Derivation of Bathymetry from High-resolution Optical Satellite Imagery and USV Sounding Data

References

• Lyzenga, D. R. 1985. Shallow-water bathymetry using combined lidar and passive multispectral scanner data. International Journal of Remote Sensing 6(1):115–125.
   Web of Science ®Google Scholar
• Ping, Z. 1982. Mathematics model for visible remote sensing of water depth. Oceanologia et Limnologia Sinica (03):225–230. ( in Chinese).
   Google Scholar
• Li, T., J. Yi, and Y. Li, et al. 1991. The extraction of geomorphologic remote sensing information on shallow sea under-water terrain and its application. Remote Sensing of Environment China (01):22–29. ( in Chinese).
   Google Scholar
• Chen, Q., R. Deng, Y. Qin, et al. 2012. Water depth extraction from remote sensing image in Feilaixia reservoir. Acta Scientiarum Naturalium Universitatis Sunyatseni (01):122–127. ( in Chinese).
   Google Scholar
• Polcyn, F. C., and I. J. Sattinger. 1969. Water depth determination using remote sensing techniques. Proceedings of the 6th International Symposium on Remote Sensing of Environment, Ann Arbor, Michigan, 1017–1028.
   Google Scholar
• Polcyn, F. C., and D. R. Lyzenga. 1973. Calculation of water depth from ERTS-MSS data. Proceedings Symposium on Significant Results Obtained from ERTS-1, New Carrollton, Maryland, 1433–1436.
   Google Scholar
• Paredes, J. M., and R. E. Spero. 1983. Water depth mapping from passive remote sensing data under a generalized ratio assumption. Applied Optics 22(8):1134–1135.
   PubMed Web of Science ®Google Scholar
• Benny, A. H., and G. J. Dawson. 1983. Satellite imagery as an aid to bathymetric charting in the Red Sea. The Cartographic Journal 20(1):5–16.
   Google Scholar
• Tanis, F. J., and W. A. Hallada. 1985. Evaluation of Landsat Thematic Mapper data for shallow water bathymetry. Proceedings of the 18th International Symposium on Remote Sensing of Environment, Paris, France, 629–643.
   Google Scholar
• Tanis, F. J., and H. J. Byrne. 1985. Optimization of multispectral sensors for bathymetry applications. Proceedings of the 19th International Symposium on Remote Sensing of Environment, Ann Arbor, Michigan, 865–874.
   Google Scholar
• Ji, W., D. L. Civco, and W. C. Kennard. 1992. Satellite remote bathymetry: A new mechanism for modeling. Photogrammetric Engineering & Remote Sensing 58(5):545–549.
   Web of Science ®Google Scholar
• Di, K., Q. Ding, W. Chen, et al. 1999. Shallow water depth extraction and chart production from TM Images in Nansha Islands and nearby sea area. Remote Sensing for Land & Resources (03):59–64. ( in Chinese).
   Google Scholar
• Dang, F., and Q. Ding. 2001. A study of shallow water depth extraction using landsat imagery. Remote Sensing for Land & Resources (04):53–58. ( in Chinese).
   Google Scholar
• Dang, F., and Q. Ding. 2003. A technique for extracting water depth information from multispectral scanner data in the South China Sea. Marine Science Bulletin (03):55–60. ( in Chinese).
   Google Scholar
• Stumpf, R. P., K. Holderied, and M. Sinclair. 2003. Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnology Oceanography 48(1 part 2):547–556.
   Web of Science ®Google Scholar
• Lyzenga, D. R., N. P. Malinas, and F. J. Tanis. 2006. Multispectral bathymetry using a simple physically based algorithm. IEEE Transactions on Geoscience and Remote Sensing 44(8):2251–2259.
   Web of Science ®Google Scholar
• Lyzenga, D. R. 1978. Passive remote sensing techniques for mapping water depth and bottom features. Applied Optics 17(3):379–383.
   PubMed Web of Science ®Google Scholar
• Lyzenga, D. R. 1981. Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. International Journal of Remote Sensing 2(1):71–82.
