Remote Sensing of Water Environment

  • Xiaoling Chen
  • Zhifeng Yu


Water, the hydrosphere, covers approximately 71% percent of the earth. It consists of ocean, river, lake, marsh, glacier, snow, groundwater, air moisture, and so on. Water environment, closely linked with human being’s life, is facing serious problems of pollution and eutrophication. Water resource’s protection and management has become more and more important in the world.

Water has been traditionally monitored by in situ measurement, to take point samples at regular intervals. But point samples are not adequate to observe spatial and temporal variations in a large area. Remote sensing has provided a new way to obtain water quality data over large areas simultaneously. Various kinds of remotely sensed images, including air-borne and space-borne optical (passive visible and infrared, laser), and passive and active microwave (e.g., Synthetic Aperture Radar, SAR) images, have become important information source for monitoring and detecting water quality. Satellite sensors such as CZCS (Coastal Zone Color Scanner), SeaWiFS (Sea-viewing Wide Field-of-view Sensor), MODIS (Moderate Resolution Imaging Spectroradiometer), MERIS (Medium Resolution Imaging Spectrometer) and Landsat series with various spatio-temporal and spectral resolutions can provide more timely synoptic water quality data (Chen et al. 2004). Therefore, remote sensing could be used as an independent measurement tool by water management authorities (Dekker et al. 2001, 2002).


Synthetic Aperture Radar Atmospheric Correction Ocean Color Pearl River Estuary Total Volatile Solid 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Anderson, D. M. and D. L. Garrison (eds.) (1997): The Ecology and Oceanography of Harmful Algal Blooms. Limnol. Oceanogr., 42, 1009-1305.Google Scholar
  2. Anderson, J.M., Duck, R.W. and McManus, J., 1995. Thermal radiometry: a rapid means of determining surface water temperature variations in lakes and reservoirs. Journal of Hydrology 173, pp. 131-144.Google Scholar
  3. Antoine, D., and A. Morel (1996), Oceanic primary production: 1. Adaption of spectral light-photosynthesis model in view of application to satellite chlorophyll observations, Global Biogeochem. Cycles, 10, 43-55.Google Scholar
  4. Arenz R F , Lewis WM, Saunders J F. Determination of chlorophyll and dissolved organic carbon from reflectance data for Colorado reservoirs[J ]. Int. J. Remote Sensing ,1996 ,17(8) :1 547-1 566.Google Scholar
  5. Atwell, B.H., McDonald, R.B. and Bartolucci, L.A., 1971. Thermal mapping of streams from airborne radiometric scanning. Water Resources Bulletin 7, pp. 228-243.Google Scholar
  6. Avery, T.E. and Berlin, G.L., 1992. Fundamentals of remote sensing and airphoto interpretation, Macmillan, New York.Google Scholar
  7. Behrenfeld, M. J., and P. G. Falkowski. Photosynthetic rates derived from satellite-based chlorophyll concentration, Limnology and Oceanography, 1997, 42 (1):1 -20.Google Scholar
  8. Belknap, W. and Naiman, R.J., 1998. A GIS and TIR procedure to detect and map wall-base channels in western Washington. Journal of Environmental Management 52, pp. 147-160.Google Scholar
  9. Brekke, C. and A.H.S. Solberg, Oil spill detection by satellite remote sensing. Remote Sensing of Environment, 2005. 95(1): p. 1-13.Google Scholar
  10. Buckton, D., 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, 1841-1851.Google Scholar
  11. Bukata R P,J H Jerome et al.Optical Properties and Remote Sensing of Inland and Coastal Water.CRC Press,Boca Raton,Florida 1995:362pp.Google Scholar
