AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-1377-2016GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during
the DRAGON-NE Asia 2012 campaignChoiMyungjehttps://orcid.org/0000-0002-2488-2840KimJhoonjkim2@yonsei.ac.krhttps://orcid.org/0000-0002-1508-9218LeeJaehwaKimMijinParkYoung-JeJeongUkkyoKimWoogyungHongHyunkeeHolbenBrenthttps://orcid.org/0000-0002-1251-9809EckThomas F.SongChul H.LimJae-HyunSongChang-KeunDepartment of Atmospheric Sciences, Yonsei University, Seoul, Republic
of KoreaEarth System Science Interdisciplinary Center, University of Maryland,
College Park, MD, USANASA Goddard Space Flight Center, Greenbelt, MD, USAKorea Ocean Satellite Center, Korea Institute of Ocean Science and
Technology, Ansan, Republic of KoreaDepartment of Spatial Information Engineering, Pukyong National
University, Busan, Republic of KoreaUniversities Space Research Association, Columbia, MD, USASchool of Environmental Science and Engineering, Gwangju Institute of
Science and
Technology (GIST), Gwangju, Republic of KoreaNational Institute of Environmental Research (NIER), Incheon, Republic
of KoreaJhoon Kim (jkim2@yonsei.ac.kr)1April2016931377139822July201515September201516January20162March2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/9/1377/2016/amt-9-1377-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/1377/2016/amt-9-1377-2016.pdf
The Geostationary Ocean Color Imager (GOCI) onboard the Communication,
Ocean, and Meteorological Satellite (COMS) is the first multi-channel ocean
color imager in geostationary orbit. Hourly GOCI top-of-atmosphere radiance
has been available for the retrieval of aerosol optical properties over East
Asia since March 2011. This study presents improvements made to the GOCI
Yonsei Aerosol Retrieval (YAER) algorithm together with validation results
during the Distributed Regional Aerosol Gridded Observation Networks – Northeast Asia 2012 campaign (DRAGON-NE Asia 2012 campaign). The evaluation
during the spring season over East Asia is important because of high aerosol
concentrations and diverse types of Asian dust and haze. Optical
properties of aerosol are retrieved from the GOCI YAER algorithm including
aerosol optical depth (AOD) at 550 nm, fine-mode fraction (FMF) at 550 nm,
single-scattering albedo (SSA) at 440 nm, Ångström exponent (AE) between 440
and 860 nm, and aerosol type. The aerosol models are created based on a
global analysis of the Aerosol Robotic Networks (AERONET) inversion data,
and covers a broad range of size distribution and absorptivity, including
nonspherical dust properties. The Cox–Munk ocean bidirectional reflectance
distribution function (BRDF) model is used over ocean, and an improved minimum
reflectance technique is used over land. Because turbid water is persistent
over the Yellow Sea, the land algorithm is used for such cases. The aerosol
products are evaluated against AERONET observations and MODIS Collection 6
aerosol products retrieved from Dark Target (DT) and Deep Blue (DB)
algorithms during the DRAGON-NE Asia 2012 campaign conducted from March to
May 2012. Comparison of AOD from GOCI and AERONET resulted in a Pearson
correlation coefficient of 0.881 and a linear regression equation with GOCI
AOD = 1.083 × AERONET AOD - 0.042. The correlation between GOCI
and MODIS AODs is higher over ocean than land. GOCI AOD shows better
agreement with MODIS DB than MODIS DT. The other GOCI YAER products (AE,
FMF, and SSA) show lower correlation with AERONET than AOD, but still show
some skills for qualitative use.
Introduction
Aerosols have an important role in the Earth's climate system, influencing
climate directly through scattering and absorbing radiation, and indirectly
by acting as cloud condensation nuclei (IPCC, 2013). Both ground-based and
satellite measurements show an increasing trend of aerosol optical depth
(AOD) over East Asia (IPCC, 2013; Hsu et al., 2012; Yoon et al., 2014). In
particular, the increasing trend over Asia is strongest during the dry
seasons from December to May. Furthermore, aerosol types over East Asia are
more complex than over other regions (J. Kim et al., 2007; Lee et al.,
2010a). To quantify its impact on climate, accurate observation of aerosol
over a broad area is required.
Aerosol can be detected by remote sensing from ground-based and satellite
measurement. AERONET (Aerosol Robotic Networks) is the representative global
network of ground-based sun photometers, with an absolute observation
uncertainty for a single AOD measurement of ±0.01 (Holben et al., 1998;
Eck et al., 1999). Satellite observations from low earth orbit (LEO) and
geostationary earth orbit (GEO) allow detection of aerosol properties over a
wider area. Many aerosol retrieval algorithms have been developed and
improved using multi-channel sensors in LEO such as the Moderate Resolution
Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field-of-View Sensor
(SeaWiFS), Medium Resolution Imaging Spectrometer (MERIS), Ozone Monitoring
Instrument (OMI), and Visible Infrared Imaging Radiometer Suite (VIIRS)
(Higurashi and Nakajima, 1999; J. Kim et al., 2007; Hsu et al., 2006, 2013;
Jackson et al., 2013; Kaufman et al., 1997a; Levy et al., 2007, 2013; Remer
et al., 2005; Sayer et al., 2012; Torres et al., 1998, 2007, 2012; von
Hoyningen-Huene et al., 2011). Multi-channel observations from LEO give
global coverage at high accuracy but with the disadvantage of low temporal
resolution. The uncertainty in the retrieved AOD from MODIS is reported as
±(0.03+5 %) over ocean and ± (0.05 + 15 %) over land
(Remer et al., 2008; Levy et al., 2010). Aerosol retrieval algorithms have
also been developed using meteorological imagers aboard GEO satellites, such
as the Geostationary Operational Environmental Satellite (GOES),
Geostationary Meteorological Satellite (GMS), and Multifunction Transport
Satellite (MTSAT) (Kim et al., 2008; Knapp et al., 2002; Wang et al., 2003;
Yoon et al., 2007; Urm and Sohn, 2005). These sensors provide observations at
a higher temporal resolution than LEO sensors, but have fixed observation
area and lower accuracy due to the wider spectral bands and fewer visible
channels. The magnitude of the uncertainty in the retrieved AOD using GOES
has been reported as ±0.13 (Knapp et al., 2005). Despite the extensive
observations to date, the confidence level of satellite-based globally
averaged AOD trends is still “low” (IPCC, 2013).
The Geostationary Ocean Color Imager (GOCI) onboard the Communication,
Ocean, and Meteorological Satellites (COMS) is the first multi-channel
visible- and near-infrared-wavelength sensor in GEO (Ahn et al., 2012;
Choi et al., 2012; Kang et al., 2006). The wavelength bands of the eight channels
are centered at 412, 443, 490, 555, 660, 680, 745, and 865 nm, similar to
other ocean color sensors such as the Coastal Zone Color Scanner (CZCS),
SeaWiFS, MERIS, and MODIS, but GOCI has a high spatial resolution of 500 m
× 500 m (Table 1). It observes East Asia hourly during the
daytime, a total of eight times per day. A prototype of the GOCI Yonsei
Aerosol Retrieval (YAER) algorithm was developed (Lee et al., 2010b) and is
improved in this study to include dynamic (changing with AOD) and
nonspherical aerosol models as introduced in Lee et al. (2012). Aerosol
optical properties (AOPs) such as aerosol optical depth, size information,
and absorptivity can be retrieved hourly from the GOCI YAER algorithm with
spatial resolution of 6 km × 6 km. The high temporal information on
AOPs over East Asia from GOCI is expected to help understand the diurnal
variation of aerosol properties and improve the accuracy of air quality
modeling (Park et al., 2014; Saide et al., 2014; Xu et al., 2015).
