Evaluation of Sentinel-2/MSI Atmospheric Correction Algorithms over Two Contrasted French Coastal Waters
<p>Maps of measurement locations for two coastal areas: French Guiana (<b>top</b>); English Channel (<b>bottom</b>).</p> "> Figure 1 Cont.
<p>Maps of measurement locations for two coastal areas: French Guiana (<b>top</b>); English Channel (<b>bottom</b>).</p> "> Figure 2
<p>The different configurations of the extraction box for the match-up exercise. The red circles correspond to the location in situ stations and the brown squares are the extracting boxes for retrieving data.</p> "> Figure 3
<p>Scatter plots of the estimated (y-axis) vs in situ (x-axis) R<sub>rs</sub> for the seven atmospheric correction algorithms at 443 nm, 490 nm, 560 nm, and 665 nm. The dotted line is the 1:1 line and solid lines present the linear regression between the AC retrievals and the field measurements R<sub>rs</sub>.</p> "> Figure 4
<p>Variation in the statistical parameters with the wavelength on the individual match-ups dataset. From left to right, up to bottom: RE, Bias, R<sup>2</sup>.</p> "> Figure 5
<p>Scatter plots of the estimated (y-axis) vs in situ (x-axis) R<sub>rs</sub> for the seven atmospheric correction algorithms at 443 nm, 490 nm, 560 nm, and 665 nm. The dotted line is the 1:1 line and solid lines present the linear regression between the AC retrieval and the in situ R<sub>rs</sub>.</p> "> Figure 6
<p>Variation in the statistical parameters with the wavelength on the common match-ups dataset. From left to right, up to bottom: RE, Bias; R<sup>2</sup>.</p> "> Figure 7
<p>Summary of statistics of normal extraction method (<b>the middle</b>), the tested down-shifted box (<b>the lower</b>), and right-shifted box (<b>the upper</b>). (<b>Left column</b>): RE; (<b>middle column</b>): Bias; (<b>right column</b>): R<sup>2</sup>.</p> "> Figure 8
<p>Example of an inhomogeneous pixel scene processed with NASA’s atmospheric correction processor in the Eastern English Channel on 21 September 2016. In situ measurement is presented as Pin 1.</p> "> Figure 9
<p>The example satellite image of Sentinel-2 at band 10 (1375 nm) over French Guiana on 28 November 2016. The cloud, cirrus, or haze covers all of the images.</p> ">
Abstract
:1. Introduction
2. Data
2.1. In Situ Measurements
2.2. Satellite
2.3. Match-Ups Selection
2.4. Extraction Method
3. Atmospheric Correction
3.1. Basics of Atmospheric Correction
3.2. Description of the Algorithms
4. Statistical Parameters
Scoring Scheme
5. Results
5.1. Number of Potential Match-Ups
5.2. All Match-Ups: Individual Performance
5.3. Common Match-Ups: Performance Inter-Comparison
5.4. Sensitivity to the Position of 3-by-3 Pixels Box
6. Discussion
6.1. Comparison of the First Group of AC: OC-SMART, NASA-AC, Polymer, ACOLITE and C2RCC
6.2. Comparison of the Second Group of AC: Sen2Cor and ICOR
6.3. Processing Time
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Box | N | QAS | χ2 (%) | SAM | Stot | |
---|---|---|---|---|---|---|