  12. Campbell, J., et al. (2002), Comparison of algorithms for estimating ocean primary production from surface chlorophyll, temperature, and irradiance, Global Biogeochem. Cycles, 16(3), 1035.Google Scholar
  13. Carder KL , Steward R G, Harvey G R , et al. Marine Humic and fulvic acids : Their effects on remote sensing of ocean chlorophyll [J ]. Limnol. Oceanogr. ,1989 ,34 (1) :68 -81.Google Scholar
  14. Chen Q. C., Wei J., and Shi P.(2003). Atmospheric correction of SeaWiFS imagery for turbid waters in Southern China coastal areas. Proceedings of SPIE -The International Society for Optical Engineering, 4892, 80-86.Google Scholar
  15. Chen Q. C., Pan Z.L., Shi P., Simulation of Sea Water Reflectance and Its Application in Retrieval of Yellow Subtance by Remote Sensing Data. Journal of Tropical Oceanography, 2003, 22(5):34-39.Google Scholar
  16. Chen X H, Chen Y Q, Lai G Y. Modeling of the transport of suspended solids in the estuary of Zhujiang River[J]. Acta Oceanological Sinica, 2003, 25(2): 120-127.Google Scholar
  17. Chen X. L., Li, Y. S., Li, Z. L.. Statistical characteristics of chlorophyll-a concentration in Hong Kong’s coastal waters. Journal of Geographical Science, 2002, 12 (3): 331-342.Google Scholar
  18. Chen X., Y.S. Li, Z. Liu, K. Yin, Z. Li, O.W. Wai and B. King,2004. Integration of multi-source data for water quality classification in the Pearl River estuary and its adjacent coastal waters of Hong Kong, Cont. Shelf Res. 24 (16): 1827-1843.Google Scholar
  19. Chen Z Q, Li Y, Pan J M. Distributions of colored dissolved organic matter and dissolved organic carbon in the Pear River Estuary, China. Cont. Shelf Res., 2004, 24: 1845~1856.Google Scholar
  20. Dekker, A.G., Vos, R.J., Peters, S.W.M., 2001. Comparison of remote sensing data, model results and in situ data for total suspended matter (TSM) in the southern Frisian lakes. The Science of the Total Environment 268, 197-214.Google Scholar
  21. Dekker, A.G., Vos, R.J., Peters, S.W.M., 2002. Analytical algorithms for lake water TSM estimation for retrospective analysis of TM and SPOT sensor data. International Journal of Remote Sensing 23 (1), 15-35.Google Scholar
  22. Deng M, Huang W, Li Y. Data collection of remote sensing derived suspended sediment concentration in Zhujiang River estuary[J]. Oceanologia Et Limnologia Sinica, 2002,33(4):341-348.Google Scholar
  23. Dierssen, H. M., and Smith, R. C. (2000). Bio-optical properties and remote sensing ocean color algorithms for Antarctic Peninsula waters. Journal of Geophysical Research, 105, 26301-26312.Google Scholar
  24. Dierssen, H. M., Zimmerman, R. C., Leathers, R. A., Downes, T. V., and Davis, C. O. (2003). Ocean color remote sensing of seagrass and bathymetry in the Bahamas Banks by high-resolution airborne imagery. Limnology and Oceanography, 48, 444-455.Google Scholar
  25. Doerffer, R., Fischer, J., Stössel, M., Brockmann, C.. 1989. Analysis of Thematic Mapper data for studying the suspended matter distribution in the coastal area of the German Bight (North Sea). Remote Sensing of Environment 28, 61-73.Google Scholar
  26. Doerffer, R., and Fischer, J. (1994). 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), 7457-7466.Google Scholar
  27. Doerffer, R., and Schiller, H. (1997). 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.Google Scholar
  28. D'Ortenzio, F., et al., Validation of empirical SeaWiFS algorithms for chlorophyll-alpha retrieval in the Mediterranean Sea -A case study for oligotrophic seas. Remote Sensing of Environment, 2002. 82(1): p. 79-94.Google Scholar
