6.1. Comparison of the First Group of AC: OC-SMART, NASA-AC, Polymer, ACOLITE and C2RCC
OC-SMART shows the overall best performances on our in situ dataset. For the case of individual match-ups datasets, OC-SMART yields the lowest errors and bias at two bands 443 nm and 665 nm. R2 also shows the higher values across all the bands with values ranging between 0.69 at 433 nm and 0.92 at 665 nm for the case of individual match-ups, and between 0.46 at 443 nm and 0.85 at 665 nm for the common match-ups case. The scatterplots for OC-SMART also show a satisfactory adjustment to the 1:1 line with low scattering for all wavelengths. These results show that the estimated Rrs from OC-SMART is highly accurate compared to the in situ measurement data. For the scoring scheme, OC-SMART provides the highest Stot result, which is 16.89/20 for the individual, and the second-highest Stot value (14.76) for the common match-ups dataset.
NASA-AC shows good performances as demonstrated by the statistical parameters and the scoring scheme on the common match-ups dataset. Accordingly, for the individual match-up case, the estimated Rrs
show a good correlation with the in situ data with R2
values ranging from 0.28 at 443 nm to more than 0.92 at 665 nm. The error and Bias values are also relatively low, which ranges from 31.6% at 443 nm to 56.3% at 665 nm and −3.59% at 443 nm to 31.30% at 665 nm, respectively. These values are 12% to 15% higher than OC-SMART. Especially, NASA-AC yields the highest Stot
value (17.13) for the common match-up case. NASA-AC provides good estimations of the spectra of Rrs
, as shown in the values of SAM, QAS, and χ2
. Overall, however, NASA-AC has a tradeoff that can be seen in the individual case. Despite the high efficiency in atmospheric correction, the ability to mask pixels with atmospheric noise components is still not good. This can be seen in the number of match-ups shown in Table 4
. Accordingly, the number of valid match-ups after processing by NASA-AC is the lowest, and it is strongly reduced for high wavelengths (band 665 nm only has 9 valid match-ups in the individual match-up case). This result is similar to what was mentioned by Ilori et al. [58
] and by Pahlevan et al. [10
]. This low number of match-ups can be caused by the inhomogeneity of the estimated remote sensing reflectance (Rrs
) in the 3-by-3 pixels box, as shown in Figure 8
. Besides the low number of match-ups, a part of the problem can be caused by the fact that the NASA-AC methodology requires an initial correction process which assumes water-leaving radiances are null in the NIR region [17
]. Thus, it may not be good to apply to some typical types of nearshore coastal water due to turbidity. NASA-AC can be considered as the second-best atmospheric correction algorithm on our in situ dataset.
Polymer has good statistical results and relatively high scores in the scoring scheme. The correlation between the in situ and the estimated Rrs
is relatively high. This is shown by the value of R2
, which gradually increases from 0.57 at 443 nm to 0.92 at 665 nm. This result is similar to those obtained by Pereira-Sandoval et al. [49
], who validated Polymer using in situ data from field campaigns, obtaining R2
values equal to 0.3 for 443 nm, 0.4 for 490 nm, 0.8 for 560 nm, and 0.93 for 665 nm, which shows an increase for the coefficient of variation value. Besides, those results are in the same trend of values as the ones observed here for both match-ups cases. The algorithm shows quite low performances in the blue band and the accuracy gradually increases in the green and red bands, as shown in Figure 3
and Figure 4
. From the statistical results, Polymer retrieves accurate spectra of Rrs
compared to a reference in situ dataset [55
] and low spectra differences between the in situ measurements and the retrieved Rrs
with values of QAS and SAM of 0.87 and 5.99, respectively (Table 6
). Overall, Polymer shows the value of Stot
of 14.90 and 13.74 for individual and common match-ups datasets, respectively.
Similar to Polymer, C2RCC is also an atmospheric correction algorithm with acceptable and stable performances, not affected by wavelength as much as NASA-AC. C2RCC has a relatively high correlation between the estimated Rrs
and the in situ measurements with R2
ranging from 0.4 at 443 nm to nearly 0.88 at 665 nm. Similar to Polymer, C2RCC exhibits relatively low accuracy in the blue bands, and this accuracy increases with increasing wavelength. One reason for this can be the strong Rayleigh scattering in the blue band by atmospheric gases, which might not be properly corrected [49
]. Therefore, to solve this problem, as mentioned in the study by Pereira-Sandoval et al. [49
], C2RCC is being developed and tested to create a new version capable of more accurate correction for sun-glint in the blue wavelength range. Overall, C2RCC still shows an acceptable scoring scheme with the value of Stot
are 13.31 and 14.11 for individual and common match-ups cases.
Polymer and C2RCC have quite similar statistical values for some parameters, such as relative error. However, for others, Polymer shows higher accuracies compared to C2RCC such as lower values of SAM values, which are 5.99 for Polymer and 8.76 for C2RCC (Table 6
), and higher R2
ranging from 0.57 to 0.92, while R2
for C2RCC ranging only from 0.4 to 0.88. Compared to C2RCC, Polymer has a better ability to remove invalid pixels. This is shown by the number of available match-ups after the match-up selection process, as given in Table 4
, wherein the available match-up number of Polymer is around 21 and 22, while this value for C2RCC ranges from 17 to 21. Therefore, Polymer is a more accurate atmospheric correction algorithm than C2RCC in our study.