   Google Scholar
• Wang, Y., and Y. Zhang. 2005. Study on remote sensing of water depth based on BP artificial neural networks. The Ocean Engineering (04):37–42. ( in Chinese).
   Google Scholar
• Tong, Q., B. Zhang, and L. Zheng. 2006. Hyperspectral Remote Sensing: Principle, Technology and Application. Beijing: High Education Press. ( in Chinese)
   Google Scholar
• Wang, J., and Q. Tian. 2007. Study on shallow coastal water depth derived method based on hyperspectral remote sensing. Scientia Geographica Sinica (06):843–848. ( in Chinese).
   Google Scholar
• Wang, X., and L. Zhang. 2012. The study on water depth inversion based on spectral similarity. Laser Journal 33(3):39–40. ( in Chinese)
   Google Scholar
• Mobley, C. D., L. K. Sundman, C. O. Davis, et al. 2005. Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables. Applied Optics 44(17):3576–3592
   PubMed Web of Science ®Google Scholar
• Deng, Z., X. Ye, H. Guan, et al. 2013. Remote sensing of water depth based on RBF neural network. Journal of PLA University of Science and Technology (Natural Science Edition) (1):101–106. ( in Chinese).
   Google Scholar
• Lee, Z., K. L. Carder, C. D. Mobley, et al. 1998. Hyperspectral remote sensing for shallow waters. I. A semianalytical model. Applied Optics 37(27):6329–6338.
   PubMed Web of Science ®Google Scholar
• Lee, Z., K. L. Carder, C. D. Mobley, et al. 1999. Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization. Applied Optics 38(18):3831–3843.
   PubMed Web of Science ®Google Scholar
• Lee, Z., K. L. Carder, and R. A. Arnone. 2002. Deriving inherent optical properties from water color: A multiband quasi-analytical algorithm for optically deep waters. Applied Optics 41(27):5755–5772.
   PubMed Web of Science ®Google Scholar
• Lee, Z., K. L. Carder. 2002. Effect of spectral band numbers on the retrieval of water column and bottom properties from ocean color data. Applied Optics 41(12):2191–2201.
   PubMed Web of Science ®Google Scholar
• Lee, Z., K. L. Carder, and K. Du. 2004. Effects of molecular and particle scatterings on the model parameter for remote-sensing reflectance. Applied Optics 43(25):4957–4964.
   PubMed Web of Science ®Google Scholar
• Manley, J. E. 1998. High Fidelity Hydrographic Surveys Using and Autonomous Surface Craft. MA, USA: Dept. of Ocean Engineering, Massachusetts Institute of Technology.
   Google Scholar
• Manley, J. E., A. Marsh, W. Cornforth, et al. 2000. Evolution of the autonomous surface craft Autocrat. Proceedings of IEEE Conference and Exhibition on Oceans 2000 MTS, IEEE, 1: 403–408.
   Google Scholar
• Brown, I. I., L. Jenkins, G. Meadows, et al. 2010. BathyBoat: An autonomous surface vessel for stand-alone survey and underwater vehicle network supervision. Marine Technology Society Journal 44(4):20–29.
   Web of Science ®Google Scholar
• Jin, J., J. Zhang, Y. Wang, et al. 2010. Bathymetry mapping around islands using an autonomous surface vehicle. Ocean Technology (02):5–8. ( in Chinese).
   Google Scholar
• Shen, J., and S. Pan. 2002. Discussion on technical methods of coastal bathymetry. Hydrographic Surveying and Charting 22(6):60–65. ( in Chinese)
   Google Scholar
• National Marine Data and Information Service. 2015. 2016 Tide Tables Vol.3 from the Taiwan Straits to the Beibu Gulf. Beijing: Ocean Press.
   Google Scholar
• Liu, S. 2011. System Integration for the Methods of Water-depth Inversion and Coastline Extraction Based on Remote Sensing [Dissertation]. Beijing: Chinese Academy of Science.
   Google Scholar