iCOR | Normal | 22 | 0.65 | 0.38 | 9.35 | 7.70 |
No Center | 22 | 0.67 | 0.36 | 9.01 | 8.02 | |
Upshifted | 22 | 0.64 | 0.35 | 8.99 | 7.83 | |
Downshifted | 22 | 0.64 | 0.38 | 9.70 | 7.73 | |
Left-shifted | 22 | 0.65 | 0.35 | 9.00 | 7.80 | |
Right-shifted | 22 | 0.64 | 0.37 | 9.30 | 8.25 | |
ACOLITE | Normal | 16 | 0.88 | 0.12 | 7.07 | 14.52 |
No Center | 16 | 0.88 | 0.12 | 7.09 | 14.69 | |
Upshifted | 16 | 0.87 | 0.12 | 7.21 | 13.72 | |
Downshifted | 16 | 0.86 | 0.13 | 7.26 | 15.00 | |
Left-shifted | 16 | 0.88 | 0.12 | 7.16 | 14.10 | |
Right-shifted | 16 | 0.92 | 0.12 | 7.00 | 14.75 | |
C2RCC | Normal | 21 | 1.00 | 0.14 | 8.76 | 13.31 |
No Center | 21 | 1.00 | 0.14 | 8.74 | 13.49 | |
Upshifted | 19 | 1.00 | 0.15 | 8.80 | 11.80 | |
Downshifted | 21 | 1.00 | 0.15 | 9.01 | 14.29 | |
Left-shifted | 21 | 1.00 | 0.14 | 8.98 | 12.81 | |
Right-shifted | 21 | 1.00 | 0.14 | 9.05 | 13.89 | |
Sen2Cor | Normal | 22 | 0.58 | 0.27 | 9.59 | 2.01 |
No Center | 22 | 0.58 | 0.27 | 9.58 | 2.03 | |
Upshifted | 22 | 0.57 | 0.27 | 9.64 | 1.56 | |
Downshifted | 22 | 0.57 | 0.33 | 10.05 | 1.28 | |
Left-shifted | 22 | 0.58 | 0.28 | 9.60 | 2.01 | |
Right-shifted | 22 | 0.61 | 0.33 | 10.03 | 1.25 | |
Polymer | Normal | 21 | 0.87 | 0.07 | 5.99 | 14.90 |
No Center | 21 | 0.87 | 0.07 | 5.97 | 15.08 | |
Upshifted | 21 | 0.87 | 0.07 | 6.04 | 14.30 | |
Downshifted | 22 | 0.89 | 0.07 | 6.04 | 15.30 | |
Left-shifted | 21 | 0.88 | 0.07 | 6.06 | 14.74 | |
Right-shifted | 22 | 0.89 | 0.07 | 6.09 | 15.58 | |
NASA-AC | Normal | 15 | 0.94 | 0.04 | 4.99 | 14.36 |
No Center | 14 | 0.94 | 0.04 | 4.99 | 13.82 | |
Upshifted | 14 | 0.97 | 0.03 | 4.46 | 15.42 | |
Downshifted | 11 | 0.94 | 0.05 | 5.17 | 12.14 | |
Left-shifted | 14 | 0.94 | 0.04 | 4.87 | 13.83 | |
Right-shifted | 13 | 0.92 | 0.04 | 5.09 | 13.28 | |
OC-SMART | Normal | 21 | 0.87 | 0.04 | 4.44 | 16.89 |
No Center | 20 | 0.86 | 0.04 | 4.34 | 16.66 | |
Upshifted | 19 | 0.91 | 0.04 | 4.39 | 16.54 | |
Downshifted | 21 | 0.86 | 0.04 | 4.61 | 17.06 | |
Left-shifted | 21 | 0.87 | 0.05 | 4.85 | 17.03 | |
Right-shifted | 21 | 0.83 | 0.04 | 4.52 | 17.20 |
References
- Cohen, W.B.; Goward, S.N. Landsat’s Role in Ecological Applications of Remote Sensing. BioScience 2004, 54, 535–545. [Google Scholar] [CrossRef]
- El Serafy, G.Y.H.; Schaeffer, B.A.; Neely, M.-B.; Spinosa, A.; Odermatt, D.; Weathers, K.C.; Baracchini, T.; Bouffard, D.; Carvalho, L.; Conmy, R.; et al. Integrating Inland and Coastal Water Quality Data for Actionable Knowledge. Remote Sens. 2021, 13, 2899. [Google Scholar] [CrossRef]
- Melet, A.; Teatini, P.; Le Cozannet, G.; Jamet, C.; Conversi, A.; Benveniste, J.; Almar, R. Earth Observations for Monitoring Marine Coastal Hazards and Their Drivers. Surv. Geophys. 2020, 41, 1489–1534. [Google Scholar] [CrossRef]
- Ritchie, J.C.; Zimba, P.V.; Everitt, J.H.J.P.E.; Sensing, R. Remote Sensing Techniques to Assess Water Quality. Photogramm. Eng. Remote Sens. 2003, 69, 695–704. [Google Scholar] [CrossRef] [Green Version]
