Satellite chlorophyll retrievals with a bipartite artificial neural network model

International Journal of Remote Sensing

Volumn 27, Issue 8, 2006, Pages 1563-1579

  Su, F C ^a   Ho, Chung Ru ^a   Zheng, Q ^b   Kuo, N J ^a   Chen, C T ^a  

^a NATIONAL TAIWAN OCEAN UNIVERSITY   (Taiwan)

^b UNIVERSITY OF MARYLAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COASTAL ENGINEERING; IMAGE RETRIEVAL; NEURAL NETWORKS; REMOTE SENSING; SENSOR DATA FUSION;

BIPARTITE ARTIFICIAL NETWORK NETWORK; SATELLITE CHLOROPHYLL RETRIEVALS; SEA-VIEWING WIDE FIELD-OF-VIEW SENSOR DATA; TAIWAN COASTAL WATERS;

ENVIRONMENTAL ENGINEERING;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CHLOROPHYLL; COASTAL WATER; NUMERICAL MODEL; SATELLITE IMAGERY;

ASIA; EURASIA; FAR EAST; TAIWAN;

EID: 33646817447     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431160500444814     Document Type: Article

References (37)

1. Aiken, J. and Moore, G. F. and Trees, C. C. and Hooker, S. B. and Clark, D. K.(1994) The SeaWiFS CZCS-type pigment algorithm. In. Greenbelt: NASA. SeaWiFS Technical Report Series, NASA Technical Memorandum, 104 566
2. Baum, E. and Haussler, D.(1989) What size net gives valid generalization?. In Advances in Neural Information Processing Systems 1. (pp. 81-90). Morgan Kaufmann.
3. Bricaud, A. and Morel, A. (1987) Atmospheric corrections and interpretation of marine radiances in CZCS imagery: Use of a reflectance model. Oceanol. Acta, Symposium Proceedings, 7, pp. 33-50.
4. Bukata, R. and Jerome, J. and Kondratyev, K. and Pozdnyakov, D. (1991) Satellite monitoring of optically-active components of inland waters: An essential input to regional climate change impact studies. Journal of Great Lakes Research, 17, pp. 470-478.
5. Buckton, D. and O'Mongain, E. and Danaher, S. (1999) The use of neural networks for the estimation of oceanic constituents based on the MERIS instrument. International Journal of Remote Sensing, 20, pp. 1841-1851.
6. Campbell, J. W. (1995) The lognormal distribution as a model for bio-optical variability in the sea. Journal of Geophysical Research, 100, pp. 13237-13254.
7. Clark, G. L. and Ewing, G. C. and Lorenzen, C. J. (1970) Spectra of backscattered light from the sea obtained from aircraft as a measure of chlorophyll concentration. Science, 167, pp. 1119-1121.
8. Fan, C. L. and Gao, F. and Jin, Z. H. (2004) Bayesian neural networks for life modelling and prediction of dynamically tuned gyroscopes. Lecture Notes in Computer Science, 3174, pp. 749-754.
9. Foresee, F. D. and Hagan, M. T. (1997) Gauss-Newton approximation to Bayesian learning. Proceedings of the 1997 International Joint Conference on Neural Networks, 2, pp. 1930-1935.
10. Gencay, R. and Qi, M. (2001) Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging. IEEE Transactions on Neural Networks, 12, pp. 726-734.
11. Gordon, H. R. and Morel, A.(1983). In Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery: A Review. New York NY: Springer-Verlag.
12. Gordon, H. R. and Clark, D. K. and Brown, J. W. and Brown, O. B. and Evans, R. H. and Broenkow, W. W. (1983) Phytoplankton pigment concentrations in the Middle Atlantic Bight: Comparisons between ship determinations and coastal zone color scanner estimates. Applied Optics, 22, pp. 20-36.
13. Gross, L. and Thiria, S. and Frouin, R. and Mitchell, B. G. (2000) Artificial neural networks for modelling the transfer function between marine reflectance and phytoplankton pigment concentration. Journal of Geophysical Research, 105, pp. 3483-3495.
14. Hagan, M. T. and Menhaj, M. (1994) Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5, pp. 989-993.
15. Heckerman, D. (1997) Bayesian networks for data mining. Data Mining and Knowledge Discovery, 1, pp. 79-119.
16. Ho, C.-R. and Lee, L.-S. and Kuo, N.-J. and Li, H.-W. and Chen, C.-T. (2001) Intercomparison of spaceborne ocean color measurements between OCI and SeaWiFS. Geophysical Research Letters, 28, pp. 1255-1258.
17. Hooker, S. B. and Esaias, W. E. and Feldman, G. C. and Gregg, W. W. and McClain, C. R.(1992) An overview of SeaWiFS and ocean color. In. Greenbelt: NASA. SeaWiFS Technical Report Series, NASA Technical Memorandum, 104-566
