Improving the retrieval of water inherent optical properties in noisy hyperspectral data through statistical modeling

David B. Gillis; Jeffrey H. Bowles; Wesley J. Moses

doi:10.1364/OE.21.021306

Improving the retrieval of water inherent optical properties in noisy hyperspectral data through statistical modeling

David B. Gillis, Jeffrey H. Bowles, and Wesley J. Moses

Optics Express
Vol. 21,
Issue 18,
pp. 21306-21316
(2013)
•https://doi.org/10.1364/OE.21.021306

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David B. Gillis, Jeffrey H. Bowles, and Wesley J. Moses, "Improving the retrieval of water inherent optical properties in noisy hyperspectral data through statistical modeling," Opt. Express 21, 21306-21316 (2013)

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Related Topics
Table of Contents Category
- Atmospheric and Oceanic Optics
Optics & Photonics Topics
?

The topics in this list come from the OSA Optics and Photonics Topics applied to this article.
Previously assigned OCIS codes
- Oceanic optics (010.4450)
- Probability theory, stochastic processes, and statistics (000.5490)
- Noise in imaging systems (110.4280)
- Remote sensing and sensors (280.0280)

About this Article
History
- Original Manuscript: June 10, 2013
- Revised Manuscript: August 1, 2013
- Manuscript Accepted: August 7, 2013
- Published: September 4, 2013
Virtual Issues
Virtual Journal for Biomedical Optics Vol. 8, Iss. 10

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Open Access

Abstract

The use of the Mahalanobis distance in a lookup table approach to retrieval of in-water Inherent Optical Properties (IOPs) led to significant improvements in the accuracy of the retrieved IOPs, as high as 50% in some cases, with an average improvement of 20% over a wide range of case II waters. Previous studies have shown that inherent noise in hyperspectral data can cause significant errors in the retrieved IOPs. For LUT-based retrievals that rely on spectrum matching, the particular metric used for spectral comparisons has a significant effect on the accuracy of the results, especially in the presence of noise in the data. In this study, we have compared the Euclidean distance and the Mahalanobis distance as metrics for spectral comparison. In addition to providing justification for the preference of the Mahalanobis Distance over the Euclidean Distance, we have also included a statistical description of noisy hyperspectral data.

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References

View by:
Article Order
|
Year
|
Author
|
Publication

A. Morel and L. Prieur, “Analysis of variations in ocean color,” Limnol. Oceanogr. 22(4), 709–722 (1977).
[Crossref]
R. P. Bukata, J. H. Jerome, K. Y. Kondratyev, and D. V. Pozdnyakov, Optical Properties and Remote Sensing of Inland and Coastal Waters (CRC Press, 1995).
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. Oceanogr. 34(1), 68–81 (1989).
[Crossref]
A. Gitelson, “The peak near 700 nm on radiance spectra of algae and water - relationships of its magnitude and position with chlorophyll concentration,” Int. J. Remote Sens. 13(17), 3367–3373 (1992).
[Crossref]
G. Dall’Olmo and A. A. Gitelson, “Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: experimental results,” Appl. Opt. 44(3), 412–422 (2005).
[Crossref] [PubMed]
Y. Z. Yacobi, W. J. Moses, S. Kaganovsky, B. Sulimani, B. C. Leavitt, and A. A. Gitelson, “NIR-red reflectance-based algorithms for chlorophyll-a estimation in mesotrophic inland and coastal waters: Lake Kinneret case study,” Water Res. 45(7), 2428–2436 (2011).
[Crossref] [PubMed]
C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Lu, “A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: the case of Taihu Lake, China,” Remote Sens. Environ. 113(6), 1175–1182 (2009).
[Crossref]
W. Yang, B. Matsushita, J. Chen, T. Fukushima, and R. Ma, “An enhanced three-band index for estimating chlorophyll-a in turbid case-II waters: case studies of Lake Kasumigaura, Japan, and Lake Dianchi, China,” IEEE Geosci. Remote Sens. Lett. 7(4), 655–659 (2010).
[Crossref]
C. D. Mobley, L. K. Sundman, C. O. Davis, J. H. Bowles, T. V. Downes, R. A. Leathers, M. J. Montes, W. P. Bissett, D. D. R. Kohler, R. P. Reid, E. M. Louchard, and A. Gleason, “Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables,” Appl. Opt. 44(17), 3576–3592 (2005).
[Crossref] [PubMed]
C. D. Mobley, “A numerical model for the computation of radiance distributions in natural waters with wind roughened surfaces,” Limnol. Oceanogr. 34(8), 1473–1483 (1989).
[Crossref]
C. D. Mobley and L. K. Sundman, Hydrolight 5 Ecolight 5 technical documentation, 1st Ed., (Sequoia Scientific Inc., 2008).
R. L. Lucke, M. Corson, N. R. McGlothlin, S. D. Butcher, D. L. Wood, D. R. Korwan, R. R. Li, W. A. Snyder, C. O. Davis, and D. T. Chen, “Hyperspectral Imager for the Coastal Ocean: instrument description and first images,” Appl. Opt. 50(11), 1501–1516 (2011).
[Crossref] [PubMed]
W. J. Moses, J. H. Bowles, R. L. Lucke, and M. R. Corson, “Impact of signal-to-noise ratio in a hyperspectral sensor on the accuracy of biophysical parameter estimation in case II waters,” Opt. Express 20(4), 4309–4330 (2012).
[Crossref] [PubMed]
B. C. Gao, M. J. Montes, Z. Ahmad, and C. O. Davis, “Atmospheric correction algorithm for hyperspectral remote sensing of ocean color from space,” Appl. Opt. 39(6), 887–896 (2000).
[Crossref] [PubMed]
M. J. Montes, B. C. Gao, and C. O. Davis, “A new algorithm for atmospheric correction of hyperspectral remote sensing data,” Proc. SPIE, Geo-Spatial Image and Data Exploitation II, W. E. Roper (ed.), 4383: 23–30 (2001).
[Crossref]
P. C. Mahalanobis, “On the generalized distance in statistics,” Proc. Natl. Inst. Sci. India 2(1), 49–55 (1936).
J. Hedley, C. Roelfsema, and S. Phinn, “Efficient radiative transfer model inversion for remote sensing applications,” Remote Sens. Environ. 113(11), 2527–2532 (2009).
[Crossref]
K. V. Mardia, J. T. Kent, and J. M. Bibby, Multivariate Analysis (Academic Press, 2003).
P. Billingsley, Probability and Measure, Third Ed. (John Wiley & Sons, 1995).

