Three-dimensional geographically weighted inverse regression (3GWR) model for satellite derived bathymetry using Sentinel-2 observations - Publikacja - MOST Wiedzy


Three-dimensional geographically weighted inverse regression (3GWR) model for satellite derived bathymetry using Sentinel-2 observations


Current trends of development of satellite derived bathymetry (SDB) models rely on applying calibration techniques including analytical approaches, neuro-fuzzy systems, regression optimization and others. In most of the cases, the SDB models are calibrated and verified for test sites, that provide favourable conditions for the remote derivation of bathymetry such as high water clarity, homogenous bottom type, low amount of sediment in the water and other factors. In this paper, a novel 3-dimensional geographical weighted regression (3GWR) SDB technique is presented, it binds together methods already presented in other studies, the geographically weighted local regression (GWR) model, with depth dependent inverse optimization. The proposed SDB model was calibrated and verified on a relatively difficult test site of the South Baltic near-shore areas with the use of multispectral observations acquired by a recently launched Sentinel-2 satellite observation system. By conducted experiments, it was shown that the proposed SDB model is capable of obtaining satisfactory results of RMSE ranging from 0.88 to 1.23[m] depending on the observation and can derive bathymetry for depths up to 12m. It was also shown, that the proposed approach may be used operationally, for instance, in the continuous assessment of temporal bathymetry changes, for areas important in the context of ensuring local maritime safety.


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Publikacja w czasopiśmie
artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w:
MARINE GEODESY nr 41, strony 1 - 23,
ISSN: 0149-0419
Rok wydania:
Opis bibliograficzny:
Chybicki A.: Three-dimensional geographically weighted inverse regression (3GWR) model for satellite derived bathymetry using Sentinel-2 observations// MARINE GEODESY. -Vol. 41, nr. 1 (2018), s.1-23
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1080/01490419.2017.1373173
Bibliografia: test
  1. Allen, J. G., N. B. Nelson, and D. A. Siegel. 2017. Seasonal to multi-decadal trends in apparent optical properties in the Sargasso sea. Deep-Sea Research Part I-Oceanographic Research Papers 119(Janu- ary):58-67. doi:10.1016/j.dsr.2016.11.004 otwiera się w nowej karcie
  2. Bramante, J. F., D. K. Raju, and T. M. Sin. 2013. Multispectral derivation of bathymetry in Singapore's shallow, turbid waters. International Journal of Remote Sensing 34(6):2070-88. doi:10.1080/ 01431161.2012.734934 otwiera się w nowej karcie
  3. Brando, V. E., and A. G. Dekker. 2003. Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality. IEEE Transactions on Geoscience and Remote Sensing 41(6):1378-87. doi:10.1109/TGRS.2003.812907 otwiera się w nowej karcie
  4. Brando, V. E., J. M. Anstee, M. Wettle, A. G. Dekker, S. R. Phinn, and C. Roelfsema. 2009. A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data. Remote Sensing of Environment 113(4):755-70. doi:10.1016/j.rse.2008.12.003 otwiera się w nowej karcie
  5. Chavez Jr., P. S. 1996. Image-based atmospheric corrections revisited and improved. Photogrammetric Engineering & Remote Sensing 62(9):1025-36.
  6. Cleveland, W. S., and S. J. Devlin. 1988. Locally weighted regression -an approach to regression-anal- ysis by local fitting. Journal of the American Statistical Association 83(403):596-610. doi:10.2307/ 2289282 otwiera się w nowej karcie
  7. Corucci, L., A. Masini, and M. Cococcioni. 2011. Approaching bathymetry estimation from high reso- lution multispectral satellite images using a neuro-fuzzy technique. Journal of Applied Remote Sens- ing 5(1):053515-053515-15. doi:10.1117/1.3569125 otwiera się w nowej karcie
  8. Doernhoefer, K., A. Goeritz, P. Gege, B. Pflug, and N. Oppelt. 2016. Water constituents and water depth retrieval from Sentinel-2A-A first evaluation in an oligotrophic lake. Remote Sensing 8 (11):941. doi:10.3390/rs8110941 otwiera się w nowej karcie
