The Geostationary Ocean Color Imager (GOCI) Yonsei aerosol retrieval (YAER)
version 1 algorithm was developed to retrieve hourly aerosol optical depth at
550 nm (AOD) and other subsidiary aerosol optical properties over East Asia.
The GOCI YAER AOD had accuracy comparable to ground-based and other
satellite-based observations but still had errors because of uncertainties
in surface reflectance and simple cloud masking. In addition, near-real-time
(NRT) processing was not possible because a monthly database for each year
encompassing the day of retrieval was required for the determination of
surface reflectance. This study describes the improved GOCI YAER algorithm
version 2 (V2) for NRT processing with improved accuracy based on updates to
the cloud-masking and surface-reflectance calculations using a multi-year
Rayleigh-corrected reflectance and wind speed database, and inversion channels for surface conditions. The improved GOCI AOD τG is closer to that of the Moderate Resolution Imaging
Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD than was the case for AOD from the YAER V1 algorithm. The V2
τG has a lower median bias and higher ratio within
the MODIS expected error range (0.60 for land and 0.71 for ocean) compared with
V1 (0.49 for land and 0.62 for ocean) in a validation test against Aerosol
Robotic Network (AERONET) AOD τA from 2011 to
2016. A validation using the Sun-Sky Radiometer Observation Network (SONET)
over China shows similar results. The bias of error (τG-τA) is within -0.1 and 0.1, and it is
a function of AERONET AOD and Ångström exponent (AE), scattering angle, normalized difference vegetation index (NDVI), cloud fraction and
homogeneity of retrieved AOD, and observation time, month, and year. In
addition, the diagnostic and prognostic expected error (PEE) of τG are estimated. The estimated PEE of GOCI V2 AOD is well
correlated with the actual error over East Asia, and the GOCI V2 AOD over
South Korea has a higher ratio within PEE than that over China and Japan.
Introduction
Aerosols are one of the most important components in the atmosphere with
respect to climate change and air pollution. Aerosols influence the climate
directly by scattering and absorbing solar radiance (aerosol–radiation
interactions) and indirectly by altering cloud properties (aerosol–cloud
interaction; IPCC, 2013). Two aerosol optical properties (AOPs), the aerosol optical depth and single-scattering albedo, determine
the sign and magnitude of the shortwave aerosol radiative forcing of the
atmosphere for different surface conditions (Takemura et al.,
2002). Thus, accurate AOP retrievals are important for quantifying the role
of aerosols in climate change. With respect to air pollution, ambient fine
particulate matter (PM) affects respiratory and pulmonary systems, resulting
in an increased incidence of heart disease, strokes, and lung cancer (Lim
et al., 2012). While PM information is often obtained from ground-based in situ
measurements, the coverage of ground-based measurements is limited to the
local scale and observational networks are often sparse, especially in
developing countries. However, satellite-based remote sensing can provide
aerosol information over a much broader area. Chemical transport models (CTMs) make many assumptions in predictions of PM concentrations. Modeling
accuracy can be improved significantly through data assimilation with
satellite-retrieved aerosol optical depth (AOD) products (van Donkelaar et al., 2010).
East Asia has some of the highest aerosol concentrations in the world, with
components that include desert dust, anthropogenic carbonaceous aerosols,
and sea salt (Kim et al., 2007; Yoon et al., 2014). Trends in aerosol
concentrations in East Asia do not show the same significant decreases seen
in Europe or North America (Zhang and Reid, 2010; Hsu et al., 2012), for
reasons that are still unclear (IPCC, 2013).
The Geostationary Ocean Color Imager (GOCI), launched in 2010 as the first
ocean color imager in geostationary orbit (GEO), observes East Asia eight
times per day from 00:30 to 07:30 Coordinated Universal Time (UTC; 09:30 to
16:30 Korea Standard Time (KST); Choi et al., 2012). Using the
radiance measurements from eight spectral channels (412, 443, 490, 555, 660,
680, 745, and 865 nm) with high spatial resolution (500 m × 500 m),
the GOCI Yonsei aerosol retrieval (YAER) version 1 (V1) algorithm was
developed to retrieve hourly aerosol optical properties such as aerosol
optical depth (AOD) with simple diagnostic parameters such as fine-mode
fraction (FMF), Ångström exponent (AE), and single-scattering albedo
(SSA; Choi et al., 2016). Because it has more
channels with higher spatial resolution in the visible and near-infrared
(NIR) bands compared with recent and planned advanced meteorological sensors
in GEO, including the Advanced Himawari Imager (AHI), the Advanced Baseline
Imager (ABI), and the Advanced Meteorological Imager (AMI), GOCI provides
valuable information related to AOPs. Hourly AOD from the GOCI YAER
algorithm is in good agreement with Moderate Resolution Imaging
Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite
(VIIRS) AOD over East Asia (Xiao et al., 2016). The application of GOCI
retrievals through data assimilation results in improved performance of
several air quality forecasting model predictions of AOD and PM
concentrations (Park et al., 2014; Saide et al., 2014; Jeon et al., 2016; Lee et al., 2016; Lee et al., 2017). For this reason, a need has arisen
for GOCI aerosol retrievals with near-real-time (NRT) processing for
operational air quality forecasting systems using data assimilation.
The lack of shortwave infrared (SWIR) channels in GOCI (similar to the 1.6
or 2.1 µm channels of MODIS) does not allow for the calculation of
surface reflectance in the visible range from top-of-atmosphere (TOA) reflectance in the SWIR
range (Kaufman et al., 1997). Instead, the minimum reflectivity technique
using the composite method (Herman and Celarier, 1997; Koelemeijer et al., 2003; Hsu et al., 2004) was applied in the GOCI YAER V1 algorithm.
However, this methodology prevents the GOCI YAER V1 algorithm from being
capable of near-real-time (NRT) processing because it required a monthly
database for each year encompassing the day of retrieval for the
determination of surface reflectance. In addition, the resulting retrievals
have a slightly negative bias over land and a positive bias over ocean due
to surface reflectance errors, compared with AERONET data during the
Distributed Regional Aerosol Gridded Observation Networks – Northeast Asia
2012 campaign (DRAGON-NE Asia 2012 campaign; Choi et al., 2016).
In this study, version 2 (V2) of the algorithm is developed to both
allow NRT processing and improve accuracy. Monthly and hourly surface
reflectance and wind speed determinations are modified using a
climatological database from the multi-year GOCI dataset and reanalysis wind
speed data, respectively. The surface reflectance database obtained from
multi-year Rayleigh-corrected reflectance (RCR) samples enables more
accurate surface reflectance retrievals by increasing the availability of
measurements that are not aerosol- or cloud-contaminated, compared with the
1-year samples of the V1 algorithm. The cloud masking and inversion spectral channels for aerosol retrievals were also modified for better
accuracy. Furthermore, retrieved GOCI YAER V2 AOD is evaluated using
ground-based observation data, along with comparisons with both V1 and MODIS
retrievals from March 2011 to February 2016, which is a longer evaluation
period than used in previous studies. The bias of the GOCI YAER V2 AOD is
analyzed and uncertainties are estimated to facilitate the application of
GOCI AOD in data assimilation.
The remainder of this paper is organized as follows. In Sect. 2,
improvements in the GOCI YAER V2 algorithm are summarized and a quantitative
comparison with other satellite AODs is presented. In Sect. 3, the GOCI
YAER V2 AOD is validated using ground-based sun-photometer observations
along with other satellite AOD measurements. In Sect. 4, GOCI YAER V2 AOD
errors are analyzed in relation to various parameters and expected errors
are estimated. Finally, a summary and conclusions are presented in Sect. 5.
GOCI YAER V2 algorithmOverview of the GOCI YAER V1 and V2 algorithm framework
A prototype of the GOCI YAER algorithm for use over the ocean (Lee et
al., 2010) was developed using MODIS Level 1B (L1B) top-of-atmosphere (TOA)
reflected radiance data and improved using nonspherical AOPs (Lee et
al., 2012). Then, using real GOCI L1B TOA radiance data, the GOCI YAER V1
algorithm for use over land and ocean surfaces was developed
(Choi et al., 2016). The algorithm is applied
to cloud-free and snow/ice-free pixels. Sets of 12 pixel × 12 pixel
blocks are aggregated to achieve 6 km × 6 km spatial resolution and
averaged after cloud/snow/ice masking and suitable pixel selection.
Unified aerosol models over land and ocean surfaces classify aerosols using
AOD at 550 nm, FMF at 550 nm, and SSA at 440 nm derived from the global
Aerosol Robotic Network (AERONET) inversion database (Dubovik and King,
2000; Holben et al., 1998). This aerosol type classification (Lee et al.,
2012) covers a range of AOPs: FMF from 0.1 to 1.0 at an interval of 0.1 and
SSA from 0.85 to 1.00 at an interval of 0.05. A total of 26 aerosol models
are assumed in the algorithm: nine highly absorbing, nine moderately absorbing,
and eight nonabsorbing models. Note that AOPs to calculate AOD are constructed
to account for hygroscopic growth and aggregation (Eck et al., 2003; Reid
et al., 1998). Non-spherical properties are considered using the phase
function derived from AERONET data.