2012 (1)

W. J. Moses, J. H. Bowles, R. L. Lucke, and M. R. Corson, “Impact of signal-to-noise ratio in a hyperspectral sensor on the accuracy of biophysical parameter estimation in case II waters,” Opt. Express 20(4), 4309–4330 (2012).
[Crossref] [PubMed]

2011 (2)

R. L. Lucke, M. Corson, N. R. McGlothlin, S. D. Butcher, D. L. Wood, D. R. Korwan, R. R. Li, W. A. Snyder, C. O. Davis, and D. T. Chen, “Hyperspectral Imager for the Coastal Ocean: instrument description and first images,” Appl. Opt. 50(11), 1501–1516 (2011).
[Crossref] [PubMed]

Y. Z. Yacobi, W. J. Moses, S. Kaganovsky, B. Sulimani, B. C. Leavitt, and A. A. Gitelson, “NIR-red reflectance-based algorithms for chlorophyll-a estimation in mesotrophic inland and coastal waters: Lake Kinneret case study,” Water Res. 45(7), 2428–2436 (2011).
[Crossref] [PubMed]

2010 (1)

W. Yang, B. Matsushita, J. Chen, T. Fukushima, and R. Ma, “An enhanced three-band index for estimating chlorophyll-a in turbid case-II waters: case studies of Lake Kasumigaura, Japan, and Lake Dianchi, China,” IEEE Geosci. Remote Sens. Lett. 7(4), 655–659 (2010).
[Crossref]

2009 (2)

J. Hedley, C. Roelfsema, and S. Phinn, “Efficient radiative transfer model inversion for remote sensing applications,” Remote Sens. Environ. 113(11), 2527–2532 (2009).
[Crossref]

C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Lu, “A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: the case of Taihu Lake, China,” Remote Sens. Environ. 113(6), 1175–1182 (2009).
[Crossref]

2005 (2)

G. Dall’Olmo and A. A. Gitelson, “Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: experimental results,” Appl. Opt. 44(3), 412–422 (2005).
[Crossref] [PubMed]

C. D. Mobley, L. K. Sundman, C. O. Davis, J. H. Bowles, T. V. Downes, R. A. Leathers, M. J. Montes, W. P. Bissett, D. D. R. Kohler, R. P. Reid, E. M. Louchard, and A. Gleason, “Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables,” Appl. Opt. 44(17), 3576–3592 (2005).
[Crossref] [PubMed]