  29. EPDHK. 1998. Marine water quality in Hong Kong in 1997, Prepared by Environmental Protection Department, Hong Kong.Google Scholar
  30. European Space Agency. (1998). Oil pollution monitoring. ESA brochure: ERS and its applications—Marine, BR-128, 1.Google Scholar
  31. Espedal, H.A. and T. Wahl, Satellite SAR oil spill detection using wind history information. International Journal of Remote Sensing, 1999. 20(1): p. 49-65.Google Scholar
  32. Falkowski,P. G. 1980. Light-shade adaption in marine phytoplankton,p. 99-119. In P. G. Falkpwski [ed.], primary productivity in the sea. plenum.Google Scholar
  33. Field, C. B., M. J. Behrenfeld, J. T. Randerson, and P. Falkowski (1998), Primary production of the biosphere: Integrating terrestrial and oceanic components, Science, 281, 237-240.Google Scholar
  34. Fingas, M. F., and Brown, C. E. (1997). Review of oil spill remote sensing.Spill Science and Technology Bulletin, 4, 199– 208.Google Scholar
  35. Fiscella, B., et al., Oil spill detection using marine SAR images. International Journal of Remote Sensing, 2000. 21(18): p. 3561-3566.Google Scholar
  36. Garrett, A.J. and Hayes, D.W., 1997. Cooling lake simulations compared to thermal imagery and dye tracers. Journal of Hydraulic Engineering 123, pp. 885-894.Google Scholar
  37. Gordon, H. R. Removal of atmospheric effects from satellite imagery of the ocean, Applied Optics. 1978, 17:1631-1636.Google Scholar
  38. Gordon, H. R., Clark, D.K., Clear water radiances for atmospheric correction of coastal zone color scanner imagery. Applied Optics. 1981, 20: 4175 -4180.Google Scholar
  39. Gordon, H. R., Castano, D. J., Coastal Zone Color Scanner atmospheric correction algorithm: multiple scattering effects. Applied Optics. 1987, 26: 2111-2122.Google Scholar
  40. Gordon, H. R., Brown J. W., and Evans, R. H., Exact Rayleigh scattering calculations for use with the Nimbus-7 Coastal Zone Color Scanner. Applied Optics. 1988, 27: 862-871.Google Scholar
  41. Gordon, H. R., and Clark, D. (1980). Remote sensing optical properties of a stratified ocean: an improved interpretation. Applied Optics, 19, 3428-3430.Google Scholar
  42. Gordon H. R. , Clark D. K. , Brown J. W. , Brown o. B. , Evans R. H. , and Broenkow W. W., "Phytoplankton pigment concentrations in the Middle Atlantic Bight: comparison of ship determinations and CZCS estimates," Appl. Opt. 22, 20-35(1983).Google Scholar
  43. Gordon, H. R. and Morel, A. (1983). Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery. A Review, Lecture Notes on Coastal and Estuarine Studies, R. T. Barber, N. K. Mooers, M. J. Bowman and B. Zeitzschel (eds.), Springer-Verlag, New York, 114 p.Google Scholar
  44. Gordon, H. R. and Wang, W., Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm, Applied Optics. 1994, 33: 443-452.Google Scholar
  45. Gower, J., et al., Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer. International Journal of Remote Sensing, 2005. 26(9): p. 2005-2012.Google Scholar
  46. Haltrin, V. I., Kattawar, G. W. and Weidemann, A. D. 1997. Modeling of elastic and inelastic scattering effects in ocean optics. In: Ocean Optics XIII. Proc. SPIE, 2963: 597-602.Google Scholar
  47. Han, L., Rundquist, D.C.. 1998. The impact of a wind-roughened water surface on remote measurements of turbidity. International Journal of Remote Sensing 19 (1), 195-201.Google Scholar
