# iCOR Atmospheric Correction on Sentinel-3/OLCI over Land: Intercomparison with AERONET, RadCalNet, and SYN Level-2

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

^{2}= 0.80–0.93 and R

^{2}= 0.92–0.96 for TOC reflectance and VIs, respectively. iCOR’s higher temporal smoothness compared to SYN L2 does not propagate into a significantly higher smoothness for TOC reflectance and VIs. Altogether, we conclude that iCOR is well suitable to retrieve statistically and temporally consistent AOT, TOC reflectance, and VIs over land surfaces from Sentinel-3/OLCI observations.

## 1. Introduction

## 2. Atmospheric Correction Algorithms

#### 2.1. iCOR

^{2}, being large enough to include high spectral variation and small enough to assume spatial atmospheric homogeneity. In the first retrieval step, the lowest TOA radiance values in the different visible bands are searched to determine an upper AOT boundary value for each macro-pixel. Subsequently, this AOT value is refined based on the spectral variation within the macro-pixel, using a multi-parameter end-member inversion technique. Five pixels with high spectral contrast (selected on TOA NDVI values) are represented by a linear combination of three pre-defined default vegetation spectra and a soil spectrum.

#### 2.2. SYN L2

^{2}). The AOT retrieval is performed following an approach combining Dark Object and multiple viewing angle methods derived from existing work for MERIS and AATSR [17]. For a given atmospheric aerosol model (currently a continental model is used) and a parameterized AOT computed at 0.55 μm, a set of surface reflectances is derived from OLCI/SLSTR observations. The derived error metric is then minimized with respect to angular and spectral constraints, using the Brent and Powell methods for univariate and multivariate minimization, respectively. The 300 m AOT is obtained from the relative contributions of the three surrounding super-pixel AOTs on a 15 × 15-pixel area centered at a given 300 m pixel. This interpolated 300 m AOT is then used in the atmospheric correction to obtain the SYNERGY Surface Directional Reflectance (SDR, equivalent to TOC reflectance).

## 3. Data and Methods

#### 3.1. Regions of Interest and iCOR Retrievals

#### 3.2. Validation Data

#### 3.2.1. AERONET

#### 3.2.2. 6SV Simulations Using AERONET Observations

_{o}being 0.55 μm and 0.44 μm, respectively, and α the Ångström coefficient. The median AOT

_{0.55}value obtained in the 60 min around the OLCI frame time stamp was taken as 6SV input. For ozone and TCWV, the arithmetic means of the available observations were used.

- Angular configuration: Solar zenith angle (SZA), solar azimuth angle (SAA), viewing zenith angle (VZA), and viewing azimuth angle (VAA)
- Date (day and month)
- AERONET AOT, TCWV, and ozone concentrations

- AERONET station altitude [m], obtained from the information available at https://aeronet.gsfc.nasa.gov/
- OLCI SRF information at 2.5 nm spectral resolution
- OLCI-observed TOA radiance

#### 3.2.3. RadCalNet Observations

^{2}. Considering that a Sentinel-3 OLCI FR pixel is approximately 300 m, a surface reflectance variability less than 3% is considered acceptable for the analysis, resulting in only the Gobabeb (GONA, Lat: 23.600°S, Lon: 15.119°E) and Railroad Valley (RVUS, Lat: 38.497°N, Lon: 115.690°W) sites considered suitable for this research. GONA and RVUS are desert sites, characterized by sand and gravel with some widely scattered dry grass, and by a high-desert playa surrounded by mountains, respectively. The current uncertainty estimates in the in-situ surface reflectance measurements are 3.5–5.3% for RVUS and 3.5–5.0% for GONA. Figure 2 shows the average and standard deviation in TOC reflectance measured at GONA (black) and RVUS (red) over the considered time over the two sites. The plots demonstrate a high reflectance stability over time, with standard deviations of ~0.02 at GONA and ~0.03 at RVUS.