- Hollmann, R.; Merchant, C.; Saunders, R.; Downy, C.; Buchwitz, M.; Cazenave, A.; Chuvieco, E.; Defourny, P.; de Leeuw, G.; Forsberg, R.; et al. The ESA Climate Change Initiative: Satellite Data Records for Essential Climate Variables. Bull. Am. Meteorol. Soc. 2013, 94, 1541–1552. [Google Scholar] [CrossRef] [Green Version]
- Platt, T.; Hoepffner, N.; Stuart, V.; Brown, C.C. Why Ocean Colour? The Societal Benefits of Ocean-Colour Technology. In Reports of the International Ocean Colour Coordinating Group; International Ocean Colour Coordinating Group (IOCCG): Dartmouth, NS, Canada, 2008; Volume 7, pp. 3–4. [Google Scholar]
- Gordon, H.R. Atmospheric correction of ocean color imagery in the Earth Observing System era. J. Geophys. Res. Atmos. 1997, 102, 17081–17106. [Google Scholar] [CrossRef]
- Gordon, H.R.; Wang, M. Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: A preliminary algorithm. Appl. Opt. 1994, 33, 443–452. [Google Scholar] [CrossRef] [PubMed]
- Wang, M. Atmospheric Correction for Remotely-Sensed Ocean-Colour Products. In Reports of the International Ocean Colour Coordinating Group; International Ocean Colour Coordinating Group (IOCCG): Dartmouth, NS, Canada, 2010; Volume 10, p. 6. [Google Scholar]
- Pahlevan, N.; Mangin, A.; Balasubramanian, S.V.; Smith, B.; Alikas, K.; Arai, K.; Barbosa, C.; Bélanger, S.; Binding, C.; Bresciani, M.; et al. ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters. Remote Sens. Environ. 2021, 258, 112366. [Google Scholar] [CrossRef]
- Drusch, M.; Del Bello, U.; Carlier, S.; Colin, O.; Fernandez, V.; Gascon, F.; Hoersch, B.; Isola, C.; Laberinti, P.; Martimort, P.; et al. Sentinel-2, ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sens. Environ. 2012, 120, 25–36. [Google Scholar] [CrossRef]
- Vanhellemont, Q.; Ruddick, K. Atmospheric correction of metre-scale optical satellite data for inland and coastal water applications. Remote Sens. Environ. 2018, 216, 586–597. [Google Scholar] [CrossRef]
- Siegel, D.; Wang, M.; Maritorena, S.; Robinson, W. Atmospheric Correction of Satellite Ocean Color Imagery: The Black Pixel Assumption. Appl. Opt. 2000, 39, 3582–3591. [Google Scholar] [CrossRef]
- Stumpf, R.; Arnone, R.; Gould, R.; Martinolich, P.; Ransibrahmanakul, V. A partially coupled ocean-atmosphere model for retrieval of water-leaving radiance from SeaWiFS in coastal waters. NASA Tech. Memo 2003, 206892, 51–59. [Google Scholar]
- Goyens, C.; Jamet, C.; Schroeder, T. Evaluation of four atmospheric correction algorithms for MODIS-Aqua images over contrasted coastal waters. Remote Sens. Environ. 2013, 131, 63–75. [Google Scholar] [CrossRef]
- Jamet, C.; Loisel, H.; Kuchinke, C.; Ruddick, K.; Zibordi, G.; Feng, H. Comparison of three SeaWiFS Atmospheric Correction Algorithms for Turbid Waters using AERONET-OC Measurements. Remote Sens. Environ. 2011, 115, 1955–1965. [Google Scholar] [CrossRef]
- Bailey, S.W.; Franz, B.A.; Werdell, P.J. Estimation of near-infrared water-leaving reflectance for satellite ocean color data processing. Opt. Express 2010, 18, 7521–7527. [Google Scholar] [CrossRef]