  48. Hu, C.M., et al., Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters. Remote Sensing of Environment, 2005. 97(3): p. 311-321.Google Scholar
  49. Hu, C.,Mqller-Krager, F. E., Taylor, C. J.,Myhre,D.,Murch, B.,Odriozola,A.L., et al. (2003). MODIS detects oil spills in Lake Maracaibo, Venezuela. EOS, Transactions, American Geophysical Union, 84(33), 313, 319.Google Scholar
  50. Huang, W. Satellite remote sensing applications in the Pearl River estuary and Hong Kong coastal waters. M.Sc. Project Report, Hong Kong University of Science and Technology, Hong Kong, 2001Google Scholar
  51. IOCCG (1998). Minimum Requirements for an Operational, Ocean-Colour Sensor for the Open Ocean, Reports of the International Ocean-Colour Coordinating Group, No. 1,IOCCG, Dartmouth, Canada.Google Scholar
  52. IOCCG (1999). Status and Plans for Satellite Ocean-Colour Missions: Considerations for Complementary Missions. Yoder, J. A. (ed.), Reports of the International Ocean-Colour Coordinating Group, No. 2,IOCCG, Dartmouth, Canada.Google Scholar
  53. IOCCG (2000). Remote Sensing of Ocean Colour in Coastal, and Other Optically-Complex, Waters. Sathyendranath, S. (ed.), Reports of the International Ocean-Colour Coordinating Group, No. 3, IOCCG, Dartmouth, Canada.Google Scholar
  54. James R Thomas, Havens K E, 1996. Algal bloom probability in a large subtropical lake. Water Resources Bulletin, 33(5):995-1006.Google Scholar
  55. Jessup, A.T., Zappa, C.J., Loewen, M.R. and Hesany, V., 1997. Infrared remote sensing of breaking waves. Nature 385, pp. 52-55.Google Scholar
  56. Johnsen, G. and Sakshaug, E. (1996). Light harvesting in bloom-forming marine phytoplankton: species-specificity and photoacclimation. Scient. Mar., 60: 47-56.Google Scholar
  57. Jorgensen, P. V. (1999). Standard CZCS Case 1 algorithms in Danish coastal waters. International Journal of Remote Sensing, 20, 1289-1301.Google Scholar
  58. Kahru, M., and Mitchell, B. G. (1999). Empirical chlorophyll algorithm and preliminary SeaWiFS validation for the California Current. International Journal of Remote Sensing, 20, 3423-3429.Google Scholar
  59. Karabashev, G. S. (1998). On concentration dependence of chlorophyll fluorescence in theoceanic waters of diverse trophicity. Oceanology, 38: 342-346.Google Scholar
  60. Keiner, L. E., and Yan, X. (1998). A neural network model for estimating sea surface chlorophyll and sediments from Thematic Mapper Imagery. Remote Sensing of Environment, 66, 153-165.Google Scholar
  61. Kirk, J. T. O. 1981. Monte Carlo study of the nature of the underwater light field in, and the relationships between optical properties of, turbid yellow waters. Aust. J. Freshwater Res., 32: 517-532.Google Scholar
  62. Kishino, M., Ishimaru, T., Furuya, K., Oishi, T., and Kawasaki, K. (1998). Inwater algorithm for ADEOS/OCTS. Journal of Oceanography, 54, 431-436.Google Scholar
  63. Kishino, M., A. Tanaka, and J. Ishizaka, Retrieval of Chlorophyll a, suspended solids, and colored dissolved organic matter in Tokyo Bay using ASTER data. Remote Sensing of Environment, 2005. 99(1-2): p. 66-74.Google Scholar
  64. Klemas V, Bartlett D, Philpot W et al., 1974. Coastal and estuarine studies with ERTS-1 and Skylab. Remote Sensing of Environment, 3(3): 153-174.Google Scholar
  65. Lathrop Jr., R.G., Lillesand, T M.. 1986. Use of Thematic Mapper Data to Assess Water Quality in Green Bay and Central Lake Michigan. Photogrammetric Engineering and Remote Sensing 52, 671-680.Google Scholar
