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Atmospheric Correction of True-Color RGB Imagery with Limb Area-Blending Based on 6S and Satellite Image Enhancement Techniques Using Geo-Kompsat-2A Advanced Meteorological Imager Data


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This study aims for producing high-quality true-color red-green-blue (RGB) imagery that is useful for interpreting various environmental phenomena, particularly for GK2A. Here we deal with an issue that general atmospheric correction methods for RGB imagery might be breakdown at high solar/viewing zenith angle of GK2A due to erroneous atmospheric path lengths. Additionally, there is another issue about the green band of GK2A of which centroid wavelength (510 nm) is different from that of natural green band (555 nm), resulting in the unrealistic RGB imagery. To overcome those weakness of the RGB imagery for GK2A, we apply the second simulation of the satellite signal in the solar spectrum radiative transfer model look-up table with improved information considering altitude of the reflective surface to reduce the exaggerated atmospheric correction, and a blending technique that mixed the true-color imagery before and after atmospheric correction which produced a naturally expressed true-color image. Consequently, the root mean square error decreased by 0.1–0.5 in accordance with the solar and view zenith angles. The green band signal was modified by combining it with a veggie band to form hybrid green which adjust centroid wavelength of approximately 550 nm. The original composite of true-color RGB imagery is dark; therefore, to brighten the imagery, histogram equalization is conducted to flatten the color distribution. High-temporal-resolution true-color imagery from the GK2A AMI have significant potential to provide scientists and forecasters as a tools to visualize the changing Earth and also expected to intuitively understand the atmospheric phenomenon to the general public.
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Asia-Pacific Journal of Atmospheric Sciences
Korean MeteorologicalSocie
Atmospheric Correction ofTrue‑Color RGB Imagery withLimb
Area‑Blending Based on6S andSatellite Image Enhancement
Techniques Using Geo‑Kompsat‑2A Advanced Meteorological Imager
MinsangKim1· Jun‑HyungHeo2 · Eun‑HaSohn2
Received: 27 April 2021 / Revised: 20 August 2021 / Accepted: 31 August 2021
© The Author(s) 2021
This study aims for producing high-quality true-color red-green-blue (RGB) imagery that is useful for interpreting various
environmental phenomena, particularly for GK2A. Here we deal with an issue that general atmospheric correction methods
for RGB imagery might be breakdown at high solar/viewing zenith angle of GK2A due to erroneous atmospheric path lengths.
Additionally, there is another issue about the green band of GK2A of which centroid wavelength (510 nm) is different from
that of natural green band (555 nm), resulting in the unrealistic RGB imagery. To overcome those weakness of the RGB
imagery for GK2A, we apply the second simulation of the satellite signal in the solar spectrum radiative transfer model look-
up table with improved information considering altitude of the reflective surface to reduce the exaggerated atmospheric cor-
rection, and a blending technique that mixed the true-color imagery before and after atmospheric correction which produced
a naturally expressed true-color image. Consequently, the root mean square error decreased by 0.1–0.5 in accordance with
the solar and view zenith angles. The green band signal was modified by combining it with a veggie band to form hybrid
green which adjust centroid wavelength of approximately 550 nm. The original composite of true-color RGB imagery is
dark; therefore, to brighten the imagery, histogram equalization is conducted to flatten the color distribution. High-temporal-
resolution true-color imagery from the GK2A AMI have significant potential to provide scientists and forecasters as a tools to
visualize the changing Earth and also expected to intuitively understand the atmospheric phenomenon to the general public.
Keywords Atmospheric correction· Look-up table· True-color imagery· Radiative transfer model· Rayleigh scattering·
Solar Zenith angle
1 Introduction
Atmospheric radiance is interrupted through absorption,
scattering, and diffraction along the atmospheric path. Of
these processes, scattering has the most dominant influ-
ence in the visible bandwidth and is represented by the
Rayleigh scattering effect. The effect of Rayleigh scatter-
ing is inversely proportional to wavelength; thus, the blue
band—which is the shortest band—is the most affected. As
a result, true-color RGB images contain bluish grays (Miller
etal. 2016).
To improve the quality of a visible band under the
influence of Rayleigh scattering, radiative transfer mod-
els (RTMs) have been applied in several studies (Gordon
1993; Rahman and Dedieu 1994; Fukushima etal. 1998;
Berka etal. 1999; Richter etal. 2006). RTMs can be used
to characterize the atmospheric effects of surface radiation
signals as measured by satellite sensors. They have sev-
eral advantages (He etal. 2019). For instance, they are not
limited to a specific region or satellite sensor because they
input the atmospheric conditions, geometrical information,
and sensory characteristics of areas where the atmospheric
Online ISSN 1976-7951
Print ISSN 1976-7633
Responsible Editor: Myoung Hwan Ahn.
* Jun-Hyung Heo
1 Korea Ocean Satellite Center, Korea Institute ofOcean
Science andTechnology, BusanMetropolitanCity49111,
2 National Meteorological Satellite Center, Korea
Meteorological Administration, Jincheon-gun27803, Korea
M.Kim et al.
1 3 Korean MeteorologicalSocie
correction is to be performed. Moreover, RTM methods are
known to be more accurate in simulating atmospheric effects
than the empirical line method or improved dark-object sub-
traction (Zhou etal. 2011).
The second simulation of a satellite signal in the solar
spectrum (6S) (Vermote etal. 2006) and moderate resolu-
tion atmospheric transmission (MODTRAN) (Adler-Golden
etal. 1999) methods have been frequently used in previous
research (Richter 1996; Karpouzli and Malthus 2003; Ghu-
lam etal. 2004; Sriwongsitanon etal. 2011; Franch etal.
2013). [emphasize why use 6S rather than other RTM]
However, owing to the complexity of their calculations and
consequent large processing times, these RTM methods are
inefficient in performing standby calibration over a wide
area, such as in the case of satellite imaging. To address
this problem, several studies have applied the look-up table
(LUT) method; this is an array-based method that replaces
runtime calculations with simple array indexing (Liang etal.
2001; Lyapustin etal. 2011; Dorji and Fearns 2018).
A number of previous studies have attempted to estimate
surface albedo (the atmospheric reflectance for isotropic
light) using various satellite sensors, such as the Advanced
Very High Resolution Radiometer (Csiszar and Gutman
1999; Strugnell and Lucht 2001), the Moderate Resolution
Imaging Spectroradiometer (MODIS) (Schaaf etal. 2002),
and the Spinning Enhanced Visible and InfraRed Imager of
the Meteosat (Geiger etal. 2008). Compared to the stud-
ies of polar satellites, research into geostationary satellite
orbits is sparse; however, recently launched geostationary
satellites such as the Geo-Kompsat-2A (GK2A) Advanced
Meteorological Imager (AMI) (Kim etal. 2021), Hima-
wari-8 Advanced Himawari Imager (AHI) (Bessho etal.
2016), Fengyun-4 Advanced Geosynchronous Radiation
Imager (AGRI) (Yang etal. 2017), and GOES-16 Advanced
Baseline Imager (ABI) (Schmit etal. 2016) are capable of
providing higher temporal, spatial, and spectral resolutions.
Depending on their sensor, various methods are applied to
produce true-color imagery. In the case of AHI, Rayleigh
correction is conducted using RTM modified by the National
Aeronautics and Space Administration (NASA) SeaDAS and
various image enhancement techniques such as hybrid green,
and Simple Hybrid Contrast Stretch (SHCS) were applied
(Miller etal. 2016). AGRI also applied image enhancement
technique SHCS method (Miller etal. 2016). Since the ABI
sensor does not have green band, green-like band is gen-
erated by combining red, blue, and vegetation band (Bah
etal. 2018). They are expected to be used for estimating
reflectance, and an increasing amount of research is being
undertaken into them.
As described by Vermote etal. (2006), several RTMs
mentioned in the previous paragraph are limited because
they do not assume a spherical atmosphere; hence, it difficult
to apply them to make limb observations (Vermote etal.
2006). In particular, for stationary satellites, the full-disk
area covered includes regions at a high solar zenith angle
(SZA) of over 70°. In terms of data utilization, accurate
surface reflection data are required (Ruddick etal. 2014;
Lee etal. 2015). Overcorrection problems occur in several
RTMs for polar orbits (Wang 2016) and geostationary sat-
ellites (Miller etal. 2016), which commonly assume a flat
atmosphere (Adler-Golden etal. 1999; Qu etal. 2003; Ver-
mote etal. 2006). Recently, RTMs have begun to consider
the effects of the Earth’s curvature as a pseudo-spherical
approximation (He etal. 2018). Validation has shown that
the model results are comparable to benchmarks (He etal.
2018) showing that the effects of Earth’s curvature increase
rapidly with SZA, for SZAs of 75°, 80°, and 85°. These
results indicate that curvature effects should be considered
in high-accuracy atmospheric correction. The Rayleigh scat-
tering LUT has also verified that it shows a significant bias
at high SZAs (He etal. 2018).
