Retrieval of Chlorophyll a, suspended solids, and colored dissolved organic matter in Tokyo Bay using ASTER data

Remote Sensing of Environment

Volumn 99, Issue 1-2, 2005, Pages 66-74

  Kishino, Motoaki ^a   Tanaka, Akihiko ^b   Ishizaka, Joji ^c  

^a TOKYO UNIVERSITY OF MARINE SCIENCE AND TECHNOLOGY   (Japan)

^b Nagasaki Industrial Promotion Foundation   (Japan)

^c NAGASAKI UNIVERSITY   (Japan)

Author keywords

ASTER; CDOM; Chlorophyll a; Neutral Network; SS; Tokyo Bay

Indexed keywords

BIODIVERSITY; CHLOROPHYLL; COASTAL ZONES; COMPUTER SIMULATION; INFRARED IMAGING; LAKES; RELIABILITY; REMOTE SENSING;

ADVANCED SPACEBORNE THERMAL EMISSION AND REFLECTANCE RADIOMETER; ASTER; CDOM; CHLOROPHYLL A; NEUTRAL NETWORK; SS; TOKYO BAY; VISIBLE AND NEAR-INFRARED (VNIR);

RADIOMETERS;

ASTER; CHLOROPHYLL A; DISSOLVED ORGANIC MATTER; REMOTE SENSING; SPATIAL RESOLUTION; SUSPENDED LOAD;

ASIA; EASTERN HEMISPHERE; EURASIA; FAR EAST; HONSHU; JAPAN; KANTO; TOKYO BAY; WORLD;

EID: 27144544977     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2005.05.016     Document Type: Article

References (39)