  66. Lavery, P., Pattiaratchi, C., Wyllie, A., Hick, P., 1993. Water quality monitoring in estuarine waters using the Landsat Thematic Mapper. Remote Sensing of Environment 46 (3), 268-280.Google Scholar
  67. Le'on, J. F., Chazette, P., and Dulac, F. (1999). Retrieval and monitoring of aerosol optical thickness over an urban area by spaceborne and groundbased remote sensing. Applied Optics, 38, 6918-6926.Google Scholar
  68. Legleiter, C. J. , D A. Roberts, W A. Marcus, and M A. Fonstad, "Passive optical remote sensing of river channel morphology and in-stream habitat: Physical basis and feasibility" (2004). Remote Sensing of Environment. 93 (4), pp. 493-510.Google Scholar
  69. Letelier, R.M. and Abbott, M.R. 1996. An analysis of chlorophyll fluorescence algorithms for the Moderate Resolution Imaging Spectrometer (MODIS). Rem. Sens. Environ., 58: 215-223.Google Scholar
  70. Lee, Z., Carder, K. L., Hawes, S. K., Steward, R. G., Peacock, T. G. and Davis, C. O. 1994. Model for the interpretation of hyperspectral remote-sensing reflectance. Appl. Opt., 33: 5721-5732.Google Scholar
  71. Li J., A study on determination of concentration of suspended solids in water by remote sensing.Acta Scientiae Circumstantiae, 1986,6(2):166-173.Google Scholar
  72. Li G.S, Wang F., Liang Q., Li J.L.. Estimation of Ocean Primary Productivity by Remote Sensing and Introduction to Spatio-temporal Variation Mechanism for the East China Sea. Acta Geographica Sinica, 2003, 58(4):484-493.Google Scholar
  73. Li, Y., Huang W., Fang M.. 1998. An algorithm for the retrieval of suspended sediment in coastal waters of China from AVHRR data. Continental Shelf Research, 1998, 18(5): 487-500.Google Scholar
  74. Lin, I.-I., L. S.Wen, K.-K. Liu,and W.-T.Tsai, Evidence and quantification of the correlation between radar backscatter and ocean colour supported by simultaneously acquired in situ sea truth,Geophys.Res. Lett.29, 102.1-4, 2002.Google Scholar
  75. Liu,A. K.,S.,Y.Wu,W.Y.Tseng,and W.G. Pichel,Wavelet analysis of SAR images for coastal monitoring,Can.J.Rem.Sens.,26, 494-500, 2000.Google Scholar
  76. Lu, J., Marine oil spill detection, statistics and mapping with ERS SAR imagery in south-east Asia. International Journal of Remote Sensing, 2003. 24(15): p. 3013-3032.Google Scholar
  77. Lyzenga, D. R. (1978). Passive remote-sensing techniques for mapping water depth and bottom features. Applied Optics, 17, 379-383.Google Scholar
  78. M. Wang, "Atmospheric correction of the second generation ocean color sensors," Ph.D. dissertation (University of Miami,Coral Gables, Fla., 1991).Google Scholar
  79. Mobley,Curtis D.Light and water-radiative transfer in natural waters[M].Academic Press.1994.Google Scholar
  80. Morel, A. and Prieur, L. (1977). Analysis of variations in ocean color. Limnol. Oceanogr. 22:709-722.Google Scholar
  81. Morel, A. and Bricaud, A., 1981. Theoretical results concerning light absorption in a discrete medium, and application to specific absorption of phytoplankton. Deep-Sea Res., (Part A), 28: 1375-1393.Google Scholar
  82. Morel, A. 1988. Optical modeling of upper ocean in relation to its biogenous matter content (Case I waters). J. Geophys. Res., 93: 10,749-10,768.Google Scholar
  83. Morel, A.,Ahn, Y-H., Partensky, F., Vaulot, D. and Claustre H. 1993. Prochlorococcus and Synechococcus: A comparative study of their optical properties in relation to their size and pigmentation. J. Mar. Res., 51: 617-649.Google Scholar
  84. Morel A. Consequences of a Synechococcus bloom upon the optical properties of oceanic (case I) waters. Limnol. Oceanogr. (1997) 42:1746-1754.Google Scholar
