Abstract

Above-water reflectance and surface chlorophyll a concentrations (Chl a) were measured in the Gulf of Lions, northwestern Mediterranean Sea in 2000 and 2001 in order to test Chl a inversion algorithms. Surface waters were separated in Case 2 waters in the Rhône River plume and proximal Region of Freshwater Influence (ROFI) stations, and Case 1 waters at all the other stations. Case 2 waters were characterized by R443/R555 < R443/R510 < R490/R555 < R490/R510 < 1. In the first part, we compared the concurrent reflectance measurements made with a scanning polarization radiometer (SIMBAD) and a hyperspectral Ocean Optics radiometer. The comparison of the remote-sensing reflectance (Rrs) values at SIMBAD wavelengths shows excellent agreement for Rrs values higher than 0.01 sr-1. Between the two instruments, reflectance ratios, commonly used in Chl a algorithms, show differences smaller than 2% in the Case 2 waters, and smaller than 20% in the Case 1 waters. In the second part, concurrent measurements of Chl a and of hyperspectral reflectance from 6 cruises were used to analyze the statistical performance of global (OC2, OC4) and regional regression algorithms using mainly SeaWiFS bands. The algorithms were tested first over the entire domain, then separately over the Case 1 and Case 2 waters. Chl a algorithms using band ratios such as the one presented in Bricaud et al. (2002) are suitable for the Case 1 waters. However, taking into account the large dispersion of Chl a for very close reflectance ratios in the Case 2 waters, single band ratios are not suitable for deriving Chl a. The use of a 4-wavelength parameter such as Xc, defined by Tassan (1994), leads to better results in the plume and proximal Rhône ROFI.

© 2005 Optical Society of America

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Appl. Opt. (4)

Conf. on Remote Sensing for Marine 02 (1)

V.F. Banzon, R.H. Evans, S. Marullo, R. Santoleri, and F. D�??Ortenzio, �??SeaWiFS observations of the southern Adriatic sea bloom (1998-2000): the role of atmospherically-forced deep convection, �?? presented at the Seventh International Conference on Remote Sensing for Marine and Coastal Environments, Miami, Florida, 20-22 May 2002.

Cont. Shelf Res. (1)

C. Millot, �??The Gulf of Lion�??s hydrodynamics,�?? Cont. Shelf Res. 10, 885-894 (1990).
[CrossRef]

Coral Reefs (1)

S. Ouillon, P. Douillet, and S. Andréfouët, �??Coupling satellite data with in situ measurements and numerical modeling to study fine suspended sediment transport: a study for the lagoon of New Caledonia,�?? Coral Reefs 23, 109-122 (2004).
[CrossRef]

Deep Sea Res. A (1)

A. Herbland, A. Le Bouteiller, and P. Raimbault, �??Size structure of phytoplankton biomass in the equatorial Atlantic Ocean,�?? Deep Sea Res. A 32, 819-836 (1985).
[CrossRef]

Deep-Sea Res. I (1)

A. Monaco, X. Durrieu de Madron, O. Radakovitch, S. Heussner, and J. Carbonne, �??Origin and variability of downward biogeochemical fluxes on the Rhone continental margin (NW Mediterranean),�?? Deep-Sea Res. I 46, 1483-1511 (1999).
[CrossRef]

Deep-Sea Res. II (2)

J.M. Beckers, P. Brasseur, and J.C.J. Nihoul, �??Circulation of the western Mediterranean: from global to regional scales,�?? Deep-Sea Res. II 44, 531-549 (1997).
[CrossRef]

D. Lefèvre, H.J. Minas, M. Minas, C. Robinson, P.J. le B. Williams, and E.M.S. Woodward, �??Review of gross community production, primary production, net community production and dark community respiration in the Gulf of Lions,�?? Deep-Sea Res. II 44, 801-832 (1997).
[CrossRef]

EOS Transactions (1)

J.E. Salisbury, J.W. Campbell, L.D. Meeker, and C. Vörösmarty, �??Ocean color and river data reveal fluvial influence in coastal waters,�?? EOS Transactions 82, 221 (2001).
[CrossRef]

EROS 2000 (1)

A. Morel, A. Bricaud, J.M. André, and J. Pelaez-Hudlet, �??Spatial-temporal evolution of the Rhône river plume as seen by CZCS imagery: Consequences upon primary productions in the Gulf of Lions,�?? in EROS 2000, J.M. Martin and H. Barth, eds. (Europ. Comm., Brussels, 1990), pp 45-62.

Geoph. Res. Let. (3)

H. Claustre, A. Morel, S.B. Hooker, M. Babin, D. Antoine, K. Oubelkheir, A. Bricaud, K. Leblanc, B. Quéguiner, and S. Maritorena, �??Is desert dust making oligotrophic waters greener?,�?? Geoph. Res. Let. 29, 107 (2002).