2000 (1)

B. C. Gao, M. J. Montes, Z. Ahmad, and C. O. Davis, “Atmospheric correction algorithm for hyperspectral remote sensing of ocean color from space,” Appl. Opt. 39(6), 887–896 (2000).
[Crossref] [PubMed]

1992 (1)

A. Gitelson, “The peak near 700 nm on radiance spectra of algae and water - relationships of its magnitude and position with chlorophyll concentration,” Int. J. Remote Sens. 13(17), 3367–3373 (1992).
[Crossref]

1989 (2)

C. D. Mobley, “A numerical model for the computation of radiance distributions in natural waters with wind roughened surfaces,” Limnol. Oceanogr. 34(8), 1473–1483 (1989).
[Crossref]

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. Oceanogr. 34(1), 68–81 (1989).
[Crossref]

1977 (1)

A. Morel and L. Prieur, “Analysis of variations in ocean color,” Limnol. Oceanogr. 22(4), 709–722 (1977).
[Crossref]

1936 (1)

P. C. Mahalanobis, “On the generalized distance in statistics,” Proc. Natl. Inst. Sci. India 2(1), 49–55 (1936).

Ahmad, Z.

Bissett, W. P.

Bowles, J. H.

Butcher, S. D.

Carder, K. L.

Chen, D. T.

Chen, J.

Corson, M.

Corson, M. R.

Dall’Olmo, G.

Davis, C. O.

Downes, T. V.

Fukushima, T.

Gao, B. C.

Gitelson, A.

Gitelson, A. A.

Gleason, A.

Harvey, G. R.

Hedley, J.

J. Hedley, C. Roelfsema, and S. Phinn, “Efficient radiative transfer model inversion for remote sensing applications,” Remote Sens. Environ. 113(11), 2527–2532 (2009).
[Crossref]

Huang, C.

Kaganovsky, S.

Kohler, D. D. R.

Korwan, D. R.

Le, C.

Leathers, R. A.

Leavitt, B. C.

Li, R. R.

Li, Y.

Louchard, E. M.

Lu, H.

Lucke, R. L.

Ma, R.

Mahalanobis, P. C.

P. C. Mahalanobis, “On the generalized distance in statistics,” Proc. Natl. Inst. Sci. India 2(1), 49–55 (1936).

Matsushita, B.

McGlothlin, N. R.

Mobley, C. D.

C. D. Mobley, “A numerical model for the computation of radiance distributions in natural waters with wind roughened surfaces,” Limnol. Oceanogr. 34(8), 1473–1483 (1989).
[Crossref]

Montes, M. J.

Morel, A.

A. Morel and L. Prieur, “Analysis of variations in ocean color,” Limnol. Oceanogr. 22(4), 709–722 (1977).
[Crossref]

Moses, W. J.

Ortner, P. B.

Phinn, S.

J. Hedley, C. Roelfsema, and S. Phinn, “Efficient radiative transfer model inversion for remote sensing applications,” Remote Sens. Environ. 113(11), 2527–2532 (2009).
[Crossref]

Prieur, L.

A. Morel and L. Prieur, “Analysis of variations in ocean color,” Limnol. Oceanogr. 22(4), 709–722 (1977).
[Crossref]

Reid, R. P.

Roelfsema, C.

J. Hedley, C. Roelfsema, and S. Phinn, “Efficient radiative transfer model inversion for remote sensing applications,” Remote Sens. Environ. 113(11), 2527–2532 (2009).
[Crossref]

Snyder, W. A.

Steward, R. G.

Sulimani, B.

Sun, D.

Sundman, L. K.

Wood, D. L.

Yacobi, Y. Z.

Yang, W.

Zha, Y.

Appl. Opt. (4)

IEEE Geosci. Remote Sens. Lett. (1)

Int. J. Remote Sens. (1)

Limnol. Oceanogr. (3)

A. Morel and L. Prieur, “Analysis of variations in ocean color,” Limnol. Oceanogr. 22(4), 709–722 (1977).
[Crossref]

C. D. Mobley, “A numerical model for the computation of radiance distributions in natural waters with wind roughened surfaces,” Limnol. Oceanogr. 34(8), 1473–1483 (1989).
[Crossref]

Opt. Express (1)

Proc. Natl. Inst. Sci. India (1)

P. C. Mahalanobis, “On the generalized distance in statistics,” Proc. Natl. Inst. Sci. India 2(1), 49–55 (1936).

Remote Sens. Environ. (2)

J. Hedley, C. Roelfsema, and S. Phinn, “Efficient radiative transfer model inversion for remote sensing applications,” Remote Sens. Environ. 113(11), 2527–2532 (2009).
[Crossref]

Water Res. (1)

Other (5)

R. P. Bukata, J. H. Jerome, K. Y. Kondratyev, and D. V. Pozdnyakov, Optical Properties and Remote Sensing of Inland and Coastal Waters (CRC Press, 1995).