Dark ocean surface reflectance is calculated using the Cox–Munk model (Cox
and Munk, 1954) considering Fresnel reflectance with a bidirectional
reflectance distribution function according to geometry and wind speed in a
precalculated look-up table (LUT) with temporal interpolation of ECMWF wind
speed data at 10 m above sea level (m a.s.l.) over dark ocean pixels (Dee et al., 2011). Land surface
reflectance is obtained using the minimum reflectivity technique for each
month, channel, and hour, and temporal interpolation is carried out over
land, turbid ocean, and heavy aerosol pixels in the inversion step. In the
algorithm, turbid water pixel detection is implemented using a difference of
660 nm TOA reflectance between directly observed and interpolated data from
412 and 865 nm (hereafter, Δρ660; Li et al., 2003; Choi et al.,
2016).
All eight channels are used over ocean surfaces, and different combinations
of channels are used over land, depending on surface conditions. Measured
spectral TOA reflectance can be converted to spectral AOD for all aerosol
models using the precalculated LUT, and spectral AOD can be converted to
the corresponding value at 550 nm using the assumed AE of each aerosol
model. Then, the mean value and standard deviation (SD) of AOD at 550 nm
from different channels are calculated for each aerosol model, and the three
aerosol models with the lowest SD are selected. The SD-weighted average of mean
AOD at 550 nm from the three selected aerosol models is used as the AOD at
550 nm. An identical SD-weighted average is applied to the assumed AE, FMF,
and SSA of the selected aerosol models to determine the final AE, FMF, and
SSA values. This inversion method is focused primarily on the retrieval of
AOD at 550 nm from multi-channel spectral information, and the AE, FMF, and
SSA are determined from aerosol models selected for the best AOD fit. Thus,
AOD at 550 nm is the main retrieval product, and the AE, FMF, and SSA are
considered as diagnostic parameters, or ancillary products. Note that the
discrete ordinate radiative transfer (DISORT) code of the libRadtran
software package is used to calculate TOA reflectance for the LUT
construction based on scalar calculations (i.e., intensity-only) and a
plane-parallel atmosphere approximation (Mayer and Kylling, 2005).
To improve the accuracy of AOP retrieval from GOCI measurements, AOD in
particular, the new V2 algorithm is developed through piecewise upgrades to
the V1 algorithm while retaining the structure and conceptual approach of
the original algorithm. A flow chart of the GOCI YAER V2 algorithm is
presented in Fig. 1. The improved parts of the V2
algorithm compared with V1 are the pixel masking and aggregation procedures,
implementation of the climatological surface reflectance and wind speed from
a 5-year climatological database for NRT calculations, turbid water
detection, and inversion conditions for land, turbid water, and dark ocean
pixels. The aerosol model construction and inversion method for converting
TOA reflectance to aerosol products are identical to those of V1. Details of
the refined parts of the algorithm are introduced in the following
subsections.
Flow chart of the GOCI Yonsei aerosol retrieval version 2
algorithm. Yellow indicates improvements from version 1 to version 2, and
gray indicates no change from version 1.
Cloud and other pixel masking steps of the GOCI YAER V2 algorithm.
StepConditionsClassificationReferencesMasking at 0.5 km × 0.5 km resolution 1SD of TOA reflectance at 555 nm in3 pixel × 3 pixel blocks > 0.0025Cloud over ocean (whole 9 pixels)Remer et al. (2005)Choi et al. (2016)2Ratio of maximum to minimum TOAreflectance at 412 nm in 3 pixel × 3 pixelblocks > 1.1Cloud over land (whole 9 pixels)Hsu et al. (2013)3SD of TOA reflectance at 490 nm in3 pixel × 3 pixels block > 0.015Cloud over land (whole 9 pixels)Wang et al. (2017)4Mean-weighted SD of TOA reflectance at490 nm in 3 pixel × 3 pixel blocks > 0.0025Cloud over land (whole 9 pixels)Wang et al. (2017)5TOA reflectance at 490 nm > 0.4Cloud over ocean and landRemer et al. (2005)Choi et al. (2016)6Pseudo GEMI index < 1.87Cloud over landPinty and Verstraete (1992), Kopp et al. (2014)7NDVI using TOA reflectance at 660 and865 nm < -0.01Inland water over landHsu et al. (2013)8Ratio of TOA reflectance at 490 to660 nm < 0.75, and SD of TOA reflectance at 490 nm < 0.015 (or mean-weighted SD of TOA reflectance at 490 nm < 0.0025)Homogenous dust call-back over landand oceanRemer et al. (2005)Aggregation to 6 km × 6 km resolution 9Number of available pixels after maskingamong 12 pixel × 12 pixel blocks > 72Discard darkest 20 % and brightest40 % of pixels referred to TOAreflectance at 490 nm, and averageremaining pixelsRemer et al. (2005) Levy et al. (2007) Choi et al. (2016)Additional masking in 6 km × 6 km resolution 10SD of TOA reflectance at 412 nm > 0.003 and mean TOA reflectance at 412 nm in12 pixel × 12 pixel blocks > 0.22Cloud over land and ocean11Mean TOA reflectance at 412 nm > 0.33 andmean TOA reflectance at 555 nm > 0.33Cloud over land and ocean12Mean TOA reflectance at 412 nm < 0.30 and mean TOA reflectance at 660 nm > 0.2Low aerosol signals and arid areamasking13Difference in TOA reflectance at 660 nmbetween direct-measured value andlinear-interpolated value from 412 and865 nm < -0.01Highly turbid pixel masking over oceanLi et al. (2003) Choi et al. (2016)Pixel masking and aggregation procedure
The GOCI YAER algorithm is targeted to cloud-free and snow-free pixels over
land and cloud-free, ice-free, and high-turbidity-water-free pixels over the
ocean. Therefore, several masking steps are required. The previous V1
algorithm contains simple cloud-masking techniques, which include a spatial
variability test using the SD over a 3 pixel × 3 pixel block and a
threshold for high TOA reflectance. These simple techniques remove most
cloud pixels, but some thin homogeneous (e.g., cirrus) cloud pixels remain
because of the absence of ice-crystal-sensitive 1.38 µm or other
infrared channels in GOCI. This leads to unfiltered cloud pixels being
misclassified as high-AOD pixels and raises the need for additional
filtering for successful data assimilation between models and GOCI AOD
(Xu et al., 2015). In addition, some low-AOD pixels could be
misclassified as cloud pixels in regions with highly inhomogeneous surface
reflectance. In this study, therefore, refined cloud-masking techniques are
applied, as summarized (with references) in Table 1. Most of these masking techniques were adopted from the MODIS and VIIRS
aerosol retrieval and cloud-masking algorithms. The masking procedures
consist of three stages: masking at the original 0.5 km × 0.5 km L1B pixel resolution, aggregation from 0.5 km × 0.5 km to
6 km × 6 km resolution, and additional masking at the 6 km × 6 km resolution.
At the 0.5 km × 0.5 km resolution, cloud masking over ocean
surfaces is unchanged, but the land-surface cloud-masking steps are refined.
The previous SD test of a 3 pixel × 3 pixel block over land for
identifying clouds and aerosols (Step 3 in Table 1,
except for a threshold of 0.0025) works well for moderate- and high-AOD
cases, but it over-masks heterogeneous surface reflectance pixels under low-AOD
conditions. Thus, the threshold is relaxed to 0.015, and the mean-weighted
SD test (Step 4 in Table 1) and the ratio of maximum
to minimum TOA reflectance at 412 nm within the 3 pixel × 3 pixel
grid are adopted (Step 2 in Table 1) as an
alternative. To identify aerosols and clouds using a different technique, a
pseudo Global Environment Monitoring Index (GEMI), developed by
Pinty and Verstraete (1992) and Kopp et al. (2014)
and applied in the operational VIIRS cloud-mask algorithm (Godin,
2014), is adopted (Step 6 in Table 1). The GEMI is
based on the reflectance ratio between 865 and 660 nm and is defined as
follows:
GEMI=G×1.0-0.25×G-100×Ref660-0.1251.0-100×Ref660,
where
G=200×Ref865-Ref660+150×Ref865+50×Ref660100×Ref865+100×Ref660+0.50.
Note that Ref660 and Ref865 are the TOA reflectance at 660 and
865 nm, respectively. In addition, inland water pixels are filtered out
using a normalized difference vegetation index (NDVI) calculated using the
TOA reflectance at 660 and 865 nm (Step 7 in Table 1). A dust call-back test used for ocean pixels is expanded to include both
ocean and land pixels and is coupled with a spatial homogeneity test (Step 8 in Table 1).
After masking at the 0.5 km × 0.5 km resolution level, the
remaining pixels are aggregated to the Level 2 product resolution of 6 km × 6 km. The TOA reflectance of the remaining pixels is averaged if
the number of remaining pixels is greater than 72 (Step 9 in
Table 1). In this step, the darkest 20 % and
brightest 40 % of pixels are discarded to filter out cloud shadow,
remaining cloud, and surface contamination, following
Choi et al. (2016). The quality assurance (QA) value of the V1 algorithm was determined based on the range of
retrieved AOD and the remaining number of pixels in a 12 pixel × 12 pixel block after all masking procedures were performed. A QA value of 0,
1, 2, or 3 for the V1 AOD was assigned for 6, 15, 22, or 36 remaining
pixels, respectively. In addition, retrieved AOD values between -0.05 and
3.6 were assigned a QA value of 1, 2, or 3, and retrieved AOD values between
-0.1 and -0.05 or between 3.6 and 5.0 were assigned a QA value of 0. The
lower of these two QA values for each pixel was used as the final QA value.