The specification of ocean color sensors.
SensorCZCSSeaWiFSMERISMODISGOCIPlatformNimbus-7OrbView-2EnvisatTerra/AquaCOMSPeriod24 Oct 1978– 1 Aug 19941 Aug 1997– 11 Dec 20101 Mar 2002– 8 Apr 201218 Dec 1999– current (Terra) 4 May 2002–current (Aqua)26 June 2010–currentOrbit typeLEO (sun-synchronous orbit)LEO (sun-synchronous orbit)LEO (sun-synchronous orbit)LEO (sun-synchronous orbit)GEOLocal equatorial crossing time (only for LEO), or longitude (only for GEO)12:00 descending node12:00 descending node10:00 descending node10:30 descending node (Terra) 13:30 ascending node (Aqua)128.2∘ ESwath (only for LEO)1600 km2800 km1150 km2230 kmCoverage/cycleNear-global coverage every dayGlobalcoverage everydayGlobalcoverage in 3 daysGlobal coverage nearly twice/day (long-wavechannels) or once/day (short-wave channels)Area of2500 km ×2500 km/hourly in daylight (8times per day)Spatial resolution825 m1100 m300 m (Eu-rope) 1200 m(global)1000 m500 mNo. of ocean colorchannels6815(total 36 channels)8Center wavelengths (and band width) of ocean color bands (nm)443 (20) 520 (20) 550 (20) 670 (20) 750 (100) 1150 (1000)412 (20) 443 (20) 490 (20) 510 (20) 555 (20) 670 (20) 765 (40) 865 (40)412.5 (10) 442.5 (10) 490 (10) 510 (10) 560 (10) 620 (10) 665 (10) 681.25 (7.5) 708.75 (10) 760.625 (3.75) 778.75 (15) 865 (20) 885 (10) 900 (10)412 (15) 443 (10) 488 (10) 531 (10) 551 (10) 667 (10) 678 (10) 748 (10) 870 (15) (only ocean color bands are presented.)412 (20) 443 (20) 490 (20) 555 (20) 660 (20) 680 (10) 745 (20) 865 (40)
Flowchart for GOCI YAER algorithm.
The Distributed Regional Aerosol Gridded Observation Networks – Northeast
Asia 2012 campaign (DRAGON-NE Asia 2012 campaign) took place in Korea and
Japan from 1 March to 31 May to observe aerosol properties and their
variability using a dense network of ground-based sun photometers. The
campaign provides a data set for validation of aerosol retrieval algorithms
in high spatial resolution.
This study introduces the improvements made to the GOCI YAER algorithm and
validation results during the DRAGON-NE Asia 2012 campaign. Because MODIS
data were used for the prototype algorithm before the launch of GOCI, this
study is the first to use real GOCI data. The GOCI YAER products are
validated with AERONET data from 38 sites during the DRAGON-NE Asia 2012
campaign. Intercomparison of AOPs between GOCI and MODIS Collection 6 (C6)
is also performed for the same period.
In Sect. 2, the improvements of the GOCI YAER algorithm are summarized. In
Sect. 3, some aerosol event cases are analyzed using products from the
improved algorithm. In Sect. 4, the GOCI YAER products are validated with
AERONET and MODIS. In Sect. 5, an error analysis of GOCI YAER AOD against
AERONET AOD is presented. Section 6 provides a summary and conclusions.
Improvements of the GOCI YAER algorithm
Since the distribution of GOCI Level 1B (L1B) radiation data in March 2011,
the GOCI YAER algorithm has been updated to process the real GOCI data and
to improve the data quality. Figure 1 shows the flowchart for the GOCI YAER
algorithm. The improvements made to the algorithm as compared to described
in Lee et al. (2010b) will be discussed according to the sequence shown in
the flowchart. The algorithm uses top-of-atmosphere (TOA) reflectance (ρTOA) as input data,
ρTOAλ=π⋅Lλμ0⋅E0λ,
where λ is the wavelength of each GOCI channel (412, 443, 490, 555, 660, 680,
745, and 865 nm), Lλ is the observed radiance from GOCI,
μ0 is the cosine of the solar zenith angle (θ0), and
E0 is the extraterrestrial solar flux.
Cloud masking and quality assurance
The algorithm is applied to cloud-free and snow-free pixels over land and
cloud-free and ice-free pixels over ocean. In order to mask out the cloudy
scenes, the following tests are applied:
ρTOA(490 nm) > 0.40 → cloud over land or ocean
standard deviation of 3 × 3 pixels ρTOA(412 nm)
> 0.0025 → cloud over land
standard deviation of 3 × 3 pixels ρTOA(550 nm)
> 0.0025 → cloud over ocean
The standard deviation test over land is based on the MODIS Deep Blue (DB) algorithm
(Hsu et al., 2004), and other tests are based on the MODIS Dark Target (DT) (Remer et al.,
2005). Note that ocean pixels with glint angle less than 40∘ are
also masked out. After the cloud masking, 12 × 12 GOCI 500 m
resolution pixels (resulting in 6 km × 6 km resolution) are
aggregated to be fed into the retrieval process. In this step, the darkest
20 % and the brightest 40 % of pixels in reference to ρTOA(490 nm) are discarded to remove remaining cloud, cloud shadow, and
surface contamination (Remer et al., 2005; Levy et al., 2007). The number of
L1B pixels remaining and the retrieved AOD at 550 nm determine the quality
assurance (QA) flag for each retrieval pixel, as listed in Table 2.
Thresholds of QA determination are based on the MODIS DT algorithm (Levy et
al., 2013). The GOCI YAER algorithm allows a retrieved AOD range from
-0.1 to 5.0, and QA can be only greater than 1 only when the value is in
the range between -0.05 and 3.6. The algorithm allows randomly retrieved,
small negative AOD caused by uncertainty in surface reflectance because it
is within the expected retrieval error with reference to the MODIS DT algorithm, and also has
statistical significance in low AOD range (Levy et al., 2007, 2013).
Conditions for determining pixel QA values from 0 to 3.
QANumber of pixels (N) selectedRange of retrieved AOD at 550 nmfrom possible 12 × 12 pixels06 ≤N≤ 14-0.10 ≤ AOD < 0.05, or 3.6 < AOD ≤ 5.0115 ≤N≤ 21-0.05 ≤ AOD ≤ 3.6222 ≤N≤ 35-0.05 ≤ AOD ≤ 3.6336 ≤N≤ 58 (maximum)-0.05 ≤ AOD ≤ 3.6Surface reflectance over land and ocean
The lack of a 2.1 µm channel in GOCI limits the capability of
estimating surface reflectance in the visible from the 2.1 µm TOA
reflectance as in the MODIS DT algorithm (Levy et al., 2007; Kaufman et al.,
1997b). Instead, the GOCI YAER algorithm uses the minimum reflectivity
technique to determine the surface reflectance (ρSFC) over land and
turbid water (Herman and Celarier, 1997; Hsu et al., 2004; Koelemeijer et
al., 2003). First, each scene's TOA reflectance is corrected for Rayleigh
scattering to derive the Rayleigh-corrected reflectance (RCR) (Hsu et al.,
2013). It is assumed that in a 30-day period, changes in surface reflectance
are insignificant and there is at least 1 clear day (Lee et al., 2010b).
