Generalized ocean color inversion model for retrieving marine inherent optical properties

P. Jeremy Werdell; Bryan A. Franz; Sean W. Bailey; Gene C. Feldman; Emmanuel Boss; Vittorio E. Brando; Mark Dowell; Takafumi Hirata; Samantha J. Lavender; ZhongPing Lee; Hubert Loisel; Stéphane Maritorena; Fréderic Mélin; Timothy S. Moore; Timothy J. Smyth; David Antoine; Emmanuel Devred; Odile Hembise Fanton d’Andon; Antoine Mangin

doi:10.1364/AO.52.002019

Applied Optics
Vol. 52,
Issue 10,
pp. 2019-2037
(2013)
•https://doi.org/10.1364/AO.52.002019

Generalized ocean color inversion model for retrieving marine inherent optical properties

P. Jeremy Werdell, Bryan A. Franz, Sean W. Bailey, Gene C. Feldman, Emmanuel Boss, Vittorio E. Brando, Mark Dowell, Takafumi Hirata, Samantha J. Lavender, ZhongPing Lee, Hubert Loisel, Stéphane Maritorena, Fréderic Mélin, Timothy S. Moore, Timothy J. Smyth, David Antoine, Emmanuel Devred, Odile Hembise Fanton d’Andon, and Antoine Mangin

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P. Jeremy Werdell, Bryan A. Franz, Sean W. Bailey, Gene C. Feldman, Emmanuel Boss, Vittorio E. Brando, Mark Dowell, Takafumi Hirata, Samantha J. Lavender, ZhongPing Lee, Hubert Loisel, Stéphane Maritorena, Fréderic Mélin, Timothy S. Moore, Timothy J. Smyth, David Antoine, Emmanuel Devred, Odile Hembise Fanton d’Andon, and Antoine Mangin, "Generalized ocean color inversion model for retrieving marine inherent optical properties," Appl. Opt. 52, 2019-2037 (2013)

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- Atmospheric and Oceanic Optics
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About this Article
History
- Original Manuscript: December 20, 2012
- Revised Manuscript: February 7, 2013
- Manuscript Accepted: February 11, 2013
- Published: March 22, 2013
Virtual Issues
Virtual Journal for Biomedical Optics Vol. 8, Iss. 5

Abstract

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

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Eigenvector	Description	Reference
	User-provided $a_{ϕ}^{*} (λ)$
$a_{ϕ}^{*} (λ)$	Maritorena et al. (2002) tabulated $a_{ϕ}^{*} (λ)$	[8]
	*Bricaud et al.* (1998)-derived** $a_{ϕ}^{} (λ)$ using OC-derived* $C_{a}$	[14]
	Ciotti and Bricaud (2006)-derived $a_{ϕ}^{*} (λ)$ using user-provided size fraction	[17]
	Eq. (5) with user-provided $S_{d g}$
$a_{d g}^{*} (λ)$	Eq. (5) with Lee et al. (2002)-derived $S_{d g}$	[7]
	Eq. (5) with Franz and Werdell (2010)-derived $S_{d g}$	[13]
	User-provided $a_{d g}^{*} (λ)$
	Eq. (8) with user-provided $S_{b p}$
	Eq. (8) with Hoge and Lyon (1996)-derived $S_{b p}$	[4]
	*Eq. (8) with Lee et al.* (2002)-derived** $S_{b p}$	[7]
	Eq. (8) with Ciotti et al. (1999)-derived $S_{b p}$	[15]
$b_{b p}^{*} (λ)$	Eq. (8) with Morel and Maritorena (2001)-derived $S_{b p}$	[22]
	Eq. (8) with Loisel and Stramski (2000)-derived $S_{b p}$	[6]
	User-provided $b_{b p}^{*} (λ)$
	Loisel and Stramski (2000)-derived $b_{b p}^{*} (λ)$	[6]
	Lee et al. (2002)-derived $b_{b p}^{*} (λ)$	[7]

