an appraisal of surface chlorophyll estimation by satellite remote sensing in the south china sea

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This article was downloaded by: [University of Iowa Libraries] On: 05 October 2014, At: 06:05 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK International Journal of Remote Sensing Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tres20 An appraisal of surface chlorophyll estimation by satellite remote sensing in the South China Sea S. Tang a b , C. Chen a , H. Zhan a , J. Zhang a & J. Yang a a LED , South China Sea Institute of Oceanology , Chinese Academy of Sciences , Guangzhou, 510301, China b Graduate University of the Chinese Academy of Sciences , Beijing, 100049, China Published online: 23 Oct 2008. To cite this article: S. Tang , C. Chen , H. Zhan , J. Zhang & J. Yang (2008) An appraisal of surface chlorophyll estimation by satellite remote sensing in the South China Sea, International Journal of Remote Sensing, 29:21, 6217-6226, DOI: 10.1080/01431160802175579 To link to this article: http://dx.doi.org/10.1080/01431160802175579 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &

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This article was downloaded by: [University of Iowa Libraries]On: 05 October 2014, At: 06:05Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

International Journal of RemoteSensingPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/tres20

An appraisal of surface chlorophyllestimation by satellite remote sensingin the South China SeaS. Tang a b , C. Chen a , H. Zhan a , J. Zhang a & J. Yang aa LED , South China Sea Institute of Oceanology , ChineseAcademy of Sciences , Guangzhou, 510301, Chinab Graduate University of the Chinese Academy of Sciences ,Beijing, 100049, ChinaPublished online: 23 Oct 2008.

To cite this article: S. Tang , C. Chen , H. Zhan , J. Zhang & J. Yang (2008) An appraisal of surfacechlorophyll estimation by satellite remote sensing in the South China Sea, International Journal ofRemote Sensing, 29:21, 6217-6226, DOI: 10.1080/01431160802175579

To link to this article: http://dx.doi.org/10.1080/01431160802175579

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &

Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

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An appraisal of surface chlorophyll estimation by satellite remotesensing in the South China Sea

S. TANG*{{, C. CHEN{, H. ZHAN{, J. ZHANG{ and J. YANG{

{LED, South China Sea Institute of Oceanology, Chinese Academy of Sciences,

Guangzhou, 510301, China

{Graduate University of the Chinese Academy of Sciences, Beijing, 100049, China

(Received 27 March 2007; in final form 25 March 2008 )

The OC4v4 algorithm, which is confirmed as a standard algorithm for the

retrieval of ocean chlorophyll-a concentration (chl-a) from ocean colour satellite

data, was appraised for its performance in the South China Sea (SCS). With

in situ optical data and surface chl-a data collected on six cruises from 1997 to

2005 in the SCS, analysis showed a large discrepancy between the in situ and

OC4v4-retrieved chl-a. The OC4v4 algorithm generally overestimates the chl-a in

the investigated area; this possibly results from the relatively higher absorption of

the chromophoric of dissolved organic matter (CDOM). To find a better

algorithm, correlation analysis was carried out between the chl-a and the band

ratio. Results showed that the remote sensing reflectance ratios, R412555 and R443

555,obtained the best correlation relationship. New algorithms were proposed based

on the two band ratios. Compared with OC4v4, the new algorithms show better

results and are suggested for the retrieval of chl-a from SeaWiFS or MODIS in

the SCS.

1. Introduction

Satellite remote sensing of ocean colour provides the only practical means to quickly

respond to many fundamental biological ocean properties, notably, surface

chlorophyll-a (chl-a), on basin scales. The sea viewing wide field-of-view sensor

(SeaWiFS), which is one of the most important global observational platforms for

ocean colour, is used to determine the distribution of the global phytoplankton

biomass (Gregg and Casey 2004). Different algorithms, including both empirical

(Gordon et al. 1983) and semi-analytical (Maritorena et al. 2002), have been

proposed for the retrieval of sea surface chl-a, based on SeaWiFS datasets. O’Reilly

et al. (1998) and O’Reilly (2000) presented OC2 and OC4 for the SeaWiFS

algorithm, based on a large in situ dataset, including measurements of oligotrophic

and eutrophic waters around the world’s oceans (O’Reilly et al. 1998, O’Reilly

2000). At the present time, both of the updated algorithms are in their fourth

version. OC4v4 (using 443, 490, 510 and 555 nm bands) is an empirical equation.