K. V. Mardia, J. T. Kent, and J. M. Bibby, Multivariate Analysis (Academic Press, 2003).

P. Billingsley, Probability and Measure, Third Ed. (John Wiley & Sons, 1995).

C. D. Mobley and L. K. Sundman, Hydrolight 5 Ecolight 5 technical documentation, 1st Ed., (Sequoia Scientific Inc., 2008).

M. J. Montes, B. C. Gao, and C. O. Davis, “A new algorithm for atmospheric correction of hyperspectral remote sensing data,” Proc. SPIE, Geo-Spatial Image and Data Exploitation II, W. E. Roper (ed.), 4383: 23–30 (2001).
[Crossref]

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Fig. 1 1000 Noisy variations (left) and covariance matrix (right). The latter is scaled by 10⁻⁸.

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Fig. 2 Distribution of the retrieved chl-a (left), CDOM (center) and SPM (right) values for the

L_{2}

(blue) and Mahalanobis (red) distances. The true values (indicated by arrow) are 3.0 mg m⁻³, 0.3 m⁻¹, and 2.0 g m⁻³, respectively.

Download Full Size | PPT Slide | PDF

Fig. 3 Distribution of

L_{2}

(left) and Mahalanobis (right) distances from the first noise-free spectrum to the 1000 noisy realizations. The red bars are the actual data, the line represents the fitted normal and chi-squared distributions, respectively.

Download Full Size | PPT Slide | PDF

Fig. 4 Two-band scattergram of the noisy (left) and database (right) spectra. The red curve represents the 3-sigma range of the

L_{2}

distance; the black dotted curve is the 3-sigma range for MD. The bands shown are 422 and 571 nm.

Download Full Size | PPT Slide | PDF

Fig. 5 Distribution of the

L_{2}

(left) and Mahalanobis (right) distances in the noise range. The dotted line represents the (fitted) distribution of the noisy data; the bars represent the number of database spectra at that distance. Note that the number of database spectra within the noise range is significantly smaller for the Mahalanobis distance.

Download Full Size | PPT Slide | PDF

Tables (3)

Table 1 Overview of the range / values for the parameters used in the database search. Some values have been approximated for brevity.

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Table 2 IOP characterizations and experimental results for the 52 noise-free spectra. Cols.1-3 are the input IOPs; Cols. 4-6 describe the retrieval results (out of 1000 noisy realizations); Cols. 7-12 are the avg. retrieved IOP error (see text for definition). Note that the number of correct retrievals will be strongly affected by the discretization of the various parameters in the lookup table (Table 1); as a result, a direct comparison among various levels is difficult.

View Table | View all tables in this article

Table 3 Distribution of the noisy and database spectra within a given range of the noise-free input spectrum.

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Equations (6)

Equations on this page are rendered with MathJax. Learn more.

d (x, y) = \sum_{i = 1}^{n} {(x_{i} - y_{i})}^{2} = {(x - y)}^{t} \cdot (x - y)

d_{M} (x) = {(x - μ)}^{t} \cdot Σ^{- 1} \cdot (x - μ)

d_{M} (x, y) = {(x - y)}^{t} \cdot Σ^{- 1} \cdot (x - y)

d_{M} (x, y) = \sum_{i = 1}^{n} \frac{1}{σ_{i}^{2}} {(x_{i} - y_{i})}^{2}

100 \times (\frac{true value - avg . retrieved value}{true value}) .

d (x, ρ) = \sum_{i = 1}^{n} σ_{i}^{2} (\frac{1}{σ_{i}^{2}} {(x_{i} - ρ_{i})}^{2})