1. E. Aas Two-stream irradiance model for deep waters Applied Optics 26 1987 2095 2101
2. J. Ardo, P. Pilesjo, and A.K. Skidmore Neural networks, multi-temporal Landsat thematic mapper data and topographic data to classify forest damages in the Czech Republic Canadian Journal of Remote Sensing 23 1997 217 229
3. M. Babin, and R. Doerffer Specifications for case II coastal water reference model Algorithm theoretical basis document for MERIS (ATBD 212) 1996 ESA-ESTEC 61 73
4. A. Bricaud, A. Morel, and L. Prieur Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains Limnology and Oceanography 26 1981 43 53
5. D. Buckton, E. O'Mongain, and S. Danaher The use of neural networks for the estimation of oceanic constituents based on the MERIS instrument International Journal of Remote Sensing 20 1999 1841 1851
6. H. Buiteveld, J.M.H. Hakvoort, and M. Donze The optical properties of pure water J.S. Jaffe SPIE Proceedings On Ocean Optics XII vol. 2258 1994 174 183
7. R. Doerffer Application of a two-flow model for remote sensing of substances in water Boundary-Layer Meteology 18 1979 221 232
8. R. Doerffer Imaging spectroscopy for detection of chlorophyll and suspended matter F. Toselli J. Bodechtel Imaging spectroscopy, fundamentals and prospective applications 1992 ECSC-EEC-EAEC Brussels 215 257
9. R. Doerffer, and J. Fischer Concentrations of chlorophyll, suspended matter, and gelbstoff in case II waters derived from satellite coastal zone color scanner data with inverse modeling methods Journal of Geophysical Research 99 C4 1994 7457 7466
10. R. Doerffer, and H. Schiller Pigment index, sediment and gelbstoff retrieval from directional water leaving radiance reflectances using inverse modelling technique Algorithm Theoretical Basis Document (ATBD) 2.12, Doc. No. PO-TN-MEL-GS-005 1997
11. G.M. Foody Landcover classification by an artificial neural network with ancillary information International Journal of Remote Sensing 9 1995 527 542
12. H.R. Gordon, D.K. Clark, J.L. Mueller, and W.G. Hovis Phytoplankton pigments from the Nimbus-7 Coastal Zone Color Scanner: Comparisons with surface measurements Science 210 1980 63 66
13. C. Gueymard SMART2, A simple model of the atmospheric radiative transfer of sunshine: Algorithms and performance assessment 1995 A Research Institute of the University of Central Florida 83 pp.
14. N.K. Højerslev Natural occurrences and optical effects of gelbstoff Report of department physical oceanography 1988 University of Copenhagen Copenhagen, Denmark 30 pp.
15. IOCCG Remote sensing of ocean colour in coastal, and other optically-complex, waters S. Sathyendrath Reports of the International Ocean-Colour Coordinating Group vol. 3 2000 IOCCG Dartmouth, Canada 145 pp.
16. IOCCG Models, parameters, and approaches that used to generate wade range of absorption and backscattetring spectra, Ocean Color Algorithm Working Group 2003 IOCCG http://www.ioccg.org/groups/lee_data.pdf. 11 pp.
17. J. Joseph Untersuchungen über Ober-und Unterlichtmessungen im Meere und über ihren Zusammenhang mit Durchsichtigkeitsmessungen Deutsche Hydrographische Zeitschrift 3 1950 324 335
18. L.E. Keiner, and X. Yan A neural network model for estimating sea surface chlorophyll and sediments from Thematic Mapper Imagery Remote Sensing of Environment 66 1998 153 165
19. J.T.O. Kirk Light and photosynthesis in aquatic ecosystems 1994 Cambridge University Press 509 pp.
20. M. Kishino, T. Ishimaru, K. Furuya, T. Oishi, and K. Kawasaki In-water algorithm for ADEOS/OCTS Journal of Oceanography 54 1998 431 436
21. M. Kishino, M. Takahashi, N. Okami, and S. Ichimura Estimation of the spectral absorption coefficients of phytoplankton in the sea Bulletin of Marine Science 37 1985 634 642
22. L. Kou, D. Labrie, and P. Chylek Refractive indices of water and ice in the 0.65-2.5 μm spectral range Applied Optics 32 1993 3531 3540
23. U. Kronfeld Die optishen eignenschatten der ozea nischen schwebstoffe und ihre bedentung fur fernerkundung von phytoplankton 1988 GKSS Forschungszentrum D-2054 Geesthacht, Germany 153 pp.
24. A. Morel Optical properties of pure sea water N.G. Jerlov S. Nielssen Optical aspects of oceanography 1974 Academic Press London 1 24
25. J.E. O'Reilly, S. Maritorena, B.G. Mitchell, D.A. Siegel, K.L. Carder, and S.A. Garver Ocean color chlorophyll algorithms for SeaWiFS Journal of Geophysical Research 103 1998 24937 24953
26. T. Oishi, Y. Takahashi, A. Tanaka, M. Kishino, and A. Tsuchiya Relation between the backward as well as total scattering coefficients and the volume scattering functions by cultured phytoplankton Journal of the School of Marine Science and Technology Tokai University 53 2002 1 15 (in Japanese with English abstract)
27. N. Okami, M. Kishino, S. Sugihara, N. Takematsu, and S. Unoki Analysis of ocean color spectra (III). Measurements of optical properties of sea water Journal of Oceanographic Society of Japan 38 1982 362 372
28. J.C. Pernetta, and J.D. Milliman Land-Ocean interactions in the coastal zone implementation plan IGBP Report vol. 33 1995 Stockholm, 215 pp.
29. R.M. Pope, and E.S. Fry Absorption spectrum (380-700) of pure water: II. Integrating cavity measurement Applied Optics 36 1997 8710 8723
30. R. Preisendorfer Hydrologic optics, vols. I-VI 1976 U.S. Department of Commerce
31. C.S. Roesler, M.J. Perrry, and K.L. Carder Modeling in situ phytoplankton absorption from total absorption spectra in productive inland marine waters Limnology and Oceanography 34 1989 1510 1523
32. S. Sathyendranath, L. Prieur, and A. Morel A three-component model of ocean colour and its application to remote sensing of phytoplankton pigments in coastal waters International Journal of Remote Sensing 10 1989 1373 1394
33. H. Schiller, and R. Doerffer Fast computational scheme for inverse modeling of multispectral radiances: Application for remote sensing of the ocean Applied Optics 32 1993 3280 3285
34. H. Schiller, and R. Doerffer Neural network for emulation of an inverse model-Operational derivation of Case II water properties from MERIS data International Journal of Remote Sensing 20 1999 1735 1746
35. A.K. Skidmore, B.J. Turner, W. Brikhof, and E. Knowles Performance of a neural network: Mapping forests using GIS and remotely sensed data Photogrammetric Engineering and Remote Sensing 63 5 1997 501 514
36. SNNS (1995). Stuttgart Neural Network Simulator. User Manual, Version 4.1. University of Stuttgart, Institute for parallel and distributed high performance systems (anonymous ftp ftp.informatik.uni-stuttgart.de). 312 pp.
37. A. Tanaka, M.. Kishino, R. Doerffer, H. Schiller, T. Oishi, and T. Kubota Development of a neural network algorithm for retrieving concentrations of chlorophyll, suspended matter and yellow substance from radiance data of the ocean color and temperature scanner Journal of Oceanography 60 2004 519 530
38. X. Zhang, C. Li, and Y. Yuan Application of neural networks to identify vegetation types from satellite images Al Applications 11 1997 99 106
39. Y. Zhang, J. Pulliainen, S. Koponen, and M. Hallikainen Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data Remote Sensing of Environment 81 2002 327 336