- Brajard, J.; Santer, R.; Crépon, M.; Thiria, S. Atmospheric correction of MERIS data for case-2 waters using a neuro-variational inversion. Remote Sens. Environ. 2012, 126, 51–61. [Google Scholar] [CrossRef]
- Doerffer, R.; Schiller, H. The MERIS case 2 water algorithm. Int. J. Remote Sens. 2007, 28, 517–535. [Google Scholar] [CrossRef]
- Fan, Y.; Li, S.; Han, X.; Stamnes, K. Machine learning algorithms for retrievals of aerosol and ocean color products from FY-3D MERSI-II instrument. J. Quant. Spectrosc. Radiat. Transf. 2020, 250, 107042. [Google Scholar] [CrossRef]
- Goyens, C.; Jamet, C.; Ruddick, K.G. Spectral relationships for atmospheric correction. II. Improving NASA’s standard and MUMM near infra-red modeling schemes. Opt. Express 2013, 21, 21176–21187. [Google Scholar] [CrossRef] [Green Version]
- Huang, X.; Zhu, J.; Han, B.; Jamet, C.; Tian, Z.; Zhao, Y.; Li, J.; Li, T. Evaluation of Four Atmospheric Correction Algorithms for GOCI Images over the Yellow Sea. Remote Sens. 2019, 11, 1631. [Google Scholar] [CrossRef] [Green Version]
- Kuchinke, C.P.; Gordon, H.; Harding, L.W.; Voss, K. Spectral optimization for constituent retrieval in Case 2 waters II: Validation study in the Chesapeake Bay. Remote Sens. Environ. 2009, 113, 610–621. [Google Scholar] [CrossRef]
- Ruddick, K.; Ovidio, F.; Rijkeboer, M. Atmospheric Correction of SeaWiFS Imagery for Turbid Coastal and Inland Waters. Appl. Opt. 2000, 39, 897–912. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Schroeder, T.; Behnert, I.; Schaale, M.; Fischer, J.; Doerffer, R. Atmospheric correction algorithm for MERIS above case-2 waters. Int. J. Remote Sens. 2007, 28, 1469–1486. [Google Scholar] [CrossRef]
- Steinmetz, F.; Deschamps, P.-Y.; Ramon, D. Atmospheric correction in presence of sun glint: Application to MERIS. Opt. Express 2011, 19, 9783–9800. [Google Scholar] [CrossRef] [Green Version]
- Vanhellemont, Q.; Ruddick, K. Acolite for Sentinel-2, Aquatic Applications of MSI Imagery. In Proceedings of the 2016 ESA Living Planet Symposium, Prague, Czech Republic, 9–13 May 2016. [Google Scholar]
- Jamet, C.; Thiria, S.; Moulin, C.; Crepon, M. Use of a Neurovariational Inversion for Retrieving Oceanic and Atmospheric Constituents from Ocean Color Imagery: A Feasibility Study. J. Atmos. Ocean. Technol. 2005, 22, 460–475. [Google Scholar] [CrossRef]
- Felde, G.; Anderson, G.; Cooley, T.; Matthew, M.; Adler-Golden, S.; Berk, A.; Lee, J. Analysis of Hyperion Data with the FLAASH Atmospheric Correction Algorithm. In Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium, Toulouse, France, 21–25 July 2003; Volume 1, pp. 90–92. [Google Scholar]
- Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH). n.d. Available online: https://www.l3harrisgeospatial.com/docs/flaash.html (accessed on 7 February 2022).
- Hagolle, O. MACCS/MAJA, How It Works. 2015. Available online: https://labo.obs-mip.fr/multitemp/maccs-how-it-works/ (accessed on 7 February 2022).
