Assessment of satellite ocean color products of MERIS, MODIS and SeaWiFS along the East China Coast (in the Yellow Sea and East China Sea)

ISPRS Journal of Photogrammetry and Remote Sensing 87 (2014) 137–151

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Assessment of satellite ocean color products of MERIS, MODIS and SeaWiFS along the East China Coast (in the Yellow Sea and East China Sea) Tingwei Cui a,⇑, Jie Zhang a, Junwu Tang b, Shubha Sathyendranath c, Steve Groom c, Yi Ma a, Wei Zhao b, Qingjun Song b a

First Institute of Oceanography (FIO), State Oceanic Administration (SOA), Qingdao, China National Satellite Ocean Application Service (NSOAS), SOA, Beijing, China c Plymouth Marine Laboratory (PML), Plymouth, Devon, UK b

a r t i c l e

i n f o

Article history: Received 1 April 2013 Received in revised form 30 October 2013 Accepted 31 October 2013 Available online 5 December 2013 Keywords: Oceanography Comparison Retrieval Algorithm Satellite Accuracy Optical

a b s t r a c t The validation of satellite ocean-color products is an important task of ocean-color missions. The uncertainties of these products are poorly quantified in the Yellow Sea (YS) and East China Sea (ECS), which are well known for their optical complexity and turbidity in terms of both oceanic and atmospheric optical properties. The objective of this paper is to evaluate the primary ocean-color products from three major ocean-color satellites, namely the Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Through match-up analysis with in situ data, it is found that satellite retrievals of the spectral remote sensing reflectance Rrs(k) at the blue-green and green bands from MERIS, MODIS and SeaWiFS have the lowest uncertainties with a median of the absolute percentage of difference (APDm) of 15–27% and root-mean-square-error (RMS) of 0.0021–0.0039 sr1, whereas the Rrs(k) uncertainty at 412 nm is the highest (APDm 47–62%, RMS 0.0027–0.0041 sr1). The uncertainties of the aerosol optical thickness (AOT) sa, diffuse attenuation coefficient for downward irradiance at 490 nm Kd(490), concentrations of suspended particulate sediment concentration (SPM) and Chlorophyll a (Chl-a) were also quantified. It is demonstrated that with appropriate in-water algorithms specifically developed for turbid waters rather than the standard ones adopted in the operational satellite data processing chain, the uncertainties of satellite-derived properties of Kd(490), SPM, and Chl-a may decrease significantly to the level of 20– 30%, which is true for the majority of the study area. This validation activity advocates for (1) the improvement of the atmosphere correction algorithms with the regional aerosol optical model, (2) switching to regional in-water algorithms over turbid coastal waters, and (3) continuous support of the dedicated in situ data collection effort for the validation task. Ó 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

1. Introduction By exploring the spectral signal measured by space-borne ocean-color instruments with dedicated atmosphere correction and in-water bio-optical algorithms, various ocean and atmosphere parameters may be retrieved. The primary data products provided by ocean-color satellites in an operational manner include ocean spectral remote sensing reflectance Rrs(k), aerosol optical thickness (AOT) sa, diffuse attenuation coefficient for the ⇑ Corresponding author. Address: Ocean Physics and Remote Sensing Department, First Institute of Oceanography, State Oceanic Administration of China, 6 Xianxialing Road, High-Tech Park, Qingtao 266061, China. Tel.: +86 532 88896419; fax: +86 532 88967761. E-mail addresses: [email protected], cuitingwei@fio.org.cn (T. Cui).

downwelling irradiance at 490 nm Kd(490), concentrations of Chlorophyll (Chl-a), and suspended particulate matter (SPM). These data products have been extensively applied in the research of climate change (Behrenfeld et al., 2006), biogeochemical cycles (Stramski et al., 2007), fishery (Platt et al., 2003), water quality (Hu et al., 2010), and marine ecosystems (Platt and Sathyendranath, 2008). Quantification of the uncertainties of the satellite ocean-color products through extensive validation activities has been identified as one of the vital and indispensable tasks for the ocean-color missions (Hooker and McClain, 2000). Most of these activities, from the satellite perspective, have been conducted for individual or specific missions (e.g. Gregg and Casey, 2004; Bailey and Werdell, 2006; Marrari et al., 2006; Aiken et al., 2007; Cui et al., 2010a;

0924-2716/$ - see front matter Ó 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.isprsjprs.2013.10.013

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Mélin et al., 2011), and only a small number of these studies have addressed the comparison of ocean-color products by different missions (Antoine et al., 2008; Blondeau-Patissier et al., 2004; Mélin et al., 2007a,b, 2010; Morel et al., 2007; Zibordi et al., 2004, 2006, 2009a, 2011). From the geographic perspective, the validation activities in coastal waters are confronted with much larger uncertainties for the optical complexities of ocean and atmosphere than those of open oceans. Along the East China Coast there are two regional seas, namely the Yellow Sea (YS) and East China Sea (ECS), which are abundant in numerous oceanographic phenomena and processes. Some of these include the Yangtze River diluted water, Kurshio current (Jian et al., 1998; Liu and Gan, 2012), radiative sand ridge shaped by strong tidal current (Wang et al., 1999), oxygen depletion area off the Yangtze River estuary (Li et al., 2002; Zhu et al., 2011), massive algal (macroalgae) bloom or green tide (Sun et al., 2008a; Ye et al., 2008), and cross-shelf front (Yuan et al., 2008). In most of these studies, satellite ocean-color data from the Coastal Zone Color Scanner (CZCS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS) have played an important and unique role (Hu et al., 2010; Jia and Zhang, 2010; Shi and Wang, 2009; Yuan and Hsueh, 2010; Cui et al., 2012). As a result of the strong terrestrial influence (dust aerosol, black carbon aerosol, river discharge) and complicated hydrographic dynamics (Liu and Zhou, 1999; Li et al., 2006; Zhao et al., 2006), the ocean and atmosphere in the YS and ECS have exhibited complex optical properties (Li et al., 2003a; Song and Tang, 2006; Tang et al., 2004; Zhao et al., 2005). For this particular ocean–atmosphere system, validation of the primary satellite ocean-color products by the major missions is critical to further enhancing our understanding of its unique features and processes. However, due to the limited validation studies (e.g. Dong et al., 2007; Sun et al., 2008b; Li and Chen, 2010) during the lifespan of SeaWiFS and MERIS sensors, science communities interested in regional oceanography possess only a very limited range of information concerning the uncertainty level of the satellite ocean-color products, as a result of which the satellite data application research has been adversely affected. In this paper, with the high quality in situ bio-optical dataset, the uncertainties of the major ocean-color products of MERIS, MODIS and SeaWiFS in the YS and ECS were evaluated and compared, including Rrs(k), sa, Kd(490), SPM and Chl-a. In addition, the sources of the uncertainties, as well as the implications for the improvement of atmospheric correction algorithm, and the development of the in-water retrieval algorithm and regional oceanography application are discussed.

2. Study area The YS and ECS are the marginal sea of the northwest Pacific Ocean, and are bordered by mainland China from the west, Taiwan Island from the south and the Korea Peninsula from the north-east (Fig. 1a). The area is linked with the Bohai Sea by the Bohai Strait from the northwest, and with the South China Sea by the Taiwan Strait from the southwest. The average water depths of the YS and ECS are about 40 m and 1000 m, respectively. The circulation in this area is mainly influenced by the Kuroshio Current, a famous western boundary current of the north Pacific subtropical gyre (Yuan et al., 2008). The Yangtze River, the largest river in the region, carries not only large amounts of freshwater but also very large amounts of suspended sediment to this location; therefore the coastal water near the estuary is well known for its extremely high turbidity (Wang et al., 2007; Wang and Shi, 2007).

