Source: http://aoot.osa.org/oe/abstract.cfm?uri=oe-27-8-A294
Timestamp: 2019-04-25 10:21:50+00:00

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The optimal exploitation of the oceanic information provided by recent high spatial resolution sensors such as Landsat 8-OLI is strongly conditioned by the quality of the water reflectance signal retrieval. One main issue stands in the ability to correct water pixels for the contamination of the sun glint, which might induce a seasonal or permanent loss of data according to the latitude. The SWIR information now provided for the most recent high spatial resolution sensors was used for evaluating the sun glint level and correcting the radiative signal for its effect. This has been performed transposing historical empirical formalisms based on the NIR signal. An automated SWIR-based sun glint correction procedure was then developed using a 4-year OLI archive gathered over very turbid waters of French Guiana (227 scenes). This procedure allows the practical limitations associated with past similar empirical methods (sensitivity to water turbidity and manual image per image correction) to be overcome. While a satisfactory preservation of the information over sun glint free pixels was observed, comparison exercises based on in situ Rrs data gathered in sun glint affected areas emphasize the relevance of the proposed methodology (correction by a factor of 14 of the averaged bias in the Rrs values after removing sun glint effects). Current limitations in the applicability of this SWIR-based empirical automated method are mainly associated with the presence of high cloud coverage, thin clouds in the OLI scene or highly spatially variable marine or atmospheric signal (around 47%, 42% and 11%, respectively, of the total of 12% of failure over French Guiana OLI archive). The potential large applicability of the procedure developed in this work was eventually demonstrated over contrasted coastal environments.
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Fig. 1 Landsat-8 OLI true color images illustrating the coverage over the coastal waters of French Guiana (4 OLI scenes).
Fig. 2 Number of valid pixels gathered from the OLI archive over the coastal waters of French Guiana. Rrs values have been processes using ACOLITE pixel per pixel SWIR processing from April 2013 to April 2017 (265 scenes).
Fig. 3 Performance of the three SWIR based methods of this study over the scene LC82270562015258LGN00 (15 September 2015). Transect (a) used (true color image) to estimate the linear relationship (b) between the band SWIR 2 and the other OLI bands B1 (443 nm), B5 (655 nm) and B6 (1609 nm) and to illustrate the sun glint correction provided by the Hedley-SWIR and Joyce-SWIR. (c to e) Performance of the latter two different correction methods over RTOA values at 443, 655 and 865 nm, respectively. (f to h) Relative difference (in %) between RTOA values before and after the sun glint correction at 443, 655 and 865 nm, respectively. Note that the results for the Lyzenga-SWIR approach are coincident with those provided by Joyce –SWIR and are thus here omitted.
Fig. 4 Illustration of the sensitivity of the method to the offset value definition for two OLI scenes LC82270562016261LGN00 (17 September 2016) (a) and LC82260572014072 (13 March 2017) (b). Panels (c) and (d) show the two kind of distributions of SWIR2 in the non glinted area, while the relative errors (e and f) in the RTOA restitution related to the use of the three different offsets: mean, mode and averaged minimum (lower than 10%) are illustrated in panels e and f for the pixels identified by a red star in the panel a and b, respectively.
Fig. 5 Synthetic flowchart describing the different steps corresponding to the SWIR2 based automated empirical correction procedure proposed in the frame of this study.
Fig. 6 Illustration of the impact of the sun glint correction process on the SWIR signal (LC82270562015338 scene – 4 December 2015) along a transect (a) in French Guiana water. Panels (b) and (c) show the distribution of the SWIR 2 and of the ratio SWIR 1 and SWIR 2 before and after correction, respectively.
Fig. 7 Illustration over two coastal contrasted OLI scenes (coastline orientation, glint free water masses distribution) over French Guiana of the performance of the SWIR-based sun glint empirical automated procedure. RTOA values before (a) and after correction (b) and Rrs values before (c) and after correction (d) at 483 nm for the scene LC82260572014264 (21 September 2014). RTOA values before (e) and after correction (f) and Rrs values before (g) and after correction (h) at 655 nm for the scene LC82270562015242 (30 August 2015).
Fig. 8 Illustration of the Rrs preservation after the correction of the sun glint effect using average values over French Guiana coastal sun glint free areas for the 227 OLI scenes considered in the frame of this study. The solid and dashed black lines represent the 1:1 and the ± 20% error lines, respectively. The same representation is provided in panel f) for the Rrs(443)/Rrs(565) ratio.
Fig. 10 Validation of the sun glint correction method through match-up data gathered in sun glint affected coastal areas of French Guiana (a). The panel b) illustrates the percentage of sun glint present in the sampled stations, estimated using Eq. (9), while the comparison between in situ and OLI Rrs data (before and after correction) is provided for the OLI visible bands in panels c to f, making a distinction between the points with sun glint percentage in the SWIR2 lower or greater than 10%.
Fig. 11 Illustration of the potential applicability of the automated procedure developed over French Guiana coastal waters over contrasted coastal sites in Mexico (a to d, LC08_L1TP_023047_20170713_20170726_01_T1) and in Vietnam (e to f, LC08_L1TP_124053_20170615_20170628_01_T1). Values of RTOA and Rrs for band 561nm before (a,c,,e,g)and after (b,d,f,h) sun glint correction. Land and cloud masks are represented in grey.
Table 1 Comparison of the coefficients of the SWIR based relationships considered in the Hedley-SWIR, Lyzenga-SWIR and Joyce-SWIR method used for correcting the French Guiana sample OLI image in Fig. 3 from the sun glint effects.
Table 2 Slope values (mean, minimum, maximum, standard deviation and variation coefficient) computed from the whole data set gathered over French Guiana coastal waters (227 images) for each OLI band.
Table 3 Statistics values illustrating the preservation of the TOA signal (DN) over sun glint free pixels after the correction the whole OLI data set gathered over French Guiana using the automated procedure defined in Fig. 5.
Table 4 Statistics of the matchup comparison between in situ and OLI-ACOLITE Rrs data (before and after sun glint correction).
Comparison of the coefficients of the SWIR based relationships considered in the Hedley-SWIR, Lyzenga-SWIR and Joyce-SWIR method used for correcting the French Guiana sample OLI image in Fig. 3 from the sun glint effects.
Slope values (mean, minimum, maximum, standard deviation and variation coefficient) computed from the whole data set gathered over French Guiana coastal waters (227 images) for each OLI band.
Statistics values illustrating the preservation of the TOA signal (DN) over sun glint free pixels after the correction the whole OLI data set gathered over French Guiana using the automated procedure defined in Fig. 5.
Statistics of the matchup comparison between in situ and OLI-ACOLITE Rrs data (before and after sun glint correction).

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