This study aimed to produce high-quality, true-color
RGB images. One important process is that of correcting
the sensor-measured radiance of channels affected by Ray-
leigh scattering through the atmosphere. Rayleigh scatter-
ing is dominant within the visible and near-infrared bands;
hence, its effects need to be mitigated. We built an LUT that
is computed using a 6S RTM, to convert the radiance to the
atmospherically corrected reflectance. Our LUT applies a
minimum curvature surface (MCS) technique to augment
the LUT in terms of geometric parameters. Inspection of
the resulting LUT shows that the atmospheric correction
coefficients dramatically increase over SZAs and view zenith
angles (VZAs) of 70°. This caused the reflectance near the
limb area exceeds the correction values. To mitigate this
reflectance, we applied limb correction according to the SZA
and VZA. The GK2A AMI, which has a visual area specifi-
cation similar to that of the AHI sensor in Himawari-8, con-
tains a green band centered at 510 nm. The band is slightly
shifted toward the blue band compared to the 550 nm found
in many other sensors (MODIS, Landsat, and VIIRS) (Miller
etal. 2016; Broomhall etal. 2019). For this reason, the veg-
etation in the true-color RGB images is browner and the
bare ground is redder than that of the aforementioned sen-
sors. One solution to this problem is hybrid-green adjust-
ment, which combines the green visible band with the near-
infrared band (870 nm) to mimic green grass vegetation.
We applied hybrid green instead of the original green band.
True-color RGB imagery incorporating atmospheric cor-
rection shows up dark. One remedial method is histogram
equalization. This method brightens the image by expanding
the narrow color distribution. True-color RGB imagery is
useful in detection and analysis.
In this paper, we describe the atmosphere-corrected, true-
color RGB imaging procedure from GK2A AMI. Section2
describes the data used in this study and the methodology for
Atmospheric Correction ofTrue‑Color RGB Imagery withLimb Area‑Blending Based on6S and…
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Korean MeteorologicalSocie
obtaining the atmospherically corrected visible bands and
image enhancements. The preliminary results of applying
each process—along with their respective final products—
are described in Section3. Section4 discusses the limita-
tions and future works. The paper is concluded in Section5.
2 Data andMethodology
2.1 Data
2.1.1 GK2A AMI
GEO-KOMPSAT-2A is a geostationary meteorologi-
cal satellite that was launched on December 5, 2018 and
began operation on July 24, 2019. It was designed to take
over the meteorological functions of the Communication,
Ocean, and Meteorological Satellite (COMS), performing
meteorological and space-weather observation tasks using
the AMI. The AMI exhibits a superior observational per-
formance to the COMS in terms of spatial, temporal, and
spectral resolutions (Kim etal. 2021).
In particular, the augmented array of channels—the
number of available channels was expanded from five to
16—has led to the improvement of satellite measurement
capacities (Table1). The composite of red, green, and
blue visible light channels enable true-color imagery; this
represents a milestone improvement over the gray-color
imagery previously available. Three water-vapor channels
facilitate the detection of water-vapor signals at different
heights, and the various infrared channels detect atmos-
pheric gases and subtle changes in convective signals.
Spatial and temporal resolutions were also enhanced.
The red channel has a resolution of 0.5km, the two other
visible channels and the 860 nm channel have a resolution
of 1km, and the infrared channels have 2km spatial reso-
lution. The observation cycle of the AMI has a duration of
10min for the full disk and 2min for the Korean Peninsula
region. Its payload covers a full disk area centered at 0°N,
128.3°E; this includes Asia and Oceania, which contain
various land-cover types and diverse climate phenomena.
Table2 compares the full width at half-maximum
(FWHM) responses and the spatial resolutions of GEO
(AMI, AHI, AGRI, and ABI) sensors for specific bands
of focus. There is only a subtle difference between AMI
and AHI, because they use the same sensor (Bessho etal.
2016). AGRI and ABI has no green band, which means
that it must take advantage of near-band values to obtain
imaginary green values (Schmit etal. 2016). On the other
hand, the AMI and AHI feature a green band; however,
this signal is slightly blue-shifted, which results in another
imaginary green value, referred to as “hybrid green”
(Miller etal. 2016). Outside of the visible range, the AMI
features a cirrus band (1.4μm); however, the cloud parti-
cle size band (2.2μm) is absent. Both are included in the
AHI and ABI.
Table 1 GEO-KOMPSAT-2A channel specifications, center of wave-
length, bandwidth, and resolution (Kim etal. 2021)
GK2A chan-
nel specifica-
GK2A band # Center of
Visible 1 0.47 0.43–0.48 1
2 0.51 0.50–0.52 1
3 0.64 0.63–0.66 0.5
Near Infrared 4 0.86 0.85–0.87 1
5 1.37 1.37–1.38 2
6 1.61 1.60–1.62 2
Water Vapor 7 3.83 3.74–3.96 2
8 6.2 6.06–6.42 2
9 6.9 6.89–7.01 2
Infrared 10 7.3 7.26–7.43 2
11 8.6 8.44–8.76 2
12 9.6 9.54–9.72 2
13 10.4 10.25–10.61 2
14 11.2 11.08–11.32 2
15 12.3 12.15–12.45 2
16 13.3 13.21–13.39 2
Table 2 Comparison of the GEO-KOMPSAT-2A advanced meteoro-
logical imager (AMI) (Kim etal. 2021), Himwari-8 advanced Hima-
wari imager (AHI) (Bessho etal. 2016), Advanced Geosynchronous
Radiation Imager (AGRI) (Yang etal. 2017), and GOES-R advanced
baseline imager (ABI) (Schmit et al. 2016) sensor in full width at
half-maximum (FWHM), and spatial resolution at nadir for selected
visible and near-infrared bands
No. FWHM(μm) Res. (km) No. FWHM(μm) Res. (km) No. FWHM (μm) Res. (km) No. FWHM (μm) Res. (km)
Blue 1 0.43–0.48 1.0 1 0.43–-0.48 1.0 1 0.45–0.49 1.0 1 0.45–-0.49 1.0
Green 2 0.50–0.52 1.0 2 0.50–0.52 1.0 - - - - - -
Red 3 0.63–0.66 0.5 3 0.63–0.66 0.5 2 0.55–0.75 0.5 2 0.59–0.69 0.5
Vege. 4 0.85–0.87 1.0 4 0.85–0.87 1.0 3 0.75–0.90 1.0 3 0.85–0.89 1.0
M.Kim et al.
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2.1.2 ECMWF CAMS Near‑real‑time Data
The European Centre for Medium-Range Weather Fore-
casts (ECMWF) has produced the Copernicus Atmosphere
Monitoring Service (CAMS) dataset. CAMS also offers
an atmospheric analysis service, which focuses on atmos-
pheric composition, including aerosols, chemical species,
and greenhouse gases (Massart etal. 2016; Inness etal.
2019). The CAMS analysis is produced using the ECM-
WF’s four-dimensional variational (4DVar) system (Enge-
len and McNally 2005) within the Integrated Forecasting
System (IFS; version CY42r1 for 2016 and CY43r1 for
2017), which is one of the world’s leading operational
global weather-prediction systems. The transport of trac-
ers such as CO2 is assessed online by the IFS model con-
currently with the meteorological forecast. Because the
semi-Lagrangian advection scheme in the IFS does not
conserve mass, a global mass fixer is applied to restore
mass conservation to the global budget (Agust-Panareda
etal. 2014). The IFS model used in CAMS has a horizon-
tal resolution of approximately 40km and 137 vertical
levels. Further information about the IFS model can be
found online (https:// www. ecmwf. int/ en/ forec asts/ docum
entat ion- and- suppo rt/ chang es- ecmwf- model/ ifs- docum
entat ion). The forecast contains information pertaining to
gases in the lowest layer of the atmosphere (troposphere)
and the ozone higher up (stratosphere). It also contains
data about desert dust, sea salt, organic matter, black car-
bon, and sulfate particles (aerosols). The initial conditions
of the forecasts (analyses) are obtained by combining a
previous forecast with satellite observations of the aero-
sol, ozone, carbon monoxide, nitrogen dioxide, and sul-
fur dioxide levels, via a process called data assimilation.
In this study, we used a three-parameter geostationary
environmental monitoring spectrometer, which measures
six-hourly instantaneous values of the total column ozone
(TCO), total column water vapor (TPW), and total aerosol
optical depth (AOD) at 550 nm from the CAMS dataset,
interpolating them onto a 0.25° longitude × 0.25° latitude
grid (Table3). This dataset was re-projected to the GK2A
full-disk region.
2.2 Methodology
Figure1 illustrates the procedure of atmospherically correct-
ing true-color imagery. The sensor-measured reflectance is
reduced by atmospheric scattering. The Rayleigh scattering
effect is inversely proportional to wavelength, thus visible
channels needed to be corrected accordingly. Without reduc-
tion, we constructed an LUT by considering geometric and
atmospheric conditions, using the 6S RTM to correct atmos-
pheric effects in visible channels.
Inspection of the created LUT showed that the atmos-
pheric correction coefficients were dramatically increased
over 70° of SZA and VZA (Miller etal. 2016). As a result,
the reflectance near the limb area exceeded the correction
values. To mitigate reflectance, we applied limb correction
according to the SZA and VZA.
The GK-2A green-channel wavelength (510 nm) differs
from the green grass vegetation wavelength (550 nm) taken
from the spectral database of NASA’s Advanced Spaceborne
Thermal Emission and Reflection Radiometer. One solution
is the hybrid-green method, which combines the visible light
band with the near-infrared band (870 nm) to mimic green
grass vegetation. We applied hybrid green instead of the
original green band.
True-color RGB imagery incorporating atmospheric cor-
rection shows up dark. One remedial method is histogram
equalization. This method aims to increase the brightness
of the image by expanding the narrow color distribution.
True-color RGB imagery is useful for detection and analysis.