  9. Eugenio, F., J. Marcello, and J. Martin. 2015. High-resolution maps of bathymetry and benthic habitats in shallow-water environments using multispectral remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing 53(7):3539-49. doi:10.1109/TGRS.2014.2377300 otwiera się w nowej karcie
  10. Forfinski-Sarkozi, N. A., and C. E. Parrish. 2016. Analysis of MABEL bathymetry in Keweenaw Bay and implications for ICESat-2 ATLAS. Remote Sensing 8(9):772. doi:10.3390/rs8090772 otwiera się w nowej karcie
  11. Fotheringham, A. S., M. E. Charlton, and C. Brunsdon. 1998. Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and Planning A 30(11):1905-27. doi:10.1068/a301905 otwiera się w nowej karcie
  12. Giardino, C., G. Candiani, M. Bresciani, Z. Lee, S. Gagliano, and M. Pepe. 2012. BOMBER: A tool for estimating water quality and bottom properties from remote sensing images. Computers & Geo- sciences 45(August):313-18. doi:10.1016/j.cageo.2011.11.022 otwiera się w nowej karcie
  13. Guzinski, R., E. Spondylis, M. Michalis, S. Tusa, G. Brancato, L. Minno, and L. B. Hansen. 2016. Exploring the utility of bathymetry maps derived with multispectral satellite observations in the field of underwater archaeology. Open Archaeology 2(1):243-263. doi:10.1515/opar-2016-0018. otwiera się w nowej karcie
  14. Hamylton, S. M., J. D. Hedley, and R. J. Beaman. 2015. Derivation of high-resolution bathymetry from multispectral satellite imagery: a comparison of empirical and optimisation methods through geo- graphical error analysis. Remote Sensing 7(12):16257-73. doi:10.3390/rs71215829 otwiera się w nowej karcie
  15. Harborne, A. R., and P. J. Mumby. 2005. Technical note: Simple and robust removal of sun glint for mapping shallow-water benthos. International Journal of Remote Sensing 26(10):2107-12. doi:10.1080/01431160500034086. otwiera się w nowej karcie
  16. Hedley, J., C. Roelfsema, B. Koetz, and S. Phinn. 2012. Capability of the Sentinel-2 mission for tropical coral reef mapping and coral bleaching detection. Remote Sensing of Environment The Sentinel Mis- sions -New Opportunities for Science 120(May):145-55. doi:10.1016/j.rse.2011.06.028 otwiera się w nowej karcie
  17. Hoge, F. E., and P. E. Lyon. 1996. Satellite retrieval of inherent optical properties by linear matrix inversion of oceanic radiance models: an analysis of model and radiance measurement errors. Jour- nal of Geophysical Research: Oceans 101(C7):16631-48. doi:10.1029/96JC01414 otwiera się w nowej karcie
  18. Jena, B., P. J. Kurian, D. Swain, A. Tyagi, and R. Ravindra. 2012. Prediction of bathymetry from satel- lite altimeter based gravity in the Arabian sea: mapping of two unnamed deep seamounts. Interna- tional Journal of Applied Earth Observation and Geoinformation 16(June):1-4. doi:10.1016/j. jag.2011.11.008 otwiera się w nowej karcie
  19. Kanno, A., Y. Koibuchi, and M. Isobe. 2011. Statistical combination of spatial interpolation and multi- spectral remote sensing for shallow water bathymetry. Ieee Geoscience and Remote Sensing Letters 8 (1):64-67. doi:10.1109/LGRS.2010.2051658 otwiera się w nowej karcie
  20. Kay, S., J. D. Hedley, and S. Lavender. 2009. Sun glint correction of high and low spatial resolution images of aquatic scenes: A review of methods for visible and near-infrared wavelengths. Remote Sensing 1(4):697-730. doi:10.3390/rs1040697 otwiera się w nowej karcie
  21. Knudby, A., S. K. Ahmad, and C. Ilori. 2016. The potential for landsat-based bathymetry in Canada. Canadian Journal of Remote Sensing 42(4):367-78. doi:10.1080/07038992.2016.1177452 otwiera się w nowej karcie
  22. Kobryn, H. T., K. Wouters, L. E. Beckley, and T. Heege. 2013. Ningaloo reef: shallow marine habitats mapped using a hyperspectral sensor. Plos One 8(7):e70105. doi:10.1371/journal.pone.0070105 otwiera się w nowej karcie
  23. Kulawiak, M., A. Chybicki, and M. Moszynski. 2010. Web-based GIS as a tool for supporting marine research. Marine Geodesy 33(2,3):135-53. doi:10.1080/01490419.2010.492280 otwiera się w nowej karcie
  24. Kyriakidis, I., K. Karatzas, A. Ware, and G. Papadourakis. 2016. A generic preprocessing optimization methodology when predicting time-series data. International Journal of Computational Intelligence Systems 9(4):638-51. doi:10.1080/18756891.2016.1204113 otwiera się w nowej karcie
