Specific inherent optical quantities of complex turbid inland waters, from the perspective of water classification

Sun, Deyong; Li, Yunmei; Wang, Qiao; Le, Chengfeng; Lv, Hongbo; Huang, Changchun; Gong, Shuai

doi:10.1039/c2pp25061f

Cited by 32 publications

(33 citation statements)

References 62 publications

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“…Most of the approaches rely on in situ observations (Boss et al, 2004;Twardowski et al, 2001). Recently, a new water classification method, NTD675 (Normalized Trough Depth of spectral reflectance at 675 nm), was proposed in Sun et al (2012) to improve Chla retrievals. The NTD675 method uses the Chla/TSM ratio to characterize the particulate discrepancies, and then links the ratio to spectral reflectance, leading to improved algorithm performance (Sun et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Influence of a red band-based water classification approach on chlorophyll algorithms for optically complex estuaries

Sun

Qiu

et al. 2014

Remote Sensing of Environment

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Section: Introductionmentioning

confidence: 99%

Influence of a red band-based water classification approach on chlorophyll algorithms for optically complex estuaries

Sun

Qiu

et al. 2014

Remote Sensing of Environment

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“…The relationship between b Ã bp 440 ð Þand C TSM also seems to have a similar phenomenon (e.g., class3 in Fig. 8), where b Ã bp 440 ð Þ decreases with increasing C TSM Sun et al 2012). However, class 2 and the total points apparently do not have this phenomenon.…”

Section: Discussionmentioning

confidence: 69%

“…A new band was introduced to offset the influence of particle absorption in the near-infrared band, and the three-band model was developed into a four-band model (Le et al 2009a;Yang et al 2010). Previous studies suggested that the classification of R rs (k) could also benefit the inversion model to obtain more accurate retrieval result (Lubac and Loisel 2007;Le et al 2011;Sun et al 2011Sun et al , 2012. However, single criterion is hard to distinguish the complex water optical properties.…”

Section: Introductionmentioning

confidence: 99%

Specific inherent optical properties of highly turbid productive water for retrieval of water quality after optical classification

Huang

Chen

et al. 2014

Environ Earth Sci

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Assessments of specific inherent optical properties (SIOPs) and their variability in highly turbid and productive inland waters are essential for the accurate estimation of water quality. A new optical classification method including two classification criteria [i.e., normalized remote sensing reflectance slope (NS), and normalized remote sensing reflectance depth (ND)] was developed to divide remote sensing reflectance into four classes, i.e., class 1 (NS \ -0.0017 and ND \ 0.21) is low turbid and productive water; class 2 (NS \ -0.0017 and ND [ 0.21) is low turbid and high productive water; class 3 (NS [ -0.0017 and ND \ 0.09) is high turbid and low productive water; and class 4 (NS [ -0.0017 and ND [ 0.009) is high turbid and high productive water. The relationships between phytoplankton absorption at 440 nm [a ph (440)] and chlorophyll-a concentration [C chla ] as well as between particle backscattering coefficient at 440 nm [b bp (440)] and total suspended matter concentration (C TSM ) after classification were obtained from a large number of in situ data in Lake Taihu. The measured specific phytoplankton absorption ½a Ã ph k ð Þ and particle backscattering coefficient ½b Ã bp k ð Þ show significant variations even within the same class. The mean values of a Ã ph k ð Þ at 440 nm ½a Ã ph 440 ð Þ for each class are 0.048 ± 0.013, 0.060 ± 0.012, 0.083 ± 0.021, and 0.056 ± 0.017 m 2 /mg, respectively. The mean values of b Ã bp k ð Þ at 440 nm ½b Ã bp 440 ð Þ for each class are 0.035 ± 0.01, 0.024 ± 0.004, 0.041 ± 0.009, and 0.038 ± 0.009 m 2 /g, respectively. The power functions of SIOPs and water constituents' concentration indicate that a Ã ph 440 ð Þ and b Ã bp 440 ð Þ vary with C chla and C TSM . The validation results show that our proposed values for a Ã ph 440 ð Þ and b Ã bp 440 ð Þ cover a very wide range of water optical properties, which are characterized from clear water to highly turbid productive water. The validation results also suggest that the retrieval accuracy of C chla and C TSM bio-optical model was improved after classification. The root mean square error (RMSE) of C chla was improved from 14.18 to 7.43 lg/L (mean value of all classes) and RMSE of C TSM was improved from 32.98 to 26.10 mg/L (mean value of all classes). Thus, the temporal and spatial variation of a Ã ph 440 ð Þ and b Ã bp 440 ð Þ should be considered in the bio-optical retrieval model of water quality.

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“…Many [17,18] developed optical classification schemes based mainly on light penetration parameters, including euphotic zone depths (EZD), Secchi depth (SD), the spectral irradiance reflectance (R), the total attenuation coefficient (c), the diffuse attenuation coefficient for downwelling irradiance (Kd), and their ratio (Kd/c). Some studies classified waters using only water quality parameters (e.g., SD, turbidity, chlorophyll-a concentration (chl-a), yellow substance, and sea surface temperature) [19] or inherent optical properties (IOPs; e.g., absorption and scattering coefficients) [15,[20][21][22]. Some studies used a combination of water quality parameters and optical properties [21,23,24].…”

Section: Introductionmentioning

confidence: 99%

Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance

Shen

Zhang

et al. 2015

Remote Sensing

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Determining the dominant optically active substances in water bodies via classification can improve the accuracy of bio-optical and water quality parameters estimated by remote sensing. This study provides four robust centroid sets from in situ remote sensing reflectance (Rrs (λ)) data presenting typical optical types obtained by plugging different similarity measures into fuzzy c-means (FCM) clustering. Four typical types of waters were studied: (1) highly mixed eutrophic waters, with the proportion of absorption of colored dissolved organic matter (CDOM), phytoplankton, and non-living particulate matter at approximately 20%, 20%, and 60% respectively; (2) CDOM-dominated relatively clear waters, with approximately 45% by proportion of CDOM absorption; (3) nonliving solids-dominated waters, with approximately 88% by proportion of absorption of nonliving particulate matter; and (4) cyanobacteria-composed scum. We also simulated spectra from seven ocean color satellite sensors to assess their classification ability. OPEN ACCESSRemote Sens. 2015, 7 14732POLarization and Directionality of the Earth's Reflectances (POLDER), Sentinel-2A, and MEdium Resolution Imaging Spectrometer (MERIS) were found to perform better than the rest. Further, a classification tree for MERIS, in which the characteristics of Rrs (709)/Rrs (681), Rrs (560)/Rrs (709), Rrs (560)/Rrs (620), and Rrs (709)/Rrs (761) are integrated, is also proposed in this paper. The overall accuracy and Kappa coefficient of the proposed classification tree are 76.2% and 0.632, respectively.

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Specific inherent optical quantities of complex turbid inland waters, from the perspective of water classification

Cited by 32 publications

References 62 publications

Influence of a red band-based water classification approach on chlorophyll algorithms for optically complex estuaries

Influence of a red band-based water classification approach on chlorophyll algorithms for optically complex estuaries

Specific inherent optical properties of highly turbid productive water for retrieval of water quality after optical classification

Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance

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