- Guillou, N.; Rivier, A.; Chapalain, G.; Gohin, F. The impact of tides and waves on near-surface suspended sediment concentrations in the English Channel. Oceanologia 2017, 59, 28–36. [Google Scholar] [CrossRef]
- Lubac, B.; Loisel, H. Variability and classification of remote sensing reflectance spectra in the eastern English Channel and southern North Sea. Remote Sens. Environ. 2007, 110, 45–58. [Google Scholar] [CrossRef]
- Loisel, H.; Meriaux, X.; Poteau, A.; Artigas, L.F.; Lubac, B.; Gardel, A.; Lesourd, a. Analyze of the Inherent Optical Properties of French Guiana Coastal Waters for Remote Sensing Applications. J. Coast. Res. 2009, 56, 1532–1536. [Google Scholar]
- Vantrepotte, V.; Gensac, E.; Loisel, H.; Gardel, A.; Dessailly, D.; Mériaux, X. Satellite assessment of the coupling between in water suspended particulate matter and mud banks dynamics over the French Guiana coastal domain. J. South Am. Earth Sci. 2013, 44, 25–34. [Google Scholar] [CrossRef]
- Mograne, M.A.; Jamet, C.; Loisel, H.; Vantrepotte, V.; Mériaux, X.; Cauvin, A. Evaluation of Five Atmospheric Correction Algorithms over French Optically-Complex Waters for the Sentinel-3A OLCI Ocean Color Sensor. Remote Sens 2019, 11, 668. [Google Scholar] [CrossRef] [Green Version]
- Bailey, S.; Werdell, J. A multi-sensor approach for the on-orbit validation of ocean color satellite data products. Remote Sens. Environ. 2006, 102, 12–23. [Google Scholar] [CrossRef]
- Concha, J.; Bracaglia, M.; Brando, V. Assessing the influence of different validation protocols on Ocean Colour match-up analyses. Remote Sens. Environ. 2021, 259, 112415. [Google Scholar] [CrossRef]
- Bulgarelli, B.; Kiselev, V.; Zibordi, G. Simulation and analysis of adjacency effects in coastal waters: A case study. Appl. Opt. 2014, 53, 1523–1545. [Google Scholar] [CrossRef]
- Bulgarelli, B.; Kiselev, V.; Zibordi, G. Adjacency effects in satellite radiometric products from coastal waters: A theoretical analysis for the northern Adriatic Sea. Appl. Opt. 2017, 56, 854–869. [Google Scholar] [CrossRef]
- Bulgarelli, B.; Zibordi, G. On the detectability of adjacency effects in ocean color remote sensing of mid-latitude coastal environments by SeaWiFS, MODIS-A, MERIS, OLCI, OLI and MSI. Remote Sens. Environ. 2018, 209, 423–438. [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]
- Sterckx, S.; Knaeps, S.; Kratzer, S.; Ruddick, K. SIMilarity Environment Correction (SIMEC) applied to MERIS data over inland and coastal waters. Remote Sens. Environ. 2015, 157, 96–110. [Google Scholar] [CrossRef]
- Vanhellemont, Q.; Ruddick, K. Turbid wakes associated with offshore wind turbines observed with Landsat 8. Remote Sens. Environ. 2014, 145, 105–115. [Google Scholar] [CrossRef] [Green Version]
- Vanhellemont, Q.; Ruddick, K. Atmospheric correction of Sentinel-3/OLCI data for mapping of suspended particulate matter and chlorophyll-a concentration in Belgian turbid coastal waters. Remote Sens. Environ. 2021, 256, 112284. [Google Scholar] [CrossRef]
- Müller-Wilm, U.; Louis, J.; Richter, R.; Gascon, F.; Niezette, M. Sentinel-2 Level-2A Prototype Processor: Architecture, Algorithms and First Results. In Proceedings of the ESA Living Planet Symposium, Edinburgh, UK, 9–13 September 2013. [Google Scholar]
- Fan, Y.; Li, W.; Gatebe, C.K.; Jamet, C.; Zibordi, G.; Schroeder, T.; Stamnes, K. Atmospheric correction over coastal waters using multilayer neural networks. Remote Sens. Environ. 2017, 199, 218–240. [Google Scholar] [CrossRef]
- Nurgiantoro, N.; Laode, M.; Kurniadin, N.; Putra, S.; Azharuddin, M.; Hasan, J.; Langumadi, Y. Assessment of atmospheric correction results by iCOR for MSI and OLI data on TSS concentration. IOP Conf. Ser. Earth Environ. Sci. 2019, 389, 012001. [Google Scholar] [CrossRef]
- Pereira-Sandoval, M.; Ruescas, A.; Urrego, P.; Ruiz-Verdú, A.; Delegido, J.; Tenjo, C.; Soria-Perpinyà, X.; Vicente, E.; Soria, J.; Moreno, J. Evaluation of Atmospheric Correction Algorithms over Spanish Inland Waters for Sentinel-2 Multi Spectral Imagery Data. 2019, 11, 1469. Remote Sens. 2019, 11, 1469. [Google Scholar] [CrossRef] [Green Version]
- 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. [Google Scholar] [CrossRef]
- Guanter, L. New Algorithms for Atmospheric Correction and Retrieval of Biophysical Parameters in Earth Observation Application to ENVISAT/MERIS Data. In Departament de Física de la Terra i Termodinàmica; Universitat de Valéncia: Valéncia, Spain, 2007; Available online: https://www.tesisenred.net/handle/10803/9877;jsessionid=402C46F935666E5FF2CEF46F057954A7.tdx2#page=1 (accessed on 3 January 2022).