#### 3.2.4. Sentinel-3 SYN L2

#### 3.3. Validation Methods

#### 3.3.1. Sampling Strategy

#### 3.3.2. Vegetation Indices

#### 3.3.3. RadCalNet Intercomparison

- RadCalNet TOC reflectances were cubically interpolated from 30 min to 1 sec for every wavelength;
- TOC reflectance values at different wavelengths were extracted at the sensor overpass time;
- TOC reflectances from point 2 were cubically interpolated from 10 nm to 0.1 nm (${\rho}_{\lambda}^{H})$;
- TOC reflectances from point 3 were convolved with the OLCI mean SRF ($SR{F}_{\lambda}$), using Equation (5) [22]:$${\rho}_{RC}=\frac{{{\displaystyle \sum}}_{{\lambda}_{1}}^{{\lambda}_{2}}{\rho}_{\lambda}^{H}\times SR{F}_{\lambda}}{{{\displaystyle \sum}}_{{\lambda}_{1}}^{{\lambda}_{2}}SR{F}_{\lambda}}$$

_{1}and λ

_{2}define the OLCI band delimitation wavelengths at Full Width at Half Maximum (FWHM). Finally, ${\rho}_{RC}$ is the convolved RadCalNet TOC reflectance converted into the OLCI spectral bands.

#### 3.3.4. Validation Metrics

_{s}and RMPD

_{u}). The coefficient of determination (R

^{2}) indicates the agreement or covariation between two datasets with respect to the linear regression model, summarizing the total explained variance by this model.

_{n+1}) and the corresponding linear interpolation between the two extremes P(d

_{n}) and P(d

_{n+2}) The Time Series Smoothness Index (TSI) is an estimate of the time series noise [26], and is defined such that a lower value indicates less temporal noise and thus a smoother time series profile.

## 4. Results

#### 4.1. Intercomparison with 6SV Simulations Using AERONET Input

^{2}spectral signatures are shown in Figure 5. The results are presented separately for S3A and S3B in the left and right columns, respectively. For all statistical metrics, a similar signature can be seen for S3A and S3B. For Accuracy, SYN has lower negative values (larger negative bias) than iCOR up to ~0.5 μm. Further, the SYN negative bias changes into positive from ~0.55 μm onwards, while for iCOR a small negative bias remains until 0.8 μm, becoming slightly positive beyond this wavelength. The Precision evolution for iCOR and SYN is similar, especially for S3A. However, for S3B differences in Precision are larger, with SYN > iCOR up to ~0.65 μm and iCOR > SYN beyond this wavelength, with differences further increasing (i.e., more scattered TOC reflectance retrievals) for λ > 0.75 μm. Uncertainty is larger for SYN compared to iCOR in the BLUE and NIR channels, which indicates a larger spread for SYN. The R

^{2}increases towards 1 for both iCOR and SYN with increasing wavelength, with SYN having lower values than iCOR at λ < 0.55 μm, with this difference being more prominent for S3B.

#### 4.2. Intercomparison with RadCalNet Observations

#### 4.3. Intercomparison with Sentinel-3 SYN L2

#### 4.3.1. Statistical Consistency iCOR Versus SYN L2

^{2}= 0.92–0.96). The systematic bias between iCOR and SYN L2 NDVI is relatively large (0.01–0.03), resulting from an intercept well above 0 (except for NAUS) and slopes close to 1. The systematic bias is lower for EVI (0.00 to 0.02) and very low for NDWI (below 0.005). The unsystematic bias (i.e., scatter around the regression line) is also larger for NDVI (0.02–0.07) and EVI (0.01–0.07), compared to NDWI (within 0.01).