Atmospheric optical properties here are complex, as a result of terrestrial influence (e.g. dust and black carbon) and the Asian monsoon (Liu and Zhou, 1999). The atmospheric correction of satellite ocean-color images in this area is further complicated by a complex mixture of aerosols, which are characterized by their absorption features (Mélin et al., 2010).

3. Methods 3.1. In situ data The in situ bio-optical data were acquired in the ocean optics experiment conducted in spring (March–April) and autumn (September), 2003. The locations of the sampling stations (150 in total) for the two seasons were similar in terms of spatial distribution (Fig. 1b) as well as the histogram of water depth (Fig. 1c). These data are regarded as the most comprehensive, high quality bio-optical observation results along the China Coast (Tang et al., 2004) and have been widely adopted by the ocean-color communities for studies of ocean-color constituents retrievals (Tang et al., 2004, 2005; Ma et al., 2004, 2005; Sun et al., 2008b; Zhang et al., 2010; Siswanto et al., 2011), ocean inherent optical properties (Song and Tang, 2006; Wang et al., 2005, 2006; Cui et al., 2010b; Qing et al., 2011), aerosol optical properties and atmospheric correction algorithms (Zhao et al., 2005; Wang et al., 2007). The dataset and adopted measurement methodologies have been described systematically and exhaustively by Tang et al. (2004) and Zhang et al. (2010), thus only a brief review of them is given here. The protocol and recommendations stated in the National Aeronautics and Space Administration (NASA) ocean optics protocols (Mueller and Fargion, 2002) were strictly followed. All of the instruments were calibrated before the cruise and all the radiometric stability of apparent optical properties (AOPs) instruments were tracked in situ during the cruise with the SeaWiFS Quality Monitor (Satlantic, Inc., SQM-II). (1) Apparent optical properties (AOPs) Two methods were used, namely the profiling and above-water measurements (Mueller and Fargion, 2002). In waters with high turbidity, the above-water measurement is preferred to determine Rrs(k), which is defined as the ratio of the water-leaving radiance to the downward irradiance above the surface (Z = 0+). The profiling method is believed to have a large error due to the strong absorption (self-shadowing) and difficulty in determining the interval for the near surface extrapolation in the data processing. Two profiling systems (Satlantic SPMR and BioSpherical PRR 800) and two above-water systems (Satlantic SAS-II and ASD FieldSpec Dual VNIR) were simultaneously utilized in the experiment. In the clear or moderately turbid waters, the profiling and abovewater results were matched to within 15% in the visible bands, and the two above-water results were within 10% at most stations. Rrs(k) spectra at major ocean-color bands are shown in Fig. 2, which demonstrates the feature of Case 2 turbid water (Morel and Prieur, 1977), with the reflectance peak at the green band. Kd(k) (defined as the logarithmic derivative of downward irradiance with respect to depth) was determined by the profiling measurements. The downwelling irradiance profiles were used to calculate the Kd(k) by applying an exponential fit over the depth range from subsurface (Z = 0) to the penetration depth Z90(k), defined as the depth at which the downwelling irradiance decreased to e1 of its value at the surface (Gordon and McCluney, 1975). The histogram of the Kd(490) data is shown in Fig. 3, which illustrates that the study area is dominated by waters with Kd(490) in the range of 0.1–0.5 m1, although extremely turbid waters can also be found.

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Water depth / m Fig. 1. (a) Map of the study area. (b) Locations of the in situ sampling sites. The circles and triangles represent the sites in spring and autumn, respectively. (c) Histogram of water depth at these in situ sites.

(2) Ocean-color components’ concentrations

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400

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Wavelength / nm Fig. 2. Rrs(k) spectra at major ocean-color bands (in logarithm scale to accommodate the large magnitude of variability).

For SPM measurements, the water samples were filtered with the 0.45 lm pore size filter and vacuum filtration system. After the water-sample filtering process was completed, the container was flushed again with pure water and the water inside the container. Then the filter pad was flushed with 50 cm3 distilled water three times to remove the salt. The weighting of the dry-weight of filter-pad was carried out using an electronic analytic scale with 0.01 g/m3 accuracy. The blank filter and sampled filter-pad were scaled several times, and two successive weight readings were within 0.01 g/m3. Variability of replicate measurements was approximately 15% (Tang et al., 2004). The histogram of SPM measurements is shown in Fig. 4, which indicates that the typical SPM value in the study area is less than 5 g/m3. For the Chl-a measurements, water samples were filtered through the Whatman GF/F glass microfiber filters with a nominal 0.7 lm pore size, under a vacuum of less than 5  104 Pa. The volumes of the water samples were 200 ml for the mesotrophic areas and 300–500 ml for the relatively clear areas. The filters were analyzed immediately with a Turner fluorometer. Analyses of

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Kd (490) / m-1 Fig. 3. Histogram of in situ Kd(490).

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SPM / g m-3 Fig. 4. Histogram of in situ SPM (in logarithm scale to accommodate the large magnitude of variability).

T. Cui et al. / ISPRS Journal of Photogrammetry and Remote Sensing 87 (2014) 137–151

replicate measurements showed an average difference of about 10%. Typical Chl-a is less than 5 mg/m3, as shown in Fig. 5. High performance liquid chromatography (HPLC) measurements were also performed at each site. The Chl-a data by fluorometry rather than HPLC was adopted, due to the considerable discrepancy found between two independent HPLC measurements. Water samples were filtered through 0.2 lm polycarbonate filters for the measurements of absorption of colored dissolved organic matter (CDOM) ag(k). The filtrates were stored in bottles and refrigerated in darkness before the optical density measurement. Two spectrophotometers (GBC Cintra20, Carry-100) were independently operated by different groups for the CDOM absorption spectra, and nearly identical results from the two groups were obtained (Tang et al., 2004). The histogram of ag(400) is shown in Fig. 6, which indicates that the typical range is 0.1–0.3 m1. (3) Aerosol optical properties

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Aerosol optical thickness (AOT), sa, defined as the integrated extinction coefficient over a vertical column of unit cross-section, was measured by a sunphotometer CIMEL CE317 at the bands of 440, 550, 670, 870 and 1020 nm. In view of the effect of the vessel perturbation on the observation, 15 consecutive measurements were taken as one group and filtered to exclude the abnormal ones during the data processing. The measurement uncertainty was estimated to be ±0.02. After logarithm transformation, AOT measurements were linearly regressed with the wavelength to yield the Angstrom exponent a (Zhao et al., 2005). The median and standard deviation (SD) of sa(870) and a were 0.20 ± 0.39 and 1.05 ± 0.45, the histograms of which are shown in Fig. 7. The statistics of the bio-optical parameters described above as well as water depth are illustrated in Table 1, for the entire dataset as well as the spring and autumn subsets. By comparison it can be seen that the study area is characterized by higher SPM load and more turbid atmosphere in spring relative to autumn, whereas other parameters, e.g. the concentrations of chlorophyll a and CDOM, as well as Kd(490), have lower values in spring than in autumn. 3.2. Satellite data MERIS Level 2 (L2) data products of Reduced Resolution (MER_RR_2P, version MEGS-7.4) were collected from European

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ag (400) / m-1 Fig. 6. Histogram of in situ ag(400).