To increase the number of samples to find clear pixels, it is also assumed
that the surface reflectance is homogeneous over 12 × 12 pixels;
therefore L1B resolution data are used for determining the minimum reflectance.
Thus, the spatial resolution of surface reflectance is the same as the
aerosol retrieval resolution of 6 km × 6 km. To allow for changes
of surface reflectance with sun–satellite geometry, RCRs at a given hour
during the day are composited for each month. The maximum number of samples
available to determine surface reflectance at a pixel is 144 pixels × 30 days, a total of 4320 samples. Samples are sorted in
ascending order according to RCR at 412 nm and selected from the darkest
1 to 3 %. At 412 nm, the variability of surface reflectance is lower
and atmospheric signals such as Rayleigh scattering or aerosol reflectance
are higher than at longer wavelengths. Thus, the RCR at 412 nm is used to
find clear pixels during the 30-day window. According to Hsu et al. (2004), a
surface reflectance database can be obtained by finding the minimum value of
the 412 nm RCR within a given month, which corresponds to about 3 % for
the window. In this process, cloud shadows which could lead to false
reflectance should not be selected to evaluate surface reflectance. For
example, Lee et al. (2010b) selected the second minimum value, and Fukuda et
al. (2013) used the modified minimum reflectance method using first and
second minimum values to avoid cloud shadow effects for determining surface
reflectance. In the GOCI YAER algorithm, the maximum number of L1B pixel samples
for one surface reflectance pixel at a given time is 144 pixels × 30 days,
a total of 4320 samples. Therefore, using only the first or second minimum
threshold is not appropriate for the GOCI YAER algorithm. Instead, darkest
0–1 % pixels are assumed to be cloud shadow and are thus excluded, empirically.
Therefore, thresholds for the lower and upper bound are set as 1 % and
3 %, respectively. The RCRs of selected pixels are averaged for each
channel, giving a surface reflectance corresponding to the middle of each
month (day 15). Finally, linear interpolation according to retrieval date is
applied.
Surface reflectance on 15th of the month, 13:30 local
standard time (LST) at 443 nm (left column) and 660 nm (right column): March
(upper row), April (middle row), and May (lower row).
Figure 2 shows examples of surface reflectance at 443 and 660 nm; the
difference in the surface reflectance between ocean and land is smaller at
443 nm than 660 nm. The high 660 nm surface reflectance near the coast of
China in the Bohai Sea and in the northern East China Sea shows turbid water
with values comparable to the land surface reflectance over northern China
and higher than southern China; this clearly shows a semi-permanent presence
of turbid water pixels during the 30 days. From March to May, surface
reflectances decrease over land because of melting snow and increasing
vegetation. According to von Hoyningen-Huene et al. (2003), who described
the aerosol retrieval algorithm using ocean color sensors, pixels with a
surface reflectance of less than 0.15 correspond to areas fully or partly covered
with vegetation. Also, Zhang et al. (2011) described that the
operational GOES AOD retrieval algorithm use a simple threshold of 0.15
surface reflectance to remove bright surface reflectance pixels. Final
selected channels for retrieving aerosol over land are those of which
surface reflectances are less than 0.15.
On the other hand, it is assumed that ocean surface reflectance varies with
geometry and wind speed (Cox and Munk, 1954); the wind speed at 10 m above sea level is used in a radiative transfer model to calculate the
look-up table (LUT). The nodal points of wind speed in the LUT calculation
are 1, 3, 5, 7, 9, and 20 m s-1, which are the default nodal points of
libRadtran package. Using the European Centre for Medium-Range Weather Forecasts'
(ECMWF) wind speed reanalysis data with 0.25∘× 0.25∘ spatial resolution every 6 h, the LUT is
interpolated to each pixel's wind speed to retrieve the AOD over the ocean.
Turbid water detection
Retrieving aerosol properties over turbid water is challenging due to the
variability of the turbid water and high surface reflectance. Half of the
ocean in the GOCI observation area is the Yellow Sea with very high
year-round turbidity. If the ocean surface is assumed over turbid water, the
surface reflectance can be underestimated, and thus AOD can be
overestimated. The previous GOCI YAER algorithm (Lee et al., 2010b) used the
surface reflectance ratio for turbid water detection, which is the
ratio of surface reflectance at 640 and 860 nm. If turbid water pixels are
detected, the surface reflectance from the second minimum RCR during the
previous 30-day period is used for AOD retrieval. Persistent areas of turbid
water during the previous 30 days can be detected in this way, but it is hard to
detect rapid temporal variations of turbidity. In this study, real-time
turbid water detection is applied.
According to Li et al. (2003), ρTOA at 550, 660, and 865 nm showed
higher values over turbid water than over clear water. They used the
difference between ρTOA at 550 nm and the value interpolated to
550 nm from ρTOA at 470, 1240, 1640, and 2130 nm using a linear fit on
a log–log scale. In this study, because GOCI does not have infrared (IR) channels,
Δρ660 is defined as the difference in reflectance at
660 nm between the observed ρTOA at 660 nm and linearly
interpolated between ρTOA at 412 and 865 nm to 660 nm. Increased
ρTOA due to turbid water is stronger at 660 nm than at 412 and
865 nm; therefore Δρ660 shows a higher value over turbid
water than over clear water.
Frequency and cumulative normal frequency of Δρ660 over the Yellow Sea and over clear water.
Δρ660 and DAI images at 13:30 LST on (a),
(b) 26 April 2012 (no dust case) and (c), (d) the following day (dust case),
respectively.
25 March 2012, 13:30 LST (a) true color image and (b)Δρ660.
To determine the threshold of Δρ660 for
distinguishing turbid and clear water over the ocean, hourly data for the
first and fifteenth day of each month for 3 years from March 2011 to
February 2014 are analyzed. The analysis is implemented over two distinct
areas: the Yellow Sea (115–126∘ E, 30–40∘ N) and an area of clear water (130–140∘ E,
25–30∘ N), as in Lee et al. (2010b). A strict threshold for defining pixels as clear water is necessary
to prevent misdetection of less turbid water as aerosol. Figure 3 shows the
cumulative normal distribution of Δρ660, where
ratios below -0.05 are 99.0 % and 67.4 % for clear water and Yellow Sea
pixels, respectively. Finally, pixels with Δρ660
below -0.05 are not considered as turbid water; consequently, the ocean algorithm
is applied. On the contrary, pixels where Δρ660 is
above -0.05 are considered as turbid water; therefore the land algorithm is
applied. Note that the surface reflectance of turbid water pixels is
adjusted to the minimum turbidity during the 30 days; therefore surface
reflectance can be underestimated when severely turbid water occurs within
the 30 days. Values of the ratio below 0.02 comprise 99.6 % of the Yellow
Sea pixels. Therefore, pixels where Δρ660 is above
0.02 are considered as severely turbid water, and excluded from the
retrieval procedure.
The number of AERONET inversion data, and considering AE between
440 and 870 nm, FMF at 550 nm, and SSA at 440 nm for the 26 aerosol models.
The minimum and maximum values are shown because of AOD dependence. H,
M, and N denote highly absorbing, moderately absorbing, and non-absorbing models, respectively.
