Sentinel-2

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Comparison of physically and image based
atmospheric correction methods for Sentinel-2
satellite imagery
Conference Paper · August 2016
DOI: 10.1117/12.2242889
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G. Lantzanakis
Foundation for Research and Technology - Hellas
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Zina Mitraka
Foundation for Research and Technology - Hellas
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Nektarios Chrysoulakis
Foundation for Research and Technology - Hellas
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2. Comparison of physically & image based atmospheric correction
methods for Sentinel-2 satellite imagery
Giannis Lantzanakis*
, Zina Mitraka, Nektarios Chrysoulakis
Foundation for Research and Technology Hellas, N.Plastira 100, 70013 Heraklion, Greece
ABSTRACT
Atmospheric correction is the process to retrieve the surface reflectance from remotely sensed imagery by removing the
atmospheric effects (Scattering and Absorption). The process determines the optical characteristics of the atmosphere
and then applies it in order to correct the atmospheric effects on satellite images. Two main categories of atmospheric
correction methods can be identified, the ones that rely on radiative transfer modeling and the image-based ones.
In this study, four methods are compared, three physically-based (6S, FLAASH, Sen2Cor) and one image-based (DOS)
for their effectiveness on atmospheric correction of Sentinel-2 high resolution optical imagery. A Sentinel-2 image,
acquired on a clear day over Heraklion, Greece was used. Ancillary information on the aerosol optical thickness from the
Moderate Resolution Imaging Spectroradiometer (MODIS) was used for the physically based methods.
In line with similar studies using Landsat images, the physically based methods perform better than the image-based
ones also for the Sentinel-2 imagery. Nevertheless, their high computational demand and the need for ancillary
atmospheric information makes them difficult to apply. Different atmospheric correction methods showed different
results for specific land cover types, suggesting that the selection of the suitable method is also application dependent.
Keywords: 6S, FLAASH, Sen2Cor, DOS, atmospheric correction, comparison
1. INTRODUCTION
The magnitude of the signal received from a satellite sensor is dependent on several factors, particularly: reflectance of
the target; nature and magnitude of the atmospheric interactions; slope and aspect of the ground target area relative to the
solar azimuth; and angle of view of the sensor and solar elevation angles. The role of atmospheric correction is to
decompose the above signal and to extract the component that originates from the target, in order to estimate the target
reflectance. The fundamental philosophy of atmospheric correction is to determine the optical characteristics of the
atmosphere and then to apply this in order to correct for the atmospheric effects of satellite imagery [1]. Accurate
calculations of surface spectral reflectance are useful for object recognition, segmentation and material classification.
Two main categories of atmospheric correction methods can be identified, the ones that rely on radiative transfer
modeling and the image-based methods. The later rely only on the information of the image in question and they are
mainly using statistical analysis of the satellite TOA(top-of-atmosphere) observations. On the other hand, the
atmospheric correction methods that rely on radiative transfer modeling, require independent data for atmospheric
optical characteristics at the time of image acquisition. These methods employ radiative transfer models.
The European satellite, Sentinel-2A was put into orbit by the European Space Agency (ESA) on 23 June 2015. Sentinel-
2 is ESA’s high resolution mission, providing frequent high resolution information with two satellites, the second
planned for launched in mid-2016 [2]. Sentinel-2 carries a Multispectral Instrument (MSI) [3], recording in 13 spectral
channels, covering a wide range of wavelengths from the 440 up to 2200 nm. The spectral channels 2, 3, 4 and 8
corresponding to blue, green, red and near infrared, have a resolution of 10 m. The spectral channels 5, 6, 7, 8a, 11 and
12 have a resolution of 20 m, while 1, 9 and 10 of 60 m.
In this study, four atmospheric correction methods are compared, three physically-based (6S,FLAASH,Sen2Cor) and one
image-based (DOS) for simulating the interaction between land and atmosphere and retrieve surface reflectance for
Sentinel-2 imagery.
6S (Second Simulation of Satellite Signal in the Solar Spectrum) [4] is an advanced NASA's radiative transfer
code for a wide range of atmospheric, spectral, and geometric conditions..