#### 4.3.2. Temporal Consistency iCOR vs. SYN L2

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Donlon, C.; Berruti, B.; Buongiorno, A.; Ferreira, M.H.; Féménias, P.; Frerick, J.; Goryl, P.; Klein, U.; Laur, H.; Mavrocordatos, C.; et al. The global monitoring for environment and security (GMES) sentinel-3 mission. Remote Sens. Environ.
**2012**, 120, 37–57. [Google Scholar] [CrossRef] - Attema, E.P.W. The active microwave instrument on-board the ERS-1 satellite. Proc. IEEE
**1991**, 79, 791–799. [Google Scholar] [CrossRef] - Louet, J.; Bruzzi, S. ENVISAT mission and system. In Proceedings of the IEEE 1999 International Geoscience and Remote Sensing Symposium, Hamburg, Germany, 28 June–2 July 1999; p. 16. [Google Scholar]
- Passot, X. VEGETATION image processing methods in the CTIV. Proc. Veg.
**2001**, 2, 3–6. [Google Scholar] - Kravitz, J.; Matthews, M.; Bernard, S.; Griffith, D. Application of Sentinel 3 OLCI for chl-a retrieval over small inland water targets: Successes and challenges. Remote Sens. Environ.
**2020**, 237, 111562. [Google Scholar] [CrossRef] - Pahlevan, N.; Smith, B.; Schalles, J.; Binding, C.; Cao, Z.; Ma, R.; Alikas, K.; Kangro, K.; Gurlin, D.; Hà, N.; et al. Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. Remote Sens. Environ.
**2020**, 240, 111604. [Google Scholar] [CrossRef] - Gower, J.; King, S. The distribution of pelagic Sargassum observed with OLCI. Int. J. Remote Sens.
**2020**, 41, 5669–5679. [Google Scholar] [CrossRef] - Wang, X.; Ling, F.; Yao, H.; Liu, Y.; Xu, S. Unsupervised Sub-pixel water body mapping with sentinel-3 OLCI image. Remote Sens.
**2019**, 11, 327. [Google Scholar] [CrossRef] [Green Version] - Zhang, L.; Wylie, B.; Loveland, T.; Fosnight, E.; Tieszen, L.L.; Ji, L.; Gilmanov, T. Evaluation and comparison of gross primary production estimates for the Northern Great Plains grasslands. Remote Sens. Environ.
**2007**, 106, 173–189. [Google Scholar] [CrossRef] [Green Version] - Brown, L.A.; Dash, J.; Lidon, A.L.; Lopez-Baeza, E.; Dransfeld, S. Synergetic Exploitation of the Sentinel-2 missions for validating the Sentinel-3 ocean and land color instrument terrestrial chlorophyll index over a vineyard dominated Mediterranean environment. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2019**, 12, 2244–2251. [Google Scholar] [CrossRef] - De Keukelaere, L.; Sterckx, S.; Adriaensen, S.; Knaeps, E.; Reusen, I.; Giardino, C.; Bresciani, M.; Hunter, P.; Neil, C.; Van Der Zande, D.; et al. Atmospheric correction of Landsat-8/OLI and Sentinel-2/MSI data using iCOR algorithm: Validation for coastal and inland waters. Eur. J. Remote Sens.
**2018**, 51, 525–542. [Google Scholar] [CrossRef] [Green Version] - Doxani, G.; Vermote, E.; Roger, J.C.; Gascon, F.; Adriaensen, S.; Frantz, D.; Hagolle, O.; Hollstein, A.; Kirches, G.; Li, F.; et al. Atmospheric correction inter-comparison exercise. Remote Sens.
**2018**, 10, 352. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Renosh, P.R.; Doxaran, D.; de Keukelaere, L.; Gossn, J.I. Evaluation of atmospheric correction algorithms for sentinel-2-MSI and sentinel-3-OLCI in highly turbid estuarine waters. Remote Sens.
**2020**, 12, 1285. [Google Scholar] [CrossRef] [Green Version] - Holben, B.N.; Eck, T.F.; Slutsker, I.; Tanré, D.; Buis, J.P.; Setzer, A.; Vermote, E.; Reagan, J.A.; Kaufman, Y.J.; Nakajima, T.; et al. AERONET—A federated instrument network and data archive for aerosol characterization. Remote Sens. Environ.
**1998**, 66, 1–16. [Google Scholar] [CrossRef] - Bouvet, M.; Thome, K.; Berthelot, B.; Bialek, A.; Czapla-Myers, J.; Fox, N.P.; Goryl, P.; Henry, P.; Ma, L.; Marcq, S.; et al. RadCalNet: A radiometric calibration network for earth observing imagers operating in the visible to shortwave infrared spectral range. Remote Sens.
