Research papers

Spatio-temporal stability and abnormality of chlorophyll-a in theNorthern South China Sea during 2002–2012 from MODIS imagesusing wavelet analysis

Meiling Liu a, Xiangnan Liu a,n, Aohui Ma b, Ting Li c, Zhihong Du a

a School of Information Engineering, China University of Geosciences, Beijing 100083, Chinab Tianjin Institute of Geotechnical Investigation & Surveying, Tianjin 300191, Chinac State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

a r t i c l e i n f o

Article history:Received 11 September 2013Received in revised form20 December 2013Accepted 26 December 2013Available online 3 January 2014

Keywords:Chl-a concentrationsMODIS imagesWavelet transformStationary levelAnomalous variability

a b s t r a c t

Detecting a regular pattern of chlorophyll-a (Chl-a) in the ocean can provide a preliminary scientificunderstanding of regional environmental changes. The objective of this research was to identify thepotential of a wavelet transform to capture and describe both the stationary level and anomalousvariability of Chl-a. An 11-year time series (from July 2002 to December 2012) of the ModerateResolution Imaging Spectroradiometer (MODIS) chlorophyll-a product in the Northern South ChinaSea (NSCS) was collected. The Data INterpolating Empirical Orthogonal Functions (DINEOF) was used toreconstruct the original MODIS data. The approximation and detailed components from the originalseries of the MODIS Chl-a data were considered to be a source of the stationary level and anomalousvariability of Chl-a, respectively. The stationary level of the Chl-a concentration was characterized by theChl-a concentration of the coastal areas that was higher than that of the open ocean area, as well asmonthly, seasonal and annual averaged Chl-a concentrations concentrating on between 0.05 and0.25 mg m�3. The anomalous variability of Chl-a has a short-oscillating period of 0.5 years; specifically,the Chl-a negative amplitude occurred in spring and autumn, and the positive amplitude was recorded inwinter and summer. Furthermore, a long-oscillating period of four years, that is, the inter-annualsingularity of Chl-a, primarily appeared in May 2003, May 2007 and May 2011. The maxima of the Chl-aconcentration were dominated by between 0.5 and 1 mg m�3. The peak winter Chl-a concentration wasmainly located in the open ocean area, and the peak summer Chl-a concentration was mostly limited tothe coastal region. This study suggests that a wavelet transform is promising for detecting the anomalousand stationary variability of ocean parameters.

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1. Introduction

The phytoplankton pigment Chl-a concentrations are consideredan important indicator of eutrophication in marine ecosystems thatmay affect human life (Smith 2006; Werdell et al., 2009). Thedescriptions of temporal and spatial distribution of Chl-a concen-trations have always been the subjects of interest in seas and oceans(Werdell et al., 2009; Xu et al., 2011; Zhang, et al., 2011a, 2011b).At present, the development of effective methodologies to analyzethe spatio-temporal data is one of the most challenging issuesfacing the remote sensing community (Bruzzone et al., 2003).A small number of researchers have attempted to analyze thespatial and temporal variations of Chl-a based on statisticalapproaches using in situ measured data and oceanic remotely

sensed data, such as the Moderate Resolution Imaging Spectro-radiometers (MODIS) Chl-a product, SeaWIFS (Yoder et al., 2001;Carder et al., 2004; Uz and Yoder, 2004; Zhang et al., 2006;Mendonca et al., 2010; Williams et al., 2013; Brickley and Thomas,2004; Iida and Saitoh, 2007). Several of these studies focused onanalyzing the spatial variation or temporal variability of Chl-a bytreating the spatial and temporal scales of variability separately.Other studies focused on synchronously analyzing the spatio-temporal variability of Chl-a by applying several methods, such asthe Empirical Orthogonal Function (EOF), which decomposes thespatio-temporal variability into a set of orthogonal functions(spatial maps) and the corresponding principal components (timeseries) (Brickley and Thomas, 2004; Iida and Saitoh, 2007). Thesestudies focused on identifying the dominant spatial and temporalpatterns of Chl-a concentrations. However, little research has beenpublished on the irregular, abnormal or non-stationary variation ofChl-a concentrations, as well as the dominant or stationary spatial-temporal variation of Chl-a. Studies of abnormal Chl-a variations

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Continental Shelf Research

0278-4343/$ - see front matter & 2014 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.csr.2013.12.010

n Corresponding author. Tel.: þ86 1082321276; fax: þ86 1082322095.E-mail addresses: liumeiling427@126.com (M. Liu), liuxncugb@163.com (X. Liu).

Continental Shelf Research 75 (2014) 15–27

can highlight the localized abrupt changes or discontinuities thatresult from disturbance events, such as El Nino-Southern Oscilla-tion. While the stationary level of Chl-a can provide averagepatterns, such as the seasonal mean, monthly mean and annualmean, it essentially acts as a smoothing procedure by eliminatingnoise and anomalous variations. Therefore, the detection of sta-tionary level and anomalous variations of Chl-a is essential to obtaina comprehensive understanding of the spatial-temporal variationsof Chl-a.

