Atmospheric and sunglint correction for retrieving chlorophyll-a in a productive tropical estuarine-lagoon system using Sentinel-2 MSI imagery

Tavares, Matheus Henrique; Lins, Regina Camara; Harmel, Tristan; Fragoso, Carlos Ruberto; Martinez, Jean‐Michel; Marques, David da Motta

doi:10.1016/j.isprsjprs.2021.01.021

Cited by 41 publications

(6 citation statements)

References 90 publications

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“…The different performances of C2-Nets along different scenarios could be explained by the different range of training datasets (Table 4) and the minimization of adverse effects by the NN [35]. The C2RCC showed better performance in oligotrophic-mesotrophic waters, but in ultraoligotrophic waters (clear waters with Z SD > 3 m and [Chl-a] < 2.5 mg/m 3 ) accurate retrieval of Rrs was complicated (e.g., Figure A2f) as also observed in [13].…”

Section: Performance On Retrieval Of Rrs With C2-netsmentioning

confidence: 99%

“…The C2XC was suitable for more complex waters (up to hypereutrophic reservoirs) but also improved the performance of C2RCC in some mesotrophic scenarios, such as coastal waters in Figure A2h. Although C2X has been observed to be more suitable for coastal and complex waters than for clear waters [13,32,35], the reduced number of valid match-ups (33%) and lower consistency in the accuracy along the spectrum observed with this processor made it the most uncertain C2-Net. This could be related to the large training range of IOPs of C2X, which may allow good Rrs retrievals to be achieved in different types of scenarios (e.g., Figure A2b,e)-including oligotrophic-clear and eutrophic-turbid lakes [36]-although the width of IOP ranges may also introduce a risk of diverging from the actual solution from the inversion, leading to confusion [33].…”

Section: Performance On Retrieval Of Rrs With C2-netsmentioning

confidence: 99%

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Towards the Combination of C2RCC Processors for Improving Water Quality Retrieval in Inland and Coastal Areas

et al. 2022

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Sentinel-2 offers great potential for monitoring water quality in inland and coastal waters. However, atmospheric correction in these waters is challenging, and there is no standardized approach yet, but different methods coexist under constant development. The atmospheric correction Case 2 Regional Coast Colour (C2RCC) processor has been recently updated with the C2X-COMPLEX (C2XC). This study is one of the first attempts at exploring its performance, in comparison with C2RCC and C2X, in inland and coastal waters in the east of the Iberian Peninsula, in retrieving water surface reflectance and estimating chlorophyll-a ([Chl-a]), total suspended matter ([TSM]), and Secchi disk depth (ZSD). The relationship between in situ ZSD and Kd_z90max product (i.e., the depth of the water column from which 90% of the water-leaving irradiance is derived) of the C2RCC processors demonstrated the potential of this product for estimating water clarity (r > 0.75). However, [TSM] and [Chl-a] derived from the different processors with default calibration factors were not suitable within the targeted scenarios, requiring recalibration based on optical water types or a shift to dynamic algorithm blending approaches. This would benefit from switching between C2RCC and C2XC, which extends the potential for improving surface reflectance estimates to a wide range of scenarios and suggests a promising future for C2-Nets in operational monitoring of water quality.

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Section: Performance On Retrieval Of Rrs With C2-netsmentioning

confidence: 99%

Section: Performance On Retrieval Of Rrs With C2-netsmentioning

confidence: 99%

Towards the Combination of C2RCC Processors for Improving Water Quality Retrieval in Inland and Coastal Areas

et al. 2022

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“…Although some oceanic AC processors with a mean sea level height and the NIR black-pixel assumption are currently being developed, they are not applicable to inland waters [8][9][10]. Owing to the interference produced by adjacency effects and the aforementioned sun glint, the processing of AC processors might become more difficult [11][12]. Different processors are required to achieve the threshold criterion of 30% reflectance error across all bands of sensors according to the Global Climate Observing System [7].…”

Section: Introductionmentioning

confidence: 99%

“…In essence, sensor design, atmospheric surface parameters, and AC processors all have a significant impact on the quality of water surface reflectance [15]. The MultiSpectral Instrument (MSI) onboard the Sentinel-2 satellite has recently sparked the attention of the scientific community in water quality mapping [11,[16][17][18][19][20]. The free acquisition of image data at resolutions of 10 m × 10 m at nadir by the Copernicus Sentinel-2 satellites has spurred applications for small-area inland waterways [7,[21][22].…”