18. Hovis, W. A.(1981) The NIMBUS-7 Coastal Zone Color Scanner (CZCS) program. In Oceanography From Space. New York: Plenum.
19. Ioccg. (1998) Minimum requirements for an operational ocean-colour sensor for the open ocean. In Reports of the International Ocean Colour Coordinating Group. No.1, IOCCG, Dartmouth, Canada, 46 pp
20. Krasnopolsky, V. and Breaker, L. and Gemmill, W. (1995) A neural network as a nonlinear transfer model for retrieving surface wind speeds from the special sensor microwave imager. Journal of Geophysical Research, 100, pp. 11033-11045.
21. Lathrop, R. and Lillesand, T. (1986) Use the Thematic Mapper data to assess water quality in Green Bay and central Lake Michigan. Photogrammetric Engineering and Remote Sensing, 52, pp. 671-680.
22. Kahru, M. and Mitchell, B. G. (1999) Empirical chlorophyll algorithm and preliminary SeaWiFS validation for the California Current. International Journal of Remote Sensing, 20, pp. 3423-3429.
23. Keiner, L. E. and Brown, C. W. (1999) Estimating oceanic chlorophyll concentrations with neural networks. International Journal of Remote Sensing, 20, pp. 189-194.
24. Keiner, L. E. and Yan, X. H. (1998) A neural network model for estimating sea surface chlorophyll and sediments from thematic mapper imagery. Remote Sensing of environment, 66, pp. 153-165.
25. MacKay, D. J. C. (1992) Bayesian interpolation. Neural Computation, 4, pp. 415-447.
26. Mathworks Inc.(1998). In Neural Network Toolbox User's Guide. Natick MA: The MathWorks Inc. Fifth printing, Version 3
27. Morel, A. and Prieur, L. (1977) Analysis of variations in ocean color. Limnology and Oceanography, 22, pp. 709-722.
28. Mueller, J. L. and Austin, R. W.(1995) Ocean optics protocols for SeaWiFS validation. In. Greenbelt: NASA. SeaWiFS Technical Report Series, NASA Technical Memorandum, 104-566
29. O'Reilly, J. and Maritorena, S. and Mitchell, B. G. and Siegel, D. A. and Carder, K. D. and Garver, S. A. and Kahru, M. and McClain, C. (1998) Ocean color algorithms for SeaWiFS. Journal of Geophysical Research, 103, pp. 24937-24953.
30. O'Reilly, J. E. and Maritorena, S. and Siegel, D. A. and O'Brien, M. C. and Toole, D. and Mitchell, B. G. and Kahru, M. and Chavez, F. P. and Strutton, P. and Cota, G. F. and Hooker, S. B. and McClain, C. R. and Carder, K. L. and Muller-Karger, F. and Harding, L. and Magnuson, A. and Phinney, D. and Moore, G. F. and Aiken, J. and Arrigo, K. R. and Letelier, R. and Culver, M. (2000) Ocean color chlorophyll algorithms for SeaWiFS, OC2, and OC4: Version 4. In. Greenbelt: NASA. SeaWiFS Technical Report Series, NASA Technical Memorandum, 104-566
31. Pattiaratchi, C. and Lavery, P. and Wyllie, A. and Hick, P. (1994) Estimates of water quality in coastal waters using multi date Landsat Thematic Mapper data. International Journal of Remote Sensing, 15, pp. 1571-1584.
32. Schiller, H. and Doerffer, R. (1999) Neural network for emulation of an inverse model-Operational derivation of Case II water properties from MERIS data. International Journal of Remote Sensing, 20, pp. 1735-1746.
33. Tang, D. L. and Kawamura, H. and Lee, M. A. and Dien, T. V. (2003) Seasonal and spatial distribution of chlorophyll-a concentrations and water conditions in the Gulf of Tonkin, South China Sea. Remote Sensing of Environment, 85, pp. 475-483.
34. Tassan, S. (1993) An improved in-water algorithm for the determination of cholorophyll and suspended sediment concentration from Thematic Mapper data in coastal waters. International Journal of Remote Sensing, 14, pp. 1221-1229.
35. Vehtari, A. and Lampinen, J. (2000) Bayesian MLP neural networks for image analysis. Pattern Recognition Letters, 21, pp. 1183-1191.
36. Whitlock, C. and Kuo, C. and LeCroy, S. (1982) Criteria for the use of regression analysis for remote sensing of sediment and pollutants. Remote Sensing of Environment, 12, pp. 151-168.
37. Zhang, T. and Fell, F. and Liu, Z. S. and Preusker, R. and Fischer, J. and He, M. X. (2003) Evaluating the performance of artificial neural network techniques for pigment retrieval from ocean color in Case I waters. Journal of Geophysical Research, 108, pp. 3286-3297.