Empirical ocean-color algorithms to retrieve chlorophyll-a, total suspended matter, and colored dissolved organic matter absorption coefficient in the Yellow and East China Seas

Siswanto, Eko; Tang, Junwu; Yamaguchi, Hiroichiro; Ahn, Yu-Hwan; Ishizaka, Joji; Yoo, Sinjae; Kim, Sang Woo; Kiyomoto, Yoko; Yamada, Keiko; Chiang, Connie Y.; Kawamura, Hiroshi

doi:10.1007/s10872-011-0062-z

Cited by 168 publications

(126 citation statements)

References 36 publications

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“…In this study, we used the Chl-a algorithm of Siswanto et al [24] to obtain satellite-derived Chl-a data. In regions with nLw 555 > 2 mW·cm −2 ·µm −1 ·sr −1 , the regionally tuned Tassan-like algorithm was used:…”

Section: Chl-a Algorithmmentioning

confidence: 99%

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Seasonal and Interannual Variability of Satellite-Derived Chlorophyll-a (2000–2012) in the Bohai Sea, China

et al. 2017

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Abstract:Knowledge of the chlorophyll-a dynamics and their long-term changes is important for assessing marine ecosystems, especially for coastal waters. In this study, the spatial and temporal variability of sea surface chlorophyll-a concentration (Chl-a) in the Bohai Sea were investigated using 13-year (2000-2012) satellite-derived products from MODIS and SeaWiFS observations. Based on linear regression analysis, the results showed that the entire Bohai Sea experienced an increase in Chl-a on a long-term scale, with the largest increase in the central Bohai Sea and the smallest increase in the Bohai strait. Distinct seasonal patterns of Chl-a existed in different sub-regions of the Bohai Sea. A long-lasting Chl-a peak was observed from May to September in coastal waters (Liaodong bay, Qinhuangdao coast, and Bohai bay) and the central Bohai Sea, whereas Laizhou bay had relatively low Chl-a in early summer. In the Bohai strait, two pronounced Chl-a peaks occurred in March and September, but the lowest Chl-a was in summer. This pattern was quite different from those in other regions of the Bohai Sea. The water column condition (stratified or mixed) was likely an important physical factor that affects the seasonal pattern of Chl-a in the Bohai Sea. Meanwhile, increased human activity (e.g., river discharge) played a significant role in changing the Chl-a distribution in both coastal waters and the central Bohai Sea, especially in summer. The increasing trend of Chl-a in the Bohai Sea might be attributed to the increase in nutrient contents from riverine inputs. The Chl-a dynamics documented in this study provide basic knowledge for the future exploration of marine biogeochemical processes and ecosystem evolution in the Bohai Sea.

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Section: Chl-a Algorithmmentioning

confidence: 99%

“…We applied the Chl-a algorithm of Siswanto et al [24] to satellite data to investigate the Chl-a dynamics in the Bohai Sea. Before the application, the performance of the Chl-a algorithm was assessed based on 69 pairs of in situ Chl-a and satellite R rs (λ) data (Figure 1c).…”

Section: Validation Of Satellite-derived Chl-a In the Bohai Seamentioning

confidence: 99%

Seasonal and Interannual Variability of Satellite-Derived Chlorophyll-a (2000–2012) in the Bohai Sea, China

et al. 2017

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“…The Bohai and Yellow Seas are characterized by optically-complex case II waters due to high suspended sediment, which can cause an overestimation of satellite derived Chl-a using global chlorophyll algorithms, particularly in the coastal waters (Gregg and Casey, 2004;IOCCG, 2000;Siswanto et al, 2011). Previous studies showed that the satellite-derived Chl-a values along the China coast are within the acceptable range because the correlations between retrieved and in situ measured Chl-a concentration are significant (Sun et al, 2010;Xiu et al, 2007;Zhang et al, 2006).…”

Section: Providing Confidence In the Satellite Chl-a Datamentioning

confidence: 99%

“…Sediment re-suspension caused by wind and tidal currents can impact on the accuracy of satellite-derived Chl-a concentrations, particularly in coastal waters (Gong et al, 2003;Shi and Wang, 2010). Efforts have been made to improve the accuracy of satellite-derived Chl-a in local Case-II seawaters (Shi and Wang, 2012;Siswanto et al, 2011;Wang and Shi, 2007).…”

Section: Introductionmentioning

confidence: 99%

Trends of satellite derived chlorophyll-a (1997–2011) in the Bohai and Yellow Seas, China: Effects of bathymetry on seasonal and inter-annual patterns