  25. Lafon, V., J. M. Froidefond, F. Lahet, and P. Castaing. 2002. SPOT shallow water bathymetry of a moderately turbid tidal inlet based on field measurements. Remote Sensing of Environment 81 (1):136-48. doi:10.1016/S0034-4257(01)00340-6 otwiera się w nowej karcie
  26. Lee, K. R., R. C. Olsen, and F. A. Kruse. 2012. Using multi-angle worldview-2 imagery to determine ocean depth near the island of Oahu, Hawaii. In Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. Shen S. S. and Lewis P. E. (eds.), vol. 8390, 83901I. Bellingham: Spie-Int Soc Optical Engineering. otwiera się w nowej karcie
  27. Lee, Z., K. L. Carder, C. D. Mobley, R. G. Steward, and J. S. Patch. 1998. Hyperspectral remote sensing for shallow waters. I. A semianalytical model. Applied Optics 37(27):6329-38. otwiera się w nowej karcie
  28. Liu, Z. 2013. Bathymetry and bottom albedo retrieval using Hyperion: a case study of Thitu Island and reef. Chinese Journal of Oceanology and Limnology 31(6):1350-55. doi:10.1007/s00343-013-2287-8 otwiera się w nowej karcie
  29. Luchinin, A. G., and M. Y. Kirillin. 2016. Temporal and frequency characteristics of a narrow light beam in sea water. Applied Optics 55(27):7756-62. doi:10.1364/AO.55.007756 otwiera się w nowej karcie
  30. Lyzenga, D. R. 1981. Remote sensing of bottom reflectance and water attenuation parameters in shal- low water using aircraft and landsat data. International Journal of Remote Sensing 1, 2(1):71-82. otwiera się w nowej karcie
  31. 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):2251-59. doi:10.1109/ TGRS.2006.872909 otwiera się w nowej karcie
  32. Lyzenga, D. R. 1977. Reflectance of a flat ocean in limit of zero water depth. Applied Optics 16(2):282- 83. doi:10.1364/AO.16.000282 otwiera się w nowej karcie
  33. Ma, S., Z. Tao, X. Yang, Y. Yu, X. Zhou, and Z. Li. 2014. Bathymetry retrieval from hyperspectral remote sensing data in optical-shallow water. IEEE Transactions on Geoscience and Remote Sensing 52(2):1205-12. doi:10.1109/TGRS.2013.2248372 otwiera się w nowej karcie
  34. Mahiny, A. S., and B. J. Turner. 2007. A comparison of four common atmospheric correction meth- ods. Photogrammetric Engineering & Remote Sensing 73(4):361-68. doi:10.14358/PERS.73.4.361 otwiera się w nowej karcie
  35. Martins, V. S., C. C. F. Barbosa, L. A. S. de Carvalho, D. S. F. Jorge, F. de Lucia Lobo, and E. M. L. de Moraes Novo. 2017. Assessment of atmospheric correction methods for Sentinel-2 MSI images applied to amazon floodplain lakes. Remote Sensing 9(4):322. doi:10.3390/rs9040322 otwiera się w nowej karcie
  36. Mishra, D. R., S. Narumalani, D. Rundquist, and M. Lawson. 2005. High-resolution ocean color remote sensing of benthic habitats: a case study at the Roatan Island, Honduras. IEEE Transactions on Geoscience and Remote Sensing 43(7):1592-1604. doi:10.1109/TGRS.2005.847790 otwiera się w nowej karcie
  37. Mishra, D., S. Narumalani, D. Rundquist, and M. Lawson. 2006. Benthic habitat mapping in tropical marine environments using quickbird multispectral data. Photogrammetric Engineering & Remote Sensing 72(9):1037-1048. otwiera się w nowej karcie
  38. Mishra, D. R., S. Narumalani, D. Rundquist, M. Lawson, and R. Perk. 2007. Enhancing the detection and classification of coral reef and associated benthic habitats: a hyperspectral remote sensing approach. Journal of Geophysical Research: Oceans 112:C08014. doi:10.1029/2006JC003892 otwiera się w nowej karcie
  39. Mobley, C. D., L. K. Sundman, C. O. Davis, J. H. Bowles, T. V. Downes, R. A. Leathers, M. J. Montes, et al. 2005. Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables. Applied Optics 44(17):3576-92. doi:10.1364/AO.44.003576 otwiera się w nowej karcie
  40. Moszynski, M., M. Kulawiak, A. Chybicki, K. Bruniecki, T. Bielinski, Z. Lubniewski, and A. Stepnow- ski. 2015. Innovative web-based geographic information system for municipal areas and coastal zone security and threat monitoring using EO satellite data. Marine Geodesy 38(3):203-24. doi:10.1080/01490419.2014.969459 otwiera się w nowej karcie
  41. Moussavi, M. S., W. Abdalati, A. Pope, T. Scambos, M. Tedesco, M. MacFerrin, and S. Grigsby. 2016. Derivation and validation of supraglacial lake volumes on the Greenland ice sheet from high-reso- lution satellite imagery. Remote Sensing of Environment 183(September):294-303. doi:10.1016/j. rse.2016.05.024 otwiera się w nowej karcie