The wavelet transform (WT) is well known to hold consider-able promise in the analysis of non-stationary and stationaryvariations of Chl-a concentration; it can provide more insight intothe structure and the observed variability of Chl-a concentrations.Recently, WT has been explored for use in time series analysis,such as the El Nino-Southern Oscillation (Torrence and Compo,1998; Belonenko, 2005), atmospheric sciences (Gamage andBlumen, 1993; Domingues et al., 2005; Andreo et al., 2006) andtemperatures (Baliunas et al., 1997) and hydrology (Smith et al.,1998; Saco and Kumar, 2000; Coulibaly et al., 2000). Waveletsare functions that decompose a complex signal into componentsub-signals. When applied to Chl-a data, wavelets can revealthe temporal variation in the amplitudes and phases of the hiddenperiodicities, as well as the drift in the dominant periods (Nakken,1999). WT has not only been used to analyze stationary changes,it also showed an advantage in the detection of highly non-stationary or abnormal processes of Chl-a concentrations whenused for Chl-a time series. Recently, several researchers haveapplied WT to help identify the cyclic variations of Chl-a concen-trations. For example, Zhang et al. (2012) analyzed the possiblecycle and relationships of chlorophyll concentration in the areasurrounding the Bohai Bay area, Yangtze River Delta Region andSouth China Sea. Bashmachnikov et al. (2013) identified the intra-annual and inter-annual non-stationary cycles of chlorophyllconcentrations in the Northeast Atlantic using SeaWiFS, MODISand MERIS data. The above studies demonstrated the potential ofwavelet analysis for identifying the change cycle of Chl-a concen-trations in the study area. The studies focused on analyzing thewavelet cycle (such as the intra- and inter-annual variations) ofChl-a concentrations by applying the zonally or locally averagedtime series data. In fact, the preferable wavelet cycle of Chl

variability depends on the area of the ocean. In this study, waveletanalysis was explored to analyze the spatio-temporal stability andabnormality of Chl-a using the decomposed signals of high-frequency and low-frequency components.

2. Description of study area and data

2.1. Study area

The study area was in the Northern South China Sea (NSCS) andcovered an area from 171N to 241N and 1091E to 1211E. Themainland of China was located to its north and northwest sides,Taiwan Island was located to its northeast, Luzon Island waslocated to its southeast and Hainan Province was located to itssouthwest (Fig. 1). The South China Sea (SCS) is the largestmarginal sea in the Southeast Asia tropics that connects to thePacific Ocean and the Indian Ocean, with a total area of 3.5 millionkm2 extending from the equator to 221N. It has wide continentalshelves from the northwest to the south, extensive runoff fromseveral large rivers, such as the Mekong River and the Pearl Riverand a deep basin reaching 4700 m deep. Furthermore, the SCS iswithin the East Asia monsoon region, which may change thephysical and chemical environments during different seasons. Inaddition, the SCS is a region of complex circulation mechanisms,hydrological characteristics and ecological structure it may play animportant role in the surrounding environment.

2.2. Data collection

In this study, the daily Aqua-MODIS Level 2 Chl-a images fromJuly 2002 to December 2012 were obtained from the NASA God-dard Space Flight Centre (GSFC) through the following website:⟨oceancolor.gsfc.nasa.gov/cgi/browse.pl?sen=am⟩. The spatial reso-lution of the original images was 1 km. We used monthly averagecomposites because they can provide reasonable coverage of ourstudy site. Therefore, the monthly chlorophyll concentrations werecalculated by taking the arithmetic mean at each pixel of theimages obtained for each month. Shang et al. (2011) demonstratedthat Chl concentrations derived from MODIS in the SCS

Fig. 1. Location of study area.

M. Liu et al. / Continental Shelf Research 75 (2014) 15–2716

appropriately and accurately represent the data and can reveal thespatio-temporal variations of Chl concentrations.

2.3. Data preprocessing

These MODIS images had been processed with the algorithmof the OC3M ocean color algorithm (O’Reilly, 21 co-authors, 2000).Each image was re-projected onto the WGS 84 UTM zone 51 Northcoordinate system using the ENVI 4.6 map projection tool, and there-projected images were clipped to focus on the study region usingAOI (Area of interest) created previously by ArcGIS Desktop 9.3. Wethen masked the cloud cover using the Model Maker tool andgenerated composites with multi-temporal images belonging to eachmonth using Mosaic tool by ERDAS IMAGINE 9.2. Monthly Chl-acomposites were formed by arithmetically averaging all daily

available scenes for each month on a pixel-by-pixel basis. Somestudies have demonstrated that MODIS overestimated the Chl-avalue in the turbid coastal waters of NSCS due to interferences fromsuspended sediments or colored dissolved organic matter (Zhanget al., 2006; Shang et al., 2011). Therefore, the shallow sea area in thestudy area that were lower 50 m in depth were discarded in thisstudy because the turbidity is high in shallow areas (o50 m).

3. Method

The methodology to study the spatio-temporal stability andabnormality of Chl-a using the WT was divided into three steps,as illustrated in Fig. 2. First, a series of complete spatio-temporalinput maps were obtained by reconstructing the missing data

Fig. 2. Flow chart of the methodology used for studying the spatio-temporal stability and abnormality of Chl-a analysis.