In the V2 algorithm, however, the retrieval is implemented if the number of
remaining pixels is greater than 28, and the QA classification is
eliminated. In addition, only pixels with retrieved AOD between -0.05 and
3.6 are included in the calculations. Small negative AOD values can be
caused by surface reflectance errors in this algorithm. These are assumed to
fall within the range of expected retrieval errors and are statistically
significant under low-AOD conditions when compared with results from the
MODIS DT algorithm (Levy et al., 2007, 2013). The threshold of maximum
AOD of 3.6 is based on Lee et al. (2012), who considered the
probability distribution of AOD in the region.
After the pixel aggregation procedure, merged TOA reflectance at the 6 km × 6 km resolution is filtered again. Bright and inhomogeneous
pixels within a 12 pixel × 12 pixel block are filtered using the
mean and SD at 412 nm (Step 10 in Table 1), and
pixels with high TOA reflectance at both 412 and 660 nm are also filtered
out (Step 11 in Table 1). Furthermore, pixels with
low atmospheric signal (dark at 412 nm) but high surface signal (bright at
660 nm), such as in arid areas, are also filtered out to avoid
misidentification of the bright surface signal as aerosol (Step 12 in
Table 1).
Climatological land surface reflectance database from multi-year samples
In the GOCI YAER algorithm, surface reflectance over land is handled
differently to that over ocean. A minimum reflectance technique to determine
the surface reflectance from the composite Rayleigh-corrected reflectance (RCR) for each month and hour is applied over all land and turbid-water
pixels in the V1 algorithm. The GOCI YAER V1 algorithm was not capable of
NRT processing because it required an adjacent 2-month database encompassing
the day of retrieval for the determination of surface reflectance.
To achieve NRT retrieval in the V2 algorithm, climatological land-surface
reflectance for each channel, hour, and month are calculated over the 5-year
period from March 2011 to February 2016. The V1 surface reflectance database
was calculated at a 6 km × 6 km resolution by the aggregation of
12 pixel × 12 pixel data to extend the number of RCR samples. The
V1 surface reflectance calculation assumes that surface reflectance within a
6 km × 6 km area is homogeneous. The V1 surface reflectance
calculation resulted in slightly negatively biased AOD at low AOD over South Korea
and Japan during spring 2012, which means that the surface reflectance was
overestimated (Choi et al., 2016). In the V2
algorithm, temporal RCR samples are expanded from a 1-year period to a 5-year period,
thereby improving performance under low aerosol conditions and reducing the
negative bias in reflectance of the V1 algorithm. The spatial resolution of
climatological land surface reflectance used in the V2 algorithm is 0.5 km × 0.5 km for the L1B TOA reflectance,
an improvement over the 6 km × 6 km resolution used in the V1 algorithm. This higher resolution
can capture highly spatially variable surface reflectance and improve the
identical pixel matching between TOA and surface reflectance during pixel
aggregation. The maximum number of composite 5-year RCR samples used to
determine the surface reflectance of a single pixel is 155 (31 days × 5 years). The darkest samples (the lowest 0–1 % of the
aggregate sample) are assumed to be cloud shadow and the brightest samples
(3–100 % of the aggregate sample) are assumed to be affected by
aerosols and/or clouds. Thus, the darkest 1–3 % of the RCR samples
are averaged and used to determine surface reflectance, as in the V1
algorithm. According to Hsu et al. (2004), surface reflectance can be
obtained by finding the minimum RCR for each month, which corresponds to
∼ 3 % of the aggregate sample. The darkest 0–1 % of
pixels are assumed, based on empirical grounds, to be cloud shadow and are
thus excluded. This composite procedure is implemented for each month, hour,
and channel. Monthly surface reflectance climatological data correspond to
the middle of each month (day 15) and are linearly interpolated to the
retrieval date. Major year-to-year land use changes over the 5-year period
would result in an artificial AOD bias and should be addressed in future
work.
Climatological ocean surface wind speed database from multi-year samples
To calculate dark ocean surface reflectance, the GOCI YAER V1 algorithm uses
the ECMWF wind speed at 10 m a.s.l. from reanalysis data, which has
a 6 h temporal resolution and 0.25∘× 0.25∘
spatial resolution. The ECMWF data are interpolated to hourly resolution for
use with observations. In the V2 algorithm, the wind speed from a 5-year
average of climatological data is used. The wind speed from 5 years of data
for each month, hour, and 0.25∘× 0.25∘ area is
averaged. This approach captures seasonal effects, such as higher (lower)
wind speeds in winter (summer), and variations in the spatial distribution
of wind speed, such as the higher (lower) wind speed in the open sea
(coast). As in the land surface reflectance calculations, climatological
wind speed data for each month correspond to the middle of each month (day 15) and are linearly interpolated to the retrieval date.
Refined pixel allocation for the land, turbid water, and dark ocean
algorithms and inversion conditions
The GOCI YAER V1 land algorithm is applied not only over land pixels but
also over highly turbid or high-AOD ocean pixels; the ocean algorithm is
applied only over dark ocean surface pixels. A pixel with Δρ660 below -0.05 is assumed to be dark ocean and is processed using the
dark ocean algorithm. Pixels with Δρ660 between -0.05 and
-0.01 are classified as turbid water and thus use the land algorithm.
Pixels with Δρ660 above -0.01 are assumed to be highly
turbid water and removed (Step 13 in Table 1). In
some cases, ocean pixels have Δρ660 above -0.05 with
extremely low TOA reflectance which could result from a combination of low
aerosol concentrations and dark ocean surface reflectance. Misidentification
of these pixels as turbid water results in negative AOD over the dark ocean.
Therefore, a threshold test to identify extremely dark ocean pixels using
TOA reflectance at 660 nm is included in the V2 algorithm. The dark ocean
algorithm is applied to pixels with Δρ660 between -0.05 and
-0.01 and TOA reflectance at 660 nm of below 0.07.
The channels selected for the inversion from measured reflectance to aerosol
optical properties are different for land, turbid water, and dark ocean
pixels. In the V1 algorithm, the land and turbid water pixels use channels
between 412 and 680 nm with surface reflectance less than 0.15, and the dark
ocean pixels use all eight channels. In the V2 algorithm, the channels used
for land pixels are the same as in the V1 algorithm, but the channels
selected for turbid water and dark ocean pixels have been changed. In the
atmospheric correction for ocean color retrieval, the main assumption is
that water-leaving radiance is close to zero in the NIR range, and thus
NIR bands are used for estimating aerosol loading in the atmosphere. The
aerosol signal in the visible range is estimated from NIR measurements
and the known relationships of aerosol signals in the visible and NIR
range for various aerosol types. Ocean color in the visible range is then
retrieved after the atmospheric correction. When AOPs are the main
retrieval target, however, water-leaving radiance is estimated as a
climatological value or neglected. Both approaches have limitations, as the
accurate separation of ocean color and aerosol signals is difficult. Because
water-leaving radiance is not considered in the current ocean surface
reflectance calculations of the GOCI YAER algorithm, channels impacted by
high water-leaving radiance are excluded in the V2 algorithm to minimize
artifacts (Ahn et al., 2012). Thus, only two channels (412 and
865 nm) are used with the climatological surface reflectance database over
turbid-water pixels, and four channels (412, 443, 745, and 865 nm) are used
with the climatological surface wind speed database over dark ocean pixels.
(a) GOCI RGB images and AOD for (b) GOCI V1 all QA, (c) GOCI V1
QA3, (d) GOCI V2, (e) MODIS/Aqua DT, (f) MODIS/Aqua DB, and (g) VIIRS EDR
algorithms on 5 May 2015 over Northeast Asia.
Comparison of GOCI YAER V2 AOD with other data
To evaluate the new masking techniques and climatological data used in the
V2 algorithm, a retrieved dataset of GOCI YAER V2 AOD for 5 May 2015 is
compared with that of the V1 algorithm under two scenarios: using all the
quality assured (all QA; QA = 0, 1, 2, or 3) pixels and using only the
highest quality assured (QA = 3) pixels. The V2 products are also compared
with MODIS/Aqua DT and DB, and VIIRS EDR products
(Fig. 2). The overpass times of MODIS and VIIRS
are generally near 04:00 UTC over the Korean Peninsula, and thus GOCI 04:30 UTC results are selected for the comparison.
Most land pixels over the Korean Peninsula and Japan are not filtered out
and are retrieved as low AOD in the DT, DB, and EDR algorithms. The DB
algorithm retrieves high AOD over the bright surface of Manchuria located
near 44∘ N, 126∘ E, but the DT and EDR do not retrieve
AOD for those pixels because the algorithms are optimized for dark surface
reflectance. The DT, DB, and EDR AOD are 0.7–1.2 for land pixels over
Hebei in China, located near 38∘ N, 117∘ E. Bright sun
glint results in the masking of ocean pixels over the Yellow Sea and East
Sea for MODIS and VIIRS, respectively. The EDR algorithm captures an aerosol
plume, resulting in AODs of ∼ 0.8 over the northern Yellow
Sea, which is not captured by the DT algorithm, and the DT algorithm
captures an aerosol plume resulting in AODs of ∼ 0.6 over the
East Sea close to Hokkaido, Japan, which is missed by the EDR algorithm.