Section: Introductionmentioning

confidence: 99%

Performances of Atmospheric Correction Processors for Sentinel-2 MSI Imagery Over Typical Lakes Across China

Song

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The launched MultiSpectral Instrument (MSI) equipped on Sentinel-2 satellites offers a powerful tool for observing the biogeochemical parameters of inland waters on a large scale. The proper use of atmospheric correction processors is essential for acquiring accurate satellite remote-sensing reflectance and downstream products. Therefore, we compared the performances of typical atmospheric correction processors, such as Sen2Cor, C2RCC-nets, C2RCC-C2X, Acolite, iCOR, Polymer, SeaDas/l2gen, and 6S processors, for MSI imagery over lake groups (N = 296) across China collected from 2016 to 2020. Linear fitting between corrected reflectance and in situ spectral measurements was used to assess performance; for the single lake, we additionally evaluated the performance of atmospheric correction processors in typical Chagan Lake in 2021. For largescale lake groups with different water quality backgrounds, the SeaDas/l2gen and C2RCC processors performed best for all band match-ups, and the C2RCC processor had the smallest errors. The SeaDas/l2gen processor works well for the signal bands (490, 560, 665, 704, and 740 nm), followed by the signal bands (560, 665, and 704 nm) of the C2RCC processor. For large-scale observations, this study revealed that Sentinel-2 satellite optical MSI imagery related to the C2RCC processor can be used to monitor aquatic systems with high-frequency investigations. For the signal band, the SeaDas/l2gen processor was used to select potential match-ups for the availability of MSI data related to the empirical models of processors. Our results may help satellite users select appropriate atmospheric correction processors for large-scale lake observations.

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“…The latest generation of mid-resolution multispectral sensors, with free image availability, such as the Landsat-8 (L8) and Sentinel-2 (S2) satellites, offer advanced opportunities for synoptic view of the entire area of interest, fine-scale, and high-frequency monitoring [28]. These satellites were not specifically designed for water observation but are promising for a detailed analysis of water quality [15,29,30], thanks to their fine radiometric sensitivity [31,32]; 10 to 30-m spatial resolution; high revisit frequency (every 2-3 days combining L8 and S2 satellites) and the improved configuration of the spectral bands in the visible and near infrared range [33].…”

Section: Introductionmentioning

confidence: 99%

Remote Analysis of the Chlorophyll-a Concentration Using Sentinel-2 MSI Images in a Semiarid Environment in Northeastern Brazil

Aranha

Martinez

Souza

et al. 2022

Water

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In this paper, the authors use remote-sensing images to monitor the water quality of reservoirs located in the semiarid region of Northeast Brazil. Sentinel-2 MSI TOA Level 1C reflectance images were used to remotely estimate the concentration of chlorophyll-a (chl-a), the main indicator of the trophic state of aquatic environments, in five reservoirs in the state of Ceará, Brazil. A three-spectral band retrieval model was calibrated using 171 water samples, collected from November 2015 through July 2018 in 5 reservoirs. For validation, 71 additional samples, collected from August 2018 through December 2019, were used to ensure a robust accuracy assessment. The TOA Level 1C products performed very well, achieving a relative RMSE of 28% and r2 = 0.80. Data on wind direction and speed, solar radiation and reservoir volume were used to generate a conceptual model to analyze the behavior of chl-a in the surface waters of the Castanhão reservoir. During 2019, the reservoir water quality showed strong variation, with concentration fluctuating from 30 to 95 µg/L We showed that the end of the dry season is marked by strong eutrophic conditions corresponding to very low water inflows into the reservoir. During the rainy season there is a large decrease in the chl-a concentration following the increase of the lake water storage. During the following dry season, satellite data show a progressive improvement of the trophic state controlled by wind intensity that promotes a better mixing of the reservoir waters and inhibiting the development of most phytoplankton.

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Atmospheric and sunglint correction for retrieving chlorophyll-a in a productive tropical estuarine-lagoon system using Sentinel-2 MSI imagery

Cited by 41 publications

References 90 publications

Towards the Combination of C2RCC Processors for Improving Water Quality Retrieval in Inland and Coastal Areas

Towards the Combination of C2RCC Processors for Improving Water Quality Retrieval in Inland and Coastal Areas

Performances of Atmospheric Correction Processors for Sentinel-2 MSI Imagery Over Typical Lakes Across China

Remote Analysis of the Chlorophyll-a Concentration Using Sentinel-2 MSI Images in a Semiarid Environment in Northeastern Brazil

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