		NOMAD					IOCCG
		$N$	$r^{2}$	Slope (SE)	Ratio	MPD	$N$	$r^{2}$	Slope (SE)	Ratio	MPD
	412	217	0.49	0.99 (0.05)	1.26	28.2	437	0.92	1.08 (0.01)	0.98	10.1
	443	217	0.52	1.00 (0.05)	1.27	28.8	437	0.93	1.07 (0.01)	1.00	9.3
$b_{b p}$	490	217	0.57	1.04 (0.05)	1.29	30.0	437	0.94	1.07 (0.01)	0.99	8.2
	555	217	0.60	1.06 (0.05)	1.30	32.8	437	0.95	1.09 (0.01)	0.91	11.8
	670	217	0.64	1.09 (0.05)	1.27	31.3	437	0.96	1.09 (0.01)	0.90	13.0
	412	649	0.90	1.14 (0.01)	0.89	20.5	421	0.99	1.03 (0.00)	1.03	7.2
	443	657	0.90	1.13 (0.01)	0.88	21.8	427	0.99	1.03 (0.01)	0.99	6.1
$a$	490	657	0.88	1.13 (0.02)	0.84	24.6	432	0.98	1.03 (0.01)	0.98	8.7
	555	654	0.81	1.21 (0.02)	0.77	37.4	436	0.95	1.11 (0.01)	0.73	29.9
	670	609	0.80	1.18 (0.02)	1.11	37.4	424	0.86	1.01 (0.02)	1.50	58.6
	412	654	0.83	1.12 (0.02)	0.84	28.8	426	0.98	1.02 (0.01)	1.01	13.1
	443	662	0.81	1.10 (0.02)	0.78	35.1	429	0.97	1.04 (0.01)	0.90	15.9
$a_{d g}$	490	662	0.76	1.06 (0.02)	0.65	43.2	435	0.95	1.07 (0.01)	0.80	25.0
	555	656	0.71	1.03 (0.02)	0.53	52.7	437	0.91	1.08 (0.02)	0.58	44.1
	670	621	0.61	0.96 (0.03)	0.32	69.8	426	0.82	1.05 (0.02)	0.36	64.2
	412	676	0.76	1.23 (0.02)	0.94	27.5	419	0.81	1.18 (0.03)	1.16	32.8
	443	682	0.76	1.21 (0.02)	0.98	25.9	419	0.79	1.13 (0.03)	1.20	30.3
$a_{ϕ}$	490	682	0.76	1.23 (0.02)	1.08	27.2	422	0.78	1.13 (0.03)	1.34	41.4
	555	673	0.78	1.22 (0.02)	1.25	42.5	422	0.84	1.10 (0.02)	1.36	52.5
	670	680	0.82	1.12 (0.02)	1.33	43.3	422	0.81	1.00 (0.02)	2.01	100.8

		SeaWiFS					MODISA
		$N$	$r^{2}$	Slope (SE)	Ratio	MPD	$N$	$r^{2}$	Slope (SE)	Ratio	MPD
	412	123	0.30	0.77 (0.07)	0.93	25.2	56	0.69	0.92 (0.07)	1.00	13.2
	443	123	0.31	0.79 (0.07)	0.92	25.0	56	0.69	0.95 (0.08)	1.02	17.2
$b_{b p}$	490	123	0.32	0.82 (0.07)	0.90	24.2	56	0.69	1.00 (0.08)	0.99	18.2
	555a	123	0.32	0.84 (0.07)	0.87	25.2	56	0.68	1.05 (0.09)	0.99	18.8
	670b	123	0.31	0.87 (0.07)	0.83	28.5	56	0.63	1.06 (0.09)	1.04	23.0
	412	192	0.74	1.12 (0.04)	0.87	30.9	21	0.45	0.76 (0.14)	0.89	36.9
	443	192	0.81	1.07 (0.03)	0.81	25.5	21	0.73	0.77 (0.10)	0.88	16.9
$a$	490	192	0.80	1.01 (0.03)	0.76	29.3	21	0.84	0.79 (0.07)	0.79	21.1
	555a	192	0.67	1.03 (0.05)	0.68	42.3	21	0.86	0.74 (0.07)	0.75	28.9
	670b	180	0.69	1.19 (0.05)	0.87	45.0	17	0.47	0.91 (0.19)	1.88	87.8
	412	192	0.51	1.12 (0.06)	0.86	45.7	20	0.07	0.96 (0.27)	0.88	40.2
	443	192	0.51	1.08 (0.06)	0.78	49.8	20	0.09	0.96 (0.27)	0.81	34.8
$a_{d g}$	490	192	0.48	1.01 (0.06)	0.64	54.2	20	0.11	0.96 (0.26)	0.68	41.8
	555a	191	0.42	0.93 (0.06)	0.50	62.5	20	0.12	0.96 (0.26)	0.46	55.3
	670b	183	0.44	0.82 (0.05)	0.34	72.0	20	0.13	0.98 (0.26)	0.32	67.7
	412	195	0.72	1.17 (0.05)	0.73	35.5	25	0.85	1.14 (0.09)	0.91	22.5
	443	197	0.68	1.14 (0.05)	0.80	31.5	25	0.82	1.20 (0.11)	0.90	31.0
$a_{ϕ}$	490	197	0.68	1.12 (0.05)	0.86	30.1	25	0.81	1.13 (0.11)	0.93	33.2
	555a	186	0.71	1.17 (0.05)	0.95	44.8	25	0.82	1.14 (0.10)	0.97	36.6
	670b	195	0.73	1.05 (0.04)	1.11	48.6	24	0.81	1.24 (0.12)	1.35	49.3