The all QA GOCI V1 predicts low AOD in Korean Peninsula and Japan areas, but
cloud contamination results in high and inhomogeneous AOD, especially near
the edge of the cloud cover. Sun-glint-masked ocean pixels are located at
lower latitudes for GOCI than for MODIS and VIIRS. Thus, aerosol plumes
detected by MODIS and VIIRS are both detected by GOCI. Although the GOCI
YAER algorithm targets dark land surface reflectance pixels, as do MODIS DT
and VIIRS EDR, the aerosol plume over bright land surfaces in Manchuria
captured by the DB algorithm is also detected. However, whether these pixels
are from cloud contamination, bright land surface reflectance, or high AOD
cannot be determined.
When only pixels with QA = 3 are applied to the V1 algorithm, most high-
and inhomogeneous-AOD pixels, typically caused by unfiltered cloud
contamination, are removed, but low-AOD pixels over land in South Korea and Japan
are also removed. There are two possible reasons for the extensive masking
in V1 using only QA = 3 pixels for the case of low AOD over land. The
spatial inhomogeneity test of the V1 algorithm is a simple SD of 3 pixel × 3 pixel TOA reflectance with one fixed threshold, regardless of
TOA reflectance. Although this approach works well in high-AOD cases, in
low-AOD cases, inhomogeneous surface reflectance signals contribute to high
SD and result in excessive masking. Another possible explanation is that these
pixels have an AOD below -0.05 because of an overestimation of surface
reflectance.
Compared with the V1 algorithm, the spatial variability tests of the V2
cloud-masking algorithm consist of the same simple SD test (except for a
relaxed threshold), an additional mean-weighted SD test, and a ratio test of
the brightest and darkest pixels, relative to TOA reflectance. In addition,
darker land surface reflectance is obtained from the climatological data,
and this results in increased AOD compared with the large negative AODs seen
from the V1 algorithm. Thus, fewer pixels are filtered out using the GOCI V2
algorithm and are retrieved as positive low AOD. The V2 AOD also shows fewer
inhomogeneous features near the edges of clouds, similar to the MODIS and
VIIRS AOD.
Long-term validation of GOCI YAER V2 AOD and AEGround-based measurements and ancillary satellite data
Two ground-based observation networks – the Aerosol Robotic Network (AERONET) and the Sun-Sky Radiometer Observation Network (SONET) – are used
to quantify the accuracy of GOCI YAER V2 AOD (τG-V2) using data from March 2011 to February 2016. AERONET is
a ground-based aerosol remote sensing network of CIMEL sun-sky radiometer
photometers maintained by the NASA Goddard Space Flight Center (Holben et
al., 1998). Spectral AOD and AE are retrieved from direct solar irradiance
measurements, and other optical/microphysical properties such as the volume
size distribution and refractive indices are retrieved from the inversion of
spectral AOD with diffuse-sky radiance measurements. Uncertainties in
AERONET AOD (τA) in the visible and NIR have been
reported as ±0.01 (Eck et al., 1999), which is much lower than is
typical for satellite-retrieved AOD because of the minimal
surface-reflectance effects in direct solar irradiance measurements and the
highly accurate calibration. Thus, AERONET AOD is often used as the
reference dataset for satellite AOD validation. The fully calibrated and
cloud-screened AERONET Version 2 Level 2.0 AOD at 550 nm and AE between 440
and 870 nm from direct measurements are used in this study
(Smirnov et al., 2000). A total of 27 AERONET sites within the
GOCI observation domain, excluding specific short-period campaign sites, are
selected for this analysis. Also, AERONET Version 2 Level 2.0 FMF at 550 nm
and SSA at 440 nm from inversion products are used for the validation of
GOCI FMF and SSA (Dubovik and King, 2000), The SONET is a ground-based
aerosol remote sensing network of CIMEL sun-sky radiometers maintained by
the Institute of Remote Sensing and Digital Earth, Chinese Academy of
Sciences (Li et al., 2015). The SONET also provides spectral AOD
(τSONET) from direct sun measurements and AE. A
total of six SONET sites in China are selected for the validation of AOD at
550 nm.
In addition, the GOCI V1 AODs with all QA pixels (τG-V1allQA) and only pixels with QA = 3 (τG-V1QA3) are compared with V2 AODs to quantify improvements in
the V2 algorithm. The MODIS DT AOD (τMDT) and DB
AODs (τMDB) of the highest quality pixels (QA = 3) are also compared over the same site and during the same period to verify
the GOCI AOD accuracy. Note that the VIIRS EDR AOD is used in the
qualitative comparison in the previous section but is not included in the
present validation because the VIIRS data are only available from January
2013.
Collocation criteria between ground- and satellite-based measurements
The comparison between satellite- and ground-based data is implemented with
spatial and temporal collocation criteria. Hourly GOCI AOD pixels that are
located within a 25 km radius of each ground site, and ground-based
observation data within 30 min of each GOCI observation time, are
averaged. The averages from both datasets are included if at least one
measurement from each dataset is available. The collocation criteria used
for the MODIS data are the same as for GOCI. After the collocation, 27
AERONET sites and 6 SONET sites are matched with GOCI land AOD observations,
and 17 AERONET sites are matched with GOCI ocean AOD observations. Note that
the 27 AERONET sites matched with GOCI land AOD observations includes all 17
coastal AERONET sites matched with GOCI ocean AOD observations because the
coastal sites can be collocated with both land and ocean AOD measurements.
Statistical evaluation metrics
Following the method of Sayer et al. (2014), the statistical metrics for
the evaluation contain the number of collocation data (N); the Pearson's
linear correlation coefficient (R); the median bias (MB); the root mean square
error (RMSE); and f, the fraction of data points within the expected error range
of the MODIS DT AOD (Collection 5), EEMDT=±(0.05+0.15×τA),
as described by Levy et al. (2007). Each AOD product has an expected
error range that can vary with the algorithm performance. To compare
accuracies, the EEMDT is applied to all algorithms. Note that the
expected error range of the GOCI YAER V2 AOD
(EEG_V2) is estimated independently
in Sect. 4.2.
Comparison of AOD between AERONET/SONET and GOCI/MODIS DT/MODIS DB
over land and ocean surfaces. The x axis is land AERONET AOD, land SONET
AOD, and ocean AERONET AOD from top to bottom, and the y axis is GOCI YAER
V1 for all QA, GOCI YAER V1 for QA = 3, GOCI YAER V2, MODIS DT, and MODIS
DB from left to right. Colored pixels represent a bin size of 0.02. Black
dashed lines denote the one-to-one line, and dotted lines denote the expected
error range of MODIS DT AOD.
Statistics of land and ocean AOD comparisons between AERONET/SONET
and satellite products, as shown in Fig. 3.
Satellite AOD algorithmNRMBf within EEDTRMSELand AOD comparison with AERONET GOCI YAER V1 all QA47 8500.86-0.0150.490.24GOCI YAER V1 QA338 1830.92-0.0660.490.18GOCI YAER V245 6430.910.0100.600.16MODIS DT32280.920.0430.620.18MODIS DB34630.930.0070.730.16Land AOD comparison with SONET GOCI YAER V1 all QA12 9740.83-0.0480.450.29GOCI YAER V1 QA310 4830.88-0.1030.420.27GOCI YAER V212 2380.86-0.0210.510.24MODIS DT7330.820.1040.460.29MODIS DB12580.860.0000.670.27Ocean AOD comparison with AERONET GOCI YAER V1 all QA19 9450.830.0560.550.17GOCI YAER V1 QA318 3080.880.0430.620.13GOCI YAER V218 4990.890.0080.710.11MODIS DT6800.920.0330.730.09Validation of GOCI YAER V2 land AOD and comparison with other data
Results of a comparison between AERONET/SONET AOD and GOCI-retrieved AOD
over land and ocean surfaces are presented in Fig. 3. Statistics from the comparison are summarized in
Table 2. As seen in the qualitative comparison
results (Fig. 2), τG_V1allQA shows many overestimated points
compared with τA because of remaining cloud
contamination. About 20 % of pixels are filtered out with the QA = 3
criteria (τG_V1QA3), and this
results in a reduction of the number of overestimated points, decreasing
RMSE from 0.24 to 0.18, and increasing R from 0.86 to 0.92. However,
underestimated points due to the overestimation of surface reflectance
remain, which results in an increase of the negative median bias from
-0.015 to -0.066. Results of a comparison of τG_V2 with τA show
fewer overestimated points compared with those of τG_V1allQA because of the improved pixel
masking. This results in an increased N and f within
EEMDT and decreased MB and RMSE compared with
τG_V1QA3. The increased N comes
from the low-AOD points that are filtered out in τG_V1QA3. The number of underestimated points in the low-AOD range decreased
because of decreased surface reflectance using the 5-year samples. This
results in lower bias (MB = 0.010), decreased RMSE (0.16), and increased f within
EEMDT (0.60). The R of 0.91 is similar to that of
τG_V1QA3 (0.92). The N between
τA and τG_V2 is about 14 times greater than the corresponding τMDT and τMDB, mostly because of the
hourly data available from GOCI compared with the twice-daily overpass data
from MODIS. The spread of data points from MODIS and AERONET relative to the
one-to-one line is lower than that from GOCI and AERONET, and this results
in higher f within EEMDT( 0.62 for
τMDT and 0.73 for τMDB). The R and RMSE of τMDT and
τMDB are similar to those of τG_V2. The MB of τMDB
is closest to zero, and τMDT has a positive MB of
0.043. The overestimation of τMDT has been
attributed to the urbanization effect of the biased reflectance estimation
(Munchak et al., 2013) and has been corrected in the MODIS DT research
algorithm (not used here) using the modified urban surface-reflectance
algorithm (Gupta et al., 2016).