M. Liu et al. / Continental Shelf Research 75 (2014) 15–27 17

based on the data interpolation method. Second, the WT wasapplied to the MODIS image series to decompose the originalseries into approximation coefficients and detail coefficients series.The third step consisted of deriving information from a seriesof different key features. The detailed procedure was defined asfollows.

3.1. DINEOF

Due to the cloud coverage of the MODIS images over the NSCS,MODIS pixel values are missing over the entire time series. Thewavelet analysis generally requires a complete time series of inputmaps without data voids. Therefore, a method to reconstruct missingdata based on the Data INterpolating Empirical Orthogonal Functions(DINEOF) decomposition was applied to a large data set to obtaincomplete Chl-a data. The DINEOF reconstruction method was pre-sented by Beckers and Rixen (2003) and used by Beckers et al. (2006).It is a self-consistent, parameter-free technique to reconstruct gaps indata that does not require this type of a-priori information. Recently,DINEOF has been widely used to reconstruct incomplete oceano-graphic data sets (Alvera-Azcarate et al., 2005; Miles and He, 2010;Volpe et al., 2012). This technique presents some advantages overmore classical approaches (such as optimal interpolation), especiallywhen working on Chl-a data sets (Miles and He, 2010). Chlorophyll iswell known to be characterized by different scales of variability anddifferent background concentrations in coastal or open ocean areas.This method identifies dominant spatial and temporal patterns inChl-a data sets and fills in missing data. Thus, DINEOF was applied toreconstruct the missing Chl-a data in this study.

3.2. Discrete wavelet analysis

In essence, the wavelet transform method decomposes a signalat different spatial or time scales onto a set of basis functions. WTis performed using dilated and shifted versions of the motherwavelet, ψ ðλÞ, to produce a set of wavelet basis functions,ψ s;bðλÞ

� �, by the following equation (Eq. (1)):

ψ s;bðλÞ ¼1ffiffis

p ψλ�bs

� �ð1Þ

where s represents the scaling factor and b the shifting factor, thenormalization, 1=

ffiffis

p, ensures that the wavelets all have the same

energy at every scale. Let the discrete signal be a vector of Nobservations, f ðtiÞ, i¼1…N, where ti ¼ t0þ iΔt, t0 is an offset, andΔt is the sampling period. The discrete wavelet transform (DWT)of Chl-a concentration, f ðtiÞ, in ti is given by (Eqs. (2) and (3)):

Wf ðj; kÞ ¼ ⟨f ;ψ j;k⟩¼Z þ1

�1f ðtiÞs� j=2ψ ðs� jti�bkÞdti ð2Þ

Wj;k ¼ ⟨f ðtiÞ;ψ j;kðtiÞ⟩ ð3ÞIf DWT is implemented using a dyadic wavelet, the basis

function is given by (Eq. (4)):

ψ j;kðtiÞ ¼ 2� j2ψ ð2� jti�kÞ ð4Þ

where Wj;k is wavelet coefficient, j is the jth decomposition levelor step and k is the kth wavelet coefficient at the jth level. Thewavelet coefficients provide information on temporal change inthe contributions of various periodic components (Bashmachnikovet al., 2013). In practice, a DWT is implemented using a fastalgorithm based on pairs of high pass and low pass filters. DWThas been defined to analyze the signals with a smaller set of scalesand specific number of translations at each scale. In DWT, thesignals are decomposed into a hierarchical structure of detail andapproximations at limited levels. The wavelet approximation, ðaÞ,and detail, ðdÞ, coefficient vectors are generated at each level of

decomposition (denoted by subscript numbers) of an originalsignal, f ðtiÞ. An inverse discrete wavelet transform can accuratelyreconstruct the original signal because all of the information in theoriginal signal is contained in the approximation coefficients at aparticular level in addition to the detail coefficients at that leveland previous levels (Eq. (5)).

f ðλÞ ¼ ajðtiÞþ ∑j

i ¼ 1diðtiÞ ð5Þ

Here, the approximation coefficient, aj, represents the high-scale, low-frequency components of a signal and is associated withaverages over scales, j. Therefore, it can capture the stationarylevel of the original signal (Percival and Walden, 2000). Detailcoefficients, dj, represent the low-scale, high-frequency compo-nents. High-frequency components are well suited to detecttransient changes and hence are associated with fluctuations andabnormal variations on a scale, j.

In this study, we used a routine written in MATLAB (TheMathworks Inc.) to apply discrete wavelet decompositions to eachof the Chl-a concentrations. The Morlet and Meyer wavelet havebeen applied to study the periodic variations and fluctuations ofclimate and hydrological factors (Domingues et al., 2005;Belonenko, 2005; Andreo et al., 2006; Belonenko et al., 2009).In this study, several mother wavelets have been tested. TheDaubechies wavelet (‘db5’) yielded a satisfactory result. Specifi-cally, ‘db5’ was chosen due to (i) its regularity condition thatassures the smoothness of the reconstructed signal, (ii) a pureperiod suitable to analyze the stability and abnormality and(iii) the ease of implementation and its low cost computation.Table 1 shows the period and semi-period corresponding to thedifferent decomposition scales for the db5 wavelet, with thecenter frequency of the wavelet in Hz (νc¼0.6667) and monthlyanalyzed period (as in our Chl-a time series).