3. FLAASH (Fast Line-of-sight Atmospheric Analysis of Hypercubes) [5] is an atmospheric correction module in
the ENVI software that corrects wavelengths in the visible through near-infrared and shortwave infrared regions
Sen2cor (Sentinel-2 Level-2A Atmospheric Correction Processor) [6] is an ESA's Prototype Processor for
processing Sentinel-2 Top of Atmosphere reflectance (Level 1C) data into Bottom of Atmospheric corrected
(Level 2A) data. It additionally performs a Scene Classification of the corresponding input.
DOS (Dark Object Subtraction) [7] is a simple empirical atmospheric correction method for satellite imagery
available in ENVI.
The above mentioned atmospheric correction methods are applied for a case study, using Sentinel-2 data. The data used
and the methodology applied to atmospherically correct the Sentinel-2 data are presented in Section 2. Comparisons
among the different methods were held, to identify the differences among them and the results are presented and
discussed in Section 3. Conclusion follow in Section 4.
2. DATA AND METHODOLOGY
2.1 Data
In this study, a Sentinel-2 level 1C product [8] image was used, acquired on a clear day over Heraklion, Greece
(09.01.2016, 9:30 GMT). The scene covers an extent of 13 x 11 km2
over the broader area of Heraklion, Greece (Fig. 1),
and includes a wide range of surface cover i.e. buildings, roads, industrial areas, parks, an airport, and agricultural
surfaces, mainly olive trees and vineyards.
Figure 1. True-colour composition (4-3-2) of the Sentinel-2 simulated image of the study area, the broader area of
Heraklion, Greece (09.01.2016).
Information on the Sentinel-2 MSI spectral response function was available from ESA (Sentinel-2 MSI Technical
Guides). Ancillary information on the aerosol optical thickness (AOT) at 550 nm from the MODIS, MOD02_L2 [9] was
used for the physically based methods. A Digital Surface Model (DSM) of 0.8 m was available from the National
Cadastre of Greece.

4. 2.2 Methodology
Four different atmospheric correction methods were applied for the Sentinel-2 scene, i.e. the 6S, the FLAASH, the
Sen2Cor and the DOS methods. The necessary input for all methods was prepared, then the different models were run
independent and the individual surface reflectance products were then compared. The overall approach followed in this
study is presented in Fig. 2.
For applying the atmospheric corrections methods, it was necessary to have information on the TOA radiance measured
by the Sentinel-2 MSI, the aerosol optical thickness (or visibility) at the time of Sentinel-2 acquisition and information
on the surface elevation. Thus pre-processing of the available data was necessary.
Figure 2. Methodology flowchart
The Sentinel-2 level-1C product was converted from DN to TOA reflectance by dividing with a scale factor of 10000.
Following, TOA reflectance was converted to TOA radiance by RADTOA = REFTOA·d(t)·ES·cos( S)· -1
[10], where
RADTOA is the TOA radiance, REFTOA is the TOA reflectance, t is the Julian day, d(t) is a correction to take the sun-
Earth distance variation into account, ES is the equivalent extra-terrestrial solar spectrum and depends on the spectral
response of the Sentinel-2 MSI bands and S: is the zenith angle (ESA, 2016). This was done for bands 2, 3, 4, 5, 6,
7,8a,11,12. Bands 1, 9, 10 were excluded since they are atmospheric bands designed for aerosol, water vapor, cirrus
clouds.
For the FLAASH method, the AOT ( A550) was converted to visibility (VIS) by VIS = 3.9449/( A550 – 0.08498) [11]. To
adjust the physically-based methods to the sensor in question, information on the MSI spectral response was necessary.
The MSI spectral response function was used in the 6S model, while for FLAASH, the mean wavelength of each band
was used.
By applying the different atmospheric correction methods, three surface reflectance products were estimated. TOA
reflectance information was used for the DOS method; TOA radiance, DEM and AOT was used for 6S; radiance and
visibility was used for FLAASH, as shown in Fig. 2. All input data used in this study were resampled to 10 m resolution.
The surface reflectance products created using the different methods, were then compared to evaluate the performance of
the methods. The differences between all the image products were estimated to assess the spatial differences among
methods and common statistics were computed. The results were also evaluated against higher resolution surface cover
information.