Parameter	Range	% of database
chl-a	0, 0.05. 0.1, …, 0.95	10.1
	1, 1.1, …, 2	7.5
	2, 3, 4, 5, …, 9	36.5
	10, 11, …, 19	27.8
	20, 25, .., 60	18.1
CDOM	0, 0.01, …,0.19	13.4
	0.2, 0.3, …, 0.9	43.3
	1.0, 1.1, …, 1.9	31.3
	2, 2.5, …, 4	12.0
SPM	0, 0.1, …, 0.9	12.3
	1, 1.5, …, 9.5	53.3
	10, 10.5, ..., 20	34.3
Sediment	None	4.7
	Yellow clay	2.7
	Red clay	52.1
	Calcareous sand	22.5
	Bukata	3.6
	Brown Earth	14.4
Phase Function	bb = 0.004	6.9
	bb = 0.008	5.6
	bb = 0.012	17.8
	bb = 0.015	10.5
	bb = 0.02	18.6
	bb = 0.016	18.6
	bb = 0.024	19.6

Noise-freeparameters			No.Correct		Imp.(%)	Average Error (%)
Noise-freeparameters			No.Correct			Chl-a		CDOM		SPM
Chl-a	CDOM	SPM	L₂	MD		L₂	MD	L₂	MD	L₂	MD
3	0.3	2	518	688	32.8	5.0	2.3	3.3	3.3	11.5	7.0
3	0.3	6	782	866	10.7	0.0	−1.0	0.0	0.0	3.3	1.2
3	0.3	10	857	889	3.7	−1.7	−1.3	0.0	0.0	0.5	0.1
3	0.3	16	889	913	2.7	−1.7	−0.7	0.0	0.0	0.2	0.0
3	1	2	255	383	50.2	−1.3	1.3	6.0	2.0	−2.5	9.0
3	1	6	439	615	40.1	−2.7	−1.7	7.0	3.0	9.7	6.7
3	1	10	447	530	18.6	−3.0	−2.7	3.0	3.0	6.2	3.7
3	1	16	650	733	12.8	−2.3	−1.3	−3.0	−1.0	2.4	0.7
3	1.6	2	315	408	29.5	−3.0	0.0	8.8	5.6	−34.5	−19.5
3	1.6	6	483	610	26.3	−1.3	−0.7	5.0	0.0	1.2	4.8
3	1.6	10	428	527	23.1	−6.3	−4.7	4.4	2.5	3.7	2.0
3	1.6	16	514	583	13.4	−3.7	−2.3	−1.9	−1.3	2.6	1.4
10	0.3	2	441	652	47.8	6.0	3.1	0.0	0.0	11.5	6.0
10	0.3	6	504	602	19.4	2.3	1.0	6.7	3.3	4.2	1.8
10	0.3	10	576	621	7.8	0.0	−0.1	−3.3	0.0	1.3	0.3
10	0.3	16	671	700	4.3	−0.4	−0.1	0.0	0.0	0.2	0.1
10	1	2	318	410	28.9	3.8	3.6	2.0	0.0	16.0	18.5
10	1	6	322	466	44.7	1.8	0.9	4.0	1.0	7.7	4.5
10	1	10	223	307	37.7	0.4	1.1	0.0	0.0	7.6	4.8
10	1	16	455	487	7.0	0.0	0.1	−2.0	0.0	1.4	0.4
10	1.6	2	223	293	31.4	3.3	3.0	5.0	0.0	−14.0	11.0
10	1.6	6	304	394	29.6	3.8	2.8	2.5	−0.6	4.2	6.2
10	1.6	10	207	301	45.4	0.4	0.6	2.5	−0.6	6.1	4.4
10	1.6	16	399	410	2.8	0.0	0.0	−1.3	−0.6	1.2	0.6
18	0.3	2	505	609	20.6	2.9	1.2	−10.0	−3.3	10.0	4.5
18	0.3	6	366	428	16.9	1.8	0.9	−6.7	−3.3	3.2	1.0
18	0.3	10	505	515	2.0	0.1	0.2	0.0	0.0	0.1	0.0
18	0.3	16	546	561	2.7	0.0	0.2	0.0	0.0	0.2	0.0
18	1	2	347	442	27.4	5.8	4.5	−5.0	−4.0	17.5	16.5
18	1	6	244	285	16.8	4.3	3.8	−6.0	−5.0	9.0	7.2
18	1	10	231	268	16.0	1.9	1.4	−2.0	−1.0	2.2	0.9
18	1	16	347	372	7.2	−0.1	−0.2	−3.0	−1.0	2.1	0.6
18	1.6	2	245	301	22.9	6.4	4.9	−3.7	−3.1	9.5	17.0
18	1.6	6	250	298	19.2	4.3	3.5	−3.7	−3.1	10.5	8.5
18	1.6	10	218	266	22.0	2.4	2.7	−3.1	−1.9	5.2	3.7
18	1.6	16	262	283	8.0	0.6	0.7	−2.5	−1.9	3.6	2.4
45	0.3	2	820	943	15.0	0.8	0.4	−6.7	−3.3	8.5	2.0
45	0.3	6	737	860	16.7	0.6	0.5	−6.7	−3.3	4.2	1.0
45	0.3	10	856	928	8.4	−0.1	−0.1	−3.3	0.0	1.5	0.3
45	0.3	16	920	975	6.0	−0.1	0.0	−3.3	0.0	0.6	0.1
45	1	2	631	775	22.8	1.6	1.6	−4.0	−3.0	14.0	11.0
45	1	6	604	712	17.9	1.6	1.6	−5.0	−3.0	7.5	5.2
45	1	10	607	694	14.3	0.8	1.0	−3.0	−1.0	2.4	1.1
45	1	16	792	862	8.8	−0.1	0.0	−1.0	0.0	0.7	0.2
45	1.6	2	504	577	14.5	3.5	3.0	−3.1	−2.5	17.5	16.0
45	1.6	6	592	680	14.9	2.1	1.8	−3.1	−2.5	9.7	7.8
45	1.6	10	574	618	7.7	1.4	1.2	−2.5	−1.9	4.4	3.5
45	1.6	16	669	711	6.3	0.8	0.8	−1.9	−0.6	2.3	1.3
0.5	0.2	1	448	649	44.9	−14.0	−4.0	−5.0	−5.0	10.0	9.0
0.5	0.2	2	695	817	17.6	−20.0	−10.0	0.0	0.0	10.0	7.0
0.5	0.5	1	198	287	44.9	−26.0	−4.0	−2.0	−6.0	−31.0	−26.0
0.5	0.5	2	522	657	25.9	−30.0	−20.0	2.0	−2.0	2.0	5.0