**2019**, 11, 2401. [Google Scholar] [CrossRef] [Green Version] - Berk, A.; Anderson, G.P.; Acharya, P.K.; Bernstein, L.S.; Muratov, L.; Lee, J.; Fox, M.; Adler-Golden, S.M.; Chetwynd, J.H., Jr.; Hoke, M.L.; et al. MODTRAN5: 2006 Update; Shen, S.S., Lewis, P.E., Eds.; International Society for Optics and Photonics: Bellingham, WA, USA, 2006; p. 62331F. [Google Scholar]
- North, P.R.J.; Brockmann, C.; Fischer, J.; Gomez-Chova, L.; Grey, W.; Heckel, A.; Moreno, J.; Preusker, R.; Regner, P. MERIS/AATSR synergy algorithms for cloud screening, aerosol retrieval and atmospheric correction. In Proceedings of the 2nd MERIS/AATSR User Workshop, ESRIN, Frascati, Italy, 22–26 September 2008; ESA Publications Division, European Space Agency: Noordwijk, The Netherlands, 2008; pp. 22–26. [Google Scholar]
- Sterckx, S. iCOR-OLCI Plugin for SNAP Toolbox—Software User Manual, 2019. Available online: https://cdn2.hubspot.net/hubfs/2834550/marketing/MAILS/iCOR/iCORpluginUserManual_OLCI_v1.0.pdf (accessed on 27 October 2020).
- Vermote, E.F.; Tanré, D.; Deuzé, J.L.; Herman, M.; Morcrette, J.J. Second simulation of the satellite signal in the solar spectrum, 6s: An overview. IEEE Trans. Geosci. Remote Sens.
**1997**, 35, 675–686. [Google Scholar] [CrossRef] [Green Version] - Vermote, E.; Tanre, D.; Deuze, J.L.; Herman, M.; Morcrette, J.J. 6S User Guide Version 3, Appendix III, 55 pp, 2006. Available online: https://ltdri.org/files/6S/6S_Manual_Part_2.pdf (accessed on 28 November 2019).
- Vermote, E.; Justice, C.O.; Bréon, F.M. Towards a generalized approach for correction of the BRDF effect in MODIS directional reflectances. IEEE Trans. Geosci. Remote Sens.
**2009**, 47, 898–908. [Google Scholar] [CrossRef] - Jing, X.; Leigh, L.; Pinto, C.T.; Helder, D. Evaluation of RadCalNet output data using Landsat 7, Landsat 8, Sentinel 2A, and Sentinel 2B Sensors. Remote Sens.
**2019**, 11, 541. [Google Scholar] [CrossRef] [Green Version] - Claverie, M.; Vermote, E.F.; Franch, B.; Masek, J.G. Evaluation of the Landsat-5 TM and Landsat-7 ETM+ surface reflectance products. Remote Sens. Environ.
**2015**, 169, 390–403. [Google Scholar] [CrossRef] - Duveiller, G.; Fasbender, D.; Meroni, M. Revisiting the concept of a symmetric index of agreement for continuous datasets. Sci. Rep.
**2016**, 6, 1–14. [Google Scholar] [CrossRef] - Weiss, M.; Baret, F.; Garrigues, S.; Lacaze, R. LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: Validation and comparison with MODIS collection 4 products. Remote Sens. Environ.
**2007**, 110, 317–331. [Google Scholar] [CrossRef] - Claverie, M.; Ju, J.; Masek, J.G.; Dungan, J.L.; Vermote, E.F.; Roger, J.C.; Skakun, S.V.; Justice, C. The harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sens. Environ.
**2018**, 219, 145–161. [Google Scholar] [CrossRef] - Meygret, A.; Santer, R.P.; Berthelot, B. ROSAS: A Robotic Station for Atmosphere and Surface Characterization Dedicated to on-Orbit Calibration; Butler, J.J., Xiong, X., Gu, X., Eds.; International Society for Optics and Photonics: Bellingham, WA, USA, 2011; p. 815311. [Google Scholar]
- Carr, S.B. The Aerosol Models in MODTRAN: Incorporating Selected Measurements from Northern Australia; Australian Government Department of Defence Science and Technology Organisation: Edinburgh, Australia, 2005.
- Fraser, R.S.; Kaufman, Y.J. The relative importance of aerosol scattering and absorption in remote sensing. IEEE Trans. Geosci. Remote Sens.
**1985**, GE-23, 625–633. [Google Scholar] [CrossRef] - Bourg, L.; Smith, D.; Rouffi, F.; Hénocq, C.; Bruniquel, J.; Cox, C.; Etxaluze, M.; Polehampton, E. S3MPC OPT Annual Performance Report-Year 2019. Sentinel-3 Mission Performance Center. 2020. Available online: https://sentinel.esa.int/documents/247904/1848151/Sentinel-3-Optical-Annual-Performance-Report-2019.pdf (accessed on 27 October 2020).