This agrees with what was presented by Pahlevan et al. [10
]. The study of Pahlevan has assessed the performance of a total of six different ACs on Landsat-8 and Sentinel-2. Over 1000 match-ups from seven different optical water types for both fresh and coastal water have been applied. The results of this study show that OC-SMART and NASA-AC are the two best atmospheric corrections for working in coastal waters. Polymer also provides high-quality estimates of the remote-sensing reflectance. However, overall, it does not surpass the performances of OC-SMART and NASA-AC [10
Unlike other ACs in this group, ACOLITE shows lower performances. For the correlation between estimated and in situ Rrs
values vary between 0.55 at 443 nm and 0.74 at 665 nm. It can be seen from this result that the correlation of ACOLITE Rrs
is low in the blue band. This also can be seen from scatterplots in Figure 3
, with the regressed line for ACOLITE being distinguishable from the 1:1 line. SAM value of ACOLITE is the second-highest value (7.07) compared to the other atmospheric correction algorithms in this group. The Stot
value of ACOLITE for the individual and common match case are 14.52 and 13.26, respectively. Although the Stot
value of ACOLITE in the all match-up case is relatively high compared to C2RCC and NASA-AC, the statistical parameters of ACOLITE are lower than these 2 ACs. For instance, the RE values of ACOLITE at 443 and 655 nm are 2 times higher than that of C2RCC and NASA-AC. The Bias values of C2RCC and NASA-AC at 443 and 490 nm are negative and range from −11% to −2%, while the Bias values of ACOLITE at these two bands range from 22% to 53%. This low performance could be due to the ACOLITE’s mechanism, which masks pixels containing clouds, fog, or cirrus. ACOLITE’s masking process allows the removal of areas with cirrus clouds using cirrus detection from the 1375 nm band. These cirrus clouds pixels at 1375 nm are applied to mask the remaining bands of MSI. Therefore, as shown in Figure 9
of French Guiana scene on 28, November 2016, when some of the S2 satellite images at 1375 nm are almost entirely covered by cloud or haze, the masking process of ACOLITE will remove the whole scene. This will result in all extracted values being NaN. Overall, ACOLITE shows the lowest performances in the first group.
6.2. Comparison of the Second Group of AC: Sen2Cor and ICOR
The second group of AC (Sen2Cor and iCOR), in general, shows lower performances compared to the first group. This is understandable as both Sen2Cor and iCOR mechanisms mostly depend on the presence of land, as those algorithms are land-based method [54
] and improved by Guanter [51
] method for Sen2Cor and iCOR, respectively. These methods generate scene parameters requiring a distribution of pixels containing land. In the absence of land, these algorithms use default values of the aerosol optical thickness, which was the case for three match-ups in the English Channel and one match-up in French Guiana in this study. Therefore, it is suggested that the tested versions of these processors should better be applied for inland scenes.
In detail, in the second group, Sen2Cor shows relatively low performances in the visible bands, especially for the blue band. At 443 nm, R2
is nearly 0.36, which indicates a low accuracy and correlation between estimated and in situ Rrs
, as suggested by Figure 4
. The data points are highly scattered around the 1:1 line for bands between 443 and 560 nm. Furthermore, the total scheming score (Stot
) is very low (2.01 and 4.19 for individual and common match-ups cases, respectively) on our dataset. This suggests that this algorithm shows poor performances in reproducing the spectral shape of the in situ data. This poor performance could partly come from the fact that the approach of this algorithm is based on the “Dense Dark Vegetation”, which means that the AC will perform better in the high presence of vegetation. One example is in Ruescas et al. [59
] as the authors used Sen2Cor to correct the atmosphere in Albufera lagoon. They showed good accuracies in the retrievals and concluded that it was due to the high concentration of chlorophyll-a all year round in the lagoon, especially in spring and summer when there is an additional resonance of crop rice around the lagoon [59
]. Therefore, in the condition of lesser vegetation, the accuracy of Sen2Cor can significantly decrease.
Compared to Sen2Cor, iCOR has a relatively high correlation between the estimated and in situ Rrs
, as shown by the value of R2
which gradually increases from 0.44 at 443 nm to 0.86 at 665 nm, while the R2
value of Sen2Cor varies from 0.36 at 443 nm to 0.61 at 665 nm. However, except for this statistical parameter, iCOR showed higher values in all other indicators such as Bias and RE in 665 nm, which is 30% higher compared to Sen2Cor. This leads to a low value of Stot
(7.7 and 3.37 for individual and common dataset cases respectively). Notably, iCOR is the only algorithm that does the adjacency correction, which is SIMilarity Environmental Correction (SIMEC), before performing the atmospheric correction [43
]. This means that in order to perform the correction process, iCOR needs two conditions to be met. First, one condition is to have at least one spectral band in the wavelength range between 680 nm and 730 nm, where strong reflections from land plants and weak reflections from water can be obtained. Second, there must be at least one reference band in the NIR region [60
]. However, in the case of extremely high turbidity such as coastal waters, the reflectance spectrum of NIR varies considerably [61
] and, maybe, this could be the reason for lower performances. Besides, the other reason can be that iCOR uses a fixed aerosol model for the rural area, which is not suitable for the coastal area, which needs a marine or pollution aerosol model [62
]. Another reasonable explanation for these lower performances is that iCOR requires a combination of pure green vegetation and bare soil endmember, and when these conditions cannot be satisfied to calculate the AOT value, it is recommended to manually set the appropriate AOT value for the area [42
]. However, in this study, instead of using a specific AOT value for each region, the AOT value used by iCOR was set to default.