2.2.1 6SV2.1
The 6S is a basic RTM; it has been used for the calcula-
tion of LUTs in the satellite atmospheric correction algo-
rithms developed by Vermote etal. (2006). It is designed
to simulate the reflection of solar radiation from the atmos-
phere–surface coupling system over a wide range of air,
spectrum, and geometric conditions. This enables accu-
rate simulation of satellite observations, consideration
of the elevated targets, and modeling of the composite
atmosphere of realistic molecules and aerosols. 6S is a
highly accurate radiative transfer model considering wide
range of atmospheric conditions. Compared with Simpli-
fied Method for the Atmospheric Correction (SMAC), 6S
showed better performance [Proud etal. 2010]. The lat-
est updates include a public release of its vector version
(6SV), which considers the Stoke’s parameter and polari-
zation contribution; this version is based on the successive
orders of scattering (SOS) approximations. The accuracy
of radiative transfer (RT) calculations can be varied by
changing the number of calculation angles and parameters
(Vermote etal. 2006). The 6S model atmosphere consists
of several layers, and the model solves the RT equations
Table 3 European Centre for Medium-Range Weather Forecasts
(ECMWF) Copernicus Atmosphere Monitoring Service (CAMS)
data characteristics
Parameters [unit] Spatial resolution
Total column ozone [
atm cm]
12.5 Daily
Total precipitable water [
]12.5 Daily
Aerosol optical depth at 550 nm 12.5 Daily
Atmospheric Correction ofTrue‑Color RGB Imagery withLimb Area‑Blending Based on6S and…
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Korean MeteorologicalSocie
numerically, layer by layer. The final result is a sum of
successive computations, which is performed by integrat-
ing over the Fourier series decomposition. Unlike other
RT codes, the 6S offers an innovative working system.
The 6SV calculates surface reflectivity, not remote sens-
ing reflectance—which is total surface incident irradiance
(downward irradiance) divided by aquatic luminance. In
the calculation, the apparent reflectance value is arbitrarily
set (assuming 0, 0.5, 1.0 in the model) and the radiance is
inversely calculated. In this process, the 6S model gener-
ates the atmospheric background, returning transmittance,
and top of atmosphere (TOA) reflectance. It provides cor-
rection coefficients to compute the ground reflectance,
given a TOA radiance. It was publicly released in May
2005, and the latest version of the 6SV code (6SV2.1),
released in June 2015, is now available for download:
http:// 6s. ltdri. org/ pages/ downl oads. html.
The land surface reflectance in the 6SV atmospheric
correction mode is calculated using the following
is the TOA reflectance;
is the SZA;
is the
is the relative azimuth angle (RAA);
is the gase-
ous transmission of atmospheric gases such as H2O, CO2,
and O3;
is the total reflectance due to molecular and
aerosol scattering;
represent the atmos-
pheric transmittance from sun to target and target to satellite,
respectively; and
is the atmospheric reflectance for a Lam-
bertian, homogeneous target. The 6SV computes all trans-
mittance and atmospheric reflectance using the user-defined
parameters and subroutines, which contain vertical profiles
of atmospheric temperature, pressure, and absorbing gases
Fig. 1 Flowchart of the atmospheric correction process for true-color imagery
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1 3 Korean MeteorologicalSocie
according to altitude. This model provides three atmospheric
correction coefficients for removing atmospheric effects. The
land surface reflectance can be calculated using Eq.(3) with
the atmospheric correction coefficients:
is the atmospherically corrected reflectance; L
is the TOA radiance [
]; and
are atmospheric correction coefficients which represent
the inverse of the transmittance, the scattering term of the
atmosphere, and the spherical albedo, respectively.
2.2.2 Building a6SV2.1 LUT
The 6SV was developed specifically to determine atmos-
pheric corrections for satellite, it allows users to define a
down-looking geometry. Despite the direct atmospheric cor-
rections of reflectance available with 6S, most studies have
performed atmospheric corrections using LUT approaches.
Using a LUT can prevent duplicate calculations for identical
conditions in limited computational environments.
First, for building our LUT, input parameters were set
using the following preconditions described in Table4. For
geometric conditions, each SZA and VZA was set between
0° and 80°, in increments of 5°. The RAA had a range from
0° to 180°, in increments of 10°. The range and intervals of
atmospheric conditions were selected based on climatology
of the ECMWF CAMS data from 2015 to 2018 (Fig.2).
The TCO had a range of 0.25–0.40atm-cm and was varied
in 0.05atm-cm increments. TPW had a range of 0–5
and was varied in 1
increments. The AOD had a range
of 0.01–0.3, with irregular intervals. The reflectance height
was segmented between ground level and a height of 10km,
in 2km intervals. The spectral conditions were configured
using the spectral response function of GK2A.
The three pre-calculated atmospheric correction coeffi-
cients were stored in the LUT. To expand the LUT, an MCS
technique was applied. The MCS is a mathematical method
for constructing smooth surfaces from irregularly spaced
data. The surface of minimum curvature corresponds to the
minimum of the Laplacian power or—in the alternative for-
mulation—satisfies the bi-harmonic differential equation.
Physically, it models the behavior of an elastic plate. In the
one-dimensional case, the minimum curvature leads to the
natural cubic spline interpolation. In the two-dimensional
case, a surface can be interpolated with bi-harmonic splines
or gridded with an iterative finite-difference scheme (Smith
and Wessel 1990). In most practical cases, the minimum
atm =
Fig. 2 Range and intervals of atmospheric conditions, selected based
on the climatology of the ECMWF CAMS data from 2015 to 2018
Atmospheric Correction ofTrue‑Color RGB Imagery withLimb Area‑Blending Based on6S and…
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Korean MeteorologicalSocie
curvature technique produces a clear, smooth surface (Rabah
and Kaloop 2013). In this study, it was assumed that the
atmospheric correction coefficients would change smoothly
in response to the SZA and VZA when all other conditions
are held constant. The SZA and VZA are specified starting
from 5° with an interval of 0.5° in the final version of the
After atmospheric correction of the reflectance, the com-
posite true-color imagery featured a particular limb region
highlighted in red, whereas the uncorrected true-color
imagery did not. The red coloration was a result of the blue
and green bands used in the RGB composite being overcor-
rected with respect to the red band. The 6S was designed
using a parallel-plane atmosphere assumption in the RT
model (which reduces the Rayleigh scattering effect), and
the impact was exaggerated at high zenith angles, such that
the Earth’s limb appeared red. It is possible to mitigate over-
correction at the limb by reducing the Rayleigh scattering
according to the atmospheric pathlengths and storing the
level of reflectance in the LUT. RT codes set reflectance
points at ground level. Additional height levels were added
into the LUT, and the height indexes were defined as in the
The purpose of cloud height index is to reduce excessive
Rayleigh scattering as the optical distance increases at high
zenith angle, assuming an altitude of reflective surface. To
estimate altitude of reflective surface, the brightness tem-
perature value of the infrared channel was used since the
altitude at which radiation occurs can be inferred from the
temperature of the radiation surface. It is applied all regions
regardless of latitudes and atmospheric states.
2.2.3 Image Processing
A difference in spatial resolution occurs when multi-band
datasets are generated from different bands. The 640 nm
band has the highest spatial resolution (about 500m), other
visible channels and vegetation channels have a 1km spa-
tial resolution, and infrared channels have a 2km spatial
resolution. To compose true-color imagery, each band is
required to have equal spatial resolution; thus, all bands
(except the 640 nm band) were downscaled to a 500m spa-
tial resolution. Spline interpolation—a statistical downscal-
ing method—was used to sharpen resolution; it is a form of
interpolation in which the interpolant is a special kind of
piecewise polynomial, called a spline. The spline interpo-
lation is preferred over polynomial interpolations because
the interpolation errors can be reduced, even when using
low-degree polynomials for the spline. Spline interpolation
avoids the problem of Runge’s phenomenon, in which oscil-
lations occur between points when interpolating with high-
degree polynomials.
The uncorrected true-color imagery results in brown
vegetation and red bare soil (Fig.3). The explanation for
these unexpected colors is that the signal center of the green
Table 4 Configuration of second simulation of a satellite signal in the
solar spectrum (6S) interpolated look-up table (LUT)
Parameters [unit] Min Max Number of
Solar zenith angle [°] 0 70 15 (5)
View zenith angle [°] 0 70 15 (5)
Relative azimuth angle [°] 0 180 19 (10)
Total column ozone [
atm cm]
0.25 0.40 4 (0.05)
Total precipitable water [
]0 5 6 (1)
Aerosol optical depth at 550 nm 0.01, 0.05, 0.1,
0.2, 0.3
Reflectance height [km] 0 10 6 (2)
Table 5 Reflectance height level and range of brightness temperature
corresponding to height index
Height index Reflectance height level Range of brightness
temperature (BT)
0 Ground level (0km) BT ≥ 260K
1 2km 260K > BT ≥ 250K
2 4km 250K > BT ≥ 240K
3 6km 240K > BT ≥ 230K
4 8km 230K > BT ≥ 220K
5 10km 220K > BT Fig. 3 Step 1: GK-2 A full-disk simple composite of native RGB-
band true-color image, taken on 2019/12/01 0300 UTC
M.Kim et al.
1 3 Korean MeteorologicalSocie
band (510 nm) in the AMI sensor is slightly blue-shifted
compared with that of other sensors (MODIS, VIIRS, etc.),
which are centered at 550 nm for the green band. Subtle
differences produce color changes in true-color imagery, as
described by Miller etal. (2016).
