Mapping South Baltic Near-Shore Bathymetry Using Sentinel-2 Observations

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Abstract

One of the most promising new applications of remote observation satellite systems (RO) is the near-shore bathymetry estimation based on spaceborn multispectral imageries. In recent years, many experiments aiming to estimate bathymetry in optically shallow water with the use of remote optical observations have been presented. In this paper, optimal models of satellite derived bathymetry (SDB) for relatively turbid waters of the South Baltic Sea were presented. The obtained results were analysed in terms of depth error estimation, spatial distribution, and overall quality. The models were calibrated based on sounding (in-situ) data obtained by a single-beam echo sounder, which was retrieved from the Maritime Office in Gdynia, Poland. The remote observations for this study were delivered by the recently deployed European Space Agency Sentinel-2 satellite observation system. A detailed analysis of the obtained results has shown that the tested methods can be successfully applied for the South Baltic region at depths of 12-18 meters. However, significant limitations were observed. The performed experiments have revealed that the error of model calibration, expressed in meters (RMSE), equals up to 10-20% of the real depth and is, generally, case dependent. To overcome this drawback, a novel indicator of determining the maximal SDB depth was proposed. What is important, the proposed SDB quality indicator is derived only on the basis of remotely registered data and therefore can be applied operationally.

1. D. R. Lyzenga, N. R. Malinas, and F. J. Tanis, “Multispectral bathymetry using a simple physically based algorithm,” IEEE Trans. Geosci. Remote Sens., vol. 44, no. 8, pp. 2251–2259, Aug. 2006.

2. A. Chybicki et al., “GIS for remote sensing, analysis and visualisation of marine pollution and other marine ecosystem components,” 2008 1st International Conference on Information Technology, Gdansk, 2008, pp. 1-4. doi: 10.1109/INFTECH.2008.4621628

3. Stumpf, R.P., Holderied, K., Sinclair, M., 2003. Determination of water depth with high-resolution satellite imagery over variable bottom types. Limonology Oceanogr. 48, 547–556. doi:10.4319/lo.2003.48.1_part_2.0547

4. R. Lyzenga, D., 1981. Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. Int. J. Remote Sens. 2, 71–82.

5. R. Lyzenga, D., 1978. Passive remote sensing technique for mapping water depth. Appl. Opt. 17, 379–383.

6. Philpot, W.D., 1989. Bathymetric mapping with passive multispectral imagery. Appl. Opt. 28, 1569–1578. doi:10.1364/AO.28.001569

7. J. C. Sandidge, and R. J. Holyer, “Coastal bathymetry from hyperspectral observations of water radiance,” Remote Sens. Environ., vol. 65, no. 3, pp. 341–352, Sep. 1998.

8. S. M. Adler-Golden, P. K. Acharya, A. Berk, M. W. Matthew, and D. Gorodetzky, “Remote bathymetry of the littoral zone from AVIRIS, LASH, and QuickBird imagery,” IEEE Trans. Geoscis. Remote Sens., vol. 43, no. 2, pp. 337–347, Feb. 2005.

9. Sheng Ma, Zui Tao, Xiaofeng Yang, Member, IEEE, Yang Yu, Xuan Zhou, and Ziwei Li, “ Bathymetry Retrieval from Hyperspectral Remote Sensing Data in Optical-Shallow Water”, Ieee Transactions on Geoscience and Remote Sensing, Vol. 52, No. 2, February 2014

10. H. Holden and E. LeDrew, “Measuring and modeling water column effects on hyperspectral reflectance in a coral reef environment,” Remote Sens. Environ., vol. 81, nos. 2–3, pp. 300–308, Aug. 2002.

11. Haibin Su, Hongxing Liu, William D. Heyman, “Automated Derivation of Bathymetric Information from Multi-Spectral Satellite Imagery Using a Non-Linear Inversion Model”, Marine Geodesy, vol. 31, 2008.

12. E. P. Green, P. J. Mumby, A. J. Edwards, and C. D. Clark, “Remote sensing handbook for tropical coastal management,” in Coastal Management Sourcebooks 3, A. J. Edward, Ed. Paris, France: UNESCO, 2000.

13. Poliyapram V., Venkatesh R., Shinji M., 2016, Satellite-derived bathymetry using adaptive geographically weighted Regression model, Marine Geodesy, vol. 39:6, 458-478.

14. Jensen, J. R. 2007. Remote sensing of the environment: An earth resource perspective, 2nd ed. Upper Saddle River, NJ: Prentice Hall

15. Mishra, D., S. Narumalani, D. Rundqulst, and M. Lawson. 2006. Benthic habitat mapping in tropical marine environments using QuickBird multispectral data. Photogrammetric Engineering & Remote Sensing 72:1037–1048.

16. Lyzenga, D. R., N. P. Malinas, and F. J. Tanis. 2006. Multispectral bathymetry using a simple physically based algorithm. Geoscience and Remote Sensing, IEEE Transactions on 44:2251–2259.

17. Green, E. P., P. J. Mumby, A. J. Edwards, and C. D. Clark. 2000. Remote sensing handbook for tropical coastal management. Paris: A. J. Edwards, UNESCO

18. Drusch, M. & 14 co-authors (2012). Sentinel-2: ESA’s optical high-resolution mission for GMES operational services, Rem. Sens. Env. (accepted).

19. Sentinels Scientific Data Hub, Technical Guide, European Space Agency, source: https://scihub.copernicus.eu/userguide/ (accessed on 22/12/2016)

20. Hellenic National Sentinel Data Mirror Site. Operated by the National Observatory of Athens. Source: https://sentinels.space.noa.gr/ (accessed on 22/12/2016)

21. National French Copernicus Site, Centre National D’Etudes Spatiales, Source: https://copernicus.cnes.fr/en/ground-segment-1 (accessed on 22/12/2016)

22. Sentinel Application Platform (SNAP), available at Science Toolbox Exploitation Platform (STEP), European Space Agency, source: http://step.esa.int/main/toolboxes/snap/ (accessed on 22/12/2016)

23. Sen2cor - Sentinel-2 Level 2A product generation and formatting, available at Science Toolbox Exploitation Platform (STEP), European Space Agency, source: http://step.esa.int/main/third-party-plugins-2/sen2cor/ (accessed on 22/12/2016)

Polish Maritime Research

The Journal of Gdansk University of Technology

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CiteScore 2017: 0.99

SCImago Journal Rank (SJR) 2017: 0.280
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