**Figure 2.**Average surface reflectance and standard deviation over time at the GONA (black) and RVUS (red) site. Values for RVUS are slightly shifted towards larger wavelengths for clarity reasons. RadCalNet observations were resampled to the OLCI spectral bands, see text for more details.

**Figure 3.**Flow chart of the methodology adopted for temporal and spectral matching between TOC RadCalNet and OLCI TOC reflectances.

**Figure 4.**(

**a**,

**b**) Intercomparisons of iCOR and SYN AOT (Y axis) versus AERONET AOT (X axis) and (

**c**–

**j**) iCOR and SYN L2 TOC reflectances (Y axis) versus 6SV TOC reflectance simulations (X axis) for (

**a**,

**c**,

**e**,

**g**,

**i**) S3A and (

**b**,

**d**,

**f**,

**h**,

**j**) S3B OLCI bands (

**c**,

**d**) Oa03, (

**e**,

**f**) Oa09, (

**g**,

**h**) Oa17, and (

**i**,

**j**) Oa21. SYN and iCOR values are indicated by red and blue dots, respectively. The black dashed lines indicate the 1:1 line, while the red and blue solid lines denote the GM linear regressions for SYN and iCOR, respectively. The error bars in the X direction span the standard deviation of the 6SV-simulated TOC reflectances for the four aerosol models: continental, maritime, urban, and desert. The legend text presents the APU values, as well as the GM linear regression equations and corresponding R

^{2}values. Note the different X and Y axis ranges for Oa03, Oa09 and Oa17, Oa21.

**Figure 5.**(

**a**–

**f**) APU and (

**g**,

**h**) R

^{2}for iCOR (blue) and SYN (red) TOC reflectances relative to 6SV TOC reflectances for bands Oa01—Oa12, Oa16—Oa18, and Oa21. Results are presented separately for (

**a**,

**c**,

**e**,

**g**) S3A and (

**b**,

**d**,

**f**,

**h**) S3B.

**Figure 6.**Average relative difference and standard deviation (vertical bars) for iCOR on S3A (black) and S3B (red) versus S3-convolved TOC reflectances at (

**a**) GONA and (

**c**) for S3A over RVUS. Values for S3B are slightly shifted towards larger wavelengths for clarity reasons. Accuracy, Precision, and Uncertainty for iCOR on S3A (solid lines) and S3B (dashed lines) versus S3-convolved TOC reflectances at (

**b**) GONA and (

**d**) for S3A over RVUS.

**Figure 7.**Pairwise iCOR AOT versus SYN L2 AOT: (

**a**) boxplot, (

**b**) GM regression density plot, and (

**c**) AOT bias frequency histogram (SYN L2—iCOR).

**Figure 8.**Boxplots of TOC reflectance comparing SYN L2 (Blue) and iCOR (green) over (

**a**–

**e**) the 5 defined ROIs (see Table 1 for their definition).

**Figure 9.**GM regression density plots comparing iCOR (Y) with SYN L2 (X) TOC reflectances for (

**a**) Oa01, (

**b**) Oa03, (

**c**) Oa06, (

**d**) Oa09, (

**e**) Oa17, and (

**f**) Oa21.

**Figure 10.**Bias frequency histograms (SYN L2—iCOR) for (

**a**) Oa01, (

**b**) Oa03, (

**c**) Oa06, (

**d**) Oa09, (

**e**) Oa17, and (

**f**) Oa21.

**Figure 11.**Systematic (RMPDs, blue) and unsystematic (RMPDu, green) difference between iCOR TOC reflectances and SYN L2 as function of wavelength, based on pairwise comparisons.

**Figure 14.**Temporal Smoothness Index (TSI) boxplots for (

**a**) AOT, (

**b**) TOC reflectance, and (

**c**) Vegetation Indices. Results were obtained for 11 AERONET sites and 4 aerosol models.

**Figure 15.**Temporal evolution of (

**a**) AOT, (

**b**) Oa03, (

**c**) Oa09, (

**d**) Oa17, and (

**e**) Oa21 TOC reflectance, and VIs [(

**f**) NDVI, (

**g**) NDWI, and (

**h**) EVI] over the Aubière LAMP AERONET site (France, WEUR, Long: 45.7610°, Lat: 3.1110°). The TSI was computed for common valid observations in the iCOR, SYN L2, and 6SV datasets.