Space Agency (ESA) in the framework of the DRAGON2 project. Bright pixel (Antoine and Morel, 1999) and neural network (NN) (Doerffer and Schiller, 2007; Schiller and Doerffer, 1999) were respectively adopted as the standard atmospheric correction and in-water algorithms for the Case 2 waters. The MERIS L2 oceancolor products mainly included the water-leaving reflectance qw(k), Chl-a, SPM, absorption of CDOM and detritus, sa as well as the Angstrom exponent a. The water-leaving reflectance qw(k) was converted to Rrs(k) by the following equation:

Rrs ðkÞ ¼ qw ðkÞ=p

ð1Þ

MODIS and SeaWiFS Level 2 products were downloaded from the NASA Goddard Earth Sciences Distributed Active Archive Center. Both sensors adopted the same atmospheric correction scheme (Gordon and Wang, 1994). MODIS data was processed by the operational software program l2gen (version 6.2.5, 2009) and the L2 data consisted of Rrs(k), Kd(490), Chl-a (by OC3 algorithm), sa(869), and a. SeaWiFS data were processed by software l2gen (version 6.1.6, 2009) and its L2 data had Rrs(k), Kd(490), Chl-a (by OC4v4 algorithm), sa(865) and a. 3.3. Match-up between in situ and satellite data

Frequency

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Fig. 5. Histogram of in situ Chl-a.

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For the Chl-a and SPM assessments, the sea surface values measured in situ were compared with the satellite retrievals. For comparison with the satellite Rrs(k), the in situ spectra were integrated by the band response function for each visible bands of MERIS, MODIS and SeaWiFS. In addition, in situ and satellite Rrs(k) spectra were corrected for the bidirectional effects using the widely adopted the look-up table method (Morel et al., 2002). As the Rrs(k) band ratios were usually adopted by bio-optical algorithms, the corresponding uncertainties were also evaluated. The match-up dataset was established according to the procedure adopted by Cui et al. (2010a), which is briefly summarized as follows. Satellite products of the pixels centered on the in situ location are extracted based on 3  3 pixel boxes. For each box, the invalid satellite pixels with negative or overflow values are eliminated and the median and SD of the remaining products are calculated. Outliers are identified as the pixels, whose values are greater than 1.5 times SD and deleted to avoid the consequence of abnormal values on the statistical results. If the number of good pixels is not less than 4, the pixel box is adopted for the match-up

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Fig. 7. Histogram of sa(870) and Angstrom exponent a.

Table 1 Statistics of the in situ datasets for the Yellow Sea and East China Sea. SD, Max, and Min represent the standard deviation, maximum and minimum, respectively.

The statistical parameters for the evaluation include the median of absolute percentage difference (APDm), root-mean-square-error (RMS), median and semi-interquartile range of satellite to in situ ratio (Ratio, SIQR) (Bailey and Werdell, 2006), and determination coefficient (R2). These quantities are calculated as follows:

Median ± SD

Average

Max.

Min.

2.10 ± 148.20 3.56 ± 202.87 1.70 ± 6.21

25.50 44.40 4.46

1762.13 1762.13 23.90

0.50 0.63 0.50

  jy  xi j APDm ¼ median i  100% xi i¼1;2...N

Chl-a (mg/m ) All(N = 150) Spring(N = 79) Autumn(N = 71)

1.32 ± 3.58 1.26 ± 2.70 1.45 ± 4.38

2.39 2.50 2.27

35.91 15.16 35.91

0.21 0.50 0.21

ag(400) (m1) All(N = 143) Spring(N = 77) Autumn(N = 69)

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi PN 2 i¼1 ðyi  xi Þ RMS ¼ N

0.16 ± 0.10 0.15 ± 0.07 0.19 ± 0.11

0.19 0.16 0.22

0. 67 0.42 0.67

0.08 0.08 0.08

Kd(490) (m ) All(N = 140) Spring(N = 71) Autumn(N = 69)

0.26 ± 0.93 0.24 ± 1.21 0.27 ± 0.49

0.57 0.68 0.47

6.79 6.79 1.95

0.06 0.06 0.07

sa(870) All(N = 71) Spring(N = 36) Autumn(N = 35)

0.20 ± 0.39 0.24 ± 0.25 0.14 ± 0.50

0.35 0.32 0.38

2.08 1.30 2.08

0.04 0.07 0.04

Water depth (m) All(N = 150) Spring(N = 79) Autumn(N = 71)

33.5 ± 17.5 35.0 ± 18.0 32.0 ± 16.9

35.1 36.7 33.5

75.0 75.0 74.0

9.5 9.5 10.0

SPM (g/m3) All(N = 150) Spring(N = 79) Autumn(N = 71)

ð2Þ

3

SIQR ¼

ðQ 3  Q 1 Þ 2

ð3Þ

ð4Þ

1

where yi is the satellite-retrieved value, xi is the in situ value and N is number of match-up data. Here, Q3 and Q1 are the third and first quartiles. All the statistics are calculated in the linear scale. APDm and RMS provide the unbiased statistics of how accurately the satellite retrieval agrees with in situ measurements. The Ratio and SIQR provide an indication of the overall bias and uncertainty. 4. Assessment of ocean and atmosphere optical properties 4.1. Rrs(k)

analysis, and the median of the pixels is compared with the corresponding in situ measurements. For all the valid match-ups, the maximal time intervals between the satellite overpass and in situ measurements is less than 6 h. The dates of the satellite images satisfying the match-up criteria are listed in Table 2. Fig. 8 illustrates the spatial distribution of the valid match-ups for MERIS (N = 24), MODIS (N = 22) and SeaWiFS (N = 29) validation, as well as their shared subsets (N = 8). It should be noted that the match-up datasets for the three satellites are not identical (see Table 3 for the comparison between the statistics for the major bio-optical properties). By comparison with the statistics shown in Table 1, it can be seen that these match-ups are typical and representative for the conditions encountered during observation in the study area.

The comparison statistics between the satellite Rrs(k) products and in situ measurements are given in Table 4, and the scatter plots are shown in Fig. 9. For MERIS radiometric products, the APDm and RMS range from 15% to 62% and 0.0011 to 0.0041 sr1 in the visible bands of 412 and 665 nm. Retrievals at 490 and 560 nm have much lower uncertainty (15–25%) than those at the blue and red bands (>45%). As indicated by the parameter ‘‘Ratio’’, MERIS Rrs(k) tends to be overestimated at the blue bands and underestimated at the red bands. The coefficient of determination R2 increases monotonically from the blue to the red. In general, MERIS Rrs(k) retrieval at 560 nm has the highest accuracy among all the bands. Suitable band ratio and addition of Rrs(k) may, to some extent, reduce the uncertainty. Among the various band combinations adopted by the in-water algorithms (Tang et al., 2004; Wang et al., 2005, 2006; Tiwari

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Table 2 Dates of the satellite images of MERIS, MODIS and SeaWiFS concurrent with in situ measurement. Major L2 satellite products are also listed. Dates of satellite images

Products

MERIS March 22nd, 2003 April 3rd, 6th, 9th, 13th, 16th, 2003 September 15th, 16th, 19th, 25th, 2003

qw(k) at 412, 443, 490, 510, 560, 620, 665, 681 and 708 nm; Chl-a, SPM; sa(443), sa(550), sa(865)

MODIS April, 5th, 14th, 15th, 16th, 2003 September 11th, 19th, 23th, 24th, 25th, 2003

Rrs(k) at 412, 443, 469, 488, 531, 547, 555, 645, 667 and 678 nm; Kd(490); Chl-a ; sa(869)

SeaWiFS March 22nd, 2003 April 6th, 15th, 16th, 2003 September 16th, 18th, 19th, 23th, 24th, 25th, 26th, 2003

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Fig. 8. Maps of the valid match-up datasets for MERIS (a), MODIS (b) and SeaWiFS (c) as well as their shared subsets (d).