3.3. Key parameters of spatio-temporal variations

To obtain successful results for the spatio-temporal variationsof Chl-a from a time series of MODIS images, the scale andfrequency of DWT should be considered. Here, the monthly area-averaged Chl-a concentrations from a shallow continental shelf(50–200 m) and sea areas deeper than 200 m between 2002 and2012 were estimated by wavelet transform using Daubechies5 mother wavelets (db5) (Fig. 3). For all possible decompositionlevels, the behavior of the wavelet approximation coefficients anddetail coefficients for 126 monthly Chl-a at levels j¼1, 2, 3, 4,5 were denoted by a1, a2, a3, a4, a5 and d1, d2, d3, d4, d5.

Fig. 3 clearly shows how the DWT decomposes the originalsignal into its large-scale approximation coefficients (aj) and fine-scale detail coefficients (dj). In general, the large-scale waveletcoefficients present a global view of the signal, and those of thefine-scale present more detailed information of the signal. There-fore, the stationary level of the signal is given by the approximationseries related to the low-frequency components (left panel inFig. 3). This behavior can be observed by comparing the a2 and

Table 1Period (p) and semi-period (p/2) of different decomposition levels using db5wavelet function and a monthly sampling period.

Level (j) Scale (a) P (Days) p/2 (Days)

1 2 90 452 4 180 903 8 360 1804 16 720 3605 32 1440 720

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a4 components. For the a2 component, the absolute maxima in thewinter and a minimum in the summer are observed with a semi-period of 90 days. For the a4 component, only the long-termvariation of the Chl-a concentration is observed. Similarly, the noiseor abnormal signal is retained in detail series related to the high-frequency components (right panel in Fig. 3). The detail compo-nents allow us to separately analyze the different contributions ofthe abnormal variation. The first levels of the detail componentscontain the high-frequency part of the signal, thus enabling theidentification of short-duration local features or the noise in thesignal. The high levels contain the low frequency part. For instance,only the modulus of extreme maxima can be observed in the d4component (with a semi-period of 360 days), which indicated theinter-annual abnormality of the Chl-a concentration during theconsidered period (i.e., 11 years).

According to Table 1, the p/2¼90 days for a¼22 and p/2¼360days for a¼24; the approximation components for level 2 a2 andlevel 4 a4 provide information about the seasonal level and annualaverage level over the considered period (i.e., 11 years). For level 2,the a2 in the seasonal average value during the period was selectedas the monthly level because the seasonal level included the monthly

level and the first level included temporal noise. The first two levels(d1þd2) and the fourth level (d4) were considered to analyze themonthly fluctuation and inter-annual abnormity, respectively.

According to above analysis, the feature parameters wereselected to study the stationary level and abnormal variation inthe temporal-spatial distribution of Chl-a concentration, such asChlaM , ChlaQ and ChlaY , which are related to the stationary level ofChl-a, and ChlaS, ΔChla, TChlamax and Chlamax, which are viewedas indictors of Chl-a abnormality (see Table 2).

The first category parameters related to the stationary level ofChl-a concentration was defined as follows.

(1) ChlaM , monthly average Chl-a indicated the monthly station-ary level, ChlaM ¼ a2 , a2 refers to the mean of the approxima-tion components of level 2 and can be regarded as the mean ofmonthly Chl-a during the period.

(2) ChlaQ , seasonal average Chl-a, which refers to the mean ofChlaM in spring (3, 4, 5), summer (6, 7, 8), autumn (9, 10, 11)and winter (12, 1, 2) during the period.

(3) ChlaY , annual average Chl-a indicated the annual stationarylevel, ChlaY ¼ a4; a4 refers to the mean of the approximation

Fig. 3. Wavelet transform for the Chl-a time series using the db5 wavelet function.

M. Liu et al. / Continental Shelf Research 75 (2014) 15–27 19

components of level 4 and can be considered as the smoothingof the original Chl-a series.

The second category parameters related to the abnormalvariation of Chl-a concentration was defined as follows.

1) ΔChla, monthly fluctuation level. According to Eq. (5), thesignal has a low-pass filtered component a2 in the secondlevel of the decomposition, f(t)¼a2þd1þd2, which indicatedthat the stationary level and thus the relationship d1þd2¼ f

(t)�a2 provides information about the portion of the signalthat can be attributed to the monthly fluctuation level.

2) Inter-annual singularity level (ChlaS): according to the theory ofthe maximum modulus (Peng et al., 2007), the modulus of thenoise signal decreased, while the modulus of the singularitysignals increased as the decomposition scales (level) of thewavelet coefficients increased. Therefore, the extreme max-imum modulus of d4 refers to the inter-annual singularity level,and it can serve to detect abrupt changes.

3) Chlamax and TChlamax refer to the maximum Chl-a and thetiming of the maximum Chl-a; they are considered an important

Table 2Key features computed from the wavelet transform to study stationary level and anomalous variability of Chl-a.