To confirm whether Δρ660 effectively detects turbid
water, two turbid water cases are selected in Fig. 4. One is a clean
atmosphere case (26 April 2012), and another case involves dust over the
northern part of the Yellow Sea (27 April 2012). To compare the sensitivity
between pixels over turbid water and those with absorbing aerosol, the Deep
Blue Aerosol Index (DAI) is calculated using GOCI TOA reflectance at 412 and
443 nm (Hsu et al., 2004, 2006; Ciren and Kondragunta, 2014). Note that DAI
and Δρ660 are plotted over cloud-free pixels, and
only positive DAI pixels are presented to check the existence of absorbing
aerosol such as dust in Fig. 4e and f, because absorbing aerosol such as
dust or smoke shows a DAI greater than 4 over ocean (Ciren and Kondragunta,
2014). The true color image for the clean case shows severe turbidity in the
ocean along the coast of eastern China and the western Korean Peninsula. The
next day, there is heavy Asian dust over northern Yellow Sea, and turbid
water is in the same position as the day before. Δρ660 shows a higher signal over turbid water (∼ 0.02) than
Asian dust (∼-0.01), while DAI shows a higher signal over
Asian dust (∼ 4.8) than turbid water (∼ 1.6).
Although heavy aerosol plumes can have Δρ660 above
-0.05 over clear water, this does not cause a significant issue because the
land algorithm is applied instead, not affecting spatial coverage.
Images of (a) GOCI true color, (b) AOD at 550 nm, (c) FMF at 550 nm,
(d) AE between 440 and 870 nm, (e) SSA at 440 nm, and (f) type for 6 May
2012, 13:30 LST. Aerosol types are colored yellow (dust), green (mixture),
orange (non-absorbing coarse type), blue (non-absorbing fine type), purple
(moderately absorbing fine type), and red (highly absorbing fine type).
As Fig. 6 except for 27 April 2012.
An additional role of Δρ660 is to detect the
remaining cloud-contaminated pixels after cloud masking. There are
inhomogeneous cloud pixels over the right half of the scene in Fig. 5. Most
cloud pixels are effectively screened by the cloud masking steps, but thin
cloud pixels remain and show high Δρ660 above 0.05
(red color). This is a similar to the “visible reflectance” anomaly of the
VIIRS aerosol algorithm (Jackson et al., 2013). Because pixels with
Δρ660 above 0.02 are considered as severe turbid
water and screened, the remaining cloud pixels are also masked using this
test. The average ratio of pixels of Δρ660 above
0.02 after cloud masking over total available ocean pixels is about 2 %
during the campaign.
Aerosol models
There are various factors to determine aerosol characteristics and aerosols' change
such as temporal and spatial variations of the direct emission, secondary
production, and meteorological transport (Yoon et al., 2011, 2012, 2014).
In addition, it is important to reflect those properties well from the perspective of
optical properties for aerosol retrieval. Assumed aerosol models play an
important role in the retrieval accuracy. To reflect global climatological
properties, AERONET inversion data (Dubovik and King, 2000) are used for
creating aerosol models to be used in the retrieval process. A classification
method for AERONET inversion data using fine-mode fraction (FMF) at 550 nm
and single-scattering albedo (SSA) at 440 nm is adopted (J. Kim et al.,
2007; Lee et al., 2010a, 2012), but there are some differences for the GOCI
YAER algorithm.
Composited AERONET data are only used for the period up to February 2011,
which is before GOCI's first observation, to separate AERONET data usages
for aerosol model construction and validation of satellite products. Global
sites are selected where the number of individual AERONET retrieval data is
greater than 10 times, giving a total of 747 sites. Observation periods of
individual AERONET sites are quite different, from few individual observations
to several years. Level 2.0 data are quality assured; consequently, each individual
observation is meaningful, even if the whole observation period is short.
Therefore, we tried to use available AERONET individual data, and a small
threshold of 10 times is applied. From those sites, the number of data that
have all the AOPs in all channels is 66 712. They are classified into 26
aerosol models according to FMF at 550 nm and SSA at 440 nm (Table 3). Note
that AOPs change as AOD varies because of the hygroscopic growth effect or
aggregation (Reid et al., 1998; Eck et al., 2003). Therefore, each aerosol
model is separated again into low, moderate, and high AOD groups
corresponding to the AOD ranges of 0.0–0.5, 0.5–0.8, and 0.8–3.6
respectively. Finally, the AOPs of each aerosol model are averaged and used
as input for the LUT calculation.
The AERONET inversion algorithm considers aerosol nonsphericity using a
mixture of polydisperse, randomly oriented homogeneous spheroids (Mishchenko
et al., 1997; Dubovik et al., 2006). Phase functions of the inversion data
including the effect of nonspherical particles are directly used for the
radiative transfer calculations.
LUT calculation and inversion procedure
Table 4 shows the node points for calculating TOA reflectances using a
discrete ordinate radiative transfer (DISORT) code of the libRadtran
software package (http://libradtran.org) (Mayer and Kylling,
2005). The input options of this radiative transfer model (RTM) to calculate
ρTOA for different aerosol conditions include the spectral phase
function and SSA; therefore the values of each model from AERONET inversion
data can be used directly. Note that the input spectral AODs for LUT
calculation are normalized to 550 nm using the climatology of each model's
Ångström exponent (AE) between 440 and 870 nm.
The inversion method is adopted from that of Lee et al. (2012). That
algorithm retrieves AOD at 550 nm using every MODIS wavelength (470, 555,
650, 860, 1240, 1630, and 2010 nm) and aerosol model, and then the
aerosol model is selected that minimized the standard deviation of the seven different
AODs retrieved from each wavelength. The final AOD is chosen from each
wavelength. By doing so, each wavelength can contribute equally to selecting
the aerosol model. In the GOCI YAER algorithm, the reference channel is the same as
550 nm and retrieval wavelengths are changed to the GOCI wavelengths.
The GOCI YAER algorithm retrieves AODs at 550 nm using whole GOCI
wavelengths' reflectance (412, 443, 490, 555, 660, 680, 745, and 865 nm) and aerosol
model over ocean. Final selected wavelengths for retrieving aerosol
properties over land are those of which surface reflectances are less than
0.15. If the number of selected wavelengths is greater than or equal to 2,
AODs at 550 nm are retrieved from that wavelength and aerosol model. The
inversion procedure to retrieve AOD is implemented using interpolation from
precalculated TOA reflectance at LUT dimensions to observed TOA reflectance
according to geometries (solar zenith angle, satellite zenith angle, and
relative azimuth angle), assumed aerosol model, wavelength, surface
reflectance, and terrain height. Then, three aerosol models are selected that
minimized the standard deviation (σ) of the different AODs retrieved
from each wavelength, defined as the square root of the average of the
squared deviations of the AODs from their average AOD. Final products of
AOD, FMF, SSA, and AE are the σ-weighted average value from three
selected models as shown in the following equations:
Final AOD at 550 nm= ∑i=13CModeli×Averaged AODModeliCModeli=1σModeli1σModel1+1σModel2+1σModel3.
Final AE between 440 and 870 nm, FMF at 550 nm, and SSA at 440 nm are
determined in the same way except that averaged AOD is replaced with assumed
AOPs as in Table 3. The GOCI YAER algorithm classifies a total of six
aerosol types using the retrieved final FMF and SSA (Table 5).
Case studies of GOCI YAER products during the DRAGON-NE Asia 2012
campaign
Aerosol types of East Asia are very diverse and complicated. Dust occurs
sporadically in the Gobi Desert and Taklamakan Desert of the continent of
Asia and anthropogenic aerosols occur in urban/industrial sites.