Table 3 Statistics of the in situ dataset for the match-ups of MERIS, MODIS and SeaWiFS. SD, Max, and Min represent the standard deviation, maximum and minimum, respectively. Median ± SD

Average

Max

Min

MERIS SPM (g/m3) Chl-a (mg/m3) ag(400) (m1) Kd(490) (m1) Water depth (m) sa(865)

1.71 ± 3.53 1.15 ± 3.54 0.14 ± 0.07 0.26 ± 0.31 36.0 ± 15.1 0.17 ± 0.50

3.23 2.78 0.17 0.35 38.3 0.31

14.20 15.16 0. 34 1.32 65.0 2.09

0.63 0.33 0.10 0.07 13.0 0.04

MODIS SPM (g/m3) Chl-a (mg/m3) ag(400) (m1) Kd(490) (m1) Water depth (m) sa(865)

1.60 ± 5.54 1.09 ± 0.92 0.15 ± 0.15 0.24 ± 0.51 32.0 ± 18.1 0.08 ± 0.19

4.95 1.44 0.21 0.46 35.5 0.14

14.90 3.93 0.67 1.95 65.0 0.72

0.63 0.41 0.09 0.06 10.0 0.04

SeaWiFS SPM (g/m3) Chl-a (mg/m3) ag(400) (m1) Kd(490) (m1) Water depth (m) sa(865)

1.72 ± 6.83 1.21 ± 1.90 0.14 ± 0.16 0.27 ± 0.54 32.0 ± 17.9 0.13 ± 0.27

5.77 1.95 0.21 0.52 33.4 0.20

22.86 8.34 0.67 1.95 65.0 1.24

0.50 0.33 0.08 0.06 10.0 0.04

and Shanmugam, 2011; Hu et al., 2012), Rrs(490)/Rrs(560) and Rrs(665) + Rrs(560) have the lowest APDm, at less than 20%. For the MODIS radiometric products, the uncertainties range from 23% to 47% in APDm and 0.0024–0.0032 sr1 in RMS in the visible bands, with the minimum values found at 488 and 555 nm. Underestimation in the red bands of MODIS products is observed

but is not as remarkable as that of MERIS. MODIS Rrs(k) retrievals have more balanced performance throughout the visible domain than those of MERIS. The band combination of Rrs(488)/Rrs(555) and Rrs(667)/Rrs(555) have the highest performance with the APDm of 15%. The uncertainty of SeaWiFS Rrs(k) is in the range of 17–60% and 0.0020–0.0027 sr1, with the best retrieval performance in the bands of 490 and 555 nm. Among the different band combinations, the band ratio of Rrs(490)/Rrs(555) has the highest accuracy of APDm less than 10%. The same features of satellite-retrieved Rrs(k) by MERIS, MODIS and SeaWiFS can be succinctly summarized as follows: (1) Rrs(k) retrievals at 490 and 555 nm have the lowest uncertainties (15–27%), whereas the uncertainty at 412 nm is highest (47– 62%); (2) Suitable bands combinations can yield lower uncertainty than that of a single band, among which Rrs(490)/Rrs(555) performs best with the uncertainty of 10–15%. In order to further compare the performance of MERIS, MODIS and SeaWiFS radiometric products, we select their shared match-ups subsets (N = 8). The scatter plot between the in situ and satellite Rrs(k) at different visible bands is shown in Fig. 10, and the corresponding statistics are listed in Table 5. As shown in Table 5 and Fig. 10, differences in the accuracy of radiometric products of MERIS, MODIS and SeaWiFS do exist, and are quite apparent in certain cases. The following conclusions can be drawn: (1) The shared feature of reflectance products of the three satellites is that Rrs(k) retrievals at 490 nm for SeaWiFS and MODIS and 560 nm have lower uncertainties than those at other visible bands.

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Table 4 Statistical results for the satellite-derived radiometric products evaluation. Slope and Intercept are the linear regression results from the satellite retrievals versus the in situ ones. N is the number of samples. APDm (%)

Ratio (SIQR)

RMS

R2

MERIS Rrs(412) Rrs(443) Rrs(490) Rrs(560) Rrs(665) Rrs(490)/Rrs(560) Rrs(665)/Rrs(560) Rrs(665) + Rrs(560) Rrs(665)/Rrs(490) Rrs(560)  0.5[Rrs(443)+ Rrs(665)] Rrs(412)/Rrs(490) Rrs(412)/Rrs(510) Rrs(443)/Rrs(560) Rrs(443)/[Rrs(560) + Rrs(665)]

62 46 25 15 43 14 21 16 17 38 42 47 45 46

1.620(0.551) 1.269(0.468) 1.065(0.262) 0.959(0.146) 0.861(0.257) 1.079(0.064) 0.960(0.212) 0.919(0.152) 0.901(0.229) 0.666(0.272) 1.419(0.267) 1.466(0.321) 1.379(0.265) 1.373(0.266)

0.0041 0.0028 0.0024 0.0028 0.0011 0.2199 0.1162 0.0039 0.1375 0.0026 0.4593 0.5254 0.4654 0.4054

0.588 0.756 0.810 0.884 0.939 0.739 0.420 0.906 0.697 0.735 0.195 0.298 0.436 0.506

MODIS Rrs(412) Rrs(443) Rrs(488) Rrs(555) Rrs(667) Rrs(488)/Rrs(555) Rrs(667)/Rrs(555) Rrs(667) + Rrs(555) Rrs(667)/Rrs(488) Rrs(555)  0.5[Rrs(443)+ Rrs(667)] Rrs(412)/RrsRrs(488) Rrs(443)/Rrs(555) Rrs(443)/[Rrs(555) + Rrs(667)]

47 27 23 27 25 15 15 25 16 53 35 31 29

0.995(0.412) 0.905(0.257) 0.876(0.176) 0.773(0.139) 0.817(0.144) 1.153(0.049) 1.002(0.147) 0.788(0.129) 0.888(0.158) 0.469(0.196) 1.088(0.326) 1.301(0.226) 1.234(0.214)

0.0032 0.0028 0.0029 0.0039 0.0024 0.2653 0.0677 0.0062 0.0656 0.0037 0.3493 0.2867 0.2434

SeaWiFS Rrs(412) Rrs(443) Rrs(490) Rrs(555) Rrs(670) Rrs(490)/Rrs(555) Rrs(670)/Rrs(555) Rrs(670) + Rrs(555) Rrs(670)/Rrs(490) Rrs(555)  0.5[Rrs(443) + Rrs(670)] Rrs(412)/Rrs(490) Rrs(412)/Rrs(510) Rrs(443)/Rrs(555) Rrs(443)/[Rrs(555) + Rrs(670)]

60 32 17 17 31 9 21 21 24 29 35 40 23 24

1.362(0.359) 1.269(0.249) 1.044(0.160) 0.992(0.190) 1.110(0.436) 1.048(0.072) 1.114(0.269) 1.016(0.230) 1.105(0.228) 0.734(0.078) 1.199(0.190) 1.234(0.214) 1.216(0.136) 1.225(0.126)

0.0027 0.0023 0.0021 0.0027 0.0020 0.1655 0.0755 0.0045 0.1056 0.0019 0.3272 0.3569 0.3275 0.2490

(2) Compared to the uncertainties in the green and red bands, deterioration in Rrs(k) retrievals at the bands of 412 and 443 nm are much more pronounced for MERIS; on the contrary, MODIS and SeaWiFS Rrs(k) retrievals show relatively consistent performance throughout all of the visible bands. (3) At the bands of 412–490 nm, the SeaWiFS retrievals have lower uncertainty (9–25%) than those of MODIS (18–28%) and MERIS (22–45%). However, at the green bands, MERIS is superior to MODIS and SeaWiFS, and retrieval uncertainties for MERIS, MODIS, and SeaWiFS are in the range of 8–22%, with a total difference of less than 15%. At the red bands, the retrieval uncertainties for MERIS, MODIS and SeaWiFS are similar (24–27%), with a difference of less than 5%. Fig. 11 shows the Rrs(k) spectral comparison between the in situ measurements and satellite retrievals at the eight stations. One of the most striking features of these figures is the degradation of MERIS retrievals in the shorter wavelength bands, which is more evident at stations HDA92, HDA93, HDA94, HDA54 and HDS66. Compared to the other sites, these five stations (Kd(490) in the range of 0.07–0.35 m1) are characterized by significant overestimation in the aerosol optical thickness at 865 nm by a factor of 1–5, and underestimation of Angstrom exponent by 64–91%.