  85. Mueller, J. L. and Austin, R.W., 1995. Ocean Optics protocols for SeaWiFS validation. Revision 1.NASA Tech. Memo., Vol. 25: 66pp.Google Scholar
  86. MUMBY, P. J., C. D. CLARK, E. P. GREEN, AND A. J. EDWARDS. 1998. Benefits of water column correction and contextual editing for mapping coral reefs. Int. J. Remote Sens. 19: 203-210.Google Scholar
  87. Munday, Jr., J. C., Alföldi, T. T.. 1979. Landsat test of diffuse reflectance models for aquatic suspended solids measurement. Remote Sensing of Environment. 8: 169-183.Google Scholar
  88. Mueller, J. L. and Austin, R.W., 1995. Ocean Optics protocols for SeaWiFS validation. Revision 1.NASA Tech. Memo., Vol. 25: 66pp.Google Scholar
  89. MUMBY, P. J., C. D. CLARK, E. P. GREEN, AND A. J. EDWARDS. 1998. Benefits of water column correction and contextual editing for mapping coral reefs. Int. J. Remote Sens. 19: 203-210.Google Scholar
  90. Munday, Jr., J. C., Alföldi, T. T.. 1979. Landsat test of diffuse reflectance models for aquatic suspended solids measurement. Remote Sensing of Environment. 8: 169-183.Google Scholar
  91. O'Reilly J.E., S. Maritorena, B.G. Mitchell, D.A. Siegel, K.L. Cardel and S.A. Garver et al, Ocean color chlorophyll algorithms for SeaWiFS, Journal of Geophysical Research-Oceans 103 (1998) (C11), pp. 24937-24953.Google Scholar
  92. O'Reilly J.E., S. Maritorena, D. Siegel, M.C. O'Brien, D. Toole and B.G. Mitchell et al., Ocean color chlorophyll a algorithms for SeaWiFS, OC2, and OC4: Version 4. In: S.B. Hooker and E.R. Firestone, Editors, SeaWiFS postlaunch technical report series, SeaWiFS postlaunch calibration and validation analyses, Part 3 vol. 11, NASA/GSFC (2000), pp. 9-23.Google Scholar
  93. Orellana M.V. and M.J. Perry, An immunoprobe to measure Rubisco concentration and maximal photosynthetic rates of individual phytoplankton cells. Limnol. Oceanogr. 37 (1992), pp. 478-490.Google Scholar
  94. ORTH, R., AND K. MOORE. 1983. Chesapeake Bay: An unprecedented decline in submerged aquatic vegetation. Science 222:51-53.Google Scholar
  95. Pan J M, Zhou H Y, Hu C Y, Liu X Y, Dong L X, Zhang M. Nutrient profile in interstitial water and flux in water-sediment interface of Zhujiang estuary of China in summer[J]. Acta Oceanologica Sinica, 2002, 24(3): 52-59.Google Scholar
  96. PETERSON, B. J., AND J. W. FOURQUREAN. 2001. Large-scale patterns in seagrass (Thalassia testudinum) demographic in south Florida. Limnol. Oceanogr. 46: 1077-1090.Google Scholar
  97. Pozdnyakov, D., Grassl,H. et al, Colour of inland waters: a methodology for its interpretation, Chichester: Springer-Praxis, 2003.Google Scholar
  98. Philpot, W. D. (1989). Bathymetric mapping with passive multispectral imagery. Applied Optics, 28, 1569-1578.Google Scholar
  99. Richard G., Lathrop, Jr. (1992). Landsat Thematic Mapper Monitoring of Turbidity Inland Water Quality, Photogrammetric Engineering and Remote Sensing. 58 (4), pp465-470.Google Scholar
  100. Ritchie, J. C., Cooper, C. M., Schiebe, F. R.. 1990. The relationship of MSS and TM digital data with suspended sediments, chlorophyll, and temperature in Moon Lake, Mississippi. Remote Sensing of Environment 33, 137-148.Google Scholar
  101. Robinson, I.S., Wells, N.C. and Charnock, H., 1984. The sea surface thermal boundary layer and its relevance to the measurement of sea surface temperature by airborne and spaceborne radiometers. International Journal of Remote Sensing 5, pp. 19-45.Google Scholar