Noise-free parameters			Noisy spectra in range				Database spectra in range
Noise-free parameters			1 sigma		2.33 sigma		1 sigma		2.33 sigma
Chl-a	CDOM	SPM	L₂	MD	L₂	MD	L₂	MD	L₂	MD
3	0.3	2	866	846	970	988	244	102	378	149
3	0.3	6	858	838	970	982	65	34	99	47
3	0.3	10	848	848	974	979	66	28	118	37
3	0.3	16	849	840	977	988	67	25	104	39
3	1	2	847	842	975	988	1044	434	1439	554
3	1	6	859	838	971	981	373	158	519	201
3	1	10	874	842	978	991	330	150	469	202
3	1	16	851	852	977	980	201	78	289	121
3	1.6	2	857	834	979	988	1218	536	1661	682
3	1.6	6	855	841	978	986	437	191	633	253
3	1.6	10	852	836	977	987	296	121	396	147
3	1.6	16	852	832	979	986	192	82	275	100
10	0.3	2	851	832	977	986	155	83	238	111
10	0.3	6	850	853	976	985	121	61	170	86
10	0.3	10	848	840	977	987	126	59	193	88
10	0.3	16	845	840	976	987	112	55	169	86
10	1	2	857	842	971	980	823	369	1143	494
10	1	6	850	838	972	986	490	204	716	261
10	1	10	853	854	977	984	427	205	615	250
10	1	16	853	834	977	987	318	140	444	189
10	1.6	2	848	841	978	984	1089	556	1399	700
10	1.6	6	846	849	981	986	612	291	806	346
10	1.6	10	847	840	970	987	449	222	590	269
10	1.6	16	853	842	972	988	342	165	445	200
18	0.3	2	853	837	973	987	106	48	155	66
18	0.3	6	845	848	976	986	88	55	123	70
18	0.3	10	849	852	973	982	92	30	150	47
18	0.3	16	846	841	973	987	87	54	121	68
18	1	2	850	847	976	979	689	251	989	316
18	1	6	841	839	979	979	399	151	551	214
18	1	10	846	843	976	986	316	129	461	194
18	1	16	849	841	976	983	243	126	369	160
18	1.6	2	869	842	972	985	950	471	1212	568
18	1.6	6	860	850	974	982	532	242	706	301
18	1.6	10	857	841	975	984	413	184	536	233
18	1.6	16	861	837	969	987	313	137	418	179
45	0.3	2	855	856	974	978	51	27	71	35
45	0.3	6	856	841	971	982	57	33	91	42
45	0.3	10	847	829	974	985	71	38	91	50
45	0.3	16	836	852	976	984	70	42	107	52
45	1	2	839	849	980	988	186	77	290	110
45	1	6	849	844	974	980	188	79	274	104
45	1	10	854	841	971	987	171	84	243	106
45	1	16	854	841	972	984	168	74	231	93
45	1.6	2	856	840	967	987	411	145	613	189
45	1.6	6	851	826	973	993	272	119	389	151
45	1.6	10	853	849	978	991	201	99	276	127
45	1.6	16	866	836	972	983	175	80	230	104
0.5	0.2	1	844	859	980	981	508	259	699	336
0.5	0.2	2	848	839	972	985	355	169	520	227
0.5	0.5	1	846	842	974	985	2316	958	3214	1222
0.5	0.5	2	855	860	971	988	1544	624	2145	783