Highly absorbing and fine-dominated, non-absorbing and fine-dominated,
marine, and dust aerosols are observed similarly over East Asia (Lee et
al., 2014). East China Sea and Yellow Sea are located between the continent
of Asia and the Korean Peninsula; therefore the long-range transport of
aerosols could be detected clearly. During the DRAGON-NE Asia 2012 campaign,
there were high aerosol loading cases. Two representative cases are presented
here, the heavy pollution haze case on 6 May, and the dust case on 27 April.
On 6 May 2012, a white haze plume was detected over northeastern China and
the Yellow Sea from the true color image as shown in Fig. 6a. GOCI YAER
AOD, FMF, AE, SSA, and aerosol type are plotted in Fig. 6b–f. Note that
all pixels regardless of QA values are included in the AOD plot, while only
pixels with positive AOD are shown for the other products. High AOD ranging
from 1.2 to 2.0 is found at the center of the haze plume, with retrieved FMF
and AE of about 0.8 and 1.2, respectively. This means that the haze aerosol
is a fine-mode dominant aerosol. The retrieved SSAs at those pixels are in
the range 0.955–0.975, corresponding to non-absorbing aerosol. The detected
aerosol type of the haze is therefore classified as non-absorbing fine
aerosol, shown in blue in Fig. 6f.
The distribution of FMF, AE, and SSA over land is more inhomogeneous than
over ocean, particularly, for pixels with low AOD, which is likely due to
the higher surface reflectance, higher spatial variability, and higher
uncertainty of land surface reflectance than that of ocean. Nevertheless, it
is encouraging that there is less discrepancy between ocean and land, with
products showing a continuous distribution across the coastline for both
high (∼ 1.0) and low AOD (∼ 0.3) pixels.
Another case is a severe dust case on 27 April 2012 as shown in Fig. 7. Heavy
yellow dust plumes are evident in the GOCI true color image. These developed
in the Gobi Desert the previous day and were transported to the northern part of
the Korean Peninsula across the Yellow Sea. The dust plume has a horizontal
scale about 1000 km from inland China to the Yellow Sea, with AOD at its
center above 2.0 (red color), and about 1.2 at the edge of the plume. The
dust plume over the northern part of the Korean Peninsula is mixed with
cloud, but the plume in the southern part shows low AOD of about 0.3, with
FMF and AE of 0.3 and 0.5, respectively, corresponding to coarse-mode-dominated aerosol. SSA ranges from 0.90 to 0.92, corresponding to moderately
absorbing aerosol. From the FMF and SSA, the aerosol plume is classified as
dust, shown in yellow in Fig. 7f.
Evaluation of GOCI YAER products during the DRAGON-NE Asia 2012
campaign
Generally, in spring, various aerosol events such as yellow dust or
anthropogenic aerosol occur frequently and intensively over East Asia
(Redemann et al., 2003; Schmid et al., 2003; S. W. Kim et al., 2007).
Although the campaign was limited to the spring season, it has the advantage
of abundant ground-based observations over Korea and Japan. During the
campaign, a total of 40 sun photometers were deployed at urban sites and
coastal sites. Over the urban areas of Seoul and Osaka, in particular,
distances between AERONET sites are about 10 km, which makes validation of
satellite data possible at high spatial resolution.
MODIS onboard Aqua and Terra provides state-of-the-art global aerosol
properties, and its aerosol retrieval algorithms have been developed and
improved continuously (Remer et al., 2005; Levy et al., 2007; Hsu et al.,
2006). Recently, an updated version was released as C6 (Levy et al., 2013;
Hsu et al., 2013). MODIS aerosol products consist of Dark Target (DT) over
both ocean and land and Deep Blue (DB) products over land only. Their
validation against AERONET showed good agreement globally (Levy et al.,
2013; Sayer et al., 2013). Because the validation of GOCI using AERONET is
limited in spatial coverage, intercomparison using the satellite-based
MODIS data set is also performed for evaluating the GOCI product.
Comparison of AOD between AERONET and (a) GOCI for all QA, (b)
GOCI for QA = 3 only, (c) MODIS DT, and (d) MODIS DB. Colored pixels
represent a bin size of 0.02. The blue solid line is the linear regression
line. Black dashed and dotted lines denote the one-to-one and expected error
lines, respectively.
Therefore, GOCI YAER AOD at 550 nm, FMF at 550 nm, SSA at 440 nm, and AE
between 440 and 870 nm are evaluated using both the ground-based AERONET and
satellite-based MODIS data sets.
Validation conditions between ground-based AERONET and satellite-based GOCI
and MODIS
For the validation, 38 AERONET sites are selected, which have at least 20
days of observations. The current Level 2.0 version 2 direct-sun all points
observation products, inversion products, and the spectral deconvolution
algorithm (SDA) products are used in this study (Holben et al., 1998;
O'Neill et al., 2003; Dubovik and King, 2000). From the direct sun
measurement, AOD and Ångström exponent are used. The validation for FMF is
done using both inversion and SDA products, while the validation for SSA is
done using inversion products. Note that the almucantar observation is only
possible when the solar zenith angle is greater than 50∘ (Dubovik
et al., 2000), so inversion data are unavailable near noon.
Aerosol data from GOCI and AERONET are collocated temporally and spatially
for the comparison. The ground-based AERONET observes the sun/sky radiance
at intervals of a few minutes at a fixed location, while GOCI observes
aerosol over East Asia at hourly intervals. GOCI pixels within 25 km of an
AERONET site are averaged, and AERONET data within 30 min from GOCI
observation time are averaged. Comparison is carried out when at least one
pixel of GOCI and one temporal value of AERONET exist. Note that AERONET
does not observe AOD at 550 nm directly; therefore it is interpolated from
other channels using a quadratic fit on a log–log scale (Eck et al., 1999).
The colocation condition between AERONET and MODIS is the same as for GOCI.
Note that validation of MODIS using AERONET is performed for AOD only.
Intercomparison conditions between MODIS and GOCI
The different characteristics of MODIS and GOCI as LEO and GEO sensors,
respectively, need to be considered when intercomparison is performed.
Spatial colocation is based on the fixed grid scale over the GOCI
observation area, divided into 0.2∘× 0.2∘
latitude–longitude resolution grid cells. Therefore, MODIS and GOCI data
within the same fixed grid are separately averaged, and then matched
spatially.
Temporal colocation is based on the MODIS observation time. MODIS Level 2
aerosol data are provided as granules, and the maximum difference in scan
time in one granule is about 5 min. The maximum difference in GOCI scan
time for one scene is about 30 min, and GOCI scans the observation area
every hour. Therefore, two GOCI scenes within 1 h centered on the MODIS
overpass time are interpolated to the MODIS time, and are collocated with
MODIS temporally.
Validation of AOD
The validation involves use of the linear regression equation, and
validation metrics including the Pearson's linear correlation coefficient
(R), root mean square error (RMSE), mean absolute error (MAE), mean bias
error (MBE), and the ratio within expected error (% within EE). Note that
MBE and MAE are the mean of differences and absolute differences of value
between AERONET and GOCI, respectively. The range of expected error (EE) of
AOD is adopted from MODIS DT over land.
Figure 8 compares AOD from GOCI, MODIS DT, and MODIS DB algorithms against
AERONET at the 38 DRAGON AERONET sites. Note that only QA = 3 data of
MODIS DT and DB AOD are used for validation. A total of 9602 data points
are matched with GOCI for all QA values, and 8694 for only QA = 3 data.
