Validation of empirical and semi-analytical remote sensing algorithms for estimating absorption by Coloured Dissolved Organic Matter in the Baltic Sea from SeaWiFS and MODIS imagery

• Autorzy: Kowalczuk P. , Darecki M. , Zabłocka M. , Górecka I.
• Języki publikacji: EN

Abstrakty

EN

An extensive bio-optical data set obtained from field measurements was used to evaluate the performance of an empirical (Kowalczuk et al. 2005) and two semi-analytical algorithms: Carder et al. (1999) and GSM01 (Maritorena et al. 2002) for estimating CDOM absorption in the Baltic Sea. The data set includes coincident measurements of radiometric quantities and absorption coefficients of CDOM made during 43 cruises between 2000 and 2008. In the first stage of the analysis, the accuracy of the empirical algorithm by Kowalczuk et al. (2005) was assessed using in situ measurements of remote sensing reflectance. Validation results improved when matching points located in Gulf of Gdańsk close to the Vistula River mouth were eliminated from the data set. The calculated errors in the estimation of aCDOM(400) in the first phase of the analysis were Bias = -0.02, RMSE = 0.46 and R2 = 0.70. In the second stage, the empirical algorithm was tested on satellite data from SeaWiFS and MODIS imagery. The satellite data were corrected atmospherically with the MUMM algorithm designed for turbid coastal and inland waters and implemented in the SeaDAS software. The results of the best case scenario for estimating the CDOM absorption coefficient aCDOM(400), based on SeaWiFS data, were Bias = -0.02, RMSE = 0.23 and R2 = 0.40. The validation of the Kowalczuk et al. (2005) empirical algorithm applied to MODIS data led to a less accurate estimate of aCDOM(400): Bias = -0.03, RMSE = 0.19 and R2 = 0.29. This assessment of the accuracy of standard semi-analytical algorithms available in the SeaWiFS and MODIS imagery processing software revealed that both algorithms (GSM_01 and Carder) underestimate CDOM absorption in the Baltic Sea with mean systematic and random errors in excess of 70%. The paper presents examples of the application of the Kowalczuk et al. (2005) empirical algorithm for producing maps of the seasonal distribution of aCDOM(400) in the Baltic Sea between 2004 and 2008.

Słowa kluczowe

EN

remote sensing; ocean colour; satellite validation; SeaWiFS; MODIS; coloured dissolved organic matter; absorption

• Wydawca: Polish Academy of Sciences, Institute of Oceanology ; Elsevier
• Czasopismo: Oceanologia
• Rocznik: 2010
• Tom: No. 52 (2)
• Strony: 171--196
• Opis fizyczny: bibliogr. 53 poz., fot., tab., wykr.

Twórcy

autor

Kowalczuk P.

autor

Darecki M.

autor

Zabłocka M.

autor

Górecka I.

• Institute of Oceanology, Polish Academy of Science, ul. Powstańców Warszawy 55, PL-81-712 Sopot, Poland,