5. 3. RESULTS
a) b)
c) d)

6. e) f) m
Figure 3. shows the spectral surface reflectance for specific land cover types a)water, b)grass, c)trees, d)bare soil,
e)urban, f)bright man made materials, calculated using the four different atmospheric correction methods. With grey line
is the TOA reflectance, which is the Sentinel-2 level-1C product. With red line is the surface reflectance calculate by 6S,
with green by FLAASH, with orange by Sen2Cor and with blue by DOS.
For pixels which contains a lot of trees and for bare soil pixels, all the models compute similar
surface reflectance. For water and grass pixels, 6S, FLAASH and DOS compute similar surface
reflectance, and Sen2Cor completely different. For urban and bright man made materials(High
Albedo), all the models compute different surface reflectance.
In figure 3 for a grass pixel was used also data from JPL(Jet Propulsion Laboratory) library [12] to
compare the products of all the atmospheric correction methods that were used in this study. This
took place only for grass pixel, because this is the only one homogeneous pixel, and the surface
reflectance computed by the four different atmospheric correction methods must be close to this JP
library spectral signatures. Surface reflectance computed by 6S fits better with JP library.
4. CONCLUSION
This study compared four atmospheric correction methods for the recently launched Sentinel-2 MSI.
The methods were parametrized and applied for a Sentinel-2 scene over the case study of Heraklion,
Greece. All methods perform similarly for bare soil pixels, and pixels with more trees, and different
for water, grass, urban and bright man made materials. The Sentinel-2 is in rump-up phase, thus,
when the operational products become available, the study will be repeated to evaluate absolute
values of surface reflectance after atmospheric correction.

7. ACKNOWLEDGMENTS
Work carried out in the framework of the Hellenic Republic-Siemens Agreement partially funded by the Programmatic
Agreement Between Research Centers-GSRT 2015-2017.
REFERENCES
[1] Chrysoulakis, N., Abrams, M., Feidas, H., and Arai, K. "Comparison of atmospheric correction methods using
ASTER data for the area of Crete," International Journal of Remote Sensing, 31(24), 6347–6385 (2010).
[2] Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., and Bargellini, P. "Sentinel-2: ESA’s
Optical High-Resolution Mission for GMES Operational Services," Remote Sensing of Environment, 120, 25–36
(2012)
[3] ESA, "SENTINEL-2 MSI Introduction," <https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi>. (2016)
[4] Vermote, EF, D. Tanre, J. L. Deuze, M. Herman, and J.-J. Morcette. "Second Simulation of the Satellite Signal in the
Solar Spectrum, 6S: An Overview,” IEEE Transactions on Geoscience and Remote Sensing, 35 (3), 675–686 (1997)
[5] Vincent, R. K., X. Qin, R. M. L. McKay, J. Miner, K. Czajkowski, J.Felde, G. W., Anderson, G. P., Cooley, T. W.,
Matthew, M. W., Adler-Golden, S. M., Berk, A., and Lee, J. "Analysis of Hyperion Data with the FLAASH
Atmospheric Correction Algorithm," IEEE Transactions on Geoscience and Remote Sensing, pp. 90-92 (2003)
[6] Main-Knorn M., Pflug b., Debaecker V., and Louis J "Calibration and validation plan for the l2a processor and
products of the sentinel-2 mission," 36th International Symposium on Remote Sensing of Environment, (2015)
[7] Chavez, PS. "An Improved Dark-Object Subtraction Technique for Atmospheric Scattering Correction of
Multispectral Data. Remote Sensing of Environment," U.S. Geological Survey, 24(3), 459–479 (1988)
[8] Baillarin SJ, Meygre A., Dechoz C., Petrucci B., Lacherade S., Tremas T., Isola C., Martimort P., and Spoto F.,
"Sentinel-2 level 1 products and image processing performance" International Archives of the Photogrammetry,
Remote Sensing and Spatial Information Sciences (2012)
[9] Remer, L.A., Mattoo, S., Levy, R.C., and Munchak, L. A. "MODIS 3 km aerosol product: algorithm and global
perspective," Atmospheric Measurement Techniques 6, 1829-1844, doi: 10.5194/amt-6-1829-2013, (2013)
[10] ESA, "Level-1C Algorithm," <https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-
1c/algorithm> (2016)
[11] Retalis A., Hadjimitsis D. G., Michaelides S., Tymvios F., Chrysoulakis N., C. R. I. Clayton , and Themistocleous
K. "Comparison of aerosol optical thickness with in situ visibility data over Cyprus,"
Natural Hazards and Earth System Sciences, 10, 421-428 (2010)
[12] NASA, "Aster Spectral Library," <Retrieved from http://speclib.jpl.nasa.gov/search-1> (2016)
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