  102. Rothman, L. S., Gamache, R. R., Goldman, A., Brown, L. R., Toth, R. A., Pickett, H. M., Poynter,R. L., Flaud, J.-M., Camy-Peyret, C., Barbe, A., Husson, N., Rinsland, C. P.and Smith, A. H.1987. The HITRAN database: 1986 edition. Appl. Optics, 26: 4058-4097.Google Scholar
  103. Ruddick K.G., Ovidio F. and Rijkeboer M. , Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters, Applied Optics 39 (6) (2000), pp. 897-912.Google Scholar
  104. Smith, W.L., Knuteson, R.O. and Revercomb, H.E., 1996. Observations of the infrared radiative properties of the ocean: implications for the measurement of sea surface temperature via satellite remote sensing. Bulletin of the American Meteorological Society 77, pp. 41-51.Google Scholar
  105. Smyth, T. J., Tilstone, G. H. and Groom, S. B. (2005) Integration of radiative transfer into satellite models of ocean primary production. Journal of Geophysical Research -Oceans, 110, p. 10014.Google Scholar
  106. Solberg, A.H.S., C. Brekke, and P.O. Husoy, Oil spill detection in Radarsat and Envisat SAR images. Ieee Transactions on Geoscience and Remote Sensing, 2007. 45(3): p. 746-755.Google Scholar
  107. Solberg, A.H.S., G. Storvik, R. Solberg, and E. Volden, “Automatic detection of oil spills in ERS SAR images,” IEEE Trans. Geosci. Remote Sens., vol. 37, no. 4, pp. 1916-1924, Jul. 1999.Google Scholar
  108. Stavn, R. H. 1992. External factors and water Raman scattering in clear ocean waters: skylight, solar angle, and the air/water interface. In: Ocean Optics XI. Proc. SPIE, 1750: 138-148.Google Scholar
  109. Stumpf, R. P., Holderied, K., and Sinclair, M. (2003). Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnology and Oceanography, 48, 547-556.Google Scholar
  110. Subramaniam, A. and Carpenter, E. J. 1994. An empirically derived protocol for the detection of blooms of the marine cyanobacterium Trichodesmium using CZCS imagery. Int. J. Remote Sensing, 15: 1559-1569.Google Scholar
  111. Sugihara S., Kishino, M. and Okami, N. 1984. Contribution of Raman scattering to upward irradiance in the sea. J. Oceanogr. Soc. Japan, 40: 397-403.Google Scholar
  112. Sukenik, A., J. Bennett and P. Falkowski, Light-saturated photosynthesis-limitation by electron transport or carbon fixation?. Biochem. Biophys. Acta 891 (1987), pp. 205-215.Google Scholar
  113. Steemann Nielsen,E. 1952. The use of radiao-active carbon (C14) for measuring organic production in the sea. J. Cons. Int. Explor. Mer 18: 117-140.Google Scholar
  114. Schiller, H., and Doerffer, R. (1993). Fast computational scheme for inverse modeling of multispectral radiances: Application for remote sensing of the ocean. Applied Optics, 32, 3280-3285.Google Scholar
  115. Steidinger, K.A., Haddad, K.D., 1981. Biologic and hydrographic aspects of red tides. Bioscience 31, 814-819.Google Scholar
  116. Stumpf, R.P., 2001. Applications of satellite ocean color sensors for monitoring and predicting harmful algal blooms. J. Human Ecol.Risk Assess. 7, 1363-1368.Google Scholar
  117. Stumpf R.P. and M.A. Tyler, Satellite detection of bloom and pigment distributions in estuaries, Remote Sensing of Environment 24 (1988), pp. 385-404.Google Scholar
  118. Tanaka, A., Kishino, M.., Doerffer, R., Schiller, H., Oishi, T., and Kubota, T. (2004). 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, 519-530.Google Scholar
  119. Tassan, S.. 1987. Evaluation of the potential of the Thematic Mapper for Marine application. International Journal of Remote Sensing 8, 1455-1478.Google Scholar