Bibliografia

• 1.Aas E., Hokedal J., Sorensen K., 2005, Spectral backscattering coefficient in coastal water, Int. J. Remote Sens., 26 (2), 331-343.
• 2.Bailey S.W., Werdell P. J., 2006, A multi-sensor approach for the on-orbit validation of ocean color satellite data products, Remote Sens. Environ., 102 (1-2), 12-23.
• 3.Bélanger S., Xie H., Krotkov N., Larouche P., Vincent W. F., Babin M., 2006, Photomineralization of terrigenous dissolved organic matter in Arctic coastal waters from 1979 to 2003: Interannual variability and implications of climate change, Global Biogeochem. Cy., 20 (4), GB4005, doi:10.1029/2006GB002708.
• 4.Blough N.V., Del Vecchio R., 2002, Chromophoric DOM in the coastal environment, [in:] Biogeochemistry of marine dissolved organic matter, D. Hansell & C. Carlson (eds.),. Acad. Press, New York, 509-546.
• 5.Carder K.L., Chen F.R., Lee Z.P., Hawes S.K., 1999, Semianalytic moderate-resolution imaging spectrometer algorithms for chlorophyll a and absorption with bio-optical domains based on nitrate-depletion temperatures, J. Geophys. Res., 104 (C3), 5403-5421.
• 6.Darecki M., Stramski D., 2004, An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea, Remote Sens. Environ., 89 (3), 326-350.
• 7.Darecki M., Weeks A., Sagan S., Kowalczuk P., Kaczmarek S., 2003, Optical characteristics of two contrasting case 2 waters and their influence on remote sensing algorithms, Cont. Shelf Res., 23, 237-250.
• 8.Del Castillo C.E., Miller R. L., 2008, On the use of ocean color remote sensing to measure the transport of dissolved organic carbon by the Mississippi River Plume, Remote Sens. Environ., 112 (3), 836-844.
• 9.Del Vecchio R., Subramaniam A., Uz S. Schollaert, Ballabrera-Poy R., Brown C.W., Blough N.V., 2009, Decadal time-series of SeaWiFS retrieved CDOM absorption and estimated CO2 photoproduction on the continental shelf of the eastern United States, Geophys. Res. Lett., 36, L02602, doi:10.1029/2008GL036169.
• 10.D'Sa E. J., Hu C., Muller-Karger F.E., Carder K.L., 2002, Estimation of colored dissolved organic matter and salinity fields in case 2 waters using SeaWiFS: Examples from Florida Bay and Florida Shelf, Proc. Indian Acad. Sci. (Earth and Planetary Sciences), 111 (3), 197-207.
• 11.D'Sa E. J., Miller R. L., 2003, Bio-optical properties in waters influenced by the Mississippi River during low flow conditions, Remote Sens. Environ., 84 (4), 538-549.
• 12.Fargion G. S., Mueller J. L., 2000, Ocean optics protocols for satellite ocean color sensor validation, Revision 2, NASA Tech. Memo. 200-209966, NASA Goddard Space Flight Center, Greenbelt, MD.
• 13.Fichot C.G., Sathyendranath S., Miller W. L., 2008, SeaUV and SeaUV(C): Algorithms for the retrieval of UV/visible diffuse attenuation coefficients from ocean color, Remote Sens. Environ., 112 (4), 1584-1602.
• 14.Garver S.A., Siegel D.A., 1997, Inherent optical property inversion of ocean color spectra and its biogeochemical interpretation, 1. Time series from the Sargasso Sea, J. Geophys. Res., 102 (8), 18607-18625.
• 15.Gordon H.R., Ding K., 1992, Self-shading of in-water instruments, Limnol. Oceanogr., 37 (3), 491-500.
• 16.Gregg W.W., Casey N.W., 2004, Global and regional evaluation of the SeaWiFS chlorophyll data set, Remote Sens. Environ., 93 (4), 463-479.
• 17.Hansell D.A., Carlson C.A., 2001, Marine dissolved organic matter and the carbon cycle, Oceanography, 14 (4), 41-49.
• 18.Hooker S.B., Esaias W.E., 1993, An overview of the SeaWiFS project, Eos Trans. Amer. Geophys. Union, 74, 241-246.
• 19.Jerlov N.G., 1976, Marine optics, Elsevier, New York, 231 pp.
• 20.Johannessen S.C., Miller W. L., 2001, Quantum yield for the photochemical production of dissolved inorganic carbon in seawater, Mar. Chem., 76 (4), 271-283.
• 21.Johannessen S.C., Miller W. L., Cullen J. J., 2003, Calculation of UV attenuation and colored dissolved organic matter absorption spectra from measurements of ocean color, J. Geophys. Res., 108 (C9), 3301, doi:10.1029/2000JC000514.
• 22.Kahru M., 1997, Using satellites to monitor large-scale environmental change: A case study of cyanobacteria blooms in the Baltic Sea, [in:] M. Kahru & C.W. Brown (eds.), Monitoring algal blooms: New techniques for detecting large scale environmental change, Springer-Verl. Land. Biosci., 43-61.
• 23.Kahru M., Mitchell B.G., 1999, Empirical chlorophyll algorithm and preliminary SeaWiFS validation for the California Current, Int. J. Remote Sens., 20, 3421-3429.
• 24.Kahru M., Mitchell B.G., 2001, Seasonal and non-seasonal variability of satellite derived chlorophyll and colored dissolved organic matter concentration in the California Current, J. Geophys. Res., 106 (C2), 2517-2529.
• 25.Kahru M., Savchuk O.P., Elmgren R., 2007, Satellite measurements of cyanobacterial bloom frequency in the Baltic Sea: interannual and spatial variability, Mar. Ecol.-Prog. Ser., 343, 15-23.