**Table 1.**Overview of the AERONET stations, the associated regions of interests (ROI) and land cover according to Global Land Cover 2000 (GLC2000), geolocation, maximum allowed cloud cover per ROI, and the number of processed PDUs per sensor. CUL = Cultivated areas and cropland, BEF = Broadleaved Evergreen Forest, OTH = Other (urban), HER = Herbaceous cover, SHR = Shrubland.

Station Name | ROI and Land Cover | Lat [°] | Long [°] | Max Cloud Cover [%] | Nr. PDUs S3A | Nr. PDUs S3B |
---|---|---|---|---|---|---|

Alta Floresta | NBRA CUL | −9.8713 | −56.1045 | 20 | 29 | 25 |

Amazon ATTO Tower | NBRA BEF | −2.1442 | −58.9999 | |||

XiangHe | CHIN CUL | 39.7536 | 116.9515 | 20 | 18 | 12 |

Beijng CAMS | CHIN URB | 39.9333 | 116.3167 | |||

Bujumbura | CAFR CUL | −3.3800 | 29.3838 | 20 | 14 | 7 |

Chilbolton | WEUR CUL | 51.1445 | −1.4370 | 50 | 85 | 84 |

Aubière LAMP | WEUR OTH | 45.7610 | 3.1110 | |||

Lille | WEUR OTH | 50.6117 | 3.1417 | |||

Palaiseau | WEUR CUL | 48.7116 | 2.2150 | |||

Lake Argyle | NAUS HER | −16.1081 | 128.7485 | 5 | 63 | 52 |

Jabiru | NAUS SHR | −12.6607 | 132.8931 | |||

TOTAL | 209 | 180 |

Site Name | Site Owner | Instrumentation Maintenance | Location | Spectral Range (μm) | Surface Reflectance Variability at 500 × 500 m (%) |
---|---|---|---|---|---|

Railroad Valley(RVUS) | United States Bureau of Land Management (BLM) | Remote Sensing Group- College of Optical Sciences University of Arizona (USA) | Nevada, USA | 0.4–2.5 | 1 |

Gobabeb (GONA) | Gobabeb Research and Training Centre | National Physical Laboratory (UK) | Naukluft National Park, Namibia | 0.4–1.81 1.92–2.3 | 3 |

**Table 3.**List of validation metrics, with n the number of valid samples used for the comparison, $\sigma \left(X\right)$ and $\sigma \left(Y\right)$ the standard deviation of $X$ and $Y$, $\sigma \left(X,Y\right)$ the co-variation of $X$ and $Y$, and $\widehat{X}$ and $\widehat{Y}$ estimated using the GM regression model. $P\left({d}_{i}\right)$, $P\left({d}_{i+1}\right)$ and $P\left({d}_{i+2}\right)$ are three consecutive observations on dates ${d}_{i}$, ${d}_{i+1}$, and ${d}_{i+2}$.

Validation Metric | Formula |
---|---|

Accuracy (Acc) or mean bias | $Acc=\frac{1}{n}{\displaystyle {\displaystyle \sum}_{i=1}^{n}}{X}_{i}-{Y}_{i}$ |

Precision (Prec) or repeatability | $Prec=\sqrt{\frac{1}{n-1}{\displaystyle {\displaystyle \sum}_{i=1}^{n}}{\left({X}_{i}-{Y}_{i}-Acc\right)}^{2}}$ |

Uncertainty (Unc) or Root Mean Squared Difference (RMSD) | $Unc=RMSD=\sqrt{\mathrm{MSD}}=\sqrt{\frac{1}{n}{\displaystyle {\displaystyle \sum}_{i=1}^{n}}{({X}_{i}-{Y}_{i})}^{2}}$ |

Root of the unsystematic mean product difference (RMPDu) | $RMP{D}_{u}=\sqrt{MP{D}_{u}}=\sqrt{\frac{1}{n}{\displaystyle {\displaystyle \sum}_{i=1}^{n}}\left(\left|{X}_{i}-{\widehat{X}}_{i}\right|\right)\left(\left|{Y}_{i}-{\widehat{Y}}_{i}\right|\right)}$ |

Root of the systematic mean product difference (RMPDs) | $RMP{D}_{s}=\sqrt{MSD-MP{D}_{u}}$ |