Parameter	Range	% of database
chl-a	0, 0.05. 0.1, …, 0.95	10.1
	1, 1.1, …, 2	7.5
	2, 3, 4, 5, …, 9	36.5
	10, 11, …, 19	27.8
	20, 25, .., 60	18.1
CDOM	0, 0.01, …,0.19	13.4
	0.2, 0.3, …, 0.9	43.3
	1.0, 1.1, …, 1.9	31.3
	2, 2.5, …, 4	12.0
SPM	0, 0.1, …, 0.9	12.3
	1, 1.5, …, 9.5	53.3
	10, 10.5, ..., 20	34.3
Sediment	None	4.7
	Yellow clay	2.7
	Red clay	52.1
	Calcareous sand	22.5
	Bukata	3.6
	Brown Earth	14.4
Phase Function	bb = 0.004	6.9
	bb = 0.008	5.6
	bb = 0.012	17.8
	bb = 0.015	10.5
	bb = 0.02	18.6
	bb = 0.016	18.6
	bb = 0.024	19.6

Noise-freeparameters			No.Correct		Imp.(%)	Average Error (%)
Noise-freeparameters			No.Correct			Chl-a		CDOM		SPM
Chl-a	CDOM	SPM	L₂	MD		L₂	MD	L₂	MD	L₂	MD
3	0.3	2	518	688	32.8	5.0	2.3	3.3	3.3	11.5	7.0
3	0.3	6	782	866	10.7	0.0	−1.0	0.0	0.0	3.3	1.2
3	0.3	10	857	889	3.7	−1.7	−1.3	0.0	0.0	0.5	0.1
3	0.3	16	889	913	2.7	−1.7	−0.7	0.0	0.0	0.2	0.0
3	1	2	255	383	50.2	−1.3	1.3	6.0	2.0	−2.5	9.0
3	1	6	439	615	40.1	−2.7	−1.7	7.0	3.0	9.7	6.7
3	1	10	447	530	18.6	−3.0	−2.7	3.0	3.0	6.2	3.7
3	1	16	650	733	12.8	−2.3	−1.3	−3.0	−1.0	2.4	0.7
3	1.6	2	315	408	29.5	−3.0	0.0	8.8	5.6	−34.5	−19.5
3	1.6	6	483	610	26.3	−1.3	−0.7	5.0	0.0	1.2	4.8
3	1.6	10	428	527	23.1	−6.3	−4.7	4.4	2.5	3.7	2.0
3	1.6	16	514	583	13.4	−3.7	−2.3	−1.9	−1.3	2.6	1.4
10	0.3	2	441	652	47.8	6.0	3.1	0.0	0.0	11.5	6.0
10	0.3	6	504	602	19.4	2.3	1.0	6.7	3.3	4.2	1.8
10	0.3	10	576	621	7.8	0.0	−0.1	−3.3	0.0	1.3	0.3
10	0.3	16	671	700	4.3	−0.4	−0.1	0.0	0.0	0.2	0.1
10	1	2	318	410	28.9	3.8	3.6	2.0	0.0	16.0	18.5
10	1	6	322	466	44.7	1.8	0.9	4.0	1.0	7.7	4.5
10	1	10	223	307	37.7	0.4	1.1	0.0	0.0	7.6	4.8
10	1	16	455	487	7.0	0.0	0.1	−2.0	0.0	1.4	0.4
10	1.6	2	223	293	31.4	3.3	3.0	5.0	0.0	−14.0	11.0
10	1.6	6	304	394	29.6	3.8	2.8	2.5	−0.6	4.2	6.2
10	1.6	10	207	301	45.4	0.4	0.6	2.5	−0.6	6.1	4.4
10	1.6	16	399	410	2.8	0.0	0.0	−1.3	−0.6	1.2	0.6
18	0.3	2	505	609	20.6	2.9	1.2	−10.0	−3.3	10.0	4.5
18	0.3	6	366	428	16.9	1.8	0.9	−6.7	−3.3	3.2	1.0
18	0.3	10	505	515	2.0	0.1	0.2	0.0	0.0	0.1	0.0
18	0.3	16	546	561	2.7	0.0	0.2	0.0	0.0	0.2	0.0
18	1	2	347	442	27.4	5.8	4.5	−5.0	−4.0	17.5	16.5