• 26.Kowalczuk P., 1999, Seasonal variability of yellow substance absorption in the surface layer of the Baltic Sea, J. Geophys. Res., 104 (C12), 30047-30058.
• 27.Kowalczuk P., Darecki M., Olszewski J., Kaczmarek S., 2005, Empirical relationships between Coloured Dissolved Organic Matter (CDOM) absorption and apparent optical properties in Baltic Sea waters, Int. J. Remote Sens., 26 (2), 345-370.
• 28.Kowalczuk P., Kaczmarek S., 1996, Analysis of temporal and spatial variability of yellow substance absorption in the Southern Baltic, Oceanologia, 38 (1), 3-32.
• 29.Kowalczuk P., Sagan S., Olszewski J., Darecki M., Hapter R., 1999, Seasonal changes in selected optical parameters in the Pomeranian Bay in 1996-1997, Oceanologia, 41 (3), 309-334.
• 30.Kowalczuk P., Stedmon C.A., Markager S., 2006, Modeling absorption by CDOM in the Baltic Sea from season, salinity and chlorophyll, Mar. Chem., 101 (1-2), 1-11.
• 31.Kutser T., 2004, Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing, Limnol. Oceanogr., 49 (6), 2179-2189.
• 32.Kutser T., Paavel B., Metsamaa L., Vahtmäe E., 2009, Mapping coloured dissolved organic matter concentration in coastal waters, Int. J. Remote Sens., 30 (22), 5843-5849.
• 33.Mannino A., Russ M.E., Hooker S.B., 2008, Algorithm development and validation for satellite-derived distributions of DOC and CDOM in the U.S. Middle Atlantic Bight, J. Geophys. Res., 113, C07051, doi:10.1029/2007JC004493.
• 34.Maritorena S., Siegel D.A., Peterson A.R., 2002, Optimization of of a semi-analytical ocean color model for global-scale applications, Appl. Optics, 41 (15), 2705-2714.
• 35.Melin F., Maritorena S., 2007, Data merger success criteria, [in:] Ocean colour data merging, W. Gregg (ed.), IOCCG Rep. No. 6, Dartmouth, CA, 68 pp.
• 36.Morel A., Gentili B., 1993, Diffuse reflectance of oceanic waters, II Bidirectional aspects, Appl. Optics, 32 (33), 6864-6872.
• 37.Morel A., Prieur L., 1977, Analysis in variation of ocean color, Limnol. Oceanogr., 22 (4), 709-722.
• 38.Moses W. J., Gitelson A.A., Berdnikov S., Povazhniy V., 2009, Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data-successes and challenges, Environ. Res. Lett., 4, 045005, doi:10.1088/1748-9326/4/4/045005, 8 pp.
• 39.Mueller J.L., Austin R.W., 1992, Ocean optics protocols for SeaWiFS validation, NASA Tech. Memo. 104566, Vol. 5, NASA Goddard Space Flight Center, Greenbelt, MD, 43 pp.
• 40.Olszewski J., Sagan S., Darecki M., 1992, Spatial and temporal changes in some optical parameters in the southern Baltic, Oceanologia, 33, 87-103.
• 41.Olszewski J., Sokolski M., Kuśmierczyk-Michulec J., 1995, The method of continuous measurements of the diffusivity of the natural light field over the sea, Oceanologia, 37 (2), 299-310.
• 42.O'Reilly J.E., Maritorena S., Mitchell B.G., Siegel D.A., Carder K. L., Garver S.A., Kahru M., McClain C.R., 1998, Ocean color algorithms for SeaWiFS, J. Geophys. Res., 103 (C11), 24937-24953.
• 43.Reinart A., Kutser T., 2006, Comparison of different satellite sensors in detecting cyanobacterial bloom events in the Baltic Sea, Remote Sens. Environ., 102 (1-2), 74-85.
• 44.Ruddick K.G., Ovidio F., Rijkeboer M., 2000, Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters, Appl. Optics, 39 (6), 897-912.
• 45.Sagan S., 1991, Light transmission in the water of the southern Baltic Sea, Diss. Monogr. 2, Inst. Oceanol. PAN, Sopot, 137 pp., (in Polish).
• 46.Sagan S., 2008, The inherent optical properties of Baltic waters, Diss. Monogr. 21, Inst. Oceanol., Sopot, 244 pp., (in Polish).
• 47.Sathyendranath S. (ed.), 2000, Remote sensing of ocean colour in coastal, and other optically-complex, waters, IOCCG Rep. No. 3, Dartmouth, CA, 140 pp.
• 48.Siegel H., Gerth M., Ohde T., Heene T., 2005, Ocean colour remote sensing relevant water constituents and optical properties of the Baltic Sea, Int. J. Remote Sens., 26 (2), 315-334.
• 49.Siegel D.A., Maritorena S., Nelson N. B., Behrenfeld M. J., 2005a, In-dependence and interdependencies among global ocean color properties: Reassessing the bio-optical assumption, J. Geophys. Res., 110, C07011, doi:10.1029/2004JC002527.
• 50.Siegel D.A., Maritorena S., Nelson N.B., Behrenfeld M. J., McClain C.R., 2005b, Colored dissolved organic matter and its influence on the satellite-based characterization of the ocean biosphere, Geophys. Res. Lett., 32, L20605, doi:10.1029/2005GL024310.
• 51.Siegel D.A., Maritorena S., Nelson N.B., Hansell D.A., Lorenzi-Kayser M., 2002, Global distribution and dynamics of colored dissolved and detrital organic materials, J. Geophys. Res., 107 (C12), 3228, doi:10.1029/2001JC000965.
• 52.Sverdrup H.U., 1953, On conditions for the vernal blooming of phytoplankton, J. Cons. Cons. Int. Explor. Mer., 18 (3), 287-295.
• 53.Zibordi G., Ferrari G.M., 1995, Instrument self-shading in underwater optical measurements: Experimental data, Appl. Optics, 34 (2), 2750-2754.