Compared with OC2v2, it is more complicated, but shows a higher accuracy, which

could be expressed as follows:

C~10 a0za1Rza2R2za3R3za4R4ð Þ and R~log R443555wR490

555wR510555

� �, ð1Þ

*Corresponding author. Email: [email protected]

International Journal of Remote Sensing

Vol. 29, No. 21, 10 November 2008, 6217–6226

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2008 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160802175579

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where C is the chl-a concentration and a is a constant array [0.366, 23.067, 1.930,

0.649, 21.532]. The remote sensing reflectance ratio is constructed from band A

divided by band B and is indicated by RAB . The terms R443

555wR490555wR510

555 means the

maximum of R443555, R490

555 and R510555.

Over the past two decades, many efforts were aimed at estimating the chl-a from

satellite data (O’Reilly et al. 1998, O’Reilly 2000, Zhan et al. 2003, Carder et al.

2004, Lavender et al. 2004, Garcia et al. 2005). Furthermore, verifications of various

empirical algorithms for the retrieval of chl-a in local seas were also required and a

lot of work has been carried out in many different regions (Kahru and Mitchell

1999, Jorgensen 2004, Lavender et al. 2004, Garcia et al. 2005).

The South China Sea (SCS) is a typical marginal sea south of China, which spans 0u–23uN and 100u–121uE. As the largest and deepest marginal sea in the tropical pacific,

the SCS links the Indian Ocean and the Pacific Ocean, encompassing a portion of the

Pacific Ocean stretching roughly from Singapore and the Strait of Malacca in the

northwest, to the Strait of Taiwan in the northeast, with an area of around

3.56106 km2. About 30% of the SCS belongs to deep-sea area with an average depth of

1400 m. There are longshore currents and warm currents driven by Kuroshio in the

north, and the SCS plays an important role in the East Asian monsoon due to its high

water temperature and large heat content in the upper ocean.

The SCS is poorly understood in terms of its bio-optical characteristics, despite

the work of a few investigators (e.g. Zhang et al. 2003). Light penetration in the SCS

is generally limited to the upper 15 to 120 m, with the downward attenuation

coefficient varying from 0.04 to 0.350 m21 (Zhang et al. 2003), and the in situ chl-a

ranges from 0.02 to 1.6 mg m23 in open waters and reaches to 50 mg m23 in shelf

waters and estuaries.

The OC4v4 will be tested in the SCS in this paper, and its performance will be

verified. The origin for the errors of the retrieval of chl-a using OC4v4 will be

discussed. A regional ‘solution’ to obtain a more accurate algorithm in the retrieval

of chl-a from ocean colour remote sensing will be proposed.

2. Data and method

2.1 Shipboard measurements

The analysis presented here is based on about 160 measurements of chl-a in the SCS,

accompanied with about 50 measurements of optical profiles. The optical

measurements are less because of the absence of light. The data were collected in

six cruises from 1997 to 2005 (see figure 1 and table 1).

Under the radiometric measurement protocol for the SeaWiFS Project (Mueller

1995), the upwelling radiance just below the water surface Lu(02, l) and the

downwelling irradiance just above the water surface Es(0+ , l) were derived by the

SeaWiFS profiling multichannel radiometer (SPMR, Satlantic Inc.), which was

designed with a self-shading correction ability and nine spectral channels with centre

wavelengths at 412, 443, 490, 510, 520, 555, 565, 670 and 780 nm, respectively. The

in situ remote sensing reflectance Rrs(l) was calculated based on the NASA

standard methods (Siegel 1995), which could be derived by the ratio of Lw(l) to

Es(0+ , l), i.e. Rrs(l)5Lw(l)/Es(0

+ , l), where Lw(l) can be expressed as

Lw(l)50.543Lu(02, l).

The water samples collected at each station were filtered through 0.25 mm

Whatman GF/F glass fibre filters, and then the chl-a was derived by fluorescence

6218 S. Tang et al.

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measured by a Turner-Design 10 Fluorescence Spectrometer. The in situ chl-a ranges

from 0.026 to 3.87 mg m23 in all of the six cruises, and is from 0.026 to 1.57 mg m23,

except for the GuangDong (GD) coast cruise.