There is good agreement between AERONET and GOCI with high data counts (red
color) gathered near the one-to-one line. Because GOCI pixels with QA = 3
are less cloud contaminated than those with all QA values, there are fewer
overestimated pixels from the GOCI QA = 3 set. Thus, all validation
criteria show better results for QA = 3 than for all QA except for the
y-intercept of the linear regression line. Most comparison points are
concentrated within the EE and immediately below EE in AERONET
AOD < 0.4, but large positive biases are observed for AERONET
AOD > 0.4, which result in the increase of the y-intercept for all QA. Such pixels
seem to be contaminated by cloud so, in general, have QA less than 3.
Therefore, when only QA = 3 pixels are compared with AERONET, the
y-intercept has a more negative value of -0.042 than for all QA (0.009). The
correlation coefficient for AOD between AERONET and GOCI (QA = 3) is
0.881, which is similar to that of MODIS DT (0.906) and DB (0.876). For
slope, RMSE, MBE, and % within EE, GOCI is better than that of MODIS DT.
Munchak et al. (2013) described that MODIS DT Collection 6 AOD is biased
high over urban surfaces, and it is suspected due to the inaccurate surface
reflectance over urban in the MODIS DT operational retrievals. Otherwise,
the enhanced MODIS DB algorithm (Collection 6) shows the best result, which
controls surface reflectance differently according to surface type, giving
high accuracy regardless of surface type (Hsu et al., 2013). The ratio
within EE of MODIS DB against AERONET is 71.5 % for all AERONET sites,
which is greater than for GOCI (57.3 %).
Comparison of AOD between (a) MODIS DT and GOCI over ocean, (b)
MODIS DT and GOCI over land, and (c) MODIS DB and GOCI over land. Color
pixels represent a bin size of 0.02. The blue solid line is the linear
regression line. The black dashed line is the one-to-one line.
Results of intercomparison of AOD between GOCI and MODIS are shown in Fig. 9. Note that ocean pixels near most coastal sites are classified as turbid
water and retrieved using the land algorithm. Thus, it is hard to validate
the GOCI ocean algorithm using AERONET, but it is possible using MODIS DT
ocean AOD. Intercomparison of the ocean AOD of MODIS DT and GOCI shows good
agreement (R=0.939). The slope of the regression line is 1.019 and the
y-intercept is 0.039. Both algorithms consider wind-speed-dependent surface
reflectance. Because the ocean surface is darker than the land surface, it
is easier to detect cloud pixels over ocean and so there are fewer
overestimation points for GOCI. The GOCI AOD over ocean is retrieved from
the ocean algorithm over clear water and the land algorithm over turbid
water (or heavy aerosol loading). The AOD over turbid water pixels is not
retrieved in the MODIS DT ocean algorithm, so direct comparison over turbid
water is impossible (Lee et al., 2010b).
A common feature of comparisons of GOCI products using MODIS DT and DB over
land is that there are more scattered points above the one-to-one line than
in comparisons between AERONET and GOCI. Because cloud is effectively
cleared in AERONET Level 2 data, most collocated cases with AERONET are in
fact cloud-free cases. MODIS DT and DB use the characteristics of cloud in
visible and infrared (IR) wavelengths for cloud screening, but there are no
IR channels in GOCI. As a result, cloud screening is carried out using
visible–near IR channels only. It is more difficult to distinguish the
cloud signal clearly over land using only visible characteristics because of
bright surface reflectance, especially for urban surfaces. If cloud is not
removed correctly, its signal is considered as aerosol, and AOD is
overestimated. This explains the greater number of pixels scattered above
the one-to-one line in both comparisons over land. GOCI YAER AOD over land
is better correlated with MODIS DB (R=0.866) than DT (R=0.827), and
the linear regression line over land between GOCI and MODIS DB is also
closer to the one-to-one line than with MODIS DT. Although the surface
reflectance calculation of GOCI YAER algorithm is not exactly the same as
that of MODIS DB algorithm, the methodology of GOCI YAER algorithm is closer
with MODIS DB than MODIS DT. Precalculated surface reflectance database is
applied over arid/semiarid surfaces, which has been used in the previous MODIS
DB algorithm (Hsu et al., 2004, 2006) and enhanced MODIS DB algorithm (Hsu
et al., 2013). However, the enhanced MODIS DB algorithm used in this study for
validation adopts three different methods according to land surface types.
Over vegetated land surfaces, it takes the spectral relationship in surface
reflectance between visible and longer wavelengths, which is used in the
MODIS DT algorithm. Over urban/built-up and transitional regions, a hybrid
approach is applied by combining the Deep Blue surface database with the
angular shapes of surface bidirectional reflectance
distribution function (BRDF). Aerosol model constructions of three
algorithms are similar as the model considers fine/coarse and
absorbing/non-absorbing characteristics. However, the MODIS DB uses reflectance
at 412 nm for retrieval, similar to GOCI, while MODIS DT does not. Inversion
procedures of three algorithms are not significantly different. Both MODIS
DT and DB retrieve spectral AODs (470 and 660 nm for DT; 412, 470, and 660 nm of DB),
interpolated to the AOD at 550 nm. However, the GOCI YAER algorithm
retrieves AOD at 550 nm directly from other channels' reflectance. Hence, the
tendency and accuracy of retrieved AOD from GOCI are closer to MODIS DB than
DT.
Comparison of AE between direct AERONET and GOCI for (a)
the whole AERONET AOD range, and (b) only for AERONET AOD > 0.3. (c) AE
intercomparison between MODIS DT and GOCI over ocean only for GOCI AOD
> 0.3. Colored pixels represent a bin size of 0.05. Wavelengths
of Ångström exponents are 440 and 870 nm for AERONET and GOCI, and 550 and
860 nm for MODIS DT over ocean. Dashed and solid lines denote the same as
Fig. 9.
Comparison of FMF between (a) SDA AERONET and GOCI, and (b)
inversion AERONET and GOCI only for AERONET AOD > 0.3. (c) FMF
intercomparison between MODIS DT and GOCI over ocean only for GOCI AOD
> 0.3. Colored pixels represent a bin size of 0.05. Dashed and
solid lines denote the same as Fig. 9.
Validation of Ångström exponent, fine-mode fraction, and single-scattering
albedo
The GOCI YAER AE, FMF, and SSA are determined from the three selected
aerosol models used in retrieving the AOD. Therefore, the possible product
retrieval ranges are limited by the aerosol models. AE, FMF, and SSA can be
retrieved in the ranges of 0.0930–1.744, 0.156–0.956, and 0.871–0.970,
respectively.
Figure 10a and b show the comparison of AE between AERONET and GOCI.
The correlation coefficient is 0.594 in Fig. 10a, which is significantly
lower than for the AOD comparison (0.881). The difference in spectral
aerosol signal does not vary much with aerosol model when AOD is low, so the
error of AE can be large at low AOD. When AOD is less than 0.3, the value of
AE is about 1.3 for AERONET, but about 0.7 for the GOCI retrieval; thus
when these points are removed, the correlation coefficient increases to
0.678 in Fig. 10b. AE is underestimated from GOCI compared with AERONET
(MBE =-0.316) for the whole range although highest density of points
from AERONET and GOCI coincide.