Coefficient of determination (R^{2})
| ${R}^{2}={\left(\frac{\sigma \left(X,Y\right)}{\sigma \left(X\right)\xb7\sigma \left(Y\right)}\right)}^{2}$ |

Temporal smoothness (δ) | $\delta \left({d}_{i}\right)=\left|P\left({d}_{i+1}\right)-P\left({d}_{i}\right)-\frac{P\left({d}_{i}\right)-P\left({d}_{i+2}\right)}{{d}_{i}-{d}_{i+2}}\left({d}_{i}-{d}_{i+1}\right)\right|$ |

Time series smoothness index (TSI) | $TSI=\sqrt{\frac{{{\displaystyle \sum}}_{i=1}^{\mathrm{n}-2}\delta {\left({d}_{i}\right)}^{2}}{n-2}}$ |

Relative difference [Δ, %] | $\Delta [\%]=\frac{{X}_{i}-{Y}_{i}}{{Y}_{i}}$ |

**Table 4.**Number of selected data points for the intercomparison of iCOR and SYN with AERONET and 6SV. The number of data points before filtering on AERONET data availability are given between parentheses.

ROI | S3A | S3B | TOTAL |
---|---|---|---|

WEUR | 90 (194) | 57 (164) | |

NAUS | 3 (51) | 9 (66) | |

NBRA | 4 (41) | 2 (32) | |

CHIN | 9 (25) | 6 (12) | |

TOTAL | 106 (311) | 74 (374) | 180 (685) |

**Table 5.**Number of Sentinel-3 products selected considering the in-situ data availability and cloud-free conditions. (*) Sentinel-3 OLCI-L1 B reprocessed data by Centre National d’Études Spatiales (CNES).

Sensor | RVUS | GONA | RVUS Time Interval | GONA Time Interval |
---|---|---|---|---|

OLCI S3A | 17 | 63 | 04/09/2016–09/07/2020 | 21/07/2017–01/10/2019 |

OLCI S3B | - | 38 * | - | 15/12/2018–08/10/2019 |

ROI | N | Intercept | Slope | R^{2} | RMSD | RMPDu | RMPDs |
---|---|---|---|---|---|---|---|

All | 8.87 × 10^{6} | 0.079 | 0.581 | 0.533 | 0.144 | 0.125 | 0.072 |

WEUR | 1.48 × 10^{7} | 0.113 | 0.401 | 0.323 | 0.176 | 0.142 | 0.104 |

NAUS | 1.48 × 10^{7} | 0.104 | 0.403 | 0.141 | 0.098 | 0.097 | 0.013 |

CAFR | 3.20 × 10^{6} | 0.125 | 0.537 | 0.335 | 0.146 | 0.132 | 0.062 |

NBRA | 8.04 × 10^{6} | 0.028 | 0.799 | 0.706 | 0.130 | 0.119 | 0.052 |

CHIN | 3.42 ×10^{6} | 0.131 | 0.515 | 0.382 | 0.190 | 0.158 | 0.105 |

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**MDPI and ACS Style**

Wolters, E.; Toté, C.; Sterckx, S.; Adriaensen, S.; Henocq, C.; Bruniquel, J.; Scifoni, S.; Dransfeld, S.
iCOR Atmospheric Correction on Sentinel-3/OLCI over Land: Intercomparison with AERONET, RadCalNet, and SYN Level-2. *Remote Sens.* **2021**, *13*, 654.
https://doi.org/10.3390/rs13040654

**AMA Style**

Wolters E, Toté C, Sterckx S, Adriaensen S, Henocq C, Bruniquel J, Scifoni S, Dransfeld S.
iCOR Atmospheric Correction on Sentinel-3/OLCI over Land: Intercomparison with AERONET, RadCalNet, and SYN Level-2. *Remote Sensing*. 2021; 13(4):654.
https://doi.org/10.3390/rs13040654

**Chicago/Turabian Style**

Wolters, Erwin, Carolien Toté, Sindy Sterckx, Stefan Adriaensen, Claire Henocq, Jérôme Bruniquel, Silvia Scifoni, and Steffen Dransfeld.
2021. "iCOR Atmospheric Correction on Sentinel-3/OLCI over Land: Intercomparison with AERONET, RadCalNet, and SYN Level-2" *Remote Sensing* 13, no. 4: 654.
https://doi.org/10.3390/rs13040654