The GOCI V2 land AOD results can be recategorized as coastal or inland
according to whether each site is collocated with both GOCI ocean and land
AOD or with GOCI land AOD only. Mean AERONET AODs from coastal sites are
lower (0.28) than those from inland sites (0.42). The intercomparison
between coastal-site AERONET AOD and GOCI V2 land AOD has an R of 0.83,
RMSE of 0.144, MB of -0.004, and f within EEMDT of 0.60.
Results from inland sites have higher R (0.93), RMSE (0.171), MB (0.023), and the
same f within EEMDT (0.60). High AOD is detected
more frequently at inland sites than at coastal sites.
A comparison between SONET AOD and satellite-retrieved AOD over land reveals
that τG_V2 has higher accuracy
than τG_V1QA3, except in terms of
R. The reason for the decreased accuracy in R of τG_V2 may be the use of the same climatological
surface reflectance for each year, whereas in reality the surface
reflectance changes annually. The τMDB has the
lowest MB and RMSE and highest f within EEMDT. The
τMDT has a positive MB of 0.104.
In conclusion, most statistical parameters indicate that land τG_V2 accuracy is improved relative to
τG_V1QA3 and is comparable to
τMDT and τMDB.
Validation of GOCI YAER V2 ocean AOD and comparison with other data
The changes of the GOCI YAER algorithm over ocean surfaces between V1 and V2
include the cloud-masking techniques, the use of climatological wind speed
data instead of each date data, pixel classification thresholds, criteria
for turbid-water and dark-ocean algorithm selection, and the choice of
spectral channels. Results from the comparison of τG_V1QA3 with τG_V1allQA show decreased N and RMSE, an MB closer to
zero, and increased R and f within EEMDT, which is
similar to the results over land sites except for MB. The refinement of the
ocean algorithm from V1 to V2 results in improvement in most statistical
parameters: decreased MB from 0.043 to 0.008, increased f within
EEMDT from 0.62 to 0.71, and decreased RMSE from 0.13
to 0.11. An MB closer to zero means that the modified channel selection in the
turbid-water and dark-ocean algorithms, to avoid the effect of water-leaving
radiance variation, works effectively. The N between AERONET and GOCI V2 AOD
over ocean surfaces is about 27 times greater than that for MODIS DT AOD,
which is greater than that seen in the land comparison despite the same
difference in observation frequency. The reason for this result is that most
turbid-water pixels near the coast are filtered out in the MODIS DT
algorithm, but are included in the GOCI YAER algorithm. Compared with the
ocean τMDT, the ocean τG_V2 has slightly higher RMSE, an MB closer to zero,
slightly higher R, and slightly lower f within EEMDT.
In conclusion, most statistical parameters show that ocean τG_V2 accuracy is improved relative to
τG_V1QA3 and is comparable with
τMDT.
Relative frequency histograms of retrieved AOD from AERONET and
satellites over (a) land and (b) ocean surfaces.
Comparison of AOD histogram distribution
In Fig. 4, mean relative frequency histograms for
land τA, collocated with GOCI and MODIS land AOD,
have a mode of 0.11 (i.e., highest frequency in the range 0.105–0.115) and
right-skewed distribution. This is similar to the global τMDT and τMDB mode of 0.1 reported
by Sayer et al. (2013). The land τG_V1QA3 mode is 0.02 and those
of τG_V2, τMDT, and τMDB
are 0.12, 0.10, and 0.13, respectively, which are similar to that of
τA. Improvement in the land surface reflectance
in V2 results in a reduced difference in mode between AERONET and GOCI. The
shape of the histogram of τMDB is better matched
to that of τA in the AOD range 0.05–0.30 than to
τMDT and τG_V2. The land-targeted histograms of
τMDT and τG_V2 have a similar shape to each other. The
two histograms have lower frequency modes and higher frequency AOD between
0.3 and 0.7 compared with τA. The τG_V2 has a smoother shape due to a larger
number of coincident data points.
The mean relative frequency histograms for τA,
collocated with GOCI and MODIS ocean AODs, have a mode of 0.11, and those of
ocean τG_V1QA3 and τMDT have modes of 0.14 and 0.16, respectively. However, ocean
τG_V2 has a mode of 0.10, which is
closer to that of τA than those of
τG_V1QA3 and τMDT. Although the mode of ocean τMDT is higher than that of τA, the
magnitude of the peak is similar. The histogram distributions of ocean
τG_V1QA3 and τG_V2 have lower magnitude peaks and more
gradual decreases with increasing AOD compared to τA.
Comparison between AERONET and GOCI YAER V2 (a) land AE, (b) ocean
AE, (c) land FMF, (d) ocean FMF, (e) land SSA, and (f) ocean SSA. Note that
collocated data are only for AERONET AOD > 0.3 for the AE and FMF
comparisons, and AERONET AOD > 0.4 for the SSA comparison. Each
colored pixel represents a bin size of 0.10 for AE, 0.05 for FMF, and 0.005
for SSA. Black dashed lines denote the one-to-one line, and blue dotted
lines in the SSA comparison denote the ±0.03 and ±0.05 ranges.
Validation of GOCI YAER V2 AE, FMF, and SSA over ocean and land surfaces
The AE intercomparisons between AERONET and GOCI YAER V2 over ocean and
land surfaces are presented in Fig. 5a and b.
Only AERONET AOD > 0.3 values are included because large errors
exist in AE, due to surface reflectance errors when AOD is low. Note that
the GOCI AE is derived from the predefined values of the selected aerosol
model, not from the retrieved spectral AOD. Compared with the V1 AE accuracy
during the DRAGON-NE Asia 2012 campaign described by
Choi et al. (2016; R= 0.678 over both land
and ocean surfaces), the V2 land and ocean AE have lower linear correlations
with AERONET (R= 0.505 and 0.459, respectively) from the 5-year validation.
The DRAGON-NE Asia 2012 campaign was conducted in spring (March–May) when
long-range transport of yellow dust from the Gobi and Taklamakan deserts in
Asia, which has low AE with high AOD, is more frequent. Aerosol plumes with
low AE and high AOD can be retrieved with higher accuracy compared with the
generally low-AOD cases during other seasons. Thus, AE shows stronger linear
correlation in spring (R of 0.63 over land and 0.57 over ocean) but is lower
for other seasons (R of 0.24 over land and 0.22 over ocean). The highest
frequency of points is close to the one-to-one line, but there is a
significant discrepancy when AERONET AE is ∼ 1.3 but GOCI AE
is ∼ 0.6, particularly over land. This could be caused by
varying surface reflectance errors for each channel or perhaps by a
local-minimum problem induced from the LUT approach used for inverse
modeling.
The FMF intercomparisons between AERONET inversion data and GOCI YAER V2
are similar to those of AE, as shown in Fig. 5c
and d. This comparison also includes only AERONET AOD > 0.3 data.
AERONET inversion products are retrieved from almucantar measurements, which
are possible when the solar zenith angle is greater than 50∘
(Dubovik and King, 2000); thus, the number of points used in the
comparison are fewer than the AOD and AE from direct measurements. The
correlation coefficients of FMF over ocean and land surfaces are similar to
those of AE, as both parameters are determined primarily by aerosol size.
The SSA intercomparisons between AERONET and GOCI YAER V2 have the lowest
R (0.206 for land and 0.251 for ocean) among the products. The visible–NIR
wavelength range is more sensitive to aerosol size than absorptivity. Thus,
aerosol models are constructed more coarsely for SSA than for FMF, and the
inversion methods focus on spectral matching of AOD at 550 nm, rather than
on SSA-optimized retrieval, such as the OMI aerosol retrieval algorithm
using ultraviolet radiation (Torres et al., 2013; Jeong et al., 2016).
Nevertheless, the ratio of GOCI V2 SSA to AERONET SSA in a ±0.03 and
±0.05 range is 47.7 and 68.0 % for land and 69.7 and
88.3 % for ocean, respectively, which is comparable to the OMI SSA
presented by Jethva et al. (2014).
In conclusion, GOCI YAER V2 AE, FMF, and SSA compared with AERONET products
are more biased and have lower correlation coefficients than seen for AOD.
This indicates that the aerosol type selection is biased to coarse and
nonabsorbing aerosols. To improve the accuracy of these parameters, more
accurate surface reflectance estimations and improved inversion methods are
required.
Error analysis of GOCI YAER V2 AOD
Retrieved AOD likely has both a systematic and random error associated with
various factors, including sun–earth–satellite geometry, cloud
contamination, surface type, and assumed aerosol model, among others. An
error analysis of satellite AOD can help identify the most important
contributors to errors in these products. In this section, coincident GOCI
and AERONET AOD are analyzed to quantify systematic and random errors. A
systematic bias analysis is implemented for the four GOCI products (i.e., the
V1 land AOD with QA = 3, V2 land AOD, V1 ocean AOD with QA = 3, and V2
ocean AOD). In addition, pixel-level uncertainties in GOCI version 2 land
and ocean AOD are estimated.
Systematic bias analysisBias as a function of AERONET AOD
As shown in Fig. 6a, V1 land AOD has a negative
bias in the low-AOD range because of an overestimation of surface
reflectance. After implementing climatological surface reflectance over
land, the V2 land-AOD shows less bias than that of V1 and is close to 0 over
the whole AOD range. This results from the increased probability of finding
observation days with low aerosol loading using a 5-year dataset. The V2
ocean AOD shows a positive bias around 0.05–0.10 and high positive bias of
0.1 when AERONET AOD is ∼ 0.3. The reason for the positive
bias in ocean AOD may be an underestimation of ocean surface reflectance
when considering only climatologically averaged wind speed and geometry,
without accounting for changes in surface properties including bio-optical
properties. Details of improvements to the ocean AOD calculation are
described later. The ranges of the 16th–84th percentiles of both
land and ocean AOD become wider as AERONET AOD increases, and the shapes
of the ranges are asymmetrical.