To correct this unrealistic green color, Miller etal. (2016)
suggested a hybrid green, which is a composite of the origi-
nal green band and a vegetation band that is sensitive to
chlorophyll signals—this is used to monitor vegetation
health. The hybrid green is calculated using the following
is the hybrid-green factor (which varies from zero
to one; in this study, the hybrid-green factor was set to 0.13
empirically), and
are the reflectances of the
green and vegetation bands, respectively. The hybrid-green
factor adjusts the vegetation signal, highlighting green colors
and faded red soil. The use of the vegetation band is not
appropriate for the surface of oceans because of the strong
absorption by water of light in those wavelengths. However,
an ocean is relatively sensitive to the blue band and the prac-
tical effect of using the hybrid-green method in true-color
imagery is more pronounced.
The original true-color composite imagery appears faint
unless color distributions are enhanced. To compensate for
indistinguishable imagery, the visual enhancement technique
of histogram equalization was implemented. Histogram
equalization is an image-processing technique designed to
improve contrasts in images by redistributing the color his-
togram. A well-separated image has advantages in terms of
visibility and sharpness, particularly over blurred or dark
images. Histogram equalization method brightens the image
by expanding the narrow color distribution. Through this
adjustment, the intensities can be better distributed on the
histogram utilizing the full range of intensities evenly. This
allows for areas of lower local contrast to gain a higher con-
trast. Histogram equalization accomplishes this by effec-
tively spreading out the highly populated intensity values
which use to degrade image contrast. First, reflectance val-
ues (0–1) were rescaled to between zero and 255, because
the RGB composite imagery contained pixel values within
this range. These rescaled reflectance values were used to
compute the cumulative density function (CDF). The last
process was to convert the reflectance values by multiplying
them with respect to the CDF of each intensity.
The pixel values at high SZA or VZA can become very
large and unrealistic, owing to overcorrection by the 6S
RTM. Because the correction breaks down nonlinearly for
very long atmospheric pathlengths near the Earth’s limb,
the corrected imagery features a reddening edge (Miller
etal. 2016). Using the uncorrected reflectance datasets, we
Hybrid green
performed data-blending at the extremities of the SZA and
VZA, to remove this reddening edge and achieve true-color
imagery after the atmospheric correction process. The cor-
rected reflectance data were blended gradually according to
the zenith angles, becoming less corrected toward the edge
of the limb. The blending factor was calculated in terms
of the SZA and VZA, it was linearly decreased from 1.0
to 0.0 over zenith angles from 75° to 90° and 65° to 85°,
3 Results
3.1 Preprocessing ForAtmospheric Correction
ofTOA Reflectance
Rayleigh scattering exerts a dominant effect along atmos-
pheric pathways and is an important feature of visible chan-
nels. It is inversely proportional to the fourth power of the
wavelength. The upper images in Fig.4 are uncorrected,
and they all exhibit the Rayleigh effect. The blurred regions,
when compared with the lower images in the figure, indi-
cate that the surface reflectance suffered Rayleigh scattering
before the signal reached the satellite. The edges of Earth
(which become more pronounced moving from the red to
blue columns) experience a larger scattering effect because
the longer the optical pathlength from the target to the sat-
ellite along the atmospheric path, the greater the Rayleigh
scattering effect. Atmospherically corrected imagery, which
adjusts for the Rayleigh scattering effects, can achieves the
clearer earth imagery exemplified in the lower images of
Figure5 shows an example of atmospherically corrected
GK-2A full-disk true-color RGB imagery; it indicates that
the adjustment for Rayleigh scattering removes much of the
atmospheric haze, yields sharper contrasts between the cloud
and surface features, and enhances the overall surface detail.
Despite the atmospheric corrections, red regions remain
present where the atmospheric pathlength is long. This is a
result of overcorrection; clouds and aerosols at high zenith
angles exert an influence on the visible channel, depending
on their height and opacity. The Rayleigh correction does
not account for these clouds and aerosols; instead, it assumes
a 6S RTM. To resolve this problem, Section3.2 describes
how the LUT factors in target elevation when calculating
TOA reflectance.
The reddening of Earth’s limb is a result of overcorrec-
tion in the green and blue bands. This is consistent with the
results of Lee etal. (2015), who found that the atmospheric
correction-coefficient values computed in 6S were dramati-
cally increased above a 60° zenith angle. This may be due
to limitations of the RT code at high angles, and these limi-
tations are difficult to quantify without the corresponding
Atmospheric Correction ofTrue‑Color RGB Imagery withLimb Area‑Blending Based on6S and…
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insitu measurements. It may be possible to improve the
correction at the limb by reducing the angular increment in
the LUT (Broomhall etal. 2019). Figure6 shows the effects
of correcting for atmospheric pathlength by considering
target elevation in the LUT. By computing target elevation
separately and using them in atmospheric pathlength correc-
tions, the reddening of the Earth’s limb can be reduced. The
10.4-µm BT used to estimate the reflecting surface height
is only approximate; however, the results show that atmos-
pheric correction is improved at high zenith angles.
3.2 Image Processing
Figure7 shows the effects of attenuation in green true-color
on the Australia region. The native true-color imagery exag-
gerates the reddish soil and dark grass; this is because the
vegetation has a peak reflectance of around 550 nm due to
the presence of chlorophyll, which is why most of the other
sensors’ green bands target this peak (Broomhall etal. 2019).
However, the true-color imagery supplemented by hybrid-
green color showed more realistic vegetation and a brown
soil color. The true-color image applied with hybrid green
in Fig.7 is similar to the MODIS Terra Rayleigh-corrected
RGB images with its 555 nm green band. Several other
works using the Himawari-8 AHI true-color RGB images
applied with hybrid green (Miller etal. 2016; Broomhall
etal. 2019) are also consistent with the result from GK2A .
Figure8 shows the result of applying histogram equali-
zation to a Rayleigh-corrected, hybrid green-applied true-
color RGB image. The original image on the left-hand side
is dim and has poor contrast, whereas the right-hand image
is brighter and has better contrast (Fig.8). The color dis-
tributions of R, G, and B before histogram equalization
Fig. 4 Effect of atmospheric correction on the GK-2A full-disk visible-band imagery. Upper images represent uncorrected band imagery and
lower images represent corrected band imagery
Fig. 5 Step 2: example of atmospherically corrected GK-2A full-disk
true-color RGB imagery, same date with Fig.3
M.Kim et al.
1 3 Korean MeteorologicalSocie
were narrow, with each color having a different center (not
shown). With histogram equalization applied, the color
distribution becomes uniform, this method is effective in
improving the visual appearance of dark images. As a result,
the difficulties of distinguishing between cloud surfaces and
the ocean are alleviated, and the characteristics of various
surface types can be visualized. Moreover, for clouds (which
are highly reflective areas), a more detailed representation
is possible, thereby improving the utility of this imagery for
assessing cloud textures.
In Fig.9, the atmospherically corrected true-color RGB
imagery is blended with uncorrected imagery near the
limb. The uncorrected AMI data are more consistent for
the Earth’s limb in comparison to the MODIS imagery;
however, in the Rayleigh scattering-corrected true-color
imagery, errors increase exponentially along long opti-
cal trajectories through the atmosphere. In the blending
stage of our proposed procedure, the proportion is varied
linearly from 100 to 0 % for SZAs between 80° and 90°.
Most edge effects that had not been removed by the previ-
ous pathlength corrections were reduced. The procedure
also resulted in a more realistic transition between Earth
and space than the atmospherically corrected true-color
RGB imagery. Figure9presents the final true-color image,
in which all of the aforementioned techniques have been
Fig. 6 Step 3: effect of altitude
assignment in look-up table
(LUT): (a) fixed at ground level,
and (b) applied height using IR
brightness temperature (BT) of
Fig. 7 Step 4: comparison of
GK-2A using (a) simple com-
posite of native RGB and (b)
hybrid RGB on Australia, taken
on 2019/11/06 0300 UTC
Fig. 8 Step 5: comparison
of GK-2A using (a) atmos-
pherically corrected true-color
RGB and (b) atmospherically
corrected and enhanced true-
color RGB imagery taken on
2020/01/06 0100 UTC
Atmospheric Correction ofTrue‑Color RGB Imagery withLimb Area‑Blending Based on6S and…
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3.3 Robust Result forAtmospheric correction
ofGK2A visible bands
To obtain robust results for atmospheric correction of the
GK2A visible band with visual true-color RGB inspection,
we quantitatively examined the results of atmospheric cor-
rection based on 6S LUT along with various SZA and VZA
conditions. Figure10(a) and (b)show an example image
of the spatial distribution of SZA and VZA at 2020.07.07
03 UTC. Because a geostationary satellite is situated in a
fixed location, VZA does not change with time. However,
in the case of SZA, it changes greatly depending on the sea-
son or time; therefore, in this study, we used the reflectance
of 03 UTC in the four seasons. We sampled approximately
121million pixels per image on four channels of GK2A.
Figure10(c) and (d)show the SZA and VZA distributions of
the sampled pixels for quantitative evaluation of reflectance.
The distributions are non-uniform for both SZA and VZA.
The majority of the samples exist around 40 degrees and
the number of samples decreases with distance away from
the peak degrees.