W.W. Gregg, M.E. Conkright, P. Ginoux, J.E. O'Reilly, and N.W. Casey, �??Ocean primary production and climate: Global decadal changes,�?? Geoph. Res. Let. 30, 1809 (2003)
[CrossRef]

G. Dall'Olmo, A.A. Gitelson, and D.C. Rundquist, �??Towards a unified approach for remote estimation of chlorophyll-a in both terrestrial vegetation and turbid productive waters,�?? Geoph. Res. Let. 30, 1938 (2003).
[CrossRef]

Glob. Biogeoch. Cycles (1)

J.A. Yoder, C.R. McClain, G.C. Feldman, and W. Esaias, �??Annual cycles of phytoplankton chlorophyll concentrations in the global ocean: a satellite view,�?? Glob. Biogeoch. Cycles 7, 181-193 (1993).
[CrossRef]

Int. J. Remote Sens. (7)

M. Kahru, and B.G. Mitchell, �??Empirical chlorophyll algorithm and preliminary SeaWiFS validation for the California Current,�?? Int. J. Remote Sens. 20, 3423-3429 (1999).
[CrossRef]

E.J. D'Sa, J.B. Zaitzeff, and R.G. Steward, �??Monitoring water quality in Florida Bay with remotely sensed salinity and in situ bio-optical observations,�?? Int. J. Remote Sens. 21, 811-816 (2000).
[CrossRef]

M.A. Lodhi, and D.C. Rundquist, �??A spectral analysis of bottom-induced variation in the colour of Sand Hills lakes, Nebraska, USA,�?? Int. J. Remote Sens. 22, 1665-1682 (2001).

F. Lahet, S. Ouillon, and P. Forget, �??Colour classification of coastal waters of Ebro river plume from spectral reflectances,�?? Int. J. Remote Sens. 22, 1639-1664 (2001).

A. Cunningham, P. Wood, and K. Jones, �??Reflectance properties of hydrographically and optically stratified fjords (Scottish sea lochs) during the Spring diatom bloom,�?? Int. J. Remote Sens. 22, 2885-2897 (2001).

D.G. Bowers, G.E.L. Harker, and B. Stephan, �??Absorption spectra of inorganic particles in the Irish Sea and their relevance to remote sensing of chlorophyll,�?? Int. J. Remote Sens. 17, 2449-2460 (1996).
[CrossRef]

F. Lahet, P. Forget, and S. Ouillon, �??Application of a colour classification method to quantify the constituents of coastal waters from in situ reflectances sampled at satellite sensor wavebands,�?? Int. J. Remote Sens. 22, 909-914 (2001).
[CrossRef]

J. Atm. Ocean. Techn. (1)

S.B. Hooker, and S. Maritorena, �??An evaluation of oceanographic radiometers and deployment methodologies,�?? J. Atm. Ocean. Techn. 17, 811-830 (2000).
[CrossRef]

J. Geoph. Res. (4)

W.W. Gregg, and M.E. Conkright, �??Global seasonal climatologies of ocean chlorophyll: blending in situ and satellite data for the CZCS era,�?? J. Geoph. Res. 106, 2499-2515 (2001).
[CrossRef]

J.E. O�??Reilly, S. Maritorena, G.G. Mitchell, D.A. Siegel, K.L. Carder, S.A. Garver, M. Kahru, andC. McClain,. �??Ocean color algorithms for SeaWiFS,�?? J. Geoph. Res. 103, 24937-24953 (1998).
[CrossRef]

A. Morel, and J.M. André, �??Pigment distribution and primary production in the western Mediterranean as derived and modelled from coastal zone color scanner observations,�?? J. Geoph. Res. 96, 12685-12698 (1991).
[CrossRef]

D. Antoine, A. Morel, and J.M. André, �??Algal pigment distribution and primary production in the Eastern Mediterranean as derived from coastal zone color scanner observations,�?? J. Geoph. Res. 100, 16193-16209 (1995).
[CrossRef]

J. Mar. Syst. (5)

A. Gitelson, A. Karnieli, N. Goldman, Y.Z. Yacobi, and M. Mayo, �??Chlorophyll estimation in the southeastern Mediterranean using CZCS images: adaptation of an algorithm and its validation,�?? J. Mar. Syst. 9, 283-290 (1996).
[CrossRef]

C. Millot, �??Review paper: Circulation in the Western Mediterranean Sea,�?? J. Mar. Syst. 20, 423-442 (1999).
[CrossRef]

J.P. Béthoux, and B. Gentili, �??The Mediterranean Sea, coastal and deep-sea signatures of climatic and environmental changes,�?? J. Mar. Syst. 7, 383-394 (1996).
[CrossRef]