18	1	6	244	285	16.8	4.3	3.8	−6.0	−5.0	9.0	7.2
18	1	10	231	268	16.0	1.9	1.4	−2.0	−1.0	2.2	0.9
18	1	16	347	372	7.2	−0.1	−0.2	−3.0	−1.0	2.1	0.6
18	1.6	2	245	301	22.9	6.4	4.9	−3.7	−3.1	9.5	17.0
18	1.6	6	250	298	19.2	4.3	3.5	−3.7	−3.1	10.5	8.5
18	1.6	10	218	266	22.0	2.4	2.7	−3.1	−1.9	5.2	3.7
18	1.6	16	262	283	8.0	0.6	0.7	−2.5	−1.9	3.6	2.4
45	0.3	2	820	943	15.0	0.8	0.4	−6.7	−3.3	8.5	2.0
45	0.3	6	737	860	16.7	0.6	0.5	−6.7	−3.3	4.2	1.0
45	0.3	10	856	928	8.4	−0.1	−0.1	−3.3	0.0	1.5	0.3
45	0.3	16	920	975	6.0	−0.1	0.0	−3.3	0.0	0.6	0.1
45	1	2	631	775	22.8	1.6	1.6	−4.0	−3.0	14.0	11.0
45	1	6	604	712	17.9	1.6	1.6	−5.0	−3.0	7.5	5.2
45	1	10	607	694	14.3	0.8	1.0	−3.0	−1.0	2.4	1.1
45	1	16	792	862	8.8	−0.1	0.0	−1.0	0.0	0.7	0.2
45	1.6	2	504	577	14.5	3.5	3.0	−3.1	−2.5	17.5	16.0
45	1.6	6	592	680	14.9	2.1	1.8	−3.1	−2.5	9.7	7.8
45	1.6	10	574	618	7.7	1.4	1.2	−2.5	−1.9	4.4	3.5
45	1.6	16	669	711	6.3	0.8	0.8	−1.9	−0.6	2.3	1.3
0.5	0.2	1	448	649	44.9	−14.0	−4.0	−5.0	−5.0	10.0	9.0
0.5	0.2	2	695	817	17.6	−20.0	−10.0	0.0	0.0	10.0	7.0
0.5	0.5	1	198	287	44.9	−26.0	−4.0	−2.0	−6.0	−31.0	−26.0
0.5	0.5	2	522	657	25.9	−30.0	−20.0	2.0	−2.0	2.0	5.0

Abstract

References

2012 (1)

2011 (2)

2010 (1)

2009 (2)

2005 (2)

2000 (1)

1992 (1)

1989 (2)

1977 (1)

1936 (1)

Ahmad, Z.

Bissett, W. P.

Bowles, J. H.

Butcher, S. D.

Carder, K. L.

Chen, D. T.

Chen, J.

Corson, M.

Corson, M. R.

Dall’Olmo, G.

Davis, C. O.

Downes, T. V.

Fukushima, T.

Gao, B. C.

Gitelson, A.

Gitelson, A. A.

Gleason, A.

Harvey, G. R.

Hedley, J.

Huang, C.

Kaganovsky, S.

Kohler, D. D. R.

Korwan, D. R.

Le, C.

Leathers, R. A.

Leavitt, B. C.

Li, R. R.

Li, Y.

Louchard, E. M.

Lu, H.

Lucke, R. L.

Ma, R.

Mahalanobis, P. C.

Matsushita, B.

McGlothlin, N. R.

Mobley, C. D.

Montes, M. J.

Morel, A.

Moses, W. J.

Ortner, P. B.

Phinn, S.

Prieur, L.

Reid, R. P.

Roelfsema, C.

Snyder, W. A.

Steward, R. G.

Sulimani, B.

Sun, D.

Sundman, L. K.

Wood, D. L.

Yacobi, Y. Z.

Yang, W.

Zha, Y.

Appl. Opt. (4)

IEEE Geosci. Remote Sens. Lett. (1)

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