Figure11 shows the averaged root mean square error
(RMSE) of the difference between the original and atmos-
pheric-corrected reflectance (blue) and difference between
the original and blended reflectance after atmospheric cor-
rection (red) as a function of SZA for each band per sea-
son. Although there are slight differences depending on the
season, the reflectance RMSE range for all sampled data is
0–0.5. In general, the RMSE due to atmospheric correction
increases with the SZA. In particular, the RMSE increases
sharply as it exceeds 70 degrees. This is because the scatter-
ing effect of the original band becomes stronger as the SZA
increases. The RMSE of the reflectance difference due to
atmospheric correction becomes more noticeable with the
transition from the red channel to the blue channel. This
result is consistent with the stronger scattering effect toward
the blue channel.
Blending is a method designed to improve images in areas
with SZA greater than 70 degrees, which not only mitigates
red areas due to atmospheric over correction at high zenith
angle, but also helps with natural representation. As a result,
Fig.11 shows the effect of blending process that reduces the
rapid increase in RMSE when only atmospheric correction is
applied. Similar to the application of atmospheric correction
effect, the variability according to the season is not large,
and the scattering effect increases as the channel wavelength
decreases, hence RMSE increases.
Figure12 shows the averaged RMSE of the difference
between the original and atmospheric-corrected reflectance
(blue) and difference between the original and blended
reflectance after atmospheric correction (red) as a func-
tion of VZA. The improvement by atmospheric correction
becomes more noticeable as VZA increases. The reason is
that the longer the optical path is, the more pronounced is
the effect caused by the atmosphere. The improvement effect
increases with the blue band. In VZA, the effect of the reduc-
tion due to blending varies from band to band and season to
season, but it reduces RMSE, as a result, helps improve the
quality of images. The averaged RMSE (observation-based
reflectivity-atmospheric-corrected reflectance, and atmos-
pheric-corrected reflectance-blended reflectance) shows the
improvement of the true-color RGB imagery. Although the
improvement due to atmospheric correction is not applied in
all areas and therefore, the blending technique is applied at
high angles, the reflectance error due to the Rayleigh scat-
tering is improved. As a result, clear results can be obtained
in the actual image.
3.4 Illustrative Examples
True-color imagery is designed to represent Earth in a man-
ner similar to how normal human color vision represents it.
This real-color imagery helps in identifying surface types
(e.g., land, ocean, ice, and snow) and atmospheric proper-
ties such as cloud, fog, dust, and ash; it produces impor-
tant information by condensing three bands into one image,
with the advantage of allowing easy communication of this
information. Therefore, it is useful in enabling people to
intuitively interpret weather phenomena without special
training, and it can be very helpful to both forecasters and
the general public.
There are several examples of true-color RGB imagery
being employed to consider weather events. In satellite
Fig. 9 Final step: final true-color imagery applying all the aforemen-
tioned techniques same date with Fig.3
M.Kim et al.
1 3 Korean MeteorologicalSocie
observation, the texture of fog is flat and edges of the clouds
are sharp. Nevertheless, the cloud environments at low levels
cannot be evaluated because the satellite detects energy from
reflecting or radiating surfaces. For the same reason, fog is
difficult to distinguish from low cloud, because they both
generate optically thick and warm cloud textures. GK2A
RGB fog imagery is used to detect fog and low-level cloud.
This imagery uses different composites of red, green, and
blue channels for day and night; however, fog and low cloud
are colored as cyan. One method of distinguishing fog from
low cloud is that of animating cloud patterns. The move-
ment of fog is relatively slow compared with that of low
cloud, and the surface texture of fog is flat. Figure13 shows
an example of true-color and RGB fog images taken of the
Korean Peninsula. The fog and lower clouds are located at
the West Sea and the Liaodong Peninsula; they are distin-
guishable from the high, thick clouds over the Shandong
Peninsula (red box). However, it is difficult to discriminate
low-level cloud using the GK2A fog imagery, because they
are represented by similar colors and shapes. In the true-
color RGB imagery, they have different external cloud sur-
faces; fog has a smooth surface and is opaque owing to its
optical thickness. The animated image facilitates effective
classification because the fog is more static than the low-
level cloud.
Another example is that of sea-ice images (Fig.14). The
GK2A RGB snow and fog image (right) represents snow
and sea-ice in red. There is a large quantity of snow over
land, and sea-ice is widespread over the Sea of Okhotsk (red
area). This is also found in true-color imagery. Sea-ice has
Fig. 10 Spatial distribution of (a) SZA and (b) VZA at 2020.07.07 03 UTC. Histogram of (c) SZA and (d) VZA of sample pixels
Atmospheric Correction ofTrue‑Color RGB Imagery withLimb Area‑Blending Based on6S and…
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the same color as cloud; however, it differs in terms of its
shape and movements, sea-ice does not undergo changes of
area coverage during the daytime.
Yellow dust is a weather phenomenon that occurs in
desert areas in China and Mongolia, and in the middle Yel-
low River, where it is generated by strong winds or terrain
and falls to the surface during long-distance transportation.
The Fig.15 shows the example of yellow dust in true-color
imagery. The red box indicates yellow dust which represents
as yellow and brown color and it travels with clouds. The
Fig.15 on the right is dust RGB from GK-2A which shows
yellow dust as red and hot pink color at the same time with
true-color RGB imagery.
Figure16 shows the typhoon MAYSAK (2020) in true-
color imagery and enhanced color IR imagery from GK2A.
Example of true-color imagery shows well-defined eyewall
(red box) which is the most dangerous and destructive fea-
ture of typhoon and clear shape of convective structure.
Right part of Fig.16 also shows typhoon MAYSAK in
enhanced color IR imagery. It highlights convective struc-
ture by repeating color and gray scale compared with tradi-
tional IR color scheme so in this figure, the structure of the
typhoon and eyewall is more clearly separated.
4 Discussion
4.1 6S Parameterization forBuilding anLUT
The 6S uses sensitive RT code to predict a satellite signal at
the satellite level for a Lambertian surface. Several param-
eters are required for it to make good estimations. However,
Fig. 11 Averaged RMSE of the difference between atmospheric corrected and blended reflectance on the original reflectance as a function of
SZA for each band of (a) spring, (b) summer, (c) fall, and (d) winter season
M.Kim et al.
1 3 Korean MeteorologicalSocie
accurate model construction takes a lot of time; thus, a bal-
ance between simplicity and elaboration is required when
setting the model configuration. Fortunately, the 6S model
contains pre-prepared atmospheric conditions, which enable
the specification of seasons (summer and winter), latitudi-
nal zone (tropical, middle-latitude, and subarctic), the US
standard atmosphere model (US62), and user-defined condi-
tions; thus, it is possible to improve the model’s ability with-
out requiring the in-depth consideration of seasonality, the
selection of a suitable molecular atmosphere model, or the
frequent provision of input atmosphere data. During the pro-
cess of atmospheric correction, an LUT that contains season-
ally and latitudinally separated coefficients may give more
accurate solutions. Furthermore, the conditions are sensitive
to the aerosol model used; 6S sets “no aerosol” for simplic-
ity and offers a number of land-type options (continental,
maritime, urban, and desert). By implementing surface-type
information, reflectance can be calculated using the coef-
ficients derived from the LUT. Models differ through using
different processes and atmospheric profiles. 6S calculates
reflectance in terms of a bidirectional reflectance distribution
function (BRDF) model. Numerous BRDF subroutines in 6S
require a parameter for computing reflectance. Utilization of
the 6SV2.1’s detailed, in-built atmosphere models (which
can more effectively address latitudinal and seasonal differ-
ences in the global atmosphere) can increase accuracy and
may be implemented using the same LUT structure.
The computational costs involved in constructing an
LUT introduces limitations to the design parameter con-
ditions. One solution to this problem is to interpolate the
LUT using an appropriate method, the MCS technique is
one such validated method; it improves the quality of surface
Fig. 12 Averaged RMSE of the difference between atmospheric corrected blended reflectance on the original reflectance as a function of VZA
for each band of (a) spring, (b) summer, (c) fall, and (d) winter season
Atmospheric Correction ofTrue‑Color RGB Imagery withLimb Area‑Blending Based on6S and…
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reflectance data, by interpolating the 6S LUT for applica-
tion to the Himawari-8 AHI. Specifically, at high SZAs
(greater than 75° SZA), improvements of 45.1 %, 39.6 %,
19.8 %, and 12.57 % in the relative root mean square errors
were observed for Channels 1–4, respectively (Li etal.
2019). This method was here proposed for application to
the GK2A AMI, and the results obtained were—when the
atmospheric coefficients increased exponentially with the
SZA and VZA—also consistent with those of the linearly
interpolated method. The trends of the atmospheric coef-
ficients also confirm that, for GK2A, xa is dependent only
on the SZA, and xb is dependent on both the SZA and VZA
(not shown); this corresponds with the results of Li etal.
(2019) for Himawari-8 AHI.
Correction of the atmospheric distortion effects caused
by molecular and particulate scattering and absorption is
Fig. 13 Example of fog in the Korean Peninsula for true-color (left) and fog (right) RGB imagery, taken on 2020/02/11 0300 UTC
Fig. 14 Example of sea-ice in the East Asia for true-color (left) and day-snow-fog (right) RGB imagery, taken on 2020/02/20 0300 UTC
M.Kim et al.