Although the MODIS DT AE over land can be calculated using spectral AOD at
470 and 660 nm, intercomparison of the AE between MODIS DT and GOCI is not
done over land in this study. Levy et al. (2010) reported that AE is not
available globally at sufficient quantitative accuracy; therefore it was
removed from the operational C6 DT products (Levy et al., 2013). Therefore,
comparison is only performed over the ocean. The MODIS DT AOD over the ocean
is retrieved at 550 and 860 nm, so the AE between these two channels is
compared with the GOCI AE in Fig. 10c. Over the ocean both GOCI and MODIS
DT assume Fresnel reflectance with wind speed dependence for the surface
reflectance, and the surface reflectances is similar between GOCI and MODIS
DT over, and the surface reflectance of ocean is lower than that of land.
Therefore, high counts are well matched and the RMSE and MBE (0.357 and
0.064, respectively) are better than those of AERONET versus GOCI (0.439 and
-0.316, respectively) although the correlation coefficient is much lower at
0.376.
FMF is provided directly from SDA AERONET, or calculated using the
almucantar retrievals of fine AOD and the total AOD at 675 nm from AERONET
inversions. Both AERONET FMF products are compared with the GOCI YAER FMF in
Fig. 11a and b. Note that both comparisons are for AERONET AOD > 0.3. The correlation coefficients are 0.698 and 0.750 for SDA
and inversion AERONET, respectively. These are higher values than for AE
validation, but less than for AOD validation. High counts of AERONET are
grouped around 0.9–1.0, but those of GOCI are grouped at 0.8. GOCI FMF is
underestimated compared with AERONET for the whole FMF range. The MBE values
are -0.212 and -0.208, respectively.
The intercomparison of FMF between MODIS DT and GOCI over the ocean is
shown in Fig. 11c. The correlation is better (R=0.417 and RMSE = 0.182) than for of AE (R=0.376 and RMSE = 0.357). The validation
results for FMF are analogous to those of AE because both parameters are
sensitive to the particle size in visible wavelengths.
Figure 12 shows the results of comparing SSA between AERONET inversion and
GOCI. Only 617 points are collocated temporally and spatially because Level
2 AERONET SSA is only provided for AOD (440 nm) > 0.4 and
almucantar observation is performed when the solar zenith angle is greater
than 50∘ (Dubovik and King, 2000). The correlation coefficient is
0.353, which is the lowest among the GOCI products. Nevertheless, the
accuracy of GOCI SSA is comparable with that of OMI SSA over East Asia.
According to Jethva et al. (2014), the correlation coefficient between
AERONET and OMI SSA is 0.406. They also showed that 44.91 and 70.29 %
of OMI SSA data are within differences of ±0.03 and ±0.05 with
respect to AERONET. GOCI SSA shows higher ratios than OMI, 69.0 and
86.9 %, for the same criteria over Northeast Asia. A preliminary
redundancy test (Lee et al., 2012), which showed that GOCI SSA may be
underestimated at high SSA (∼ 0.95) and overestimated at low
SSA (∼ 0.85), is consistent with the results of GOCI SSA
validation against AERONET. The difference between absorbing and
non-absorbing aerosols is significant in the ultraviolet (UV) and shorter visible (blue)
wavelengths, and weak at longer visible (green and red) wavelengths. GOCI
YAER algorithm is optimized for AOD retrieval using aerosol model
composition classified by FMF and SSA. In the next generation GOCI-2 mission
to be launched in 2019, SSA can be retrieved more accurately utilizing the UV
channel.
Comparison of SSA between inversion AERONET and GOCI. Colored
pixels represent a bin size of 0.005. Dashed and solid lines denote the same as
Fig. 9. Red and blue dotted lines denote the ±0.03 and ±0.05
ranges, respectively.
Difference in AOD between GOCI and AERONET according to (a)
AERONET AOD, (b) scattering angle, (c) solar zenith angle, (d) relative
azimuth angle, (e) local standard time, and (f) NDVI. Each point is the
median value from 200 collocated data sorted in ascending order of each
x axis value except for local standard time. Lower and upper bounds of the
error bar at each point correspond to the 16 and 84 % points of each
bin, respectively.
GOCI AE and SSA product qualities could also be compared with other previous
studies while the region and period are different. Global MODIS DT Ångström
exponent validation results with AERONET were presented in Levy et al. (2010)
and Levy et al. (2013) over land and ocean, respectively. Levy et al. (2010)
compared the MODIS DT Collection 5 Ångström exponent between 470 and
650 nm (AE_470_650) and AERONET AE_470_650 over land, resulting in R of 0.554 and a linear
regression equation with MODIS AE_470_660 = 0.6471 × AERONET AE_470_660 +
0.3342. According to Levy et al. (2013), the MODIS DT Collection 6 Ångström
exponent between 550 and 870 nm (AE_550_870)
shows more higher accuracy over ocean (R=0.612 and a linear regression
equation with MODIS AE_550_870 = 0.686
× AERONET AE_550_870 + 0.47). MODIS
DB Collection 6 Ångström exponent (over land) shows similar accuracy with
GOCI YAER Ångström exponent (R=0.45 for all AOD and R=0.68 when AOD
is greater than 0.3). These results are similar to those of GOCI YAER AE
validation (R=0.594 for all AOD and R=0.678 when AOD is greater than 0.3).
Aerosol optical properties such as Ångström exponent and single-scattering
albedo retrieved from the Polarization and Directionality of Earth's
Reflectance (POLDER) instrument on-board the Polarization and Anisotropy of
Reflectances for Atmospheric Sciences coupled with Observations from a Lidar
(PARASOL) satellite shows more accurate results. Hasekamp et al. (2011)
described that AE retrieval using polarization measurement shows higher
accuracy (R=0.85) than using intensity-only retrieval (R=0.62).
Generalized Retrieval of Aerosol & Surface Properties (GRASP) algorithm
using POLDER (Dubovik et al., 2011; Kokhanovsky et al., 2015) shows higher
accuracy in SSA (R=0.93) when AOD is greater than 0.4. These results mean
that more information such as polarization and multi-angle observation can
improve retrieval accuracy of aerosol optical properties. In conclusion,
GOCI AE, FMF, and SSA show lower accuracy than AOD. Nevertheless, these
values can be useful for qualitative studies, although not for quantitative
studies.
Error analysis of GOCI YAER AOD
Uncertainties in surface reflectance, assumed aerosol model, cloud masking,
and geometry result in systematic errors in the retrieved AOD. In this
section, the difference in AOD between GOCI and AERONET is analyzed to
quantify the respective error sources affecting the accuracy of GOCI AOD.
The difference in AOD between GOCI and AERONET is shown in Fig. 13a as a
function of AERONET AOD. The 16–84 % range for each bin widens as AOD
increases, as with satellite products. GOCI AOD has a negative bias of -0.1
against AERONET for AERONET AOD < 0.4, while there is no consistent
bias but a skewed distribution toward the positive differences for AERONET
AOD > 0.9. Main uncertainties in low AOD and high AOD are linked
to uncertainties in surface reflectance and assumptions about aerosol
microphysical properties, respectively (Sayer et al., 2013). Levy et al. (2010) also described
that systematic bias for low AOD results from overestimating the surface reflectance in the visible channels. Therefore,
the minimum reflectivity technique can overestimate surface reflectance due
to contamination by the remaining cloud or aerosol, resulting in negative
bias at low AOD. On the other hand, the accuracy at high AOD can be affected
by the assumed aerosol model or cloud masking. An insignificant bias of the
median points supports the validity of the assumed aerosol model, but a
positive skewed distribution can be attributed to the remaining cloud
contamination due to cloud masking using visible channels only. It is
difficult to distinguish aerosol and cirrus cloud without information from
IR wavelengths (Lee et al., 2013).
