Leveraging Commercial High-Resolution Multispectral Satellite and Multibeam Sonar Data to Estimate Bathymetry: The Case Study of the Caribbean Sea
2. Materials and Methods
2.1. Study Sites
2.2. In Situ and Satellite Data
2.2.1. Multibeam Echosounder Surveys
2.2.2. Pleiades Imagery
2.3. Satellite Data Preprocessing
- The cloud mask data that were supplied with the raw Pleiades imagery were not accurate or precise enough for use. All clouds over marine environments were manually delineated and masked in QGIS 3.4.
- The Pleiades data lacks the shortwave-infrared of Sentinel-2 used within the classification and regression tree (CART) classifier. A CART classifier using the Pleiades imagery was considered, but did not reflect the existing boundaries used by the island GIS departments. To ensure interoperability with existing departmental data, all terrestrial environments were masked using OpenStreetMap boundary data in QGIS 3.4.
- The modified dark pixel subtraction (DPS) method  was implemented in QGIS 3.4.
- The temporal image composition  was outside the scope of this study and was not performed.
- The sun-glint correction algorithm  was performed on the single scene Pleiades images, within QGIS 3.4.
- There was no deviation from how the PIF were extracted, modelled or applied , except for the vegetation types used to represent dark features. In this study, shallow sand was used in all sites as bright features. Thalassia testudinum (turtle grass) and Syringodium filiforme (manatee grass) were used as dark features for the Anguilla site and BVI site respectively. The location used to extract the bright and dark featured are displayed in Figure 2.
- The 3 × 3 low pass filter was applied in QGIS 3.4.
2.4. Empirical Satellite-Derived Bathymetries (SDB)
2.5. Accuracy and Error
3.1. SDB Estimations and Accuracies
3.2. SDB Vertical Errors
4.1. Suitability of the Pleiades Imagery for High-Resolution SDB Estimations
4.2. Thematic, Geographical and Methodological Comparisons
4.3. The Pros and Cons of the Current SDB Approach
- High-spatial-resolution and accurate SDB calculations of 2 m in a tropical environment which could fit into Zone A2 of CATZOC and be used for navigation purposes.
- Efficient in time, technical capacity, and computation (in comparison to state-of-the-art physics-based, photogrammetric, and adaptive-based methods).
- Minimization of statistical bias of neighbouring observations according to the first law of geography  by implementing two geographically independent (distance of ~147 km) MBES-derived datasets.
- Reduction of radiometric differences between the Pleiades images employed in SDB training and validation (through the use of pseudo-invariant features) which could have inflicted greater vertical errors otherwise.
- Two-year difference between the used Pleiades imagery for SDB calibration and validation and the in situ data from the site of BVI. However, while this temporal difference should theoretically impose quantitative disagreements, in this case, due to the broader absence of river runoffs in the northeast Caribbean Sea, we do not expect it to have influenced the SDB estimations.
- Empirical SDB methods like [7,8] assume homogeneous and unique water column conditions and bottom types. Here, the Anguilla and BVI benthos feature a mixture of seagrasses, sand, rocks, sponges, corals, and algae; and in conjunction with increased sedimentation in the satellite image from BVI, they violate the aforementioned empirical assumption and might have affected our observations.
- The cost of the herein in situ information by MBES survey data might be expensive and elusive for other SDB-related projects, applications, and studies. Nevertheless, the initial development and application of the current SDB processing chain have exhibited accurate results with the use of low-cost bathymetric systems and data.
4.4. Back to the Future: The Three Actors for Global SDB Coverage
- NASA’s GEDI (Global Ecosystem Dynamics Investigation)—a two-year, high-resolution spaceborne LiDAR mission deployed on the International Space Station on 5 December 2018 ;
- NASA’s ICESat-2 (Ice, Cloud and Land Elevation Satellite-2)—a three-year, satellite-based LiDAR mission equipped with its ATLAS (Advanced Topographic Laser Altimeter System) sensor, launched on September 15 2018, whose suitability for SDB extractions in different natural environments has been already explored [38,39];
- DLR’s EnMAP (Environmental Analysis and Mapping Program) —an envisaged five-year spaceborne imaging spectroscopy mission. Expected for launch in 2020, EnMAP will offer VNIR + SWIR hyperspectral data of 30 m spatial sampling and four-day temporal revisit. This will unlock new SDB mapping and monitoring ventures with a minimum spectral sampling distance of 7.5 nm and a signal-to-noise ratio of 400:1 in the VNIR wavelength range .
Conflicts of Interest
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|Study Site||Date of Acquisition||Sensor Azimuth||Sensor Viewing Angle||Solar Azimuth||Solar Elevation|
|Anguilla||15 August 2016||180.33||23.66||98.13||68.01|
|BVI||25 January 2016||179.99||2.51||147.75||46.49|
|Model Depth||Intercept||B1 Coefficient||B2 Coefficient||R-Squared Value||RMSE Value, Metres (m)|
|Study Site||Model||Training Points||Validation Points|
|British Virgin Islands||0–10 m|
|Study Site||Depth Range, Metres (m)||R-Squared Value||RMSE Value, Metres (m)|
|British Virgin Islands||0–10||0.44||1.39|
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Pike, S.; Traganos, D.; Poursanidis, D.; Williams, J.; Medcalf, K.; Reinartz, P.; Chrysoulakis, N. Leveraging Commercial High-Resolution Multispectral Satellite and Multibeam Sonar Data to Estimate Bathymetry: The Case Study of the Caribbean Sea. Remote Sens. 2019, 11, 1830. https://doi.org/10.3390/rs11151830
Pike S, Traganos D, Poursanidis D, Williams J, Medcalf K, Reinartz P, Chrysoulakis N. Leveraging Commercial High-Resolution Multispectral Satellite and Multibeam Sonar Data to Estimate Bathymetry: The Case Study of the Caribbean Sea. Remote Sensing. 2019; 11(15):1830. https://doi.org/10.3390/rs11151830Chicago/Turabian Style
Pike, Samuel, Dimosthenis Traganos, Dimitris Poursanidis, Jamie Williams, Katie Medcalf, Peter Reinartz, and Nektarios Chrysoulakis. 2019. "Leveraging Commercial High-Resolution Multispectral Satellite and Multibeam Sonar Data to Estimate Bathymetry: The Case Study of the Caribbean Sea" Remote Sensing 11, no. 15: 1830. https://doi.org/10.3390/rs11151830