All
	NOMAD				IOCCG
	$N_{%}$	$N_{total}$	Med	SIQR	$N_{%}$	$N_{total}$	Med	SIQR
$Δ R_{rs}$	90	964	1.68	1.05	90	500	1.04	0.49
$Δ b_{b p}$	97	223	26.94	15.64	87	500	8.52	6.80
$Δ a$	88	735	27.75	17.28	87	500	8.56	5.90
$Δ a_{d g}$	90	735	51.02	28.38	87	500	27.25	20.19
$Δ a_{ϕ}$	87	780	29.32	19.17	84	500	35.83	23.25
Oligotrophic
	NOMAD				IOCCG
	$N_{%}$	$N_{total}$	Med	SIQR	$N_{%}$	$N_{total}$	Med	SIQR
$Δ R_{rs}$	98	51	1.14	1.38	100	100	0.59	0.32
$Δ b_{b p}$	100	1	93.27	NA	100	100	5.61	3.00
$Δ a$	94	37	53.56	28.94	100	100	6.26	2.60
$Δ a_{d g}$	97	37	99.83	36.75	100	100	28.38	16.06
$Δ a_{ϕ}$	93	43	20.67	11.75	100	100	22.81	11.42
Mesotrophic
	NOMAD				IOCCG
	$N_{%}$	$N_{total}$	Med	SIQR	$N_{%}$	$N_{total}$	Med	SIQR
$Δ R_{rs}$	90	477	1.56	0.76	100	125	1.22	0.37
$Δ b_{b p}$	97	127	33.65	15.79	100	125	6.51	3.84
$Δ a$	93	326	25.71	16.23	100	125	6.59	3.22
$Δ a_{d g}$	96	326	46.53	28.81	100	125	30.72	22.95
$Δ a_{ϕ}$	94	353	23.93	13.22	100	125	31.44	19.20
Eutrophic
	NOMAD				IOCCG
	$N_{%}$	$N_{total}$	Med	SIQR	$N_{%}$	$N_{total}$	Med	SIQR
$Δ R_{rs}$	88	436	1.91	1.51	82	275	1.29	0.70
$Δ b_{b p}$	96	95	19.90	12.06	77	275	13.31	11.04
$Δ a$	82	372	28.25	17.31	76	275	14.56	8.77
$Δ a_{d g}$	84	372	48.47	25.94	77	275	25.00	21.75
$Δ a_{ϕ}$	80	384	40.27	30.36	71	275	54.49	34.19

		MPD				Median
Run	$N$	$b_{b p}$	$a$	$a_{d g}$	$a_{ϕ}$	$Δ R_{rs}$	$Δ b_{b p}$	$Δ a$	$Δ a_{d g}$	$Δ a_{ϕ}$
GIOP-DC	437	NA	NA	NA	NA	1.04	8.52	8.56	27.25	35.83
$S_{b p} - 33 %$	440	5.19	5.17	7.58	2.98	0.99	11.23	11.70	32.14	34.69
$S_{b p} + 33 %$	436	5.65	5.70	8.82	2.90	1.14	11.40	10.70	23.51	39.12
$S_{d g} - 33 %$	448	18.96	33.44	101.73	46.59	1.61	16.27	19.08	32.94	31.95
$S_{d g} + 33 %$	399	3.77	8.41	40.10	32.92	1.23	9.44	8.95	79.90	59.32
$S_{d g}$ from [7]	439	3.20	5.33	20.40	14.58	1.10	8.65	9.80	22.25	34.42
$C_{a} - 33 %$ in [14]	419	2.02	2.92	1.48	7.25	1.19	8.79	8.83	28.62	31.10
$C_{a} + 33 %$ in [14]	437	1.56	2.28	1.14	5.90	1.10	8.12	9.17	26.79	40.09
Fixed $C_{a}$ in [14]	369	4.57	7.89	2.60	21.68	1.46	11.30	11.53	30.97	26.70
$a_{ϕ}^{*}$ from [17]	357	8.33	12.72	7.04	22.23	1.20	14.26	16.75	38.30	23.13
$G$ from [22]	422	9.99	6.15	7.49	14.12	1.16	11.50	13.64	37.49	36.24
Matrix inversion	475	4.60	3.68	2.24	7.41	1.73	9.15	9.43	24.79	36.82
$400 \leq λ \leq 600 nm$	424	0.23	0.21	0.08	0.38	0.92	8.76	8.78	31.94	36.55

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Applied Optics