The next comparison is the difference in AOD between GOCI and AERONET
plotted against the scattering angle in Fig. 13b. GOCI AOD is underestimated
at scattering angles near 115 and 140∘ and
overestimated at 145∘ and above 160∘. Scattering angle
is calculated using solar zenith angle, satellite zenith angle, and relative
azimuth angle. GOCI is on geostationary orbit; therefore satellite zenith and
azimuth angles are fixed. Therefore, relative azimuth angle between sun and
satellite varies according to local standard time only. Solar zenith angle
varies according to local standard time and season. Scattering angle
contains such complicated error sources, which makes the scattering angle dependency
of AOD difference between GOCI and AERONET difficult to interpret; therefore
AOD error analyses according to solar zenith angle and relative azimuth
angle are also presented.
GOCI AOD errors according to solar zenith angle as Fig. 13c are close to zero
at 30, 40, 50, and 60∘ solar zenith angle, and show fluctuating
pattern between them. LUT node points of solar zenith angle are constructed
at 10∘ interval, and linear interpolation to observed solar zenith
angles in inversion procedure could cause this error pattern. The fluctuation
tendency of error as underestimation at scattering angles could also be
caused by the interpolation error in the inversion procedure. Subdivision of
5∘ interval for node point of LUT calculation or online calculation
could improve this interpolation error (Jeong et al., 2016).
Error tendency according to relative azimuth angle as Fig. 13d shows less
fluctuant shape, and underestimation at low relative azimuth angle. Both
conditions of low azimuth angle and high solar zenith angle correspond to
the early morning or late afternoon as local standard time. Therefore,
errors analyzed according to the fixed local standard time as shown in Fig. 13e
show underestimation at 09:30, 15:30, and 16:30. Plane-parallel atmosphere
approximation or scalar calculation in the RTM could result in less accurate
Rayleigh scattering calculation for surface reflectance using the minimum
reflectivity technique.
The method for determining surface reflectance is applied equally to all
pixels regardless of surface type. To test the accuracy as a function of
surface type, the normalized difference vegetation index (NDVI) is adopted,
defined as (ρTOA(865 nm) -ρTOA(660 nm)) / (ρTOA(865 nm) +ρTOA(660 nm)). Generally, it is negative over
ocean and positive over land. It is close to 1 when the surface is green
because of vegetation growth, while it is close to zero over less green
areas. Figure 13f shows the difference in AOD between GOCI and AERONET
plotted against NDVI. Note that negative NDVI is possible when GOCI ocean
pixels are collocated with AERONET at coastal sites. The difference is small
(0–0.05) and the bias is for low NDVI (-0.4 to 0.1). However, the
difference decreases linearly from 0.05 to -0.2 as NDVI increases from 0.1
to 0.6, due to the limitation in minimum reflectivity technique with a search
window of 1 month during the dynamic vegetation change in the spring
season and its reference at 412 nm channel. AOD is significantly
underestimated by GOCI with increasing vegetation cover, thus surface type
must be considered to improve the algorithm as included in the enhanced
MODIS DB algorithm (Hsu et al., 2013). Additionally, this may be partially
due to the most densely vegetated surfaces in both Korea and Japan being
forested mountains. Because aerosol concentration decreases exponentially as
altitude increases generally, any GOCI retrievals made over the hills or
mountains have lower AOD than the values located in the valley or low-altitude-level area. NDVI is largest over the forested mountain slopes which
extend to the upper part of the aerosol layer, therefore the GOCI retrievals
are underestimated as NDVI increases.
Conclusions
Since its development the prototype over-ocean GOCI YAER algorithm over the
ocean (Lee et al., 2010b) was further developed to include nonspherical
aerosol models for better performance for dust cases (Lee et al., 2012).
However, the algorithm has only been tested using MODIS data, and limited to
ocean surfaces. Here, based on the heritage, the GOCI YAER algorithm is
extended to land surfaces and tested using real GOCI data. GOCI has the
advantages of high spatial (500 m × 500 m) and temporal (hourly)
resolution using eight channels in visible and near-infrared wavelengths.
Therefore, other properties such as FMF, AE, and SSA as well as AOD can be
retrieved at a 6 km × 6 km resolution.
Different surface reflectance assumptions and channels are applied for the
land and ocean. Turbid water is detected according to Δρ660, and the land algorithm is applied to it for better performance. In
addition, nonsphericity and dynamical properties of aerosol are reflected in
the aerosol models.
The DRAGON-NE Asia 2012 campaign in spring has enabled the evaluation of
GOCI YAER products over 38 sites in Korea and Japan using AERONET data and
MODIS over East Asia. AOD from the GOCI YAER shows good agreement with
AERONET with a correlation coefficient of 0.881, which is similar to that
of MODIS DT (R=0.906) and DB (R=0.876). The fraction of AOD data
falling within the expected error for GOCI is 57.3 %, which is worse than
MODIS DB (71.5 %) but similar to MODIS DT (54.2 %). In the
intercomparison between GOCI and MODIS, GOCI and MODIS DT show good
agreement over ocean with high correlation (R=0.939). Over land, GOCI
YAER shows better agreement and less bias with MODIS DB (R=0.866, RMSE
= 0.192) than MODIS DT (R=0.827, RMSE = 0.284) likely due in part to
similar retrieval conditions in both GOCI and MODIS DB. For size parameters
such as AE and FMF, GOCI agrees less well with AERONET (R=0.594–0.750)
and tends to underestimate (MBE =-0.381 to -0.208). Over ocean, the
comparison of size parameters between GOCI and MODIS DT shows significantly
poorer agreement (R=0.376–0.417), but data points with high frequency
are well matched. For the SSA, GOCI shows low correlation of 0.353 with
AERONET, but the range of SSA (0.90–0.95) is well matched each other. In
conclusion, GOCI YAER AOD shows high accuracy against MODIS, and other
aerosol parameter products can be used qualitatively, although their accuracy
is less than AOD.
From the error analysis, GOCI YAER AOD shows a negative bias of -0.1 for
low AOD (< 0.4), and the negative bias increases as NDVI becomes
higher. It is necessary to improve the accuracy of surface reflectance over
vegetated areas for the next version, and possibly account for the elevation
of forested mountains relative to the aerosol vertical profile.
The current version of LUT was calculated by using a scalar RTM, libRadtran;
this RTM is less accurate for calculating Rayleigh scattering for the short
visible wavelengths (∼ 400 nm). A vector RTM might be helpful
in improving the accuracy of the GOCI YAER algorithm in the future. The
current validation period is limited to spring season in 2012, and thus the
seasonal dependence of accuracy is not presented in this study. Nearly 5
years of GOCI data have been accumulated since March 2011, which will allow
long-term validation and analysis to be carried out to investigate retrieval
accuracies and uncertainties in the near future.
Acknowledgements
We thank the Korean Institute of Ocean Science and Technology (KIOST) for
the development and application of GOCI in this research. We also thank all
principal investigators and their staff for establishing and maintaining the
AERONET sites of the DRAGON-NE Asia 2012 campaign used in this investigation. We
also thank the MODIS science team for providing valuable data for this
research. This research was supported by the GEMS program of the Ministry of
Environment, Korea, and the Eco Innovation Program of KEITI (2012000160002).
Edited by: M. Schulz
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