1 3 Korean MeteorologicalSocie
desirable whenever comparisons are to be made with data
acquired under different atmospheric or geometric con-
ditions. The absolute atmospheric correction of optical,
remotely sensed data relies on RT codes. Several RT codes
are available with different features; however, the most
popular RT codes are 6S and MODTRAN. Callieco and
Dell’Acqua (2011), compared two RT codes under identi-
cal geometric and atmospheric profile conditions. Their
results showed that the mean relative difference between
the simulated transmittances—obtained by subtracting the
6S transmittance from that of the MODTRAN—is small, at
around 3.5 %. This is a result of the different assumptions
used in calculating the light scattering process (Lee etal.
4.2 Limitation of6S RTM
Earth has a spherical-shell atmosphere (SSA); however,
almost all RT codes—including those of 6SV—are based
on a parallel-plane atmosphere model, which assumes that
Fig. 15 Example of dust in the East Asia for true-color (left) and dust (right) RGB imagery, taken on 2021/03/15 0200 UTC
Fig. 16 Example of typhoon MAYSAK (2020) in East China Sea for true-color (left) and enhanced IR imagery (right), taken on 2020/09/01
0200 UTC
Atmospheric Correction ofTrue‑Color RGB Imagery withLimb Area‑Blending Based on6S and…
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the Earth’s surface and atmosphere are flat. In this case,
RTMs tend to overcorrect the TOA reflectance above SZAs
and VZAs of 70°; this issue is exacerbated when the two
zenith angles approach one another. To resolve this problem,
the Monte Carlo solver is an ideal approach, outperforming
the SOS approximation (used in 6SV) or discrete ordinate
(DISORT) code solver (used in MODTRAN, PSTAR, etc.);
however, this method is not suitable for constructing an LUT
because the computational cost is excessively high. Recently,
an RT solver previously used in SOS and DISORT has been
developed for improving the SSA model through pseudo-
correction (Callieco and Dell’Acqua 2011; He etal. 2018).
The atmospheric correction could succeed for high zenith
angles, where the RT code alters the SSA structure model.
Some discussions remain around Rayleigh scattering at
high zenith angles. This region contains very long optical
paths, and high cloud can lead to further erroneous correc-
tion. The blending of Earth’s limb regions entails the dif-
ficult task of discerning which region suffers the largest
Rayleigh scattering effect. Broomhall etal. (2019) used a
BT of 10.4μm and alternatively applied another blending,
instead of an uncorrected visible composite; this removed
red edges and improved continuous nighttime scenes. Fig-
ure17 shows an example of application of this method to
GK2A. Although there is already a natural darkening around
Earth’s edge, brownish shadows may still appear in some
regions where the SZA is large.
The solar radiation directly reflected by water surfaces is
computed exactly using the Snell–Fresnel laws in the 6S; how-
ever, the sun-glint problems remain unresolved. System vicari-
ous calibration (SVC) enables relative radiometric calibration
to be achieved for satellite ocean color sensors. This could
minimize uncertainty in the water-leaving radiance measured
from TOA radiance and resolve the problems cause by sun
glint (Zibordi etal. 2015).
4.3 Image Enhancement
At the extremities of SZA and VZA, data are blended for
aesthetic reasons (Miller etal. 2016; Broomhall etal. 2019)
applied this method—although in a slightly edited form—by
moving the input-uncorrected RGB composite of the visible
band into the infrared band. Overcorrected reflectance—which
produces a red edge—can be blended with BT at the solar
transition region. As exemplified by Broomhall etal. (2019),
the blended imagery at high SZAs effectively removed the
red edge along the Earth’s limb. Figure17 displays the result
of blending using a BT of 10.4μm for GK2A. The visible
imagery is blended with black at the limb, with the propor-
tion varying linearly from 100 to 0 % for VZAs between 75°
and 90°.
This imagery technique has an additional advantage in
that it provides visually continuous information during night-
time. The histogram equalization enhances color contrasts by
redistributing the color histogram. The effect of histogram
equalization is a distinctive brightening of the true-color
images. However, the color distribution may be narrowed by
the amount of light in the sample, such as during sunset or
sunrise. Figure18shows a true-color scene at various times.
The solar noon case shows Earth with a refined color distribu-
tion. As expected, it depicts ocean, land, cloud, and vegetation
well. Images taken at other times—such as during sunrise and
sunset—are low in brightness; in these, almost all features
except clouds are shaded. The lower panels show the histo-
grams of normalized reflectance—for pixel values between 0
and 255—before (dotted) and after (line) applying histogram
equalization. The shape of the histogram before equalization in
the solar noon case differs from the others, which exhibit wider
distributions. After histogram equalization was applied, their
shape became very similar. This suggests that corrective color
redistribution may be possible; however, the input dataset has
a low performance in discriminating subtle differences in the
reflecting surface type.
5 Conclusions
This paper described the production of atmospherically cor-
rected, true-color GK2A RGB imagery. The main process is
separated into absolute atmospheric correction and relative
Fig. 17 Result of blending using a brightness temperature (BT) of
10.4μm for GK2A. The visible imagery is blended with black at the
limb, with the proportion varying linearly from 100–0 % for VZAs
between 75° and 90° same date with Fig.8
M.Kim et al.
1 3 Korean MeteorologicalSocie
atmospheric correction, based on image-processing tech-
niques. LUTs represent a practical approach to performing
atmospheric corrections that require the processing of large
quantities of data (GK2A produces 22,000 × 22,000 and
11,000 × 11,000 pixel scenes of 0.5 and 1km resolutions
every 10min, respectively).
To generate true-color imagery, all datasets must have
an identical spatial resolution; thus, the visible (excluding
the 640 nm band), vegetation, and infrared channels were
downscaled to 500m (the spatial resolution of the 640 nm
band—the sharpest channel). During statistical downscal-
ing, spline interpolation was used to sharpen resolution. An
LUT was built considering the geometric parameters (SZA,
VZA, and RAA), atmospheric parameters (TCO, TPW, and
AOD), and reflectance height from the ground (up to 10km).
The three pre-calculated atmospheric correction coefficients
were stored in the LUT. To augment the LUT, an MCS tech-
nique was applied.
The RTM can be extended to distinguish cloud-top
heights. In this case, the pathlength is described for each
pixel, which avoids the requirement for pathlength cor-
rection during the atmospheric correction process. These
enhancements have the potential to improve the accuracy
of the Rayleigh-corrected reflectance, particularly at higher
SZAs and VZAs. Adding the RTM’s capacity to perform
Rayleigh corrections with defined pathlengths would require
an appropriate elevation model and cloud-height product;
thus, many more RT computations would need to be per-
formed (a unique set for each selected pathlength), and an
extra dimension would be required in the LUT structure.
It is unlikely that the visible appearance of the true-color
imagery would be significantly improved by including path-
length as an extra variable in the atmospheric correction
process, except perhaps for the highest VZAs and SZAs. To
quantitatively confirm the effect of atmospheric correction,
RMSE was analyzed in accordance with SZA and VZA and
found to be in the range of 0–0.5. As SZA increases, the
effect of atmospheric scattering is enhanced and the RMSE
due to atmospheric correction increases. In particular,
RMSE increases significantly as it exceeds 70 degrees. As
the scattering is stronger as it goes to the shorter band, the
improvement of the reflectivity is noticeable.
Each original band that composes a true-color image has
a different spatial resolution. To match the resolutions, spline
interpolation was used. Downscaled imagery is expected to
express coastlines and cloud surfaces in detail. Utilizing the
methods of Miller etal. (2016), it is possible to produce a
hybrid green band that better matches the peak reflectance
of chlorophyll (~ 555 nm). The initial atmospherically cor-
rected true-color RGB images contained large, unrealistic
values for long atmospheric pathlengths near Earth’s limb,
owing to overcorrection by the 6S RTM (Miller etal. 2016).
Fig. 18 Example of true-color imagery and frequency plot of reflectance before (dotted) and after (line) histogram equalization
Atmospheric Correction ofTrue‑Color RGB Imagery withLimb Area‑Blending Based on6S and…
1 3
Korean MeteorologicalSocie
One method of overcoming this difficulty is to blend the
atmospherically corrected true-color RGB imagery with
uncorrected imagery near the limb. It was found that the
quantitative improvement of the reflectivity according to the
blending technique increased the correction effect according
to the cloud height as the VZA increased.
Even after atmospheric correction, true-color imagery
appeared dark and lacked contrast. Histogram equalization,
which adjusts the brightness and contrast of images by redis-
tributing the histogram of the RGB composite, was used
to produce vivid true-color images. True-color information
provides a practical method for interpreting a wide variety
of environmental phenomena. High-temporal-resolution
true-color imagery from the GK2A AMI provides a tool for
scientists and forecasters to visualize the changing Earth
and has considerable potential to engage the general public
in an intuitive manner. We anticipate that our procedure,
with its successful integration of a number of sub-processes
pertaining to the GK2A AMI, will constitute a significant
step in this direction.
Authors’ Contributions Minsang Kim conceived and designed the
experiments; Minsang Kim performed the experiments; Minsang Kim,
Jun-Hyung Heo, and Eun-Ha Sohn analyzed the data; Minsang Kim,
Jun-Hyung Heo, and Eun-Ha Sohn contributed materials and analysis
tools; Minsang Kim and Jun-Hyung Heo wrote the paper; Eun-Ha Sohn
managed the whole process of the research.
Funding This work was funded by the Korea Meteorological Adminis-
tration’s Research and Development Program “Technical Development
on Weather Forecast Support and Convergence Service using Meteoro-
logical Satellites” under Grant (KMA2020-00120).