- Berk, A.; Anderson, G.; Acharya, P.; Bernstein, L.; Muratov, L.; Lee, J.; Fox, M.; Adler-Golden, S.; Chetwynd, J.; Hoke, M.; et al. MODTRAN5, 2006 update. Defense and Security Symposium. In Proceedings of the SPIE, Orlando, FL, USA, 17 April 2006; Volume 6233. [Google Scholar]
- Brockmann, C.; Doerffer, R.; Peters, M.; Kerstin, S.; Embacher, S.; Ruescas, A. Evolution of the C2RCC Neural Network for Sentinel 2 and 3 for the Retrieval of Ocean Colour Products in Normal and Extreme Optically Complex Waters. In Proceedings of the Living Planet Symposium, Prague, Czech Republic, 9–13 May 2016. [Google Scholar]
- Kaufman, Y.J.; Sendra, C. Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery. Int. J. Remote Sens. 1988, 9, 1357–1381. [Google Scholar] [CrossRef]
- Wei, J.; Lee, Z.; Shang, S. A system to measure the data quality of spectral remote-sensing reflectance of aquatic environments. J. Geophys. Res. Ocean 2016, 121, 8189–8207. [Google Scholar]
- Müller, D.; Krasemann, H.; Brewin, R.J.W.; Brockmann, C.; Deschamps, P.-Y.; Doerffer, R.; Fomferra, N.; Franz, B.A.; Grant, M.G.; Groom, S.B.; et al. The Ocean Colour Climate Change Initiative: I. A methodology for assessing atmospheric correction processors based on in situ measurements. Remote Sens. Environ. 2015, 162, 242–256. [Google Scholar] [CrossRef] [Green Version]
- Kruse, F.A.; Lefkoff, A.B.; Boardman, J.W.; Heidebrecht, K.B.; Shapiro, A.T.; Barloon, P.J.; Goetz, A.F.H. The spectral image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 1993, 44, 145–163. [Google Scholar] [CrossRef]
- Ilori, C.O.; Pahlevan, N.; Knudby, A. Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing. Remote Sens. 2019, 11, 469. [Google Scholar] [CrossRef] [Green Version]
- Ruescas, A.; Pereira-Sandoval, M.; Tenjo, C.; Ruiz-Verdú, A.; Steinmetz, F.; De Keukelaere, L. Sentinel-2 atmospheric correction inter-comparison over two lakes in Spain and Peru-Bolivia. In Proceedings of the Colour and Light in the Ocean from Earth Observation (CLEO) Workshop, Frascati, Italy, 6–8 September 2016. [Google Scholar]
- Sterckx, S.; Knaeps, E.; Ruddick, K. Detection and correction of adjacency effects in hyperspectral airborne data of coastal and inland waters: The use of the near infrared similarity spectrum. Int. J. Remote Sens. 2011, 32, 6479–6505. [Google Scholar] [CrossRef]
- Doron, M.; Bélanger, S.; Doxaran, D.; Babin, M. Spectral variations in the near-infrared ocean reflectance. Remote Sens. Environ. 2011, 115, 1617–1631. [Google Scholar] [CrossRef]