Status Parameter Description Meaning Formula

Stationarystatus

ChlaM Monthly averaged Chl-a concentration Mean of monthly Chl-a during the period ChlaM ¼ a2

ChlaQ Mean of four seasonal Chl-a ChlaQ ¼ a2 Mean of Chl-a for spring (3, 4, 5),summer (6, 7, 8),autumn (9, 10, 11)and winter (12, 1, 2) during the period

ChlaQ ¼ a2

ChlaY Mean of the inter-annual component ChlaY ¼ a4 Mean of Chl-a during the period ChlaY ¼ a4

Abnormalstatus

ChlaS Extreme value of Chl-a corresponding to the d4component

Singularity value of Chl-a during the period –

ΔChla Variability of Chl-a corresponding to the sum of thedetail component until level two

Amplitude of monthly Chl-a ΔChla¼d1þd2

TChlamax Timing of the maximum Chl-a corresponding tooriginal data

Data of maximum Chl-a –

Chlamax Maximum Chl-a corresponding to original data maximum Chl-a –

Fig. 4. Monthly averaged Chl-a concentration during 2002–2012 from MODIS image. (Note: the high Chl-a concentration area in the central part is associated with PratasIsland).

M. Liu et al. / Continental Shelf Research 75 (2014) 15–2720

feature to characterize the Chl-a dynamics because they arerelated to the external environment factor.

4. Result and discussion

4.1. Stationary level of Chl-a

4.1.1. Monthly average Chl-aThe distribution of monthly averaged Chl-a concentration from

2002 to 2012 is shown in Fig. 4. This figure shows that the Chl-aconcentration was relatively low in the open ocean of the NSCS,and high Chl-a concentrations were limited only to a narrow bandalong the coast. The spatial distribution of Chl-a in NSCS ispredominantly influenced by the oceanic topography and thecoastal upwelling (Tang et al., 2003; Zhang et al., 2006). FromNovember to February, the relative higher values of the meanconcentration (40.25 mg m�3) extend to the open ocean. DuringMarch–October, the mean Chl-a value decreased distinctly off-shore from the coastal area; namely, Chl-a (40.25 mg m�3)remained connected to the coast, and the lower Chl-a(o0.25 mg m�3) appeared in the open ocean of the NSCS andmaintained a stable distribution. Fig. 7a shows the variation of themonthly area-averaged Chl-a concentrations over time. The mean

value of the entire study area decreased from January to July andthen increased from August to December. The area was maximizedin January and minimized in July. Table 3 shows the percentage ofthe average monthly Chl-a concentrations by area for each con-centration classification. The maximum value of Chl-a concentra-tion coverage area (0.25 and 0.45 mg m�3) was observed betweenDecember and January. In the ten other months, the maximumcoverage area of Chl-a concentration was between 0.05 and0.25 mg m�3. In oligotrophic subtropical water, the in-situ Chlconcentrations were under 0.25 mg m�3 most of the year, whichagreed with previous results (Vedernikov and Demidov, 1999;Mendonca et al., 2010). In addition, approximately 90% of the areashowed concentrations from 0.05–0.25 to 0.25–0.45 mg m�3

each month.

4.1.2. Seasonal average Chl-aThe distribution of the 11-year seasonally averaged Chl-a concen-

tration during the study period is shown in Fig. 5. This figure clearlyshows that the variability of Chl-a was spatially and seasonally large.High Chl-a concentrations were observed in winter, and low Chl-aconcentrations were found in spring, summer and autumn. The areawhere the Chl-a concentration exceeded 0.25 mgm�3 was wider inthe winter than in the other seasons, due to the phytoplankton bloomin the SCS off Luzon in thewinter (Tang et al., 1999). The distribution ofChl-a concentration did not significantly differ in spring, summer andautumn. Fig. 7b presents the variation of seasonal area-averaged Chlconcentrations over time. The area-averaged Chl-a concentrationdecreased from spring to summer and then increased from summerto winter. The area-averaged Chl-a concentration was maximized inwinter and minimized in summer, which agreed with the results of aprevious study (Tang et al., 2003). Table 4 shows the averaged seasonalChl-a concentrations by area (%) for each concentration classification.These values show a seasonal pattern for almost each concentrationcategory. The coverage area of Chl-a concentration was maximizedbetween 0.05 and 0.25 mgm�3, irrespective of the season. In addition,the regions containing 0.05–0.25 mgm�3 increased from spring tosummer and then decreased. The second maximum value in Chl-aconcentration coverage area was between 0.25 and 0.45 mgm�3.In the area containing 0.25–0.45 mgm�3 distributed along the coastal

Fig. 5. Seasonal averaged Chl-a concentration during 2002–2012 from MODIS image.

Table 3The area percentage of monthly averaged Chl-a concentration in each concentra-tion classification during 2002–2012.

