Above-water measurements of reflectance and chlorophyll-a algorithms in the Gulf of Lions, NW Mediterranean Sea

Sylvain Ouillon; Anne A. Petrenko

doi:10.1364/OPEX.13.002531

Optics Express
Vol. 13,
Issue 7,
pp. 2531-2548
(2005)
•https://doi.org/10.1364/OPEX.13.002531

Above-water measurements of reflectance and chlorophyll-a algorithms in the Gulf of Lions, NW Mediterranean Sea

Sylvain Ouillon and Anne A. Petrenko

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Sylvain Ouillon and Anne A. Petrenko, "Above-water measurements of reflectance and chlorophyll-a algorithms in the Gulf of Lions, NW Mediterranean Sea," Opt. Express 13, 2531-2548 (2005)

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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.

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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.

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

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Fig. 3. Example of concurrent reflectance measurements using SIMBAD and Ocean Optics SD1000 radiometers (Gulf of Lions, 16^th February 2001).

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Fig. 4. SIMBAD and Ocean Optics concurrent remote sensing reflectance values at 3 visible wavelengths.

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Fig. 5. Comparison of SIMBAD and Ocean Optics concurrent reflectance ratios.

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Fig. 6. Comparison of Chl a estimates by SIMBAD and by Ocean Optics using the OC2v4 algorithm [11].

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Fig. 7. Chl a estimates using the OC2v4 and OC4v4 algorithms vs. measured Chl a.

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Fig. 8. Measured Chl a vs. reflectance ratios in the Gulf of Lions (N=32).

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Fig. 9. Estimates vs. in situ Chl a by several algorithms on the complete Sarhygol dataset (distal+proximal ROFI).

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Fig. 10. Estimated vs. in situ Chl a in the distal Rhône ROFI using several algorithms.

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Fig. 11. Chl a vs. X_cmod in the proximal Rhône ROFI and regression relationship.

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Fig. 12. Modeled Chl a vs. in situ Chl a in the proximal Rhône ROFI using two algorithms.

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Tables (5)

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.

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Equations (18)

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E_{d} (λ) = \frac{π}{R_{g} (λ)} L_{d} (λ)

R_{rs} (λ) = \frac{L_{w} (λ)}{E_{d} (λ)} = \frac{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) = \overset{̅}{x} = \frac{1}{n} \sum_{i = 1}^{n} x_{i}

stdev (x) = {[\frac{1}{n - 1} {\sum_{i = 1}^{n} (x_{i} - \overset{̅}{x})}^{2}]}^{1 ⁄ 2}

MNB = mean (\frac{y_{a \lg} - y_{obs}}{y_{obs}}) . 100

rms = stdev (\frac{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} = {\frac{R 443}{R 555} . [\frac{R 412}{R 490}]}^{+ n}

{Chl a = 2.513 [\frac{R 443}{R 555}]}^{+ 2.827}

Chl a = 6.258 \cdot \exp (- 1.344 [\frac{R 443}{R 555}])

Chl a = 7.113 \cdot \exp (- 1.496 [\frac{R 443}{R 550}])

Chl a = 5.677 \cdot \exp (- 1.221 [\frac{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}

Cruise	Dates	SZ ^a Min (deg.)	Nb of SIMBAD scans	Nb of O. Optics scans	Nb of concurrent scans by Simbad and by O. Optics
SARHYGOL 2	25–26 apr 2000	30.1	0	3	0
SARHYGOL 3	14–15 jun 2000	19.7	4^b	8	0
SARHYGOL 4	11–12 sep 2000	38.3	9	3	3
SARHYGOL 5	10–11 nov 2000	60.4	5	8	5
SARHYGOL 6	16 feb 2001	55.1	5	7	5
SARHYGOL 8	14–15 jun 2001	19.4	10^c	3	0

Type		Salinity	Chl a (mg/m³)	Seston ^a (mg/l)	NO₂ (µM)	NO₃ (µM)	PO₄ (µM)
Case 1 waters	Min	37.4	0.06	1.56	0.00	0.02	0.01
	Max	38.2	1.6	3.60	0.25	1.13	0.29
Case 2 waters	Min	8.7	0.4	5.34	0.44	14.74	0.36
	Max	29.6	3.8	38.60	1.41	43.97	0.76

