Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations

Bi, Shun; Li, Yunmei; Wang, Qiao; Lyu, Heng; Liu, Ge; Zheng, Zhuo; Du, Chunyu; Mu, Meng; Xu, Jie; Lei, Shaohua; Miao, Shihai

doi:10.3390/rs10071002

Cited by 49 publications

(18 citation statements)

References 78 publications

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“…Similar to the previous study [23], R rs (400), R rs (412), and R rs of NIR bands longer than 709 nm were omitted in the classification due to the poor performance of the atmospheric correction. For OLCI-derived R rs , POLYMER and C2RCC had obvious overcorrection of R rs , consistent with the study of Bi et al (2018) [51] in Lake Taihu and Lake Hongze. It was also shown that C2RCC exhibited good performances from 490 to 709 nm, and poor performances in the blue (400, 412, and 443 nm) and NIR wavebands (754-865 nm) for the highly absorbing waters in the Baltic Sea [60].…”

Section: Discussionsupporting

confidence: 86%

“…If D m 2 was lower than the threshold value D t 2 , the spectrum x belongs to the class; if D m 2 > D t 2 , the pixel would be recognized as an unclassified type. In addition, the current optical classification tool built in SNAP software [22] was also tried in this study, but it did not perform well due to the failure of atmospheric correction in the study region [48,51]. When applied to the satellite OLCI data, the wavelengths selected need to be effective in separate water types.…”

Section: Type-labeling Of the Satellite R Rs (λ)mentioning

confidence: 99%

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Optical Classification of the Remote Sensing Reflectance and Its Application in Deriving the Specific Phytoplankton Absorption in Optically Complex Lakes

Xue

Wang

et al. 2019

Remote Sensing

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Optical water types (OWTs) were identified from remote sensing reflectance (Rrs(λ)) values in a field-measured dataset of several large lakes in the lower reaches of the Yangtze and Huai River (LYHR) Basin. Four OWTs were determined from normalized remote sensing reflectance spectra (NRrs(λ)) using the k-means clustering approach, and were identified in the Sentinel 3A OLCI (Ocean Land Color Instrument) image data over lakes in the LYHR Basin. The results showed that 1) Each OWT is associated with different bio-optical properties, such as the concentration of chlorophyll-a (Chla), suspended particulate matter (SPM), proportion of suspended particulate inorganic matter (SPIM), and absorption coefficient of each component. One optical water type showed an obvious characteristic with a high contribution of mineral particles, while one type was mostly determined by a high content of phytoplankton. The other types belonged to the optically mixed water types. 2) Class-specific Chla inversion algorithms performed better for all water types, except type 4, compared to the overall dataset. In addition, class-specific inversion algorithms for estimating the Chla-specific absorption coefficient of phytoplankton at 443 nm (a*ph(443)) were developed based on the relationship between a*ph(443) and Chla of each OWT. The spatial variations in the class-specific model-derived a*ph(443) values were illustrated for 2 March 2017, and 24 October 2017. 3) The dominant water type and the Shannon index (H) were used to characterize the optical variability or similarity of the lakes in the LYHR Basin using cloud-free OLCI images in 2017. A high optical variation was located in the western and southern parts of Lake Taihu, the southern part of Lake Hongze, Lake Chaohu, and several small lakes near the Yangtze River, while the northern part of Lake Hongze had a low optical diversity. This work demonstrates the potential and necessity of optical classification in estimating bio-optical parameters using class-specific inversion algorithms and monitoring of the optical variations in optically complex and dynamic lake waters.

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Section: Discussionsupporting

confidence: 86%

Section: Type-labeling Of the Satellite R Rs (λ)mentioning

confidence: 99%

Optical Classification of the Remote Sensing Reflectance and Its Application in Deriving the Specific Phytoplankton Absorption in Optically Complex Lakes

Xue

Wang

et al. 2019

Remote Sensing

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“…Almost all available AC algorithms specially designed for coastal and turbid waters were considered. Due to the unavailability of codes, some AC algorithms were omitted from the present study [21,42,63,64]. Table 2.…”

Section: Selected Atmospheric Correction Algorithmsmentioning

confidence: 99%

“…Many studies did assess the performance of existing AC algorithms in moderately turbid waters for different satellite sensors such as SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) [28], MODIS-Aqua [29], GOCI (Geostationary Ocean Color Imager) [30], L8-OLI [31][32][33][34][35][36], S2-MSI [36][37][38][39][40][41] and S3-OLCI [42,43]. None of these studies considered the case of highly turbid waters for S3-OLCI.…”

mentioning

confidence: 99%

Evaluation of Atmospheric Correction Algorithms for Sentinel-2-MSI and Sentinel-3-OLCI in Highly Turbid Estuarine Waters