Month o0.05 0.05–0.25 0.25–0.45 0.45–0.65 0.65–0.85 40.85

1 0.00 36.39 53.21 4.51 2.23 3.652 0.00 61.32 31.22 3.00 1.88 2.583 0.01 81.04 13.78 1.93 1.39 1.854 0.01 85.34 10.09 1.70 1.28 1.575 0.04 88.36 7.15 2.01 1.11 1.326 0.30 88.12 6.72 2.60 0.94 1.327 0.93 86.40 7.00 3.12 1.10 1.458 0.35 86.41 7.08 3.38 1.22 1.569 0.03 84.99 7.49 4.04 1.72 1.72

10 0.01 79.47 11.44 3.80 2.63 2.6511 0.00 56.34 33.42 4.13 2.48 3.6312 0.00 42.30 47.01 4.48 2.35 3.86

M. Liu et al. / Continental Shelf Research 75 (2014) 15–27 21

area in spring, summer and autumn and extended to the open seaarea in winter, with an area percentage of 45%. The distribution ofseasonal Chl-a concentration was affected by meteorological forces.The NSCS was strongly influenced by the East Asia Monsoon (Ninget al., 2009; Hong et al., 2011; Qiu et al., 2011), and seasonallyreversing monsoon wind played an important role in the hydrologicalfeatures and the general circulation in the study region (Tang et al.,2003, 2011). Thus, the distribution of Chl-a in the study area generallydiffered between the southwest monsoon (generally from late May toSeptember) and the northeast monsoon (generally from November toMarch) (Hong et al., 2011; Zhang et al., 1997).

4.1.3. Annual average Chl-aThe annual cycle of average Chl-a concentrations from 2002 to

2012 is shown in Fig. 6. The spatial variation of the annual averageChl-a concentration resembled that of the monthly average Chl-aconcentration. Fig. 6 shows that the Chl-a concentration wasrelatively low in the open ocean of the NSCS and high in the

coastal areas. The Chl-a concentration in the open ocean of theNSCS was stable, except for nearby Luzon Island. The inter-annualvariations of the Chl-a concentration were higher in the narrowband along the coast. Fig. 7c presents the variation of the zonallyaveraged Chl concentrations over time. Inter-annual variation ofthe Chl-a concentration was relatively subtle, ranging from 0.21 to0.28. The maximum Chl-a concentration occurred in 2010, whilethe minimum Chl-a concentrations were observed in 2002. Table 5shows the coverage areas (%) of the average annual Chl-a con-centrations for each concentration classification. The maximumChl-a concentrations by area were between 0.05 and 0.25 mg m�3,which closely associated with the monthly average Chl-a values.According to the above analysis, the monthly average Chl-aconcentrations were below 0.25 mg m�3 for most of the year.In addition, over 70% of the area showed concentrations of0.05–0.25 mg m�3. The second maximum Chl-a concentrationcoverage areas were between 0.25 and 0.45 mg m�3. With respectto ChlaM , ChlaQ and ChlaY , the distribution pattern for thepercentages of each concentration category were similar; namely,the Chl-a concentration primarily ranged from 0.05–0.25 to 0.25–0.45 mg m�3.

In summary, the Chl-a concentration was stable and couldreveal the stationary level of Chl-a in the study area irrespectiveof the monthly, seasonal or annual cycles. The stationary level ofChl-a in the NSCS was characterized by two points. (1) The averageChl-a concentration decreased from the coastal area to the openarea. (2) The average Chl-a concentration in the NSCS primaryranged from 0.05 to 0.25 mg m�3. The ability of low-frequencycomponents of a signal to reveal the stationary level of Chl-a in

Fig. 6. Annual averaged Chl-a concentration during 2002–2012 from MODIS image.

Table 4The area percentage of seasonal averaged Chl-a concentration in each concentra-tion classification during 2002–2012.

Season o0.5 0.05–0.25 0.25–0.45 0.45–0.65 0.65–0.85 40.85

Spring 0.00 85.32 10.01 1.79 1.30 1.57Summer 0.34 87.06 7.01 3.12 1.06 1.42Autumn 0.00 79.88 11.15 3.80 2.60 2.57Winter 0.00 45.81 44.60 4.03 2.27 3.27

M. Liu et al. / Continental Shelf Research 75 (2014) 15–2722

spatial-temporal variations was validated by comparing the meanand S.D. of Chl-a concentrations derived from low-frequencycomponents and from the original data (Fig. 7). Fig. 7 indicatesthat the mean values before and after WT showed similar valuesand a similar distribution tendency. And the SD values before andafter WT were also similar. This finding indicated that theapproximation coefficients of WT can illustrate the stationary levelof Chl-a in the study area. In addition, the time variation curves forthe data were smoother after WT than before WT, which suggeststhat the approximation coefficients of WT better reflected thestationary level of Chl-a compared to the original signal becausethe approximation component was not affected by the “noise”introduced by the original data. This uncertainty has been shownto be much smaller when using the approximation componentseries, which performs as a smoothing procedure and eliminatesanomaly variations in the signal under certain scales (Martinezand AmparoGilabert, 2009).

4.2. Anomalous variability of Chl-a

4.2.1. Monthly fluctuation levelThe distribution of the monthly fluctuation level of the Chl-a

concentration during the study period is shown in Fig. 8. Fig. 8 showsthat ΔChla was different between months. The value of ΔChla waspositive in January, February, March, June, July, August and Decemberfor most regions. However, the value of ΔChla was negative in April,May, September and October for most regions. In addition, the valueof ΔChla in each month was concentrated between �0.2 and0.2 mgm�3, which widely distributed in the NSCS, while the otherconcentration classifications always scattered in the study area.The detail statistics for the area coverage of ΔChla for eachconcentration classification is shown in Table 6. Over 90% of thearea showed concentrations between �0.2–0 and 0–0.2 mg m�3

each month. Table 6 shows that the negative and positive amplitudesof the Chl-a concentration alternated. Specifically, the Chl-a ampli-tude was is positive (ranging from 0 to 0.2) from December to Marchand from June to August for most of the region. However, the Chl-aamplitude was negative (ranging from �0.2 to 0) from April to Mayand from September to November for most of the region. Theoscillating period of the wavelet was generally obvious. The monthlyfluctuation trend is regular with a period of approximately 6 months.The Chl-a positive amplitude occurred in winter and summer,whereas the Chl-a negative amplitude was recorded in spring andautumn. This pattern indicated that the original chlorophyll concen-tration was higher in winter and summer and lower in spring andautumn over this time period. These results agree with a previousstudy (Zhang et al., 2012).