Slope

Intercept

N

1.141 0.982 0.835 0.831 0.888 0.921 0.571 0.853 0.657 0.792 0.885 1.140 1.002 1.066

0.0022 0.0016 0.0012 0.0003 0.0002 0.1879 0.0671 0.0001 0.0001 0.0002 0.4267 0.3134 0.2863 0.1965

18 19 21 22 22 21 22 22 21 21 18 18 19 19

0.500 0.756 0.845 0.885 0.835 0.901 0.942 0.867 0.961 0.921 0.194 0.396 0.550

0.786 0.931 0.884 0.810 0.870 1.255 1.205 0.831 0.985 0.5280 0.747 0.612 0.756

0.0011 0.0006 0.0007 0.0005 0.0004 0.0751 0.0474 0.0009 0.0200 0.0000 1.0054 0.4027 0.2295

17 20 20 20 20 20 20 20 20 20 17 20 20

0.739 0.873 0.915 0.946 0.908 0.858 0.847 0.939 0.888 0.933 0.038 0.014 0.637 0.734

1.099 1.039 0.925 0.868 0.840 0.834 0.845 0.862 0.822 0.779 0.257 0.147 0.867 0.885

0.0009 0.0010 0.0009 0.0008 0.0004 0.2161 0.0662 0.0012 0.0550 0.0000 0.9098 0.6750 0.2880 0.1999

28 28 28 28 28 28 28 28 28 28 28 28 28 28

4.2. sa The uncertainties of sa in the near infrared band are 47%, 47% and 32% for MERIS, MODIS and SeaWiFS, respectively (see Table 6). Aerosol optical thickness tends to be somewhat overestimated by MERIS, and underestimated by SeaWiFS (see Fig. 12). MODIS overestimates the in situ ones for small sa (<0.2) and underestimates those for large sa (>0.2). Compared with that in the near infrared (NIR) band, sa retrieval accuracy in the green bands is higher (25%). The uncertainties of the retrieved Angstrom exponent by MERIS, MODIS and SeaWiFS are 57, 41 and 29%, respectively, and the underestimation trend by MERIS is noticeable. 4.3. Kd(490) The uncertainty of the MODIS Kd(490) product is 48% and the corresponding one of SeaWiFS Kd(490) is 39% (Table 7). Both types of satellite products tend to underestimate the in situ measurements (Fig. 13a). For the areas with high turbidity, e.g. Kd(490) > 0.7 m1, the underestimation factor is greater than 2. To further explore the source of the uncertainty, the standard Kd(490) retrieval algorithm is replaced by the one developed for the turbid waters (Chesapeake Bay, Wang et al., 2009). It is found

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0.1

0.1

Rrs(412) Rrs(443) Rrs(490) Rrs(560) Rrs(665)

Rrs(443) Rrs(488)

0.01

MODIS R rs (sr -1)

MERIS Rrs (sr -1)

0.01

Rrs(412)

0.001

Rrs(555) Rrs(667)

0.001

0.0001

0.0001

(a) 0.0001

0.001

0.01

(b) 1E-005 1E-005

0.1

0.0001

In situ R rs (sr -1)

0.001

0.01

0.1

In situ R rs (sr -1)

0.1 Rrs(412) Rrs(443) Rrs(490) Rrs(555) Rrs(670)

SeWiFS R rs (sr-1)

0.01

0.001

0.0001

(c) 0.0001

0.001

0.01

0.1

In situ R rs (sr -1) Fig. 9. Scatter plots of the satellite-derived Rrs(k) versus in situ measurements. (a) MERIS, (b) MODIS, (c) SeaWiFS. Solid line is 1:1 line. Dashed lines are 1:2 and 2:1 lines.

0.1

Satellite Rrs (sr )

0.01 -1

that the uncertainty of satellite Kd(490) retrievals decreases significantly to 21–27% (see Table 7) and the improvement in turbid waters (with Kd(490) > 0.5 m1) is much more compelling (Fig. 13b). All of the results described above indicate two crucial conclusions. First, much of the uncertainty in the satellite Kd(490) products can be attributed to the adopted standard in-water algorithm, which is not applicable to the study area and requires modification; and second, with the improved in-water algorithm, the satellite retrievals of Kd(490) may have an uncertainty of about 25% in the range of 0.06–2 m1, which corresponds to the water which dominates the YS and ECS (Fig. 3).

0.001

MERIS R rs(412) MERIS R rs(443) MERIS R rs(490) MERIS R rs(560) MERIS R rs(665) MODIS R rs(412) MODIS R rs(443) MODIS R rs(488) MODIS R rs(555) MODIS R rs(667) SeWiFS R rs(412) SeWiFS R rs(443) SeWiFS R rs(490) SeWiFS R rs(555) SeWiFS R rs(670)

5. Assessment of ocean-color constituent concentrations 0.0001

5.1. SPM The standard MERIS SPM products underestimate the in situ ones by 40% (Fig. 14 and Table 7). In order to identify the source of the uncertainty further, a regional SPM retrieval algorithm for the YS and ECS (Tang et al., 2004) is applied to the MERIS Rrs(k) data, which utilizes the reflectance at bands of 490, 555, and 670 nm. The agreement between the retrievals and measurements

0.0001

0.001

0.01

0.1

In situ Rrs (sr -1) Fig. 10. Scatter plots of the satellite-derived Rrs(k) versus in situ measurements for the observations concurrent with MERIS, MODIS and SeaWiFS. Solid line is 1:1 line. Dashed lines are 1:2 and 2:1 lines.

T. Cui et al. / ISPRS Journal of Photogrammetry and Remote Sensing 87 (2014) 137–151 Table 5 Comparison of the performance of MERIS, MODIS and SeaWiFS Rrs(k) products (N = 8). Slope and Intercept are the linear regression results from the satellite retrievals versus the in situ ones. N is the number of samples. APDm (%)

Ratio (SIQR)

RMS

R2

Slope

Intercept

MERIS Rrs(412) Rrs(443) Rrs(490) Rrs(560) Rrs(665)

45 37 22 8 27

1.453(0.535) 1.370(0.476) 1.223(0.252) 1.007(0.149) 1.044(0.469)

0.0020 0.0015 0.0016 0.0019 0.0007

0.939 0.972 0.975 0.982 0.996

0.847 0.852 0.846 0.874 0.909

0.0022 0.0017 0.0017 0.0012 0.0001

MODIS Rrs(412) Rrs(443) Rrs(488) Rrs(555) Rrs(667)

26 28 18 22 24

1.148(0.255) 1.149(0.273) 0.991(0.170) 0.855(0.142) 0.913(0.157)

0.0016 0.0014 0.0016 0.0033 0.0014

0.863 0.933 0.962 0.972 0.992

1.058 0.968 0.863 0.759 0.806

0.0003 0.0007 0.0007 0.0006 0.0003

SeaWiFS Rrs(412) Rrs(443) Rrs(490) Rrs(555) Rrs(670)

21 25 9 20 27

1.184(0.192) 1.143(0.173) 0.985(0.095) 0.918(0.142) 0.909(0.174)

0.0013 0.0013 0.0019 0.0032 0.0024

0.875 0.946 0.977 0.984 0.994

0.954 0.858 0.783 0.756 0.660

0.0003 0.0006 0.0007 0.0006 0.0003

is considerably improved, with the APDm of 27% (Fig. 14 and Table 7), which is close to the uncertainty of the algorithm (Tang et al., 2004). Clearly, the standard MERIS data processing chain (NN algorithm) may require tuning for improved performance in the study area. Moreover, there is another fact that may also be responsible for the accuracy improvement. The MERIS band combinations at 490, 555 and 670 nm used by the regional algorithm have relatively high accuracy, whereas the standard NN algorithm takes Rrs(k) at all the visible bands as the input. As a result, the significant uncertainties in the satellite retrieved Rrs(k) at 412 and 443 nm may transfer and accumulate in the processing chain to degrade the ultimate SPM retrievals. The regional SPM retrieval algorithm is also applied to the MODIS and SeaWiFS Rrs(k) data, and reasonable retrieval results (more accurate than those of MERIS) are also found (with an uncertainty of 19–25%) in the range of about 0.5–20 g/m3, which is the typical range of SPM in the study area (Fig. 4).