2.2 Methodology of comparison of different algorithms

To evaluate the algorithm performance, the relation square (R2), the root mean

squared error (RMSE) and the systematic error (MNB) were calculated:

Figure 1. Map of the study area and the sample stations. # The GuangDong (GD) coastcruise stations, n stations of the Open cruise in 2005, + stations of the Open cruise in 2004, eNanSha 3 cruise stations in 1999, m NanSha 2 cruise stations in 1999 and d NanSha 1 cruisestations in 1997.

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RMSE~

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

n

Xn

i~1

xi{yið Þ2s

and MNB~1

n

Xn

i~1

xi{yið Þyi

����

����, ð2Þ

where n is the number of the samples, x is the chl-a estimated from the algorithm

and y is the in situ chl-a.

In some publications, the statistic based on the RMS of the logarithm of the ratio

of the retrieved values to the in situ values was used (O’Reilly et al. 1998, Darecki et

al. 2005). To evaluate the algorithm performance, two sets of statistical parameters

were used:

log bias~1

n

Xn

i~1

log xi{yið Þ

and

log rms~

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

nPn

i~1

log2 xi{yið Þ{Pn

i~1

log xi{yið Þ� �2

n n{1ð Þ

vuuuut: ð3Þ

3. Results and discussion

Comparison of the measured chl-a and the OC4v4 algorithm-derived chl-a from

measured optical data is presented in figure 2. In general, the in situ optical data

derived chl-a by the OC4v4 algorithm overestimates the chl-a in the whole range.

When using the OC4v4 algorithm to estimate the chl-a from in situ optical data, the

result is about 1.66 times that of the in situ chl-a (figure 2). As can be seen from the

figure, the goal of 35% accuracy for satellite chl-a retrieval seems to be hard toachieve by use of the OC4v4 algorithm in the SCS.

Since in situ reflectance is not affected by atmospheric radiance, the difference

between in situ and retrieved chl-a is due to the specific optical properties of the SCS.

The relative higher chromophoric of dissolved organic matter (CDOM) absorption

in the SCS (Chen et al. 2003) may result in the overestimation for the retrieval of

chl-a with the OC4v4 algorithm, which was developed on the base of the ratio of

the blue to green remote sensing reflectance spectra. The relative higher CDOM

absorption at around 400 nm (figure 3) may increase the remote sensing reflectancein the blue spectrum, which will raise the ratio value of blue to green remote sensing

reflectance spectra.

Table 1. Sources of the optical parameters and the chl-a data.

Cruise names DatesNumber of stations

with optical dataNumber of stations

with chl-a data

NanSha 1 7–23 November 1997 0 15NanSha 2 10 April–1 May 1999 13 18NanSha 3 1–21 July 1999 0 31Open 1 16 September–5 October 2004 18 44Open 2 5–23 September 2005 15 35GD coast 5–24 September 2003 8 18

6220 S. Tang et al.

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Figure 2. Comparison of in situ chl-a and retrieved chl-a from in situ optical data withOC4v4 algorithm.

Figure 3. Distribution of the CDOM absorption at 400 nm from in situ measurement in the SCS.

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The significant overestimation of the OC4v4 algorithm necessitates the effort of

developing new regression between the remote sensing reflectance ratio and the chl-a

in the SCS. New SCS local algorithms were tried to be developed with an optimal

band combination based on the correlation relationships between the in situ remote

sensing reflectance ratios of Rij and chl-a, where i and j traversed all the bands of

412, 443, 490, 510 and 555 nm, respectively. The characteristics of the remote sensing

reflectance data most relevant to bio-optical algorithms are illustrated in figure 4.

Over the entire data domain, R412555 yields the highest correlation of R250.8693 with

chl-a, whilst a similar result is achieved by R443555, where R250.8628. The ratios R412

555

and R443555 show higher correlation than R490

555, possibly because the average measured

chl-a in the SCS is lower than the original data in OC4v4. O’Reilly et al. (1998)

found that the dispersion increases with increasing chl-a and decreasing band ratio,

especially the R412555 and R443

555. The ratios R412555 and R443

555 provide the most precise

(lowest dispersion) in chlorophyll for chlorophyll-poor waters, especially for that

less than 0.4 mg m23(O’Reilly 2000). The overall chl-a in the six cruises is about

0.29 mg m23. The average concentration used in figure 5 (chl-a and optical data) is

about 0.52 mg m23 and more than 80% of the chl-a is less than 0.4 mg m23, but the

average chl-a used in the OC4v4 algorithm is much higher than the chl-a in this

paper.