H. Simpson, �??Physical processes in the ROFI regime,�?? J. Mar. Syst. 12, 3-15 (1997).
[CrossRef]

J.J. Naudin, G. Cauwet, C. Fajon, L. Oriol, S. Terzic, J.L. Devenon, and P. Broche, �??Effect of mixing on microbial communities in the Rhone River plume,�?? J. Mar. Syst. 28, 203-227 (2001).
[CrossRef]

J. Ocean. (1)

M. Kishino, T. Ishimaru, K. Furuya, T. Oishi, and K. Kawasaki, �??In-water algorithms for DEOS/OCTS,�?? J.Ocean. 54, 383-399 (1998).

Kor. J. Remote Sens. (1)

Y.H. Ahn, J.E. Moon, and S. Gallegos, �??Development of suspended particulate matter algorithms for ocean color remote sensing,�?? Kor. J. Remote Sens. 17, 285-295 (2001).

Limnol. Ocean (1)

N.A. Welschmeyer, �??Fluorimetric analysis of chlorophyll a in the presence of chlorophyll b and pheopigments,�?? Limnol. Ocean. 39, 1985-1992 (1994).
[CrossRef]

Limnol. Ocean. (5)

K.L. Carder, R.G. Steward, G.R. Harvey, and P.B. Ortner, �??Marine humic and fulvic acids: their effects on remote sensing of ocean chlorophyll,�?? Limnol. Ocean. 34, 68-81 (1989).
[CrossRef]

N. Hoepffner, and S. Sathyendranath, �??Bio-optical characteristics of coastal waters: Absorption spectra of phytoplankton and pigment distribution in the western North Atlantic,�?? Limnol. Ocean. 37, 1660-1679 (1992).
[CrossRef]

C.D. Mobley, and D. Stramski, �??Effects of microbial particles on oceanic optics: Methodology for radiative transfer modeling and example simulations,�?? Limnol. Ocean. 42, 550-560 (1997).
[CrossRef]

D. Stramski, and C.D. Mobley, �??Effects of microbial particles on oceanic optics: A database of single-particle optical properties,�?? Limnol. Ocean. 42, 538-549 (1997).
[CrossRef]

E.M. Louchard, R.P. Reid, C.F. Stephens, C. Davis, R. Leathers, and T. Downes, �??Optical remote sensing of benthic habitats and bathymetry in coastal environments at Lee Stocking Island, Bahamas: A comparative spectral classification approach,�?? Limnol. Ocean. 48, 511-521 (2003).
[CrossRef]

Mar. Biol. (1)

G. Jacques, H.J. Minas, M. Minas, and P. Nival, �??Influence des conditions hivernales sur les productions phyto et zooplanctoniques en Méditerranée nord-occidentale, II. Biomasse et production planctonique,�?? Mar. Biol. 23, 251-265 (1973).
[CrossRef]

Ocean. Acta (1)

P. Forget, and S. Ouillon, �??Surface suspended matter off the Rhône river mouth from visible satellite imagery,�?? Ocean. Acta 21, 739-749 (1998).
[CrossRef]

Ocean. Acta. (1)

A.A. Petrenko, �??Variability of circulation features in the Gulf of Lion NW Mediterranean Sea. Importance of inertial currents,�?? Ocean. Acta 26, 323-338 (2003).
[CrossRef]

Rem. Sens. Env. (1)

M. Darecki, and D. Stramski, �??An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea,�?? Rem. Sens. Env. 89, 326-350 (2004).
[CrossRef]

Remote Sens. Env. (5)

J.M. Froidefond, L. Gardel, D. Guiral, M. Parra, and J.F. Ternon, �??Spectral remote sensing reflectances of coastal waters in French Guiana under the Amazon influence,�?? Remote Sens. Env. 80, 225-232 (2002).
[CrossRef]

K. Masuda, and T. Takashima, �??The effect of solar zenith angle and surface wind speed on water surface reflectivity,�?? Remote Sens. Env. 57, 58-62 (1996).
[CrossRef]

E. Hochberg, M.J. Atkinson, and S. Andréfouët, �??Spectral reflectance of coral-reef bottom types worldwide and implications for coral reef remote sensing,�?? Remote Sens. Env. 85, 159-173 (2003).
[CrossRef]

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F. Lahet, S. Ouillon, and P. Forget, �??A three component model of ocean colour and its application in the Ebro River mouth area,�?? Remote Sens. Envir. 72, 181-190 (2000).
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J.E. O�??Reilly, S. Maritorena, D.A. Siegel, M. O�??Brien, D. Toole, B.G. Mitchell, M. Kahru, F.P. Chavez, P. Strutton, G.F. Cota, S.B. Hooker, C. McClain, K.L. Carder, F. Müeller-Karger, L. Harding, A. Magnuson, D. Phinney, G.F. Moore, J. Aiken, K.R. Arrigo, R. Letelier, and M. Culver, �??Ocean color chlorophyll a algorithms for SeaWiFS, OC2, and OC4: Version 4,�?? in SeaWiFS Postlaunch Technical Report Series, Vol. 11: Seawifs Postlaunch Calibration and Validation Analyses Part 3, S.B. Hooker and E.R. Firestone, eds. (NASA, Greenbelt, MD, 2000), pp. 9-23.