  120. Tassan S. The effect of dissolved ‘yellow substance’ on the quantitative retrieval of chlorophyll and total suspended sediment concentrations from remote measurements of water colour[J ]. Int. J. Remote Sensing ,1988 ,9(4) :787-797.Google Scholar
  121. Tassan S. Local algorithms using SeaWiFS data for the retrieval of phytoplankton , pigments , suspended sediment , and yellow substances in coastal waters[J]. Appl. Opt., 1994, 33(12):2 369-2378.Google Scholar
  122. Tassan, S. (1993). An algorithm for the detection of the White-Tide (“mucilage”) phenomenon in the Adriatic Sea using AVHRR data. Remote Sens. Environ., 45: 29-42.Google Scholar
  123. Tassan,S.. 1992. An algorithm for the identification of benthic algae in the Venice lagoon from Thematic Mapper data. International Journal of Remote Sensing 13 (15), 2887-2909.Google Scholar
  124. Tassan,S.. 1993. An Improved In-water Algorithm for the Determination of Chlorophyll and Suspended Sediment Concentration from Thematic Mapper Data in Coastal Water. International Journal of Remote Sensing 14 (6), 1221-1229.Google Scholar
  125. Tassan S. and d'Alcalà M.R.. Water quality monitoring by Thematic Mapper in coastal environments. A performance analysis of local bio-optical algorithms and atmospheric correction procedures. Remote Sensing Environ. 45, 177-191 (1993).Google Scholar
  126. Tian L. Q.,Chen X. L.,et al. Atmospheric correction of ocean color imagery over turbid coastal waters using active and passive remote sensing. Chinese Journal of Oceanology and Limnology, 2009, 27(1): 124-128.Google Scholar
  127. Torgersen, C.E., et al, Airborne thermal remote sensing for water temperature assessment in rivers and streams. Remote Sensing of Environment, 2001. 76(3): p. 386-398.Google Scholar
  128. Torgersen, C.E., Price, D.M., Li, H.W. and McIntosh, B.A., 1999.Multiscale thermal refugia and stream habitat associations of chinook salmon in northeastern Oregon. Ecological Applications 9, pp. 301-319.Google Scholar
  129. Viollier, M. and Sturm, B. 1984. CZCS data analysis in turbid coastal water, J. Geophys. Res., 89:4977-4985.Google Scholar
  130. Weisblatt, E. A., Zaitzeff, J. B., and Reeves, C. A.: 1973,Classification of Turbidity Levels in the Texas Marine Coastal Zone, Machine Processing of Remote Sensing Data Conference Proceedings. Laboratory for Application of Remote Sensing, Purdue University, Lafayette, Indiana. October 16-18, pp. 42.Google Scholar
  131. Whitlock, C.H.,NASA TMX-73906,1976.Google Scholar
  132. Williams A N, Grabau W E, 1973. Sediment concentration mapping in tidal estuaries. Third Earth Resources Technology Satellite-1 Sym. NASA SP-351, 1347-1386.Google Scholar
  133. Yung Y K, Wong C K, Broom M J et al, 1997, Long-term changes in hydrography, nutrient and phytoplankton in Tolo Harbour, Hong Kong, Hydrobiologia, 352:107-352.Google Scholar
  134. Zeichen, M.M. and I.S. Robinson. Detection and monitoring of algal blooms using SeaWiFS imagery. 2000. Venice, ITALY: Taylor and Francis Ltd.Google Scholar
  135. Zhang, Y., Pulliainen, J., Koponen, S., and Hallikainen, M. (2002). 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, 327-336.Google Scholar
  136. Zhu X G, He Z J, Deng M. Monitoring of water color for Pearl River estuary over twenty years. Journal of Remote Sensing. 2001, 5(5): 396-401.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Xiaoling Chen
    • 1
  • Zhifeng Yu
    • 1
  1. 1.State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote SensingWuhan UniversityWuhanChina, People’s Republic

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