In order to overcome the overestimation of chl-a in the SCS, an improved OC4v4

algorithm that is the same as equation (1), but with different parameters should be

considered. The relationship between the chl-a and the maximum of the R412555, R443

555,

R490555 and R510

555 is shown in figure 5. The R2 of this relationship is lower than that of

Figure 4. Relationships between the band ratios R412555, R443

555, R490555 and R510

555 and the chl-abased on in situ measurement (all data are log transformed).

6222 S. Tang et al.

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chl-a and R412555 or chl-a and R443

555. The reason for the higher R2 of the last two

parameters cannot be explained easily without enough synchronous CDOM

absorption data. It may be because that, in higher chlorophyll waters, the chl-a

shows some relationship with the CDOM (figure 6).

Since the correlation coefficient achieved the maximum value at R412555 and R443

555,

and because the cubic or quadratic polynomial did not accomplish more significant

statistical accordance with the in situ chl-a, a simple polynomial of log–log equations

of log R412555

� �and log R443

555

� �was chosen to derive the correlation relationships with

the in situ chl-a. The new local algorithms for the SCS chl-a concentration (CSCS)

could be determined as follows:

CSCS~10{1:2554R{0:2282, where R~log R412555

� �, ð4Þ

or CSCS~10{1:4945R{0:2182, where R~log R443555

� �: ð5Þ

The performances of the OC4v4 algorithm and the new SCS local algorithms

developed in this paper are compared in table 2, where it can be found that the new

SCS local algorithm gave better results in all of the statistical targets (RMSE, MNB,

Log_rms) than the OC4v4 algorithm. The algorithm based on R412555 and R443

555 shows

similar results in the RMSE, but a slightly higher result for MNB and log_rms.

Furthermore, the new two SCS local algorithms achieve higher correlation

Figure 5. Scatter plot of band ratio and measured chl-a. The solid line marks the OC4v4algorithm and the SCS regional version of the four-band algorithm (all data are logtransformed).

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relationships between in situ and retrieved chl-a, with R2 near to 0.86 in the log–log

equation and a lower RMSE of 0.47 (table 2); this markedly conquers the

overestimation problems occurred in the retrieval with the OC4v4 algorithm.

The estimation of water-leaving radiance at 412 nm is hard to carry out, and is

sometimes negative in the SeaWiFS data (Siegel et al. 2000, Folkestad et al. 2007).

The atmospheric correction at 412 nm shows more errors than the other bands. The

use of 412 nm in estimating the chl-a requires further discussion. It is regarded that,

in oligotrophic waters, the R412555 is expected to achieve a better result, but in higher

chl-a waters, the algorithm should be applied cautiously.

4. Conclusions

In this paper, the performance of the existing OC4v4 algorithm for the retrieval of

chl-a was firstly verified in the SCS with in situ optical data. The verification

Figure 6. The relationship between the chl-a and the absorption of CDOM at 400nm basedon in situ measurements in the SCS (only Open1 and Open 2 cruises have synchronousCDOM absorption).

Table 2. Evaluation results of the algorithms. The three rows refer to the statisticalparameters between the measured and retrieved chl-a using equations (1), (4) and (5),

respectively.

Algorithm RMSE MNB Log_bias Log_rms

OC4v4 1 1.17 0.29 0.24

SCS local with R412555

0.47 0.32 — 0.21

SCS local with R443555

0.47 0.37 — 0.22

6224 S. Tang et al.

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indicated that the retrieved result derived by the OC4v4 algorithm overestimated the

chl-a in the SCS, which might result from the relatively higher CDOM absorption.

Then, new SCS local algorithms for the retrieval of chl-a were developed based on

the correlation relationships between the ratios of the blue to green remote sensing

reflectance spectra and chl-a. The R412555 and R443

555 ratios were found to have the best

correlation coefficient with chl-a. New algorithms based on R412555 and R443

555 were

developed when estimating the chl-a in the SCS. The results showed that the new

SCS local algorithms obtained a higher accuracy in the SCS with R2 over 0.86 and a

RMSE of 0.47, and notably improved the overestimation problems in the

application of the OC4v4 algorithm. However, the retrieval of chl-a in coastal

and turbid waters was not considered in this paper because of the various

components and complex optical properties and the inaccurate atmospheric

correction of case 2 waters. This requires more in situ radiometric data and further

work conducted in order to improve the local algorithm.

Acknowledgements

The authors are very grateful to the two anonymous referees for their helpful

suggestions on the manuscript.

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