5.2. Chl-a The uncertainties of satellite Chl-a products by MERIS, MODIS and SeaWiFS are 54%, 32% and 40% and 3.624, 1.228 and 1.509 mg/m3 in RMS, respectively. The overestimation trends of all of the satellite retrievals are observed (Table 7 and Fig. 15). Analogous to what has been demonstrated in the SPM validation, MERIS Rrs(k) together with a regional Chl-a retrieval algorithm developed by in situ data (Sun et al., 2008b), rather than the standard NN-based one, may yield better Chl-a retrievals with APDm of 31% in the range of about 0.3–15.0 mg/m3 (Table 7 and Fig. 15). However, when the same regional Chl-a retrieval algorithm is applied to MODIS and SeaWiFS Rrs(k), substituting standard OC3 and OC4V4 algorithms respectively, the retrievals uncertainties are found to be even larger than those of standard products by 10–15% (Table 7 and Fig. 15). The main factor for the deterioration is that the reflectance at band 412 nm is involved in the regional algorithm (but not in the standard OC3 and OC4V4), where the satellite Rrs(k) retrieval has poor accuracy, and the uncertainty accumulates to the final Chl-a retrievals. This reason may also account for the worse performance of standard satellite Chl-a products of MERIS than those of MODIS and SeaWiFS.

145

6. Discussion 6.1. Comparisons with other studies To compare with available Rrs(k) validation results in the study area, it is noted that Sun et al. (2009) obtained very similar results of MODIS by a slightly different procedure, with RMS of 0.0024– 0.0036 sr1 and APDm of 21–40%. In addition, our findings regarding the Rrs(k) comparison among the three satellites are generally in accordance with those obtained by Antoine et al. (2008) in the offshore site of the Mediterranean Sea (BOUSSOLE), and Zibordi et al. (2006) in the coastal site of the northern Adriatic Sea. The major difference is that the largest uncertainty of satellite Rrs(k) is found in the blue bands for the YS and ECS, whereas it is found in the red bands for the Mediterranean Sea (for all three missions) and Adriatic Sea (for SeaWiFS and MODIS). The overestimation of satellite sa product found here is in accordance with that reported by Li and Chen (2010) whose validation activity was dedicated for MODIS in the YS and ECS. However, they showed better accuracy performance (APDm = 35%, RMS = 0.065, N = 18) over ours, which may be attributed to the differences of the match-ups dataset in (1) the spatial distribution (e.g. they have match-ups in the North Yellow Sea, whereas we do not); (2) the temporal coverage (the majority of their match-ups are from January, February, October, and November, whereas ours are from March, April and September); and (3) the match-up procedure (their adopted spatio-temporal windows to establish the matchup are ±1 h and 10  10 km). Our results of satellite sa validation are generally in consensus with those of Mélin et al. (2010), who showed with globally distributed AERONET data that the APDm for MODIS and SeaWiFS sa increased from 20% to 22% at the blue bands to 45–48% in the near-infrared. For their match-up subsets in the Northeast Asia (along the coast of Korea and Japan), some of which were located in the YS and ECS (near the eastern boundary), consistent statistics can also be found (APDm 39–44% for MODIS and SeaWiFS in the NIR bands). MODIS Chl-a assessment in the study area performed by Sun et al. (2009) produced similar results with RMS of 1.56 mg/m3, but more severe overestimation was found by them with APDm of 103.25% and Ratio of 2.03, compared with ours of 32% and 1.146. This difference may mainly be ascribed to their limited match-up dataset (N = 9), which lacks coverage of the stations with favorable retrievals.

6.2. Improvement of atmospheric correction algorithm The significant uncertainty with satellite-derived Rrs(k) (Table 4) reveals that much more effort is still required to improve the performance of atmospheric correction algorithms along the East China Coast. Both the complex aerosol properties and significant NIR signal from turbid waters likely play an important role in the atmospheric correction performance. Aerosols may be significantly influenced by the Asian continents and monsoon climate, which contribute to absorption of particles of various anthropogenic origins, as well as desert dust under certain seasons and wind direction (Liu and Zhou, 1999; Li et al., 2003b; Mélin et al., 2010; Fukushima and Toratani, 1997). Consequently, the aerosol properties may not be well represented in the satellite aerosol models used in the atmospheric correction algorithm, thus leading to Rrs(k) errors, especially at the shorter wavebands, as revealed by the results for 412 nm. NIR optical contribution by turbid water will trigger the overestimation of the aerosol signal, which may have not been fully accounted for either. In addition, sensor calibration

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0.006

0.005

0.008 Sta. HDA93 In situ MERIS MODIS SeaWiFS

0.006

Sta. HDA94 In situ MERIS MODIS SeaWiFS

0.004

0.002

500

600

0.004

0.002

0 400

700

Wavelength (nm)

R rs (sr -1)

R rs (sr -1)

600

0 400

700

500

600

700

Wavelength (nm)

0.025

0.002

0.001

0.02 0.015 Sta. HDA91 In situ MERIS MODIS SeaWiFS

0.01

500

600

0.005 400

700

Wavelength (nm)

500

600

700

Wavelength (nm) 0.003

0.008

0.024 0.02

Sta. HDA64 In situ MERIS MODIS SeaWiFS

0.006

R rs (sr -1)

R rs (sr -1)

500

0.03 Sta. HDA54 In situ MERIS SeaWiFS MODIS

0.003

0.016 0.012 Sta. HDA63 In situ MERIS MODIS SeaWiFS

0.008 0.004 400

0.002

Wavelength (nm)

0.004

0 400

0.003

0.001

500

600

Wavelength (nm)

Sta. HDS66 In situ MERIS MODIS SeaWiFS

0.002

R rs (sr -1)

0 400

Sta. HDA92 In situ MERIS MODIS SeaWiFS

R rs (sr -1)

R rs (sr -1)

R rs (sr -1)

0.004

0.004

0.001

0.002

700

0 400

500

600

Wavelength (nm)

700

0 400

500

600

700

Wavelength (nm)

Fig. 11. Direct spectral comparison between the in situ and satellite Rrs(k) at the 8 sites concurrent with MERIS, MODIS and SeaWiFS at the same time.

(e.g. especially for MERIS, Martiny et al., 2005; Mélin et al., 2011), to a large extent, may also be a source of error. Suitable regional aerosol models for the YS and ECS, describing the distinct aerosol features in the light absorption, size distribution and refractive index, are critically required. Further observation must be strengthened at fixed stations (e.g. AERONET or AERONET-OC, Holben et al., 1998; Zibordi et al., 2009b) and during cruises for scientific purposes or operational investigations. Based on these observations, the regional aerosol optical properties and the spatio-temporal variability will be better characterized and understood. The shortwave-infrared (SWIR)-band-based algorithm has shown promising results (Wang and Shi, 2005, 2007; Wang, 2007), but only for extremely turbid waters, the dynamic range of which is limited and restricted to the estuarine zone; in contrast, as the East China Coast is mainly moderately (or even less) turbid, the SWIR band has little response. Therefore, for these waters the NIR information in the ocean-color images must be further explored, for atmosphere correction purposes (e.g. Wang et al., 2012).