Data Availability Not applicable.
Code Availability Not applicable.
Conflicts of Interest/Competing Interests The authors declare no con-
flict of interest.
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... As a result of its excellent accuracy and online availability at http://6s.ltdri. org, the 6SV model is frequently utilized today to reduce atmospheric distortions (Kim et al., 2022). In addition, it is possible to develop the 6SV model particularly to identify the right parameters that will yield the highest level of accuracy (Kim et al., 2022). ...
... org, the 6SV model is frequently utilized today to reduce atmospheric distortions (Kim et al., 2022). In addition, it is possible to develop the 6SV model particularly to identify the right parameters that will yield the highest level of accuracy (Kim et al., 2022). Aerosols, water vapor, and ozone are examples of atmospheric characteristics that influence the accuracy of the results of atmospheric correction in FLAASH, ATCOR, and 6SV, but, field measurements of these parameters are rarely available. ...
Applying atmospheric corrections on satellite images is an important step before using the satellite image for any further processing. These atmospheric corrections are broadly classified as either image-based or physics-based atmospheric corrections. From a plethora of such corrections, which is best suited for vegetation and snow mapping in the mountainous Himalayan region needs to be decided. Hence, in this work, we evaluated a total of eight atmospheric corrections models including 5 image-based namely DOS (dark object subtraction method), improved dark object subtraction method (DOS3), COST (cosine theta), apparent reflectance (Aref), QUAC (QUick Atmospheric Correction), and 3 physics-based methods, namely SIAC (Sensor Invariant Atmospheric Correction), 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum) and FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes). We found that 6SV and FLAASH were better than other methods and QUAC was the worst performer when applied to Landsat 8 OLI images of the Nepal Himalayan region which has dense vegetation and snow-covered areas. The better snow reflectance values were observed for FLAASH (B, G, R: 0.88, 0.89, 0.9; NIR: 0.83), SIAC (B, G, R: 0.85, 0.89, 0.89; NIR: 0.83) and 6SV (B, G, R: 0.87, 0.89, 0.89; NIR: 0.8) methods, whereas the FLAASH and SIAC methods exhibited higher vegetation reflectance values in the NIR band than other methods. The spectra from the standard spectral library were compared with the values of vegetation and snow spectral reflectance produced from corrected reflectance images. The mean values of snow and vegetation reflectance were higher for FLAASH, 6SV, and SIAC methods as compared to other methods. Therefore, FLAASH, 6SV, and SIAC methods, in contrast to other used atmospheric correction methods, have a high possibility of giving accurate snow and vegetation cover mapping. The snow cover and vegetation cover map prepared using NDSI and NDVI showed that areas covered under thin clouds and haze were better extracted when FLAASH, SIAC, and 6SV methods are applied as compared to other methods. Thus, this study confirms that physics-based atmospheric correction models such as FLAASH, SIAC, and 6SV methods should be used while working on satellite images of the Himalayan region where the focus is on snow and vegetation cover mapping.
... Meanwhile, recent advances in satellite remote sensing have enabled multiple geostationary satellites worldwide, covering America, Europe, and Asia. The Geostationary Korea Multi-Purpose Satellite-2A (GK2A) was launched on 5 December 2018, and has been providing various meteorological products, including SST, at intervals of 10 min on a 2 km grid [25]. However, a spatial gap-filling method to create continuous raster data is not established yet. ...
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Satellite remote sensing can measure large ocean surface areas, but the infrared-based sea surface temperature (SST) might not be correctly calculated for the pixels under clouds, resulting in missing values in satellite images. Early studies for the gap-free raster maps of satellite SST were based on spatial interpolation using in situ measurements. In this paper, however, an alternative spatial gap-filling method using regression residual kriging (RRK) for the Geostationary Korea Multi-Purpose Satellite-2A (GK2A) daily SST was examined for the seas around the Korean Peninsula. Extreme outliers were first removed from the in situ measurements and the GK2A daily SST images using multi-step statistical procedures. For the pixels on the in situ measurements after the quality control, a multiple linear regression (MLR) model was built using the selected meteorological variables such as daily SST climatology value, specific humidity, and maximum wind speed. The irregular point residuals from the MLR model were transformed into a residual grid by optimized kriging for the residual compensation for the MLR estimation of the null pixels. The RRK residual compensation method improved accuracy considerably compared with the in situ measurements. The gap-filled 18,876 pixels showed the mean bias error (MBE) of −0.001 °C, the mean absolute error (MAE) of 0.315 °C, the root mean square error (RMSE) of 0.550 °C, and the correlation coefficient (CC) of 0.994. The case studies made sure that the gap-filled SST with RRK had very similar values to the in situ measurements to those of the MLR-only method. This was more apparent in the typhoon case: our RRK result was also stable under the influence of typhoons because it can cope with the abrupt changes in marine meteorology.
... However, atmospheric effect simulation using the 6SV code is time-consuming because of complex computation; thus, it is difficult to use 6SV directly for operational atmospheric correction of a satellite image. Therefore, in this study, we used the lookup table (LUT) approach, which is widely used in 6SV-based atmospheric correction for operation and research purposes [28,62,63]. The LUT approach involves a simple index-based calculation instead of the complex calculation of 6SV, and avoids repetitive calculations; thus, it can significantly reduce processing time. ...
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Land surface reflectance (LSR) is well known as an essential variable to understand land surface properties. The Geostationary Ocean Color Imager (GOCI) be able to observe not only the ocean but also the land with the high temporal and spatial resolution thanks to its channel specification. In this study, we describe the land atmospheric correction algorithm and present the quality of results through comparison with Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ data for GOCI-II. The GOCI LSR shows similar spatial distribution and quantity with MODIS LSR for both healthy and unhealthy vegetation cover. Our results agreed well with in-situ-based reference LSR with a high correlation coefficient (>0.9) and low root mean square error (<0.02) in all 8 GOCI channels. In addition, seasonal variation according to the solar zenith angle and phenological dynamics in time-series was well presented in both reference and GOCI LSR. As the results of uncertainty analysis, the estimated uncertainty in GOCI LSR shows a reasonable range (<0.04) even under a high solar zenith angle over 70°. The proposed method in this study can be applied to GOCI-II and can provide continuous satellite-based LSR products having a high temporal and spatial resolution for analyzing land surface properties.
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We propose a methodology employing an interpolation technique on the Second Simulation of a Satellite Signal (6S) look-up table (LUT) to improve surface reflectance retrieval using Himawari-8/Advanced Himawari Imager (AHI). A minimum curvature surface (MCS) technique was used to refine the 6S LUT, and the solar zenith angle (SZA) and viewing zenith angle (VZA) increments were narrowed by 0.5°. The interpolation processing time was relatively short, about 3172 s per channel, and the interpolated xa and xb were well represented by the changes in SZA and VZA. An evaluation of the interpolated xa and xb for six cases revealed a relative mean absolute error of less than 5% for all channels and cases; however, a slight difference was evident for higher values of SZA and VZA. To evaluate the surface reflectance, we compared the surface reflectance derived using 6S LUT with that calculated using 6S only. Application of the interpolated 6S LUT showed a lower relative root mean square error (RRMSE) of 0.65% to 9.29% for all channels, than before interpolation. The improvement in surface reflectance measurements increased with the SZA. For a SZA above 75°, the RRMSE improved significantly for all channels (by 11.33–45.1%). In addition, when the MCS method was applied, the surface reflectance measurements improved without spatial discontinuity and showed good agreement with 6S results in a linear profile analyses. Thus, the method proposed can improve LUT based surface reflectance measurements in less time and increase the availability of surface reflectance data based on geostationary satellites.
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The new generation of geostationary satellite sensors is producing an unprecedented amount of Earth observations with high temporal, spatial and spectral resolutions, which enable us to detect and assess abrupt surface changes. In this study, we developed the land surface directional reflectance and albedo products from Geostationary Operational Environment Satellite-R (GOES-R) Advanced Baseline Imager (ABI) data using a method that was prototyped with the Moderate Resolution Imaging Spectroradiometer (MODIS) data in a previous study, and was also tested with data from the Advanced Himawari Imager (AHI) onboard Himawari-8. Surface reflectance is usually retrieved through atmospheric correction that requires the input of aerosol optical depth (AOD). We first estimated AOD and the surface bidirectional reflectance factor (BRF) model parameters simultaneously based on an atmospheric radiative transfer formulation with surface anisotropy, and then calculated the “blue-sky” surface broadband albedo and directional reflectance. This algorithm was implemented operationally by the National Oceanic and Atmospheric Administration (NOAA) to generate the GOES-R land surface albedo product suite with a daily updated clear-sky satellite observation database. The “operational” land surface albedo estimation from ABI and AHI data was validated against ground measurements at the SURFRAD sites and OzFlux sites and compared with the existing satellite products, including MODIS, Visible infrared Imaging Radiometer (VIIRS), and Global Land Surface Satellites (GLASS) albedo products, where good agreement was found with bias values of −0.001 (ABI) and 0.020 (AHI) and root-mean-square-errors (RMSEs) less than 0.065 for the hourly albedo estimation. Directional surface reflectance estimation, evaluated at more than 74 sites from the Aerosol Robotic Network (AERONET), was proven to be reliable as well, with an overall bias very close to zero and RMSEs within 0.042 (ABI) and 0.039 (AHI). Results show that the albedo and reflectance estimation can satisfy the NOAA accuracy requirements for operational climate and meteorological applications.
