highest water film cover and high reflectance for the water and moisture-related indexes, which can explain the confusion between these two. Moreover, bare sediment mixed was among the rarest habitat types, both in our field reference data and in the final prediction map (1.9% of the total area); hence, it had considerably fewer data for training the RF algorithm. These two \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Final Model**} & \multicolumn{1}{c}{**S2 Model**} & \multicolumn{1}{c}{**S2 + S1 Model**} & \multicolumn{1}{c}{**S2 + DEM Model**} \\ \multicolumn{1}{c}{**Stats**} & \multicolumn{1}{c}{**S2 + S1 + DEM**} & \multicolumn{1}{c}{**14 Predictors**} & \multicolumn{1}{c}{**14 Predictors**} & \multicolumn{1}{c}{**14 Predictors**} \\ \hline \multirow{2}{*}{Bare sediment sandy} & User’s Accuracy & **83.8** & 80.9 & 83.4 & 81.3 \\  & Producer’s Accuracy & **93.2** & 90.9 & 93.0 & 92.1 \\ \hline \multirow{2}{*}{Bare sediment mixed} & User’s Accuracy & **70.2** & 58.0 & 60.3 & 62.2 \\  & Producer’s Accuracy & **36.1** & 28.7 & 32.8 & 27.9 \\ \hline \multirow{2}{*}{FCB sandy} & User’s Accuracy & **71.1** & 67.1 & 70.5 & 67.3 \\  & Producer’s Accuracy & **75.2** & 63.3 & 71.6 & 64.9 \\ \hline \multirow{2}{*}{FCB mixed} & User’s Accuracy & **83.5** & 80.4 & 80.8 & 81.0 \\  & Producer’s Accuracy & 72.8 & 68.1 & **74.1** & 67.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: \(Cont.\)factors might explain the poor performance when classifying the bare sediment mixed class but a good performance when predicting bare sediment sandy areas. Despite the overall positive results in mapping sediment in terms of mud content, the separation of sediment types into only two classes based on mud content may be too simplistic from the perspective of the benthic invertebrates and the shorebirds that feed on them.