Name	Ref.	Equation	Chl a range (mg.m^-3)	R²	N
OC2v4	[11]	$Chl a = {10.0}^{(0.319 - 2.336 R_{2 S} + 0.879 R_{2 S}^{2} - 0.135 R_{2 S}^{3})} - 0.071$ $where R_{2 S} = \log_{10} (R 490 ⁄ R 555)$	0.008–64	0.883 ^a	2,804
OC4v4	[11]	$Chl a = {10.0}^{(0.366 - 3.067 R_{4 S} + 1.930 R_{4 S}^{2} + 0.649 R_{4 S}^{3} - 1.532 R_{4 S}^{4})}$ $where R_{4 S} = max [\log_{10} (R 443 ⁄ R 555), \log_{10} (R 490 ⁄ R 555),$ $\log_{10} (R 510 ⁄ R 555)]$	0.008–64	0.892 ^a	2,804
GIT96	[14]	$Chl a = 0.914 {[\frac{R 440}{R 550}]}^{- 1.86}$	0.028–0.36	0.83^b	21
L-Dorma	[15]	$Chl a = 1.49 {[\frac{R 490}{R 555}]}^{- 2.51}$	0.07-2	0.948 ^a	45
NLDorma	[15]	$Chl a = {10.0}^{(0.217 - 2.728 R_{2 S} + 0.704 R_{2 S}^{2} + 0.297 R_{2 S}^{3})} - 0.035$ $where R_{2 S} = \log_{10} (R 490 ⁄ R 555)$	0.07-2	0.941 ^a	45
BRI02	[3]	$Chl a = 2.094 {[\frac{R 443}{R 555}]}^{- 2.357}$	0.034-1	0.952 ^b	157
TAS94a	[19]	$\log C = {0.0664 + 0.0462 \log (X_{ca}) - 4.144 [\log (X_{ca})]}^{2 d}$ $where X_{ca} = {[R 443 ⁄ R 555] . [R 412 ⁄ R 490]}^{- 1.2}$ ^d	0.025-1		91 ^a
TAS94b	[19]	$\log C = {0.36 - 4.38 \log X_{cb}}^{d}$ $where X_{cb} = [R 443 ⁄ R 555] {. [R 412 ⁄ R 490]}^{- 0.5}$ ^d	1-40		-c

Zone		OC2v4	OC4v4	GIT96	L-Dorma	NL-Dorma	BRI02	TAS
Global	MNB (%)	68.91	72.05	16.73	7.82	13.23	114.37	51.17
(N=32)	rms (%)	120.76	126.47	83.30	88.06	100.27	238.41	95.51
	log_bias	0.146	0.150	-0.005	-0.053	-0.042	0.206	0.066
	log_rms	0.265	0.271	0.242	0.261	0.273	0.288	0.359
	slope	0.612	0.597	0.840	0.804	0.686	0.277	2.014
	intercept	0.180	0.184	0.180	0.222	0.254	0.305	-0.376
	R²	0.642	0.639	0.628	0.637	0.632	0.604	0.718
Distal	MNB (%)	44.86	46.36	-2.41	-16.02	-16.88	26.79	24.43
(N=24)	rms (%)	71.75	75.21	42.16	36.89	36.21	38.47	76.13
	log_bias	0.101	0.103	-0.052	-0.121	-0.125	0.083	-0.019
	log_rms	0.242	0.247	0.200	0.210	0.208	0.137	0.360
	slope	0.498	0.491	0.441	0.366	0.372	0.926	0.256
	intercept	0.165	0.168	0.093	0.081	0.078	0.049	0.176
	R²	0.904	0.884	0.925	0.897	0.887	0.899	0.356
Proximal	MNB (%)	141.05	149.13	74.15	79.35	103.58	377.12	131.43
(N=8)	rms (%)	199.33	207.84	140.93	148.56	166.67	376.38	107.45
	log_bias	0.280	0.293	0.136	0.151	0.208	0.577	0.322
	log_rms	0.303	0.304	0.312	0.304	0.302	0.312	0.212
	slope	0.270	0.263	0.135	0.199	0.262	0.504	2.083
	intercept	2.527	2.619	1.931	1.883	2.092	5.146	0.276
	R²	0.242	0.269	0.114	0.236	0.247	0.131	0.669

Zone		GL-D1	GL-D2	GLP
Global	MNB (%)	153.07	31.82	-5.97
(N=32)	rms (%)	368.56	104.25	44.67
	log_bias	0.186	0.056	-0.081
	log_rms	0.374	0.206	0.230
	slope	3.326	0.914	0.899
	intercept	-0.115	0.230	-0.076
	R²	0.585	0.666	0.869
Distal	MNB (%)	3.33	3.67
(N=24)	rms (%)	26.62	29.67
	log_bias	2.2	10-6	-2.6 10^-5
	log_rms	0.115	0.117
	slope	1.072	0.865
	intercept	-0.028	0.037
	R²	0.867	0.946
Proximal	MNB (%)			5.53
(N=8)	rms (%)			39.80
	log_bias			-3.45 10^-6
	log_rms			0.48
	slope			0.902
	intercept			0.141
	R²			0.851

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