- Guanter, L.; Alonso, L.; Moreno, J. First Results From the PROBA/CHRIS Hyperspectral/Multiangular Satellite System Over Land and Water Targets. IEEE Geosci. Remote Sens. Lett. 2005, 2, 250–254. [Google Scholar] [CrossRef]
Wavelength | Spatial Resolution |
---|---|
443 nm | 60 m |
490 nm | 10 m |
560 nm | 10 m |
665 nm | 10 m |
705 nm | 20 m |
740 nm | 20 m |
783 nm | 20 m |
842 nm | 10 m |
865 nm | 20 m |
940 nm | 60 m |
1375 nm | 60 m |
1610 nm | 20 m |
2190 nm | 20 m |
Order | In Situ | Satellite | ||
---|---|---|---|---|
Date | Time | S2A | S2B | |
1 | 21 September 2016 | 10:12:00 | 10:57:32 | |
2 | 21 September 2016 | 09:20:00 | 10:57:32 | |
3 | 21 September 2016 | 09:50:00 | 10:57:32 | |
4 | 21 September 2016 | 10:12:00 | 10:57:32 | |
5 | 21 September 2016 | 11:20:00 | 10:57:32 | |
6 | 11 October 2016 | 09:50:00 | 10:59:52 | |
7 | 11 October 2016 | 09:00:00 | 10:59:52 | |
8 | 11 October 2016 | 09:50:00 | 10:59:52 | |
9 | 28 November 2016 | 13:20:00 | 14:00:52 | |
10 | 28 November 2016 | 13:53:00 | 14:00:52 | |
11 | 28 November 2016 | 14:29:00 | 14:00:52 | |
12 | 28 November 2016 | 15:09:00 | 14:00:52 | |
13 | 19 January 2017 | 11:15:00 | 11:03:51 | |
14 | 19 January 2017 | 10:16:00 | 11:03:51 | |
15 | 19 January 2017 | 10:45:00 | 11:03:51 | |
16 | 19 January 2017 | 10:55:00 | 11:03:51 | |
17 | 19 January 2017 | 11:15:00 | 11:03:51 | |
18 | 29 April 2017 | 11:40:00 | 10:56:51 | |
19 | 29 April 2017 | 12:25:00 | 10:56:51 | |
20 | 6 July 2017 | 13:30:00 | 14:00:51 | |
21 | 6 July 2017 | 14:13:00 | 14:00:51 | |
22 | 6 July 2017 | 15:24:00 | 14:00:51 | |
23 | 1 July 2017 | 15:30:00 | 14:00:49 | |
24 | 19 April 2018 | 11:38:00 | 10:56:19 | |
25 | 30 April 2018 | 10:33:00 | 11:21:21 | |
26 | 18 July 2018 | 9:53:00 | 11:00:39 | |
27 | 23 July 2018 | 10:30:00 | 10:56:21 | |
28 | 26 July 2018 | 12:54:00 | 11:06:21 |
Study Areas | Days | Stations | Potential Match-Up |
---|---|---|---|
English Channel | 16 | 43 | 22 |
French Guiana | 14 | 60 | 8 |
Total | 30 | 103 | 30 |
iCOR | ACOLITE | C2RCC | Sen2Cor | Polymer | NASA-AC | OC-SMART | |
---|---|---|---|---|---|---|---|
443 nm | 22 | 16 | 21 | 22 | 21 | 15 | 21 |
490 nm | 22 | 16 | 20 | 22 | 21 | 15 | 21 |
560 nm | 22 | 16 | 21 | 22 | 22 | 17 | 22 |
665 nm | 21 | 13 | 17 | 21 | 22 | 9 | 22 |
RE (%) | Bias (%) | |||||||
443 nm | 490 nm | 560 nm | 665 nm | 443 nm | 490 nm | 560 nm | 665 nm | |
iCOR | 114.81 | 73.03 | 48.03 | 118.44 | 107.23 | 66.03 | 43.06 | 112.94 |
ACOLITE | 58.24 | 39.78 | 38.00 | 88.04 | 52.93 | 22.17 | 19.50 | 42.47 |