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The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis is the latest global reanalysis dataset of atmospheric composition produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), consisting of three-dimensional time-consistent atmospheric composition fields, including aerosols and chemical species. The dataset currently covers the period 2003–2016 and will be extended in the future by adding 1 year each year. A reanalysis for greenhouse gases is being produced separately. The CAMS reanalysis builds on the experience gained during the production of the earlier Monitoring Atmospheric Composition and Climate (MACC) reanalysis and CAMS interim reanalysis. Satellite retrievals of total column CO; tropospheric column NO2; aerosol optical depth (AOD); and total column, partial column and profile ozone retrievals were assimilated for the CAMS reanalysis with ECMWF's Integrated Forecasting System. The new reanalysis has an increased horizontal resolution of about 80 km and provides more chemical species at a better temporal resolution (3-hourly analysis fields, 3-hourly forecast fields and hourly surface forecast fields) than the previously produced CAMS interim reanalysis. The CAMS reanalysis has smaller biases compared with most of the independent ozone, carbon monoxide, nitrogen dioxide and aerosol optical depth observations used for validation in this paper than the previous two reanalyses and is much improved and more consistent in time, especially compared to the MACC reanalysis. The CAMS reanalysis is a dataset that can be used to compute climatologies, study trends, evaluate models, benchmark other reanalyses or serve as boundary conditions for regional models for past periods.
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Observations of top-of-atmosphere radiances from the Advanced Himawari Imager (AHI) blue, green, and red spectral bands can be used to produce high-temporal-resolution, true-color imagery at 1-km spatial resolution over the Asia–Pacific region. To enhance interpretability and aesthetic appearance of these images, the top-of-atmosphere radiance data are processed to remove the Rayleigh-scattered atmospheric component, corrected for limb effects, blended with brightness temperature data from a thermal infrared window band at night, and the resultant imagery adjusted to optimize contrast. The contribution of Rayleigh scattering to the AHI observations is calculated by interpolating radiative transfer parameters from a preconstructed set of lookup tables, which are specifically created for the Himawari-8 AHI instrument. A surface reflectance value for each pixel is calculated after the Rayleigh contribution is removed. The spectrally dependent reflectance values produced from the lookup table differ from the exact calculation by up to 18% at the planetary limb, over 100%at the solar terminator, and by less than 0.5%at low to moderate solar and sensor zenith angles. The subsequent corrections applied for limb effects mitigate the areas with high interpolation error, which slightly reduces the spatial coverage, but provides Rayleigh-corrected surface reflectance products that have interpolation errors at or below 0.5%. Resolution sharpening increases the nominal pixel size from 1000 to 500m while still producing sharp images. The resultant images are colorful, visually intuitive, high contrast, and of sufficient spatial and temporal resolution to provide a unique and complementary observational tool for use by weather forecasters and the general public alike.
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A number of approaches have been developed to generate true color images from the Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellite (GOES)-16. GOES-16 is the first of a series of four spacecraft with the ABI onboard. These approaches are complicated since the ABI does not have a green (0.55 μm) spectral band. Despite this limitation, representative true color images can be built. A methodology for generating color images from the ABI is discussed, along with corresponding examples from the Earth Polychromatic Imaging Camera on Deep Space Climate Observatory.
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Recent studies in the application of geostationary satellite sensors, such as the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the European Union’s meteorological satellite, and the Geostationary Ocean Color Imager (GOCI) from South Korea, to monitor the Total Suspended Sediment (TSS) in coastal waters have shown that the higher temporal dynamics in the coastal processes can be better achieved with high temporal resolution geostationary satellite sensors. The availability of the geostationary satellite, Himawari-8 carrying the Advanced Himawari Imager (AHI) sensor with visible, Near InfraRed (NIR) and Shortwave InfraRed (SWIR) bands over the coastal waters of Australia has prompted this study to test the feasibility of AHI in mapping TSS concentration. In this study, we present an atmospheric correction method for AHI data using two different aerosol correction methods, a combination of two SWIR bands, and a combination of one NIR and one SWIR band. The outcome from this study indicated that the atmospherically corrected AHI data at 10 min temporal resolution can be used to discern TSS concentrations greater than ∼0.15 mg/L when solar zenith angles (θ0) are less than 60°. The result of the cross-validation of AHI derived TSS concentration, with MODIS-Aqua and Lansat-8 Operational Land Imager (OLI) derived TSS concentration showed good correlation, with correlation coefficients r=0.71 and r=0.91, respectively.
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China is developing a new generation of geostationary meteorological satellites called FengYun-4 (FY-4) which is planned for launch beginning in the 2016 timeframe. Following upon the current FY-2 satellite series, FY-4 will carry four new instruments; they are the Advanced Geosynchronous Radiation Imager (AGRI), the Geosynchronous Interferometric Infrared Sounder (GIIRS), the Lightning Mapping Imager (LMI), and the Space Environment Package (SEP). The first satellite of the FY-4 series launched on 11 December 2016 is experimental and the following four or more satellites will be operational. The main objectives of the FY-4 series are to monitor rapidly changing weather systems and to improve warning and forecasting capabilities. The FY-4 measurements are aimed at accomplishing: (1) high temporal and spatial resolution imaging in 14 spectral bands from the visible (VIS), near infrared (NIR) and infrared (IR) spectral regions; (2) lightning imaging; and (3) high spectral resolution IR sounding observations over China and adjacent regions. FY-4 will also enhance the space weather monitoring and warning with SEP. Current products from FY-2 will be improved by FY-4, and a number of new products will also be introduced. FY-4’s sounding and imaging data will be used to improve applications in a wide range of ocean, land, and atmosphere monitoring plus forecasting extreme weather (especially typhoons and thunderstorms); overall FY-4 will contribute to more accurate understanding and forecasting of China’s weather, climate, environment and natural disasters. This new generation of Chinese geostationary weather satellites is being developed in parallel to the new generation of geostationary meteorological satellite systems from the international community of satellite providers and is intended to be an important contribution to the global observing system.
New generation ocean color imagers on geostationary orbits are designed to provide a much higher temporal resolution along with enhanced spatial and spectral resolutions that will open up obvious opportunities for improving the sampling frequency and resolving diurnal variability of phytoplankton and other biogeochemical properties in dynamic coastal waters. Despite the capabilities of such new generation sensors to detect the diurnal cycles of various ocean phenomena, there is a lack of knowledge on their radiometric sensitivity and signal detectability for observing the ocean color at morning or evening hours. This paper aims to explore the capability of geostationary satellite ocean color sensor for detecting ocean biogeochemical properties [chlorophyll (CHL); total suspended matter (TSM); colored dissolved organic matter (CDOM)] under high solar zenith angles (SZAs). The analysis is based upon simulations from the vector radiative transfer model for the coupled ocean-atmosphere system (PCOART-SA), which considers the earth curvature effects. The unitless differential signal-to-noise ratio (ASNR) is used as a discriminant parameter to indicate the radiometric sensitivity to variation of different biogeochemical properties. The results showed that the SZAs have a significant impact on the signal detectability for CHL variation. For typical shelf water (CHL = 1 μg/L, TSM = 1 mg/L, CDOM = 0.15 m <sup xmlns:mml="" xmlns:xlink="">-1</sup> ), with the typical observation zenith angle (OZA) = 30°, changes on theorder of ΔCHL = 0.024 μg/L (2.4% to background CHL) were detectable when SZA = 30°; when SZA > 75°, the detectable minimal ΔCHL increased to 0.77 μg/L (77%), indicating the difficulty of detecting CHL under high SZA. For CDOM, the detectability of changes (ΔCDOM) was also found to be closely related to the SZAs, i.e., changes on the order of ten times depending on the SZA conditions. However, even under extremely high SZA conditions (SZA = 80°, OZA = 30°), ACDOM = 0.007 m <sup xmlns:mml="" xmlns:xlink="">-1</sup> which is about 4.7% of the background CDOM was still detectable at 412 nm. On the other hand, under high SZA conditions (SZA = 80°, OZA = 30°), ΔTSM = 0.211 mg/L (2.1% to the background TSM) was also detectable. Overall, our results indicate that under high SZAs conditions, the geostationary satellite ocean color sensor may experience difficulty in detecting a slight change in CHL variation in productive waters, but it still can detect small changes in TSM and CDOM contents despite a reduced sensitivity at the steeper SZAs.
In this study, a vector radiative transfer model for the coupled ocean-atmosphere system with consideration of the effects of Earth curvature (named PCOART-SA) was developed using the pseudo-spherical approximation. Both downward and reflected solar beam radiation were corrected accounting for Earth curvature effects. Validation showed that the PCOART-SA results agreed well with literature benchmarks and the CDISORT and AccuRT model results. Based on PCOART-SA, Earth curvature effects on Rayleigh-scattering radiance including polarization were investigated. The results showed that the influence of Earth curvature increased rapidly with solar zenith angle, with influences up to 1%, 3%, and 12% for solar zenith angles at 75°, 80°, and 85°, respectively, which should be considered for high accuracy atmospheric correction. We also found that the Rayleigh-scattering look-up table in SeaDAS after version 7.2 showed significant bias at high solar zenith angles, which needs further investigation. Finally, using the PCOART-SA model, we generated Rayleigh-scattering look-up tables for Aqua/MODIS with consideration of Earth curvature effects, which can be directly used in SeaDAS.