In order to ameliorate the atmosphere correction of satellite ocean-color images in the intricate coastal environment, the synergistic use of active optical remote sensing data (e.g. LIDAR) may be helpful, from which the aerosol vertical structure can be inferred (Tian et al., 2009). For the design of future ocean-color missions, the dedicated instruments for the accurate aerosol retrieval may be equipped onboard to work simultaneously with the ocean-color imager to provide the auxiliary aerosol information for the atmosphere correction, especially when the coastal area monitoring is of high priority. There have been successful projects in the field of microwave remote sensing. For the altimeter satellite missions (e.g. ERS-1, T/P, JASON-1/2, ENVISAT, HY-2) of taking the sea surface height measurement as their major scientific aim, the dedicated microwave radiometers are operated onboard synchronously with the altimeters to supply the path delay of the electromagnetic wave, which is regarded as the most important source of error in accurate ranging (Keihm et al., 1995). Similarly, to achieve better salinity retrieval, the specific radar was also integrated to the Aquarius microwave sensor to provide the correction for the

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T. Cui et al. / ISPRS Journal of Photogrammetry and Remote Sensing 87 (2014) 137–151 Table 6 Statistical results for the satellite aerosol products evaluation. Slope and Intercept are the linear regression results from the satellite retrievals versus the in situ ones. N is the number of samples. APDm (%) MERIS sa (865) sa (550)

a MODIS sa (869)

a SeaWiFS sa (865)

a

Ratio (SIQR)

R2

RMS

Slope

Intercept

N

47 24 57

1.323(0.398) 1.030(0.219) 0.536(0.237)

0.494 0.494 0.694

0.238 0.347 0.062

0.184 0.246 0.164

0.191 0.206 0.347

14 14 14

47 41

1.351(0.324) 1.171(0.203)

0.166 0.505

0.088 0.315

0.068 0.595

0.102 0.678

15 15

32 29

0.797(0.229) 1.156(0.291)

0.276 0.507

0.248 0.250

0.077 0.581

0.084 0.531

22 22

Table 7 Statistical results for the validation of the satellite products and those derived from satellite Rrs with the improved in-water algorithms. Slope and Intercept are the linear regression results from the satellite retrievals versus the in situ ones. N is the number of samples.

surface roughness caused by wind and waves (Lagerloef et al., 2008).

6.3. Development of regional ocean-color algorithm

APDm (%)

Ratio (SIQR)

RMS

R2

Slope

Intercept

N

MERIS Kd(490)b Chl-aa Chl-ab SPMa SPMb

22 54 31 40 27

1.034(0.210) 1.260(0.417) 1.015(0.286) 0.641(0.200) 0.898(0.263)

0.134 3.624 1.669 1.802 1.562

0.828 0.030 0.128 0.884 0.889

1.004 0.081 0.250 1.284 1.229

0.013 1.691 1.032 1.305 0.718

21 22 18 22 22

MODIS Kd(490)a Kd(490)b Chl-aa Chl-ab SPMb

48 21 32 41 25

0.528(0.319) 1.127(0.212) 1.146(0.510) 0.618(0.131) 0.851(0.230)

0.574 0.202 1.228 1.396 1.827

0.692 0.853 0.191 0.006 0.940

0.105 0.834 0.528 0.073 0.796

0.109 0.091 1.179 1.021 0.071

20 20 20 17 20

SeaWiFS Kd(490)a Kd(490)b Chl-aa Chl-ab SPMb

39 27 40 55 19

0.678(0.276) 1.200(0.229) 1.358(0.413) 0.784(0.438) 0.998(0.168)

0.539 0.197 1.509 2.624 1.876

0.276 0.854 0.570 0.155 0.917

0.120 0.775 0.784 0.547 0.872

0.128 0.119 1.025 0.721 0.264

26 28

a

Given the uncertainty of atmosphere correction over turbid waters, the agreement between satellite retrievals and in situ measurements may increase significantly by replacing the standard inwater retrieval algorithms with regionally optimized or tuned standard algorithms. As the uncertainties of satellite retrieved Rrs(k) possess spectral dependency, involvement of a greater number bands in one algorithm does not guarantee higher accuracy. As a result of error propagation and accumulation, these retrievals would probably be degraded. From the remote sensing application point of view, when developing an in-water algorithm, we should choose Rrs(k) at those bands that are not only sensitive to the concerned geophysical parameter, but also have an acceptable uncertainty of satellite retrievals. The CDOM and Chl-a retrieval algorithms usually depend on the blue bands. Poor accuracy in satellite Rrs(412) retrievals would impact the reliable retrieval by these in-water algorithms (Morel and Gentili, 2009a,b; Shanmugam, 2011). Therefore, the substitute algorithms are required, especially those using the blue-green and red bands for the CDOM retrieval (Tiwari and Shanmugam,

Represents results of the standard satellite products. Represents results derived from the satellite Rrs(k) and improved in-water algorithms (Tang et al., 2004; Sun et al., 2008b; Wang et al., 2009). b

2011), and those using the fluorescence and NIR bands for the Chl-a estimation (Ma et al., 2011; Moses et al., 2012).

6.4. Ocean-color application to regional/operational oceanography This validation study shows that for the majority of the YS and ECS (except the extremely turbid waters located mainly near the estuary), the satellite retrievals of SPM, Chl-a, and Kd(490) by MERIS, MODIS and SeaWiFS may all reach the uncertainty level of about 25–30% with the improved or regional in-water algorithms. Our results advocate the feasibility and add the confidence of applying the satellite data as well as their merging products in the operational monitoring of ocean environment and oceanographic studies. The results also emphasize the necessity of the early re-processing of these standard satellite data of major

10

2

Satellite derived Angstom exponent

Satellite derived AOT

MERIS 865 SeaWiFS 865 MODIS 869 MERIS 550

1

0.1

0.01 0.01

0.1

In situ AOT

1

10

MERIS MODIS SeaWiFS

1.5

1

0.5

0

-0.5 -0.5

0

0.5

1

1.5

2

In situ Angstom exponent

Fig. 12. Scatter plot of satellite retrievals and shipboard measurements of the aerosol optical thickness (a) and the Angstrom exponent (b).

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T. Cui et al. / ISPRS Journal of Photogrammetry and Remote Sensing 87 (2014) 137–151

Satellite derived Kd (490) (m-1)

MODIS K d (490) (m-1)

MODIS SeaWiFS

1

0.1

MERIS MODIS SeaWiFS

1

0.1

(a) 0.1

1

In situ K d (490) (m-1)

(b) 0.01 0.01

0.1

1

In situ K d (490) (m-1)

Fig. 13. Scatter plots of the satellite derived Kd(490) versus in situ measurements.(a) Standard satellite product. (b) Kd(490) retrieved by satellite Rrs and Wang et al. (2009) model for turbid waters. Solid line is 1:1 line. Dashed lines are 1:2 and 2:1 lines.