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Figures (12)

Fig. 1.
Fig. 1.

Map of the Gulf of Lions with isobaths at 100, 1000, and 2000 m. The dotted line indicates the SARHYGOL trajectory. The optical stations are indicated by circles, with black or brown circles when both Ocean Optics and Simbad measurements were collected. Brown and yellow circles correspond to Case 2 waters. The dashed line indicates the approximate boundary between the proximal zone and the distal zone of the Rhône ROFI.

Fig. 2.
Fig. 2.

Remote sensing reflectance spectra collected in the Gulf of Lions with the 8 Case 2 stations in red.

Fig. 3.
Fig. 3.

Example of concurrent reflectance measurements using SIMBAD and Ocean Optics SD1000 radiometers (Gulf of Lions, 16th February 2001).

Fig. 4.
Fig. 4.

SIMBAD and Ocean Optics concurrent remote sensing reflectance values at 3 visible wavelengths.

Fig. 5.
Fig. 5.

Comparison of SIMBAD and Ocean Optics concurrent reflectance ratios.

Fig. 6.
Fig. 6.

Comparison of Chl a estimates by SIMBAD and by Ocean Optics using the OC2v4 algorithm [11].

Fig. 7.
Fig. 7.

Chl a estimates using the OC2v4 and OC4v4 algorithms vs. measured Chl a.

Fig. 8.
Fig. 8.

Measured Chl a vs. reflectance ratios in the Gulf of Lions (N=32).

Fig. 9.
Fig. 9.

Estimates vs. in situ Chl a by several algorithms on the complete Sarhygol dataset (distal+proximal ROFI).

Fig. 10.
Fig. 10.

Estimated vs. in situ Chl a in the distal Rhône ROFI using several algorithms.

Fig. 11.
Fig. 11.

Chl a vs. Xcmod in the proximal Rhône ROFI and regression relationship.

Fig. 12.
Fig. 12.

Modeled Chl a vs. in situ Chl a in the proximal Rhône ROFI using two algorithms.

Tables (5)

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Table 1. Characteristics of the reflectance measurements performed during SARHYGOL cruises

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Table 2. Characteristics of the two types of waters considered in this study (24 “distal ROFI” or Case 1 waters, and 8 “proximal ROFI” or Case 2 waters).

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Table 3. Global and regional Chl a algorithms used in this study. Chl a is expressed in mg.m-3.

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Table 4. Statistical performance of chlorophyll-a algorithms applied to the SARHYGOL dataset (N=32). The parameters are obtained between modeled and measured Chl a.

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Table 5. Statistical performance of optimized Chl a algorithms at SeaWiFS bands for the SARHYGOL dataset (N=32). The parameters are obtained between modeled and measured Chl a.

Equations (18)

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E d ( λ ) = π R g ( λ ) L d ( λ )
R rs ( λ ) = L w ( λ ) E d ( λ ) = L u ( λ ) ρ L sky ( λ ) E d ( λ )
R rs ( 555 ) = 0.9898 R rs ( 560 ) + 0.1322
R rs ( 555 ) = 0.9845 R rs ( 565 ) + 0.2660
mean ( x ) = x ̅ = 1 n i = 1 n x i
stdev ( x ) = [ 1 n 1 i = 1 n ( x i x ̅ ) 2 ] 1 2
MNB = mean ( y a lg y obs y obs ) . 100
rms = stdev ( y a lg y obs y obs ) . 100
log _ bias = mean ( log ( y a lg y obs ) )
log _ rms = stdev ( log ( y a lg y obs ) )
X c = R 443 R 555 . [ R 412 R 490 ] + n
Chl a = 2.513 [ R 443 R 555 ] + 2.827
Chl a = 6.258 · exp ( 1.344 [ R 443 R 555 ] )
Chl a = 7.113 · exp ( 1.496 [ R 443 R 550 ] )
Chl a = 5.677 · exp ( 1.221 [ R 443 R 560 ] )
X c mod = X ca for 0.025 < Chl a < 1.1 mg . m 3
X c mod = X cb for 1.1 < Chl a < 40 mg . m 3
Chl a = 1.609 X c mod 2.457

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