  42. Pacheco, A, A. Horta, J. O. Loureiro, and O. Ferreira. 2015. Retrieval of nearshore bathymetry from landsat 8 images: a tool for coastal monitoring in shallow waters. Remote Sensing of Environment 159(0):102-16. otwiera się w nowej karcie
  43. Pe'eri, S., C. Parrish, C. Azuike, L. Alexander, and A. Armstrong. 2014. Satellite remote sensing as a reconnaissance tool for assessing nautical chart adequacy and completeness. Marine Geodesy 37 (3):293-314. doi:10.1080/01490419.2014.902880 otwiera się w nowej karcie
  44. Pope, A., T. A. Scambos, M. Moussavi, M. Tedesco, M. Willis, D. Shean, and S. Grigsby. 2016. Estimat- ing supraglacial lake depth in west Greenland using landsat 8 and comparison with other multi- spectral methods. Cryosphere 10(1):15-27. doi:10.5194/tc-10-15-2016 otwiera się w nowej karcie
  45. Pope, R. M., and E. S. Fry. 1997. Absorption spectrum (380-700 Nm) of pure water. II. Integrating cavity measurements. Applied Optics 36(33):8710-23. doi:10.1364/AO.36.008710 otwiera się w nowej karcie
  46. Spitzer, D, and R.W. J. Dirks. 1986. Shallow water bathymetry and bottom classification by means of the Landsat and SPOT optical scanners. 1986 International Symposium/Innsbruck 0660:136-38. doi:10.1117/12.938578. otwiera się w nowej karcie
  47. Stulp, F., and O. Sigaud. 2015. Many regression algorithms, one unified model: a review. Neural Net- works 69(September):60-79. doi:10.1016/j.neunet.2015.05.005 otwiera się w nowej karcie
  48. Stumpf, R. P., K. Holderied, and M. Sinclair. 2003. Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnology and Oceanography 48(1part2):547-56. doi:10.4319/lo.2003.48.1_part_2.0547 otwiera się w nowej karcie
  49. Su, H., H. Liu, L. Wang, A. M. Filippi, W. D. Heyman, and R. A. Beck. 2014. Geographically adaptive inversion model for improving bathymetric retrieval from satellite multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing 52(1):465-76. doi:10.1109/ TGRS.2013.2241772 otwiera się w nowej karcie
  50. Su, H., H. Liu, and Q. Wu. 2015. Prediction of water depth from multispectral satellite imagery #x2014; The regression kriging alternative. IEEE Geoscience and Remote Sensing Letters 12 (12):2511-15. doi:10.1109/LGRS.2015.2489678 otwiera się w nowej karcie
  51. Su, H., H. Liu, and W. D. Heyman. 2008. Automated derivation of bathymetric information from multi-spectral satellite imagery using a non-linear inversion model. Marine Geodesy 31(4):281-98. doi:10.1080/01490410802466652 otwiera się w nowej karcie
  52. Toming, K., T. Kutser, A. Laas, M. Sepp, B. Paavel, and T. Nõges. 2016. First experiences in mapping lake water quality parameters with Sentinel-2 MSI imagery. Remote Sensing 8(8):640. doi:10.3390/ rs8080640 otwiera się w nowej karcie
  53. Vahtm€ ae, E., T. Kutser, G. Martin, and J. Kotta. 2006. Feasibility of hyperspectral remote sensing for mapping benthic macroalgal cover in turbid coastal waters-a Baltic sea case study. Remote Sensing of Environment 101(3):342-51. doi:10.1016/j.rse.2006.01.009 otwiera się w nowej karcie
  54. Vahtmaee, E., and T. Kutser. 2016. Airborne mapping of shallow water bathymetry in the optically complex waters of the Baltic sea. Journal of Applied Remote Sensing 10(May):025012. doi:10.1117/ 1.JRS.10.025012 otwiera się w nowej karcie
  55. Vinayaraj, P., V. Raghavan, S. Masumoto, and J. Glejin. 2015. Comparative evaluation and refinement of algorithm for water depth estimation using medium resolution remote sensing data. Interna- tional Journal of Geoinformatics 11(3):17-29.
  56. Vinayaraj, P., V. Raghavan, and S. Masumoto. 2016. Satellite-derived bathymetry using adaptive geographi- cally weighted regression model. Marine Geodesy 39(6):458-78. doi:10.1080/01490419.2016.1245227 otwiera się w nowej karcie
  57. Wieczorek, M. M., W. A. Spallek, T. Niedzielski, J. A. Godbold, and I. G. Priede. 2014. Use of remotely-derived bathymetry for modelling biomass in marine environments. Pure and Applied Geophysics 171(6):1029-45. doi:10.1007/s00024-013-0705-7 otwiera się w nowej karcie
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