4.2.2. Inter-annual singularity levelAccording to above discussion, the timing of d4 modulus

extreme maximum was regarded as the inter-annual singularity.

Fig. 7. Comparison of monthly, seasonal and annual area-averaged Chl-concentrations between before-WT and after-WT.

Table 5The area percentage of annual averaged Chl-a concentration in each concentrationclassification during 2002–2012.

Year o0.05 0.05�0.25 0.25�0.45 0.45�0.65 0.65�0.85 40.85

2002 0.00 84.50 8.40 3.75 1.61 1.742003 0.01 84.60 9.62 2.87 1.45 1.442004 0.01 83.86 9.77 3.27 1.57 1.522005 0.01 83.61 9.67 3.31 1.81 1.592006 0.02 82.51 11.44 2.78 1.47 1.782007 0.01 78.84 12.71 2.94 2.57 2.942008 0.03 78.77 12.68 3.89 2.46 2.172009 0.01 77.33 13.92 4.44 2.10 2.212010 0.01 71.77 18.90 3.90 2.24 3.192011 0.01 82.91 10.44 3.12 1.60 1.922012 0.01 84.29 9.05 2.97 1.76 1.92

M. Liu et al. / Continental Shelf Research 75 (2014) 15–27 23

When it refers to temporal-spatial singularity level, the distribu-tion of abnormal Chl-a concentration over the same period is wideand continuous in space. In this study, ChlaS was defined as thepercentage of abnormal Chl-a concentration by area greater thanone-fourth of the study area. The singularity level of the temporal-spatial distribution of Chl-a concentration is displayed in Fig. 9.The abnormal chlorophyll concentrations appeared in May 2003,May 2007, May 2011, January 2012 and September 2012. From2002 to 2011, this abnormal pattern followed a 4-year cycle, whichmay be attributed to immediate and delayed responses to the ElNiño Southern Ocean (ENSO) forces. Some studies demonstrated

that the ENSO resulted in abnormal Chl-a chlorophyll concentra-tions in the NSCS (Chao et al., 1996; Ose et al., 1997; Wu et al.,1999; Zhang et al., 1997; Tseng et al., 2009). In addition, thesingularities were recorded in May. The variation of the singularitymay be correlated with East Asia monsoon in the SCS. Where thereis alternating monsoons, namely the southwest monsoon fromMay to early September and the northeast monsoon from lateSeptember to April (Zhang et al., 1997; Hong et al., 2011). That is tosay, there is abrupt change for monsoons orientation in May.However, two singularities appeared within 8 months in 2012,which may have been affected by the continuity of the anomalousChl-a concentration in 2011. The Chl-a inter-annual singularity iscomplex over longer time scales. Chl-a singularity may be asso-ciated with anthropological environmental changes, climate oscil-lations, climate change and extreme atmospheric anomalies, suchas the heat wave in 2003 and the abnormal sea surface tempera-ture in 2007 (Volpe et al., 2012).

Fig. 9a–e shows that the singularity level of the Chl-a concen-tration primarily ranged from �0.1 to 0.1 over the study period.Fig. 9f shows the statistics of the singularity value in detail. Themean chlorophyll concentration was lowest in May 2007 andhighest in January 2012, when Chl-a concentration ranged from0 to 0.1 mg m�3. The coverage area of the Chl-a concentrationsingularity was maximized in May 2011 and minimized in January2012. In summary, the coverage area, mean and standard devia-tions of the Chl-a concentration singularity differed in the NSCS fordifferent months. The Chl-a concentration singularity was spatiallyand temporally large.

Fig. 8. Monthly fluctuation level of Chl-a concentration during 2002–2012 from MODIS image.

Table 6The area percentage of monthly fluctuation level for Chl-a concentration in eachconcentration classification during 2002–2012.

Month o�0.4 �0.4–0.2 �0.2–0 �0–0.2 0.2–0.4 40.4

1 0.17 0.70 11.39 86.97 0.54 0.222 0.14 0.29 36.71 62.30 0.25 0.313 0.13 0.12 19.99 78.64 0.97 0.154 0.15 0.46 92.46 6.45 0.39 0.105 0.22 1.31 92.24 6.08 0.07 0.086 0.11 0.17 46.45 52.38 0.66 0.217 0.09 0.41 10.20 84.32 3.75 1.238 0.29 1.19 21.56 76.80 0.09 0.079 0.21 1.02 57.17 40.79 0.56 0.23

10 0.19 0.71 87.86 9.65 0.75 0.8411 0.16 0.51 93.32 5.82 0.13 0.0712 0.18 0.56 22.41 75.83 0.87 0.14

