# Mapping Subtidal Marine Forests in the Mediterranean Sea Using Copernicus Contributing Mission
## 2 Materials and Methods
### Analysis Workflow  
We developed a workflow based on open access, desktop-based software applications (Figure 4). Initially, ACOLITE [37; 38; 39] was employed for atmospheric correction, retrieving aquatic reflectance from the TOAR PlanetLabs imagery. The most recent version, 20250402.0, incorporates RAdCor processing and an explicit treatment of adjacency effects for Landsat, Sentinel-2, and PlanetScope datasets [40]. Default settings were applied and the Rayleigh-corrected product was retrieved (product rhorc_\"). We also estimated the Suspended Matter Concentration using the band switching algorithm available in ACOLITE as a metric of the water quality linked with the transparency of the water column. Non-water pixels were masked through the internal process of the algorithm. Subsequent analysis was conducted  
Figure 3: The imagery from the PlanetLabs archive as visualized in R (band 6)-G (band 4)-B (band 2) and overlaid over BING imagery in QGIS. The coordinate system is EPSG 32635 WGS 84/UTM zone 35N. in QGIS (version 3.40.4, Bratislava LTR) and the EnMAP toolbox (v3.13.3) [41] for the analysis of the image, both for the supervised classification and the spectral unmixing. The classification scheme included five habitat categories: \begin{tabular}{l l} (a) & _Cystoseira_ s.l. forests; \\ (b) & Shallow soft bottoms (\textless{}15 m depth); \\ (c) & Rocky reefs with other cover; \\ (d) & Seagrass; \\ (e) & Deep soft bottoms (\textgreater{}15 m depth). \\ \end{tabular}   
The designation of the training dataset for the analysis of the satellite imagery followed the pathway of integration of annotations we created using Bing basemaps and Google satellite imagery, available in QGIS and Google Earth. For the habitats \"shallow soft bottoms\", \"deep soft bottoms\", \"rocky reefs with other cover\" and \"_Seagrass_\", we identified the habitats on these platforms with confidence given the experience we have in the Greek territorial waters from past expeditions. We selected homogeneous patches that clearly depicted the target habitats, and we evaluated the spectral separability accordingly in an iterative approach. We finalized the delineation of our training data when the lowest value was above 1.50, as was the case with _Seagrass_ vs. rocky reefs with other cover. For the class \"_Cystoseira_ s.l.\", we utilized knowledge from colleagues that had visited the site for the collection of samples in the view of taxonomic work. They provided us with polygons in which, during their mission (August 2024), they observed 100% cover of the target habitat. Table 1 details the number of polygons and annotated pixels per class. Figure 5 presents the spatial distribution of the training data, and Figure 6 depicts the spectral signatures of each class. Figure 4: The developed workflow for image processing, training and validation design and classification. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Number of Polygons** & **Number of Annotated Pixels** \\ \hline _Cystoseira_ spp. forests & 3 & 469 \\ Deep soft bottoms & 7 & 15,809 \\ _Seagrass_ primarily composed of _Posidonia oceanica_ & 26 & 26,561 \\ Rocky reefs with other cover & 12 & 27,575 \\ Shallow soft bottoms & 31 & 25,248 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary information on the annotated data used in the analysis of the imagery. Figure 5: The spatial distribution of the training data overlaid on the atmospheric corrected imagery. Figure 6: The spectral signatures of the designated classification scheme, as collected at 5 m depth from the satellite imagery. Spectral separability between classes constitutes a pivotal step in the development of the classification scheme and the selection of the training sites. It was assessed using the Transformed Divergence (TD) metric (Table 2), with index values from 0 (complete overlap) to 2 (perfect separation) [42].