MERIS standard product MERIS Rrs + regional model (RM)

Satellite retrieved SPM (mg/L)

MODIS Rrs + RM SeaWiFS Rrs + RM 10

1

0.1 0.1

1

10

In situ SPM (mg/L) Fig. 14. Scatter plots of the satellite derived SPM versus in situ measurements. The solid triangular N represents standard MERIS SPM product. The symbols 4, s and + refer to the retrievals by the regional algorithm of Tang et al. (2004) and the satellite derived Rrs of MERIS, MODIS and SeaWiFS, respectively. Solid line is 1:1 line. Dashed lines are 1:2 and 2:1 lines.

ocean-color missions with suitable algorithms for regional application. 6.5. Uncertainties and limitations of validation results 6.5.1. Uncertainties First, the uncertainties of the validation result from those inherent with in situ measurements (e.g. 15% for Rrs(k), 15% for SPM, 10% for Chla). For the spectral remote sensing reflectance data, extra uncertainties may have been induced for the bidirectional correction, as the adopted look-up table method (Morel et al., 2002) is generally applicable only to oceanic Case 1 waters. In addition, as of yet there is no established method available for the turbid coastal waters, despite the fact that there has been some progress based on the inherent optical properties (e.g. Zibordi and Berthon, 2001; Park and Ruddick, 2005; Devred et al., 2007; Lee et al., 2011).

Moreover, there may be uncertainties related with the spatiotemporal variability of the ocean and atmospheric parameters. Temporal dynamics are one of the key characteristics that the ocean and atmosphere differs from the land, especially for the coastal area with significant terrestrial and tidal influences. The choice of the time interval for the in situ-satellite matchup is dependent upon the temporal changes characteristics of the ocean and atmosphere environment, which themselves may be complex and further understanding of them is needed. The maximum time window for our match-up dataset is 6 h, during which natural variability of the atmosphere and ocean properties are likely to occur. Moreover, spatial mismatch exists during the validation, as the minimum satellite observation area is about 1  1 km, and usually a spatial average in a 3  3 box is used to match the in situ observation, which is actually performed at a single spot. The homogeneity test has been used to quantify the spatial variability in the 3  3 pixel box of the satellite measurement (Cui et al., 2010b), but the threshold is determined somewhat artificially. Recently, the impact of sub-pixel variations on the ocean-color remote sensing products has also been found (Lee et al., 2012). For turbid coastal waters, further research on the appropriate method is required to establish the match-up dataset. As a result, several effective measures may be taken to resolve this issue. First and foremost, multiple ocean-color images during one single day from the geostationary orbit satellite (e.g. Geostationary Ocean Color Imager, GOCI, Lee et al., 2010) may be used to quantify the variability scale of the geophysical parameters in space and time to provide a reference and guide for the determination of the match-up window. Additionally, the methods of scale transformation (Tao et al., 2009) and sub-pixel mapping (Verhoeye and Wulf, 2002; Mertens et al., 2003) usually adopted in land cover remote sensing may be helpful for addressing the issue of spatial mismatch.

6.5.2. Limitations Although the validation results presented here are representative, as the match-up dataset covers most of the natural variability of bio-optical properties encountered in the study area, it must be admitted that certain limitations exist. First, the size of the matchup dataset is limited. Second, the spatial distribution of the matchups is not even. No match-ups are available in the relatively clear waters of the northern YS and central ECS, or in the highly turbid waters near the Yangtze River estuary and radiative sand ridge.

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Satellite retrieved Chl-a (mg/m 3)

Satellite retrieved Chl-a (mg/m3)

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(a) 0.1 0.1

1

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(b) 0.1 0.1

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In situ Chl-a (mg/m 3)

Fig. 15. Scatter plots of the in situ Chl-a measurements versus (a) the standard satellite products, (b) the retrievals by satellite Rrs(k) and the regional algorithm by Sun et al. (2008b). Solid line is 1:1 line. Dashed lines are 1:2 and 2:1 lines.

Finally, the temporal coverage is limited to only one month of observation in spring and one month in autumn. Considering the intricate nature and variability of the ocean and atmosphere along the East China Coast, more match-ups are expected for the future validation activity to cover the complex aerosol condition and highly turbid waters.

As the first comprehensive satellite ocean-color products validation activity (in terms of the variables explored and missions involved, e.g. SeaWiFS, MODIS, and MODIS) in the YS and ECS, the work shown in this study provides the baseline to move forward, although some uncertainties and limitations exist. To perform further research in the study area, a dedicated in situ data collection effort with the consistent protocols followed is highly required for the task of validating ocean-color sensors.

7. Conclusions The satellite Rrs(k) retrievals by MERIS, MODIS and SeaWiFS show their lowest uncertainties at 490 and 555 nm, with the APDm of 15–27% and RMS of 0.0021–0.0039 sr1, whereas the Rrs(k) uncertainty at 412 nm is highest (APDm 47–62%, RMS 0.0027–0.0041 sr1). The degradation of MERIS Rrs(k) at 412 and 443 nm is significant compared to that of MODIS and SeaWiFS. Appropriate Rrs(k) band-ratios may help to reduce this uncertainty. The uncertainties of satellite retrieved aerosol properties are 32–47% for sa in the NIR bands and 29–57% for the Angstrom exponent. The significant uncertainties involved with the satellitederived Rrs(k) and sa reveal that much effort is required to improve the performance of atmospheric correction algorithms along the East China Coast, even if only the moderately turbid atmosphere and waters are considered. For this, the establishment of the regional aerosol optical model and synergistic use of AOT data retrieved by LIDAR are suggested. Kd(490) is underestimated by MODIS and SeaWiFS products by about 40–50%, and for the highly turbid area the underestimation factor was greater than 2. MERIS SPM products underestimate the in situ ones by 40% in the range of about 0.5–20 g/m3. The uncertainties of satellite Chl-a products by MERIS, MODIS and SeaWiFS are 54%, 32% and 40% respectively in the range of 0.3–15 mg/m3, and the overestimation trends may be observed. Much of the uncertainties involved in these derived products may be attributed to the in-water algorithms which are not suitable to the study area. With appropriate in-water algorithms developed specifically for the turbid waters in place of the standard ones adopted in the routine satellite data processing chain, the uncertainty of these derived properties may decrease significantly to the level of 20–30%, which holds true for the majority waters of the study area. This advocates for the re-processing of satellite data by switching to the regional in-water algorithms over the turbid coastal waters for a better application in regional oceanography studies.

Acknowledgements This work is funded by the High-tech Research and Development Program of China (No. 2007AA092102) and National Science Foundation of China (No. 61265008). The research was initiated when Tingwei Cui visited Plymouth Marine Laboratory, funded by the POGO-SCOR Fellowship programme, where he benefited from advice and facilities provided through the NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS). This is also a contribution to the CoastColour project of ESA. All crew members on the cruises are acknowledged for their hard work in collecting and analyzing the in situ data. We would like to thank ESA for the distribution of MERIS data products through the Dragon Project (IDs 5292 and 10470), Distributed Active Archive Center at the Goddard Space Flight Center, Greenbelt, MD 20771, for the production and distribution of MODIS and SeaWiFS data products. We are indebted to Dr. Eurico D’Sa and Zhongping Lee for their constructive comments and suggestions on the manuscript. We also thank the anonymous reviewers for their helpful comments on the manuscript. References Aiken, J. et al., 2007. Validation of MERIS reflectance and chlorophyll during the BENCAL cruise October 2002: preliminary validation of new demonstration products for phytoplankton functional types and photosynthetic parameters. International Journal of Remote Sensing 28 (3–4), 497–516. Antoine, D. et al., 2008. Assessment of uncertainty in the ocean reflectance determined by three satellite ocean color sensors (MERIS, SeaWiFS and MODISA) at an offshore site in the Mediterranean Sea (BOUSSOLE project). Journal of Geophysical Research 113 (C7), C07013. http://dx.doi.org/10.1029/ 2007JC004472. Antoine, D., Morel, A., 1999. A multiple scattering algorithm for atmospheric correction of remotely-sensed ocean colour (MERIS instrument): principle and implementation for atmospheres carrying various aerosols including absorbing ones. International Journal of Remote Sensing 20 (9), 1875–1916.

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