et al. 2020

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The present study assesses the performance of state-of-the-art atmospheric correction (AC) algorithms applied to Sentinel-2-MultiSpectral Instrument (S2-MSI) and Sentinel-3-Ocean and Land Color Instrument (S3-OLCI) data recorded over moderately to highly turbid estuarine waters, considering the Gironde Estuary (SW France) as a test site. Three spectral bands of water-leaving reflectance ( R h o w ) are considered: green (560 nm), red (655 or 665 nm) and near infrared (NIR) (865 nm), required to retrieve the suspended particulate matter (SPM) concentrations in clear to highly turbid waters (SPM ranging from 1 to 2000 mg/L). A previous study satisfactorily validated Acolite short wave infrared (SWIR) AC algorithm for Landsat-8-Operational Land Imager (L8-OLI) in turbid estuarine waters. The latest version of Acolite Dark Spectrum Fitting (DSF) is tested here and shows very good agreement with Acolite SWIR for OLI data. L8-OLI satellite data corrected for atmospheric effects using Acolite DSF are then used as a reference to assess the validity of atmospheric corrections applied to other satellite data recorded over the same test site with a minimum time difference. Acolite DSF and iCOR (image correction for atmospheric effects) are identified as the best performing AC algorithms among the tested AC algorithms (Acolite DSF, iCOR, Polymer and C2RCC (case 2 regional coast color)) for S2-MSI. Then, the validity of six different AC algorithms (OLCI Baseline Atmospheric Correction (BAC), iCOR, Polymer, Baseline residual (BLR), C2RCC-V1 and C2RCC-V2) applied to OLCI satellite data is assessed based on comparisons with OLI and/or MSI Acolite DSF products recorded on a same day with a minimum time lag. Results show that all the AC algorithms tend to underestimate R h o w in green, red and NIR bands except iCOR in green and red bands. The iCOR provides minimum differences in green (slope = 1.0 ± 0.15, BIAS = 1.9 ± 4.5% and mean absolute percentage error (MAPE) = 12 ± 5%) and red (slope = 1.0 ± 0.17, BIAS = −9.8 ± 9% and MAPE = 28 ± 20%) bands with Acolite DSF products from OLI and MSI data. For the NIR band, BAC provides minimum differences (slope = 0.7 ± 0.13, BIAS = −33 ± 17% and MAPE = 55 ± 20%) with Acolite DSF products from OLI and MSI data. These results based on comparisons between almost simultaneous satellite products are supported by match-ups between satellite-derived and field-measured SPM concentrations provided by automated turbidity stations. Further validation of satellite products based on rigorous match-ups with in-situ R h o w measurements is still required in highly turbid waters.

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“…The validation work exposed here is the first done over French coastal waters for the OLCI sensor. Actually, a similar work was published for OLCI AC development and validation, but over inland waters and not coastal waters [52].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Five Atmospheric Correction Algorithms over French Optically-Complex Waters for the Sentinel-3A OLCI Ocean Color Sensor

et al. 2019

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The Sentinel-3A satellite was launched on 16 February 2016 with the Ocean and Land Colour Instrument (OLCI-A) on-board for the study of ocean color. The accuracy of ocean color parameters depends on the atmospheric correction algorithm (AC). This processing consists of removing the contribution of the atmosphere from the total measured signal by the remote sensor at the top of the atmosphere. Five ACs: the baseline AC, the Case 2 regional coast color neural network AC, its alternative version, the Polymer AC, and the standard NASA AC, are inter-compared over two bio-optical contrasted French coastal waters. The retrieved water-leaving reflectances are compared with in situ ocean color radiometric measurements collected using an ASD FielSpec4 spectrometer. Statistical and spectral analysis were performed to assess the best-performing AC through individual (relative error (RE) at 412 nm ranging between 23.43 and 57.31%; root mean squared error (RMSE) at 412 nm ranging between 0.0077 and 0.0188) and common (RE(412 nm) = 24.15–50.07%; RMSE(412 nm) = 0.0081–0.0132) match-ups. The results suggest that the most efficient schemes are the alternative version of the Case 2 regional coast color neural network AC with RE(412 nm) = 33.52% and RMSE(412 nm) = 0.0101 for the individual and Polymer with RE(412 nm) = 24.15% and RMSE(412 nm) = 0.0081 for the common ACs match-ups. Sensitivity studies were performed to assess the limitations of the AC, and the errors of retrievals showed no trends when compared to the turbidity and CDOM.

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Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations

Cited by 49 publications

References 78 publications

Optical Classification of the Remote Sensing Reflectance and Its Application in Deriving the Specific Phytoplankton Absorption in Optically Complex Lakes

Optical Classification of the Remote Sensing Reflectance and Its Application in Deriving the Specific Phytoplankton Absorption in Optically Complex Lakes

Evaluation of Atmospheric Correction Algorithms for Sentinel-2-MSI and Sentinel-3-OLCI in Highly Turbid Estuarine Waters

Evaluation of Five Atmospheric Correction Algorithms over French Optically-Complex Waters for the Sentinel-3A OLCI Ocean Color Sensor

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