M. Liu et al. / Continental Shelf Research 75 (2014) 15–2724

4.2.3. Maximum levelTo investigate the effect of various oceanic environments (such

as upwelling, wind mixing and heat flux) on the abnormally strongChl-a bloom, we calculated the timing of Chl-a maxima andmaximum Chl-a concentration over the 11 years of data in everypixel-point of the study region. The distribution of TChlamax andChlamax during the study period is presented in Fig. 10. Fig. 10shows that TChlamax and Chlamax differed for different parts of thestudy area. Generally speaking, the winter-peaking Chl-a concen-tration was mainly located in the open ocean, and the summer-peaking Chl-a concentration was mainly located in the coastalregion. The spatial differences of TChlamax and Chlamax werepredominantly affected by the hydrological and meteorologicalconditions. The southwest monsoon induces coastal upwellingduring summer in different part of the SCS (Xie et al., 2003; Shanget al., 2004). In addition, the complex hydrological conditions inthe study area considerably influence the phytoplankton growthby affecting the transportation and distribution of nutrient-richwater, such as the coastal water that is discharged from rivers andriverine discharges (Suchint and Puntip, 2000). Thus, the above

factors may result in high phytoplankton biomass during thesummer in coastal areas. For the open ocean, the high chloro-phyll-a concentrations occurred in winter due to the ampleradiation of sunlight and relatively high SST in many tropic marineregions and winter upwelling from the Luzon Strait (Udarbe-Walker and Villanoy, 2001; Penaflor et al.,2007). In summary,TChlamax differed in coastal areas and open ocean areas.

According to the statistics in Fig. 10, the largest area percentageof TChlamax in four seasons decreased in the following order:winter4autumn4spring4summer. The coverage area ofTChlamax was approximately 60% in the winter. The coverage areaof TChlamax was similar in autumn, spring and summer. Thisfinding indicated that the different parts of the study showedmaximum Chl-a concentrations at different times. Fig. 10b indi-cates that the Chlamax image showed the spatial distribution, withhigher values in the narrow band along the coast and nearbyLuzon Strait and lower values in the open ocean of the NSCS. Thelargest coverage area of Chlamax for each concentration classifica-tion was 0.5–1 mg m�3, followed by 1–2 mg m�3, o0.5 mg m�3,2–4 mg m�3, 4–8 mg m�3 and 48 mg m�3. Chlamax was

Fig. 9. Spatial-temporal distribution of Chl-a inter-annual singularity.

Fig. 10. Spatial-temporal distribution of the timing of Chl-a maxima and maximum Chl-a concentration during 2002–2012.

M. Liu et al. / Continental Shelf Research 75 (2014) 15–27 25

concentrated between 0.5 and 1 mg m�3 and showed a coveragearea of 48%, which corresponded to the open ocean regions atrelatively lower values.

5. Conclusion

The wavelet analysis of Chl-a concentrations in the NSCSobtained from the MODIS Chl-a product during 2002–2012revealed the spatio-temporal stability and abnormality of theChl-a concentration. The main goal of this study was to introducea methodology based on the WT to define useful key features tostudy the stationary level and abnormal variability of Chl-aconcentration in the sea. The potential of DWT to split the originalChl-a series into approximate and detail components has beenpreviously shown. These components are considered to be asource of stationary level and anomalous variability, which areassociated with variations of the Chl-a dynamics at different timescales. ChlaM , ChlaQ and ChlaY were derived from approximationcomponents and selected as the key parameters to describe thestationary level of Chl-a. The values of ChlaM , ChlaQ and ChlaY

were higher in the coastal region than in the open oceanic region.ChlaM and ChlaQ ranged from 0.05 to 0.45 mg m�3 and showed acoverage area of 90%. The zonally averaged ChlaM value was higherin winter (12, 1, and 2) than in the summer (6, 7, and 8).In addition, the zonally averaged ChlaM value was maximized inJanuary and minimized in July. ChlaY mainly ranged from 0.05 to0.25 mg m�3 and remained stable in most of the region. ΔChla,ChlaS, TChlamax and Chlamax, which originated from the detailcomponents, were selected as the key parameters to capture theanomalous variability of Chl-a. Two major cycles dominated theChl-a dynamics with periods of 0.5-year and 4-years. Namely,ΔChla showed alternate changes in the positive amplitude (winterand summer) and negative amplitude (spring and autumn).ChlaS mostly appeared in May 2003, May 2007, May 2011, January2012 and September 2012. TChlamax and Chlamax had differentvalues nearly everywhere in the study region. Roughly speaking,the winter-peaking Chl-a concentration was mainly located in theopen ocean, and the summer-peaking Chl-a concentration wasmainly located in the coastal region.

In summary, wavelet analysis is a promising tool to study thespatio-temporal stability and abnormality of Chl-a by decompos-ing the original signal. This technique may offer insight intoinvestigations of the spatio-temporal stability and abnormality ofoceanic parameters.

Acknowledgements

This research was supported by the National Natural ScienceFoundation of China (No. U0933005) and the FundamentalResearch Funds for the Central Universities. The authors wish tothank the anonymous reviewers for their constructive commentsthat helped improve the scholarly quality of the paper.

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