Chen

Wang

2013

Progress in Oceanography

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a b s t r a c tThe spatial and temporal variability of sea surface chlorophyll-a (Chl-a) concentrations in the Bohai and Yellow Seas were analyzed, using satellite-derived Chl-a products from SeaWiFS and MODIS sensors over the period of September 1997-September 2011. A set of monthly and cloud-free Chl-a data was produced by the Data Interpolating Empirical Orthogonal Function (DINEOF) method. The results indicate that there are different Chl-a seasonal patterns existing in the Yangtze River mouth, coastal and offshore waters, respectively. In the Yangtze River mouth, a long-lasting Chl-a peak (May-September) is seen in summer. In coastal waters, two significant Chl-a maxima occur in winter-spring and late summer, respectively. In offshore waters, only one significant spring (March-April) Chl-a maximum is evident with a time lag of 1-3 months to coastal waters and the signal of autumn maximum is very weak. In coastal waters, wind-tide-thermohaline circulations and East Asia summer rainy monsoon may important physical factors to impact the seasonal pattern of Chl-a, but increased human activity (e.g., eutrophication, dam) could significantly enhance this process. In offshore waters, the impact on the circulation of the YSWC in winter and YSCW in summer in the central Yellow Sea could be important physical factor in explaining the variability of Chl-a in seasonal patterns. The decadal trends of Chl-a and sea surface temperature are decreasing in coastal waters, with a significantly positive correlation. In offshore waters, the decadal trends of Chl-a is increasing but a slight decreasing sea surface temperature trend is seen, and they indicate a negative correlation.

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“…7 The variability of absorption coefficients in open ocean waters ("case 1") has been thoroughly documented over the last several decades. [8][9][10] In recent years, methods for the accurate retrieval of total absorption in coastal or inland waters ("case 2") have also been under investigation, with proposed-models including empirical, 11 spectral optimization, 12 artificial neural network, 13 linear matrix inversion, 14 and semianalytical 15 approaches. Empirical models associate IOPs with remote sensing reflectance or the ratio of irradiance reflectance using simple or multiple regression methods.…”

mentioning

confidence: 99%

Retrieval of absorption coefficients for a drinking water source using a green–red band quasianalytical algorithm

Shi

Tao

Mao

et al. 2018

J. Appl. Rem. Sens.

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"Retrieval of absorption coefficients for a drinking water source using a green-red band quasianalytical algorithm," J. Appl. Remote Sens. 12(4), 042802 (2018), doi: 10.1117/1.JRS.12.042802. Abstract. This study proposed a green-red band quasianalytical algorithm, QAA-GRI, and evaluates its performance using an in situ dataset from Lake Qiandaohu, a drinking water source, in China. First, by shifting the reference wavelength from 555 to 510 nm, a green-red index (GRI) can be calculated from remote sensing reflectance at 510, 560, and 620 nm, and the index was then used to retrieve the total absorption coefficients at the reference band, að510Þ. Second, a semianalytical model based on að510Þ and the GRI was deduced to establish the QAA-GRI, replacing the empirical model in quasianalytical algorithm version 5 (QAA-v5). The QAA-GRI was applied to retrieve absorption coefficients from an in situ dataset of Lake Qiandaohu, and the QAA-GRI's performance was compared with that of QAA-v5 and another red-green QAAbased approach (QAA-RGR). The results demonstrate that, for this dataset, the QAA-GRI exhibited better performance (R 2 ¼ 0.81, mean absolute percentage error, MAPE ¼ 15.7%), which indicated a clear improvement in the accuracy of absorption coefficient retrieval, compared with QAA-v5 (R 2 ¼ 0.56, MAPE ¼ 21.2%) and QAA-RGR (R 2 ¼ 0.67, MAPE ¼ 22.6%). In addition, to illustrate the QAA-GRI's assumptions and its applicability, the QAA-GRI was also applied to an in situ dataset from Taihu Lake, a highly turbid and eutrophic water source. As predicted, when applied to Taihu Lake, the QAA-GRI did not perform as well as it did when applied to Lake Qiandaohu because of the former's extremely high turbidity, which can cause greater uncertainty with regard to backscattering coefficients. This study suggests that the QAA-GRI is better suited to the retrieval of absorption coefficients from drinking water resources. Our algorithm will also have potential applications to satellite data from the Medium Resolution Imaging Spectrometer or Ocean and Land Color Instrument. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Empirical ocean-color algorithms to retrieve chlorophyll-a, total suspended matter, and colored dissolved organic matter absorption coefficient in the Yellow and East China Seas

Cited by 168 publications

References 36 publications

Seasonal and Interannual Variability of Satellite-Derived Chlorophyll-a (2000–2012) in the Bohai Sea, China

Seasonal and Interannual Variability of Satellite-Derived Chlorophyll-a (2000–2012) in the Bohai Sea, China

Trends of satellite derived chlorophyll-a (1997–2011) in the Bohai and Yellow Seas, China: Effects of bathymetry on seasonal and inter-annual patterns

Retrieval of absorption coefficients for a drinking water source using a green–red band quasianalytical algorithm

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