C2RCC | 36.61 | 34.45 | 28.44 | 52.50 | −8.52 | −3.11 | 17.05 | 13.67 |
Sen2Cor | 119.69 | 73.08 | 51.72 | 89.48 | 118.57 | 61.80 | 47.22 | 81.54 |
Polymer | 38.80 | 35.90 | 30.35 | 48.91 | −13.39 | −2.31 | 2.74 | 10.31 |
NASA-AC | 31.60 | 36.37 | 28.90 | 56.30 | −3.59 | −11.25 | 1.93 | 31.30 |
OC-SMART | 27.63 | 30.00 | 32.19 | 47.85 | −2.41 | −2.84 | −6.41 | 5.88 |
R2 | ||||||||
443 nm | 490 nm | 560 nm | 665 nm | |||||
iCOR | 0.44 | 0.56 | 0.77 | 0.86 | ||||
ACOLITE | 0.55 | 0.62 | 0.63 | 0.74 | ||||
C2RCC | 0.40 | 0.61 | 0.78 | 0.88 | ||||
Sen2Cor | 0.36 | 0.41 | 0.51 | 0.61 | ||||
Polymer | 0.57 | 0.72 | 0.80 | 0.92 | ||||
NASA-AC | 0.28 | 0.50 | 0.72 | 0.94 | ||||
OC-SMART | 0.69 | 0.74 | 0.80 | 0.92 |
Box | QAS | χ2 (%) | SAM | Stot |
---|---|---|---|---|
iCOR | 0.65 | 0.38 | 9.35 | 7.70 |
ACOLITE | 0.88 | 0.12 | 7.07 | 14.52 |
C2RCC | 1.00 | 0.14 | 8.76 | 13.31 |
Sen2Cor | 0.58 | 0.27 | 9.59 | 2.01 |
Polymer | 0.87 | 0.07 | 5.99 | 14.90 |
NASA-AC | 0.94 | 0.04 | 4.99 | 14.36 |
OC-SMART | 0.87 | 0.04 | 4.44 | 16.89 |
iCOR | ACOLITE | C2RCC | Sen2Cor | Polymer | NASA-AC | OC-SMART | |
---|---|---|---|---|---|---|---|
443 nm | 14 | 14 | 14 | 14 | 14 | 14 | 14 |
490 nm | 14 | 14 | 14 | 14 | 14 | 14 | 14 |
560 nm | 16 | 16 | 16 | 16 | 16 | 16 | 16 |
665 nm | 7 | 7 | 7 | 7 | 7 | 7 | 7 |
Box | QAS | χ2 (%) | SAM | Stot |
---|---|---|---|---|
iCOR | 0.89 | 0.046 | 4.95 | 3.37 |
ACOLITE | 1.00 | 0.056 | 5.44 | 13.26 |
C2RCC | 1.00 | 0.149 | 9.77 | 14.11 |
Sen2Cor | 0.79 | 0.068 | 6.70 | 4.19 |
Polymer | 0.89 | 0.076 | 6.14 | 13.74 |
NASA-AC | 1.00 | 0.038 | 4.73 | 17.13 |
OC-SMART | 0.82 | 0.045 | 4.59 | 14.76 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Bui, Q.-T.; Jamet, C.; Vantrepotte, V.; Mériaux, X.; Cauvin, A.; Mograne, M.A. Evaluation of Sentinel-2/MSI Atmospheric Correction Algorithms over Two Contrasted French Coastal Waters. Remote Sens. 2022, 14, 1099. https://doi.org/10.3390/rs14051099
Bui Q-T, Jamet C, Vantrepotte V, Mériaux X, Cauvin A, Mograne MA. Evaluation of Sentinel-2/MSI Atmospheric Correction Algorithms over Two Contrasted French Coastal Waters. Remote Sensing. 2022; 14(5):1099. https://doi.org/10.3390/rs14051099
Chicago/Turabian StyleBui, Quang-Tu, Cédric Jamet, Vincent Vantrepotte, Xavier Mériaux, Arnaud Cauvin, and Mohamed Abdelillah Mograne. 2022. "Evaluation of Sentinel-2/MSI Atmospheric Correction Algorithms over Two Contrasted French Coastal Waters" Remote Sensing 14, no. 5: 1099. https://doi.org/10.3390/rs14051099