Difference between GOCI and AERONET AOD in terms of (a) AERONET
AOD, (b) AERONET AE, (c) scattering angle, and (d) GOCI NDVI. Each point
represents the 50th percentile of 1000 collocated data points sorted in
ascending order for each x axis value. The horizontal line through each point
represents the range of collocated data points.
Bias as a function of AERONET AE
The V2 ocean and land AOD biases are close to zero when AERONET AE is within
1.3–1.6 and the accuracy of GOCI AE is high (Fig. 6b). However, these biases increase in the positive direction as AE
deceases to 0.3 (large particles). Compared with the biases of V1, those of
V2 are reduced for all AE ranges, but the pattern of difference in AE
remains. This could be due to errors in the assumed aerosol optical
properties of extremely large particles. Assumed aerosol models based on the
global AERONET climatological database are categorized according to FMF and
SSA, and the phase functions of nonspherical properties are averaged to one
value for each model. In reality, various nonspherical shapes with the same
FMF value may be present and may result in higher error at low values of
AERONET AE. The differences may also be due to errors in aerosol type
selection during the inversion process, as suggested by the decreased
accuracy of low GOCI AE. Wavelength-dependent errors in calibration or
surface reflectance assumptions may also contribute to the observed
differences. Further investigation is required to quantify the relative
contributions of these errors.
Bias as a function of scattering angle
In Fig. 6c, the bias of ocean AOD changes from
-0.05 to 0.10 as scattering angle increases from 110 to
175∘. The bias in land AOD shows a similar trend, but with a
range of variance from -0.05 to 0.05. As the scattering angle increases to
180∘, the atmospheric contribution to total TOA reflectance
decreases compared with that from the surface because of the shorter light
path length, which leads to an increase in AOD retrieval error (Sayer et
al., 2013). This larger error at higher scattering angle is more distinct
for ocean AOD than land AOD because of the difference in surface
reflectance. The land algorithm performs characterization at each hour for
surface reflectance using the composite method to reflect the BRDF effect.
The ocean algorithm also considers geometry and wind speed in calculating
the BRDF effect. However, ocean bio-optical properties such as chlorophyll
(Chl) or color-dissolved organic matter (CDOM) are not considered in the
current ocean surface reflectance calculation. This may be the cause of the
relatively large error in ocean AOD compared with land AOD.
Bias as a function of NDVI
A bias analysis of land and ocean AOD relative to NDVI is presented in
Fig. 6d. The V2 land AOD has a bias close to zero
for NDVI > 0.4 (high vegetation), but has a positive bias of up
to 0.05 in the range 0.1 < NDVI < 0.4, which corresponds to
less vegetated areas, such as semiarid and urban regions. The method used to
determine surface reflectance from multi-year samples in the V2 algorithm is
applied to all pixels identically regardless of surface type, which can
result in a bias that varies with NDVI. The positive bias over urban areas
is similar to that of the MODIS Collection 6 DT AOD (Munchak et al.,
2013; Gupta et al., 2016). The positive bias of V1 ocean AOD is generally
lower using the V2 algorithm because the 500–600 nm channels that are
strongly affected by ocean bio-optical property variance are not used in the
V2 ocean algorithm. However, channels that are used in the V2 algorithm can
still be slightly affected by these bio-optical effects. Thus, positive
biases persist for smaller negative NDVI values, which correspond to
less turbid ocean pixels where ocean surface models that consider wind speed
are utilized.
Bias as a function of cloud contamination
Despite applying several cloud-masking techniques, the remaining
cloud-contaminated pixels may still result in high positive biases in AOD.
In this section, uncertainties due to cloud contamination are analyzed in
terms of (1) cloud fraction at the aerosol product pixel resolution (6 km × 6 km), (2) the number of GOCI aerosol pixels collocated with each
AERONET site, and (3) AOD spatial homogeneity.
Difference between GOCI and AERONET AOD in terms of (a) GOCI cloud
fraction within each aerosol product pixel (6 km × 6 km), (b) the
number of spatially collocated GOCI pixels within a 25 km distance from
AERONET sites, and (c) the spatial standard deviation of collocated GOCI
AOD. The points, dotted lines, and horizontal lines in (a, c) are as
defined in Fig. 6.
First, the cloud fraction (CF) for one 6 km × 6 km aerosol-product
pixel can be calculated using the number of 0.5 km × 0.5 km L1B pixels that remain after all masking steps. In the aggregation step
from the original L1B resolution of 0.5 km × 0.5 km to Level 2
aerosol-product resolution of 6 km × 6 km, the maximum number of
remaining pixels is 58 after performing all the individual masking processes
and discarding the darkest 20 % and brightest 40 % of pixels in a block
of 12 pixels × 12 pixels (i.e., 144 pixels). The minimum number is
set as 29, which corresponds to 50 % of the maximum value. If the number
of remaining pixels is less than 29, then AOPs of that pixel are not
retrieved. Note that pixels that are bright because of surface reflectance,
not clouds, may be counted as a high CF, but it is difficult to completely
distinguish these two cases at 500 m spatial resolution. In
Fig. 7a, the bias of ocean AOD is close to zero
for a CF of 0.0, and increases to 0.1 as the CF increases to 0.5. The bias of land
AOD is ∼ 0.05 when the CF is close to zero, approaches to zero for
0.05 < CF < 0.25, and increases up to 0.05 as the CF goes to
0.4. This positive bias under high-CF conditions is similar to that of MODIS
DT and DB AOD (Hyer et al., 2011; Shi et al., 2013). However, the
positive bias of land AOD at CF = 0, which is not observed for MODIS DT
and DB AOD, may be due to surface reflectance underestimations over bright
surface in GOCI.
Next, the bias due to cloud contamination is analyzed with reference to the
number of spatially collocated GOCI AOD pixels of each scene (NC) for
each AERONET site location (Fig. 7b). Because the
GOCI AODs within a 25 km radius around each site are averaged if at least
one pixel is available, NC can indicate the existence of clouds near the
AERONET site over a broader domain. Note that the maximum NC of ocean
AOD pixels of 40 is less than that of land (56) because ocean AOD is
generally collocated with AERONET sites located on the coast. The bias of V2
land AOD is 0.1 for NC= 1 and approaches zero as NC increases.
The V1 land AOD had a negative bias, primarily because of surface
reflectance. Thus, the bias does not change with NC. The ocean AOD bias
is 0.05 for NC= 1 and decreases for higher NC, up to 30.
However, high positive biases exist for NC > 30, which could
be due to problems in characterizing ocean surface reflectance.
Finally, the SD of the spatially collocated AODs indicates how spatially
smooth the retrieved AODs are. In the GOCI algorithm, aerosol optical
properties for each pixel are retrieved independently regardless of the
surrounding pixels, which is similar to the approach used by the MODIS DT
and DB algorithms (Hsu et al., 2013; Levy et al., 2013). The SD could
increase if cloud-contaminated pixels are misclassified as high-AOD pixels,
despite the presence of relatively low AOD in the surrounding pixels. Thus,
the SD may be an indirect indicator of cloud contamination in this
independent-pixel retrieval method. In Fig. 7c,
the bias increases positively up to ∼ 0.13 for ocean AOD and
to ∼ 0.08 for land AOD as the SD increases. The
16th–84th percentile range also becomes wider (not shown). The V1
land AOD had negative biases of -0.1 for low SD and -0.05 for high
SD and was persistently affected by surface reflectance issues and/or cloud
contamination. Note that some recent aerosol retrieval algorithms have
adopted a statistical spatial smoothness constraint for AOPs in the
inversion procedure to improve accuracy (Dubovik et al., 2011; Xu et al.,
2016).
In summary, the high cloud contamination in both each product-pixel (6 km × 6 km) and neighboring pixel (within 25 km) domains results in
high positive biases of up to 0.1. However, an independent analysis of the
cloud-contamination-only effect is complicated by various factors including
surface reflectance errors, resulting in a high bias under low
cloud-contamination conditions.
Bias as a function of hour, month, and year
The GOCI AOD consists of eight hourly observations per day from 09:30 to
16:30 KST (centered time of each measurement), and the solar zenith and
azimuth angle varies over a much wider range than that of low earth orbit (LEO)
satellites. However, it requires more sophisticated treatments for
properties such as surface reflectance, the aerosol phase function, and the
calculation of Rayleigh scattering, which may result in accuracies that vary
with measurement time. In Fig. 8a, the bias of
land AOD decreases from about -0.1 for V1 to almost zero for V2, with no
noticeable hourly dependence for V2. In contrast, the ocean AOD has a
distinct diurnal bias shape, which is close to zero at 09:30, 15:30, and
16:30 KST and ∼ 0.1 at 12:30 KST. This is consistent with the
results of the bias analysis with reference to the scattering angle.
Difference between GOCI and AERONET AOD in terms of local
observation time, month, and year. The points are as defined in
Fig. 7b.
The bias of land AOD as a function of month remains near zero
(Fig. 8b). In contrast, that of ocean AOD
increases up to 0.1 in spring (April–May) and to ∼ 0.05 in
late fall and early winter (November–December), which can likely be
attributed to monthly variations in Chl concentration over East Asia. The
climatological Chl concentration reported by Yamada et al. (2004) is highest during spring (1.2–2.7 µg L-1), lower during late fall
(0.8–1.2), and 0.2–0.4 µg L-1 during other seasons. Thus, the
change in monthly bias for ocean AOD is likely affected by Chl concentrations
in the current GOCI ocean AOD algorithm. The positive biases of the V1 ocean
AOD during spring and late fall were reduced using V2 after changing the
channel selection.
Expected errors of MODIS C6, VIIRS EDR, and GOCI over ocean and
land. μ0 and μ are the
cosine of solar zenith angle and satellite zenith angle, respectively.
τA and τS are AERONET and satellite AOD, respectively.
AlgorithmDiagnostic expected error (DEE)Prognostic expected error (PEE)ReferenceOcean MODIS DTLinear regression with biasconsideration: -0.10τA-0.02 (lower bound) and 0.10τA+0.04 (upper bound)Levy et al. (2013)VIIRS EDRLinear regression with biasconsideration: -0.238τA+0.01 (lower bound) and 0.194τA+0.048 (upper bound)Linear regression: ±(0.250τS+0.009)Huang et al. (2016)GOCI YAER V2Linear regression: ±(0.185τA+0.037)Linear regression: ±(0.206τS+0.030) Unique regression per AODrange: Table 4This studyLand MODIS DTLinear regression: ±(0.15τA+0.05)Levy et al. (2010)MODIS DBLinear regression: ±(0.20τA+0.05)Linear regression with air massfactor consideration: ±(0.56+0.086)/(1/μ0+1/μ)Sayer et al. (2013)VIIRS EDRLinear regression with biasconsideration: -0.470τA-0.01 (lower bound) and -0.0058τA+0.09 (upper bound)Linear regression: ±(0.34τS+0.023)Huang et al. (2016)GOCI YAER V2Linear regression: ±(0.137τA+0.073)Linear regression: ±(0.184τS+0.061) Unique regression per AODrange: Table 4This study
The V1 land AOD retrieved using monthly surface reflectance data for each
year shows a constant negative bias of about -0.05 from 2011 to 2015
(Fig. 8c). In contrast, the V2 land AOD retrieved
using monthly climatological surface reflectance data from the 5-year
dataset samples shows biases that are smaller than those of V1 but with
increased variation. The increased variance for V2 could be due to a
limitation of the application of climatological data, which cannot reflect
year-to-year changes in surface reflectance. The ocean AOD shows less
variation in bias compared with the V2 land AOD, but it varies more than the V1
land AOD. This may be attributable to interannual variations in ocean
surface reflectance caused by ocean bio-optical properties.
Absolute difference between GOCI YAER V2 AOD and AERONET AOD in
terms of (a) AERONET ocean AOD, (b) AERONET land AOD, (c) GOCI YAER V2 ocean
AOD, and (d) GOCI YAER V2 land AOD. The diamond, triangle, and square
symbols represent the 38th, 68th, and 95th percentiles of 200 collocated
data points sorted in ascending order of x axis value. In
(a–d), the red line in each panel is the linear least-squares fit of the
68th percentiles, and the blue and green lines are half and double the red
line values, respectively.
Uncertainty estimation for GOCI YAER V2 AOD
The uncertainty (or “expected error”) of retrieved AOD is defined as a
1 SD confidence interval corresponding to the 68th
percentile, and it is estimated from the long-term evaluation of retrieved
satellite AOD using ground-based AERONET measurements. Each
satellite-retrieved AOD has its own uncertainty based on the methods used
for surface reflectance estimations, assumed aerosol models, etc. The
expected error (EE) of retrieved AOD can be estimated as a function of both
AERONET AOD and retrieved satellite AOD. The “diagnostic” expected error (DEE) is based on AERONET AOD, which is more accurate than satellite AOD
and is thus more useful in quantitatively evaluating the algorithm, though it is
restricted to only the AERONET pixels. Alternatively, the “prognostic”
expected error (PEE), a function of retrieved satellite AOD, can be
calculated over all retrieved pixels, making it more appropriate for certain
applications, such as data assimilation with air-quality forecasting models
(Sayer et al., 2013; Shi et al., 2013). A common characteristic of EE is
that it increases linearly with AOD. Thus, a linear regression fit between
the 68th percentile of absolute error and the reference AOD (AERONET or
satellite AOD) is determined as EE. The 68th, 38th, and 95th
percentile points correspond to 1, 0.5, and 2 SD
intervals, respectively, assuming the error has a Gaussian distribution and
no bias. Thus, 0.5 and 2 times the linear least square regression equation
of the 68th percentile should correspond to the 38th and 95th
percentiles, respectively. The EEs of MODIS, VIIRS, and GOCI AOD based on
this approach are summarized in Table 3. Note that
additional factors are considered in the EE calculations for each algorithm,
such as bias information in MODIS DT over ocean surfaces and VIIRS EDR, and
geometrical air mass factors in MODIS DB (Levy et
al., 2013; Sayer et al., 2013; Huang et al., 2016).
Prognostic expected error (PEE) estimation of GOCI YAER V2 AOD
according to the AOD range. The minimum PEE is labeled “noise floor”.
GOCI AOD rangeOcean algorithmLand algorithm“Noise floor”0.0440.048-0.05≤τG<0.500.07-0.58τG+4.12τG28.81τG3+7.39τG41.50τG50.11-1.15τG+8.87τG225.05τG3+34.83τG418.93τG5τG≥0.700.00+0.25τG0.13+0.12τG0.50≤τG<0.70Highest between two fitting equationsHighest between two fitting equations
Absolute difference between GOCI YAER V2 AOD and AERONET AOD in
terms of (a) GOCI YAER V2 ocean AOD and (b) GOCI YAER V2 land AOD. The
triangle symbols represent the 68th percentiles of 200 collocated data
points sorted in ascending order of x axis value.
Comparison of observed (a) ocean and (b) land AOD error
distributions with theoretical Gaussian distributions for the linear PEE
(red) and multiple PEE (blue).
Spatial distribution of statistical evaluation metrics for GOCI
YAER V1 QA3 land AOD (first and third columns) and V2 land AOD
(second and fourth columns). Left panels show mean AERONET AOD,
correlation coefficient, and RMSE from top to bottom. Right panels show
median bias, fraction within DEE, and fraction within multiple PEE from top
to bottom.
As for Fig. 12, except for GOCI ocean AOD.
To estimate DEE and PEE of the GOCI YAER V2 AOD using a linear least-squares
regression equation, the absolute AOD difference between GOCI and AERONET is
analyzed for AERONET and GOCI AOD in Fig. 9. The
linear DEE (0.185τA+0.037) and
PEE (0.206τG+0.030) of ocean
AOD follow the 68th percentile points well (R= 0.968 and 0.971,
respectively). Doubled values of DEE and PEE for ocean AOD are well matched
with the 95th percentile points. Although the linear DEE
(0.137τA+0.073) and PEE
(0.184τG+0.061) of land AOD are
well matched with the 68th percentile points (R= 0.969 and 0.937,
respectively), the PEE of land AOD includes discrepancies that vary over the
AOD range. Significant discrepancies exist between the 95th percentile
points and doubled values of the PEE of land AOD. Due to the existence of
more complex error sources, the EE of land AOD cannot be accurately
characterized in a linear relationship with AOD (Hyer et al., 2011).
The estimated linear DEE and PEE of land AOD show similar or lower slopes
but higher offset compared with MODIS and VIIRS, which is assumed to be due
to higher surface reflectance bias in GOCI.
Instead, PEE values constructed for specific AOD ranges (“multiple PEE”) are
applied as in Fig. 10 and summarized in
Table 4. The “noise floor”, defined by
Hyer et al. (2011), is the minimum absolute error. A fifth-order
polynomial regression fit is applied for GOCI AOD < 0.5 to reflect
the curved pattern, and a linear fit is applied when GOCI AOD > 0.7. The higher of these two computed values is applied when GOCI AOD is
between 0.5 and 0.7. Both multiple PEEs show higher EE values near GOCI AOD
of 0.1 (over ocean and land) and 0.6 (over land) compared with the linear
PEEs, and thus they better match observations near the 68th percentile.
The ratio of actual error to linear and multiple PEE follows the theoretical
Gaussian distribution with a mean of zero and variance of 1
(N(0,1)) as shown in Fig. 11, which is
similar to the results obtained for MODIS DB AOD (Sayer et al., 2013).
Because the PEE of ocean AOD has a strong linear relation with GOCI AOD,
there are fewer differences between linear and multiple PEE. However, the PEE
of land AOD has a significantly different relationship with AOD, leading to
differences in the distributions of linear and multiple PEE. Although the
ratio between N0,1=-1 and
N0,1=+1 (0.683) is closer to that
of linear PEE for land AOD (0.680) than to the corresponding multiple PEE
(0.669), the peak of N0,1 is closer to
that of multiple PEE than linear PEE. In addition, all linear and multiple
PEEs of ocean and land AOD have slight positive biases compared with
N0,1. Notwithstanding, the obtained PEEs
of GOCI YAER V2 AOD, particularly multiple PEE for land AOD, generally
represent actual errors well.
Regional performance
The obtained GOCI DEE and (multiple) PEE can be used for AOD validation for
each site along with other statistical evaluation metrics presented earlier.
The validation results for all sites have been analyzed individually to
compile the results shown for each site, including the fraction of data
points within DEE and (multiple) PEE. Spatial distributions of statistical
evaluation metrics are presented in Figs. 12 and 13 for land and ocean AOD, respectively.
The average of collocated AERONET AOD is highest in China, including the
Beijing (0.69 and 0.48 with GOCI V1 and V2, respectively) and Taihu (0.70)
sites. The South Korean sites show higher annual average AERONET AOD (0.33–0.50)
than Japanese sites (0.17–0.30). For land AOD among the 27 land AERONET
sites, 21 sites show improvement in V2 according to the statistical
evaluation metrics and 6 sites have decreased accuracy in V2 compared with
V1. In addition, the GOCI V2 land AOD shows less bias and has a higher
fraction of data points within DEE and PEE over the Korean Peninsula
compared with the Chinese and Japanese sites. The sites with the worst
accuracy in V2 land AOD have a positively increased median bias. The reason
for this decrease in accuracy of some of the sites in V2 compared with V1 is
likely the way that the surface reflectance database is constructed. Surface
reflectance at the lower accuracy sites in V2, such as at Chiba University,
Kobe, Xinglong, and Osaka, is brighter (urban surfaces) than at other sites,
and the current identification threshold of the darkest 1–3 % of
observations, without considering surface type, results in climatologically
derived values for reflectance that are too dark at bright (urbanized)
surface sites. Tilstra et al. (2017) suggested that selecting
the mode of the RCR histogram improves the characterization of surface
reflectance of bright surfaces compared with selecting the minimum values of
the RCR. Choosing different thresholds for various surface types may improve
the accuracy of retrievals over sites that have high surface reflectance.
For ocean AOD, 14 sites show improvement in V2 and 3 sites have lower
accuracy in V2 than V1 among the 17 coastal AERONET sites. In contrast to
the increased median bias in land AOD, ocean AOD shows decreased median bias
from V1 to V2. However, the lower accuracy sites do not differ significantly
between V1 and V2 compared with land AOD. The fraction of data points within
DEE and PEE for V1 ocean AOD at the Japan sites is higher than at the South
Korean sites, but becomes similar in V2. The obtained DEE of V2 ocean AOD (94 %)
is higher than the theoretical 1σ fraction (68 %). However, the PEE
of V2 ocean AOD is 66 %, similar to the theoretical value. Thus, the
obtained PEE can represent the error of GOCI AOD better than DEE.
Summary and outlook
Aerosol retrieval using GOCI is unique because of hourly monitoring of
aerosols with multi-channel measurements in the visible to near-infrared
range with high spatial resolution, over East Asia where aerosol emissions
are very high, despite its limitation in observation area coverage. Hourly
GOCI AOD retrievals with high accuracy, NRT availability, and quantitatively
analyzed uncertainties are highly suitable for use with air-quality
monitoring and data assimilation in air-quality forecasting models,
particularly when rapid diurnal variations and transboundary transport are
significant.
The objective of this study is the development of an improved GOCI YAER
algorithm (V2) for NRT processing with higher accuracy. Cloud-masking
procedures were revised to prevent false masking of low-AOD pixels over
bright surfaces that was present in the previous version by adopting recent
MODIS and VIIRS cloud-masking methodology and improving existing V1
methodologies. To reduce the remaining cloud and aerosol contamination
effects in the surface reflectance database, the period of RCR samples is
expanded from a 1-year to a 5-year period, to increase the probability of
finding cloudless low-AOD cases that improve the accuracy of the
climatological surface reflectance database. In addition, the surface wind
speed data are constructed as a climatological database for NRT retrieval
without importing numerical weather forecast products. The GOCI spectral
channel selection is revised to account for specific surface conditions:
dark ocean, turbid water, and land surface. In particular, the channels from
500 to 700 nm, which are significantly affected by ocean bio-optical
variations, are excluded from ocean AOD retrievals. As a result, the area of
successful AOD retrieval and masking in the GOCI YAER V2 algorithm and the
retrieved AOD values approach the results of MODIS and VIIRS AOD
qualitatively, compared to that of GOCI YAER V1.
To confirm the improvements to GOCI AOD accuracy in V2, the retrieved GOCI
AOD and MODIS AOD are compared with ground-based East Asia AERONET and China
SONET measurements of AOD for 5 years from 1 March 2011 to 29 February 2016.
The GOCI YAER land AOD shows a significant improvement from V1 to V2 with
reduced bias from about -0.07 to 0.01 and increased f within
EEMDT from 49 to 60 %. The comparison with
SONET AOD also shows improved results with reduced bias from about -0.10
to -0.02 and increased f within EEMDT from 42
to 51 %. The GOCI YAER ocean AOD also shows reduced bias from about 0.04
to 0.01 and increased f within EEMDT from 62 to
71 %. As a result, the quality of both the GOCI YAER V2 ocean and land AOD
is more comparable to that of the MODIS DT and DB AOD products over East
Asia.
Although retrieved GOCI YAER V2 AOD shows some bias with respect to AERONET
AOD and AE, scattering angle, NDVI, cloud fraction and homogeneity of
retrieved AOD, and observation time, month, and year, it never exceeds an
absolute value of ∼ 0.1 for most variables. Accounting for the
observed increase in error with AOD, the intrinsic expected error of GOCI
YAER V2 AOD was estimated using AERONET data. The linear DEE and PEE
(0.185τA+0.037 and
0.206τG+0.030, respectively)
for ocean AOD represent the actual error well over the entire AOD range. The
linear DEE of land AOD (0.137τA+0.073) also represents the actual error well.
However, the actual error does not increase linearly with GOCI land AOD;
thus, the linear PEE of land AOD (0.184τG+0.061) shows variations over the AOD. Instead, the
use of multiple PEE, which consists of PEE values for specific GOCI AOD
ranges, improves the representation of the actual error.
Despite the algorithm improvements presented in this study, there is still
potential for future improvements. The current version of the LUT was
calculated using a scalar radiative transfer calculation, which is less
accurate for calculating Rayleigh scattering for short visible wavelengths
(∼ 400 nm), and using a plane-parallel atmosphere
approximation that is less accurate at high solar/sensor zenith angle.
Vector radiative transfer calculations (i.e., consideration of polarization) and
spherical-shell atmosphere approximations can calculate Rayleigh scattering
at high accuracy and may improve the accuracy of the GOCI YAER algorithm.
Also, recent statistically optimized aerosol retrieval algorithms utilizing
the characteristics of spatial and temporal smoothness constraints for
aerosols result in improved accuracy by increasing the aerosol signal
(Dubovik et al., 2011; Xu et al., 2016). They also enable the
simultaneous retrieval of multiple geophysical variables, such as aerosol
and surface reflectance over land and aerosol and chlorophyll concentrations
over the ocean, which can reduce the remaining error due to the predefined
surface reflectance over ocean and land surfaces in the GOCI YAER algorithm.
The second-generation GOCI (GOCI-II), scheduled to launch in 2019, which has
higher spatial resolution (∼ 250 m), more channels, including
380 nm, and daily full-disk coverage, will further improve the accuracy of
AOP retrieval. Furthermore, GOCI-II will observe East Asia simultaneously
with the Geostationary Environmental Monitoring Spectrometer (GEMS) for
trace gases (i.e., ozone, nitrogen dioxide, formaldehyde, and sulfur dioxide)
and the AMI for meteorological parameters (i.e., cloud properties).
Therefore, multi-sensor synergies contributing to a comprehensive
understanding of aerosols and trace gases, cloud, and ocean colors are
expected.
Data availability
The GOCI L1B data are available on the home page of the Korean Institute of
Ocean Science and Technology (KIOST) Korea Ocean Satellite Center (KOSC, 2018;
http://kosc.kiost.ac.kr/), and the GOCI YAER V2 aerosol data will be
available at the same site. The GOCI YAER V2 aerosol data are also available
through personal communication with the authors of the present paper. The
AERONET data were obtained from https://aeronet.gsfc.nasa.gov (GSFC, 2018). The SONET
data were obtained from http://www.sonet.ac.cn (SONET, 2018). The MODIS DT and DB aerosol
data were obtained from https://ladsweb.modaps.eosdis.nasa.gov (LAADS DAAC, 2018). The VIIRS
EDR aerosol data were obtained from https://www.class.ncdc.noaa.gov (NOAA, 2018). The
libRadtran software package was obtained from http://www.libradtran.org (Mayer et al., 2017). The
ECMWF wind speed reanalysis data were obtained from
http://apps.ecmwf.int/datasets (ECMWF, 2018).
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was funded by the Korea Meteorological Administration Research and
Development Program under grant KMIPA 2015-5010. This research was also
supported by the “Development of the integrated data processing system for
GOCI-II” funded by the Ministry of Ocean and Fisheries, South Korea. All principal investigators and
their staff are thanked for establishing and maintaining the AERONET and
SONET sites used in this investigation. The MODIS Dark Target, Deep Blue,
and VIIRS aerosol teams are thanked for providing valuable data for this
research. Jae-Hyun Ahn (KIOST KOSC) is thanked for preparing aerosol data
distribution. Edward J. Hyer (US Naval Research Laboratory) and Andrew M. Sayer (USRA/GESTAR at NASA GSFC) are thanked for useful discussion of calculating uncertainties of
satellite AOD. The editor and two anonymous reviewers are thanked for
numerous useful comments, which improved the content and clarity of the
manuscript.
Edited by: Alexander Kokhanovsky