2. Materials and Methods
2.1. Study Sites and In Situ Data
2.2. Sentinel-2 Data Preprocessing
- We employ the QA60 bitmask band which contains cloud information to mask out opaque and cirrus clouds and scale S2 L1C TOA data by 10,000 (S2 quantification value).
- We use a classification and regression tree (CART) classifier  on a b3-b8-b11 composite to mask the terrestrial environment. It is noteworthy that although the classifier is trained with selected aquatic and terrestrial points (35 and 32, respectively) around Crete Island only (Figure 1d), it is utilised in all three sites.
- To atmospherically correct the masked for clouds and land images, we implement a modified dark pixel subtraction (DPS) method after  which subtracts the mean radiance of optically deep-water pixels (>40 m) to address path radiance and two standard deviations to address sensor-related noise in all bands.
- We employ the so-called image composition where a new pseudo-image is created using—in our case—the median values of the already pre-processed images . This approach aims to further reduce image artefacts which have not been corrected by the previous preprocessing steps. In fact, 79 tiles (34 SGE, 34 SGD, 35 SKV and 35 SKU; also called granules—100 × 100 km2 ortho images projected in Universal Transverse Mercator UTM/WGS84 ) form the Samaria National Park and Apokoronas pseudo-image, while 18 tiles (34 TFK) form the Thermaikos pseudo-image.
- We apply the sun glint correction algorithm from  to the median composite. Following a user-defined set of pixels that represents sun glint of variable intensity (two polygons in the south of tSamaria National Park site in South Crete), the algorithm equals the sun glint-corrected/sun glint-polluted median composite reduced by the product of the slope of the regression of NIR b8 against b1–b4 and the difference between b8 and its minimum value. We should state here that in , the preprocessing step included the mean and not the minimum NIR signal over optically deep water. The two last steps of our preprocessing chain are performed in a GIS environment (ArcMap 10.5) as we export the sun glint-corrected median composites from the previous step for all three areas of interest.
- We implement the pseudo-invariant features (PIF)  to radiometrically normalise b2 and b3 bands which are used in the validation of the SDB models (Apokoronas and Thermaikos composites; Figure 1a,b) to the b2 and b3 of the National Samaria Park composite (Figure 1c). This technique was developed to quantitatively transform a subject multispectral image to the reference multispectral image as if they were sensed under the same atmospheric conditions, and in the case of a coastal study, the same water surface and column conditions. The PIF-based composite normalisation is employed here to decrease the spectral differences which caused high RMSEs in our first SDB validation experiments (results are not shown here). Figure 3 displays the location of the selected PIF features (44 in total for each site), with shallow sands as the bright features and Posidonia oceanica seagrasses (National Samaria Park and Thermaikos) and brown algae (Apokoronas; mainly Cystoseira sp.) as the dark features. Figure S1 shows the linear equations which are utilised in the radiometric normalisations. We select these specific features because they occupy the extremes (high, low) of the observed reflectance range in all three sites following the recommendations that can be found in ; the underlying assumption here is that the PIF changes little between the same tiles (=same site) and different composites (=different site).
- We apply a 3 × 3 low pass filter to the normalised S2 b2 and b3 bands to further reduce noise before the training and validation of the empirical SDB models.
2.3. Empirical Satellite-Derived Bathymetries (SDB)
2.4. Spatial Distribution of Model Residuals
3.1. SDB Estimations
3.2. Methodological Gains Using a PIF Normalisation and 3 × 3 Smoothing
3.3. Spatial Distribution of Model Residuals
3.4. SDB Sensitivity to Variation in Seabed Habitat and Reflectance
4.1. Cloud-Based SDB and Model Performance
4.2. On the Importance of Image Composition, PIF and the Spatial Distribution of Errors
4.3. In Situ Data Collection, Crowdsourced Information and an Outlook for SDB
- Τhe implementation of the estimated spatial error parameter as a postprocessing step to increase the statistical accuracy of the empirically derived SDB models following the work in .
- Τhe utilisation of best-available-pixel (BAP) composition, which employs a series of pixel-based scores related to distance to clouds and shadow masks, atmospheric opacity, day of the year etc., instead of image composition across a relevant time series to the user’s study region .
- Τhe use of the proposed preprocessing workflow to conduct sea- to basin-wide habitat mapping and monitoring.
- Τhe fusion of the Copernicus Sentinel-1 (also available in GEE) and the Sentinel-2 open and free image archive for the development of 10-m topobathymetric digital elevation models (DEMs)—seamless merged elevation products of terrestrial and underwater topography  useful for numerous Earth science applications including mapping and modelling of inundation, sediment transport, sea-level rise, and storm surge .
Conflicts of Interest
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|Survey Site||National Samaria Park (Training)||Apokoronas (Validation)||Thermaikos Gulf (Validation)|
|Survey area (km2)||46.3||1.3||20.6|
|Number of in situ points||4978 (25-m models)|
3230 (12-m models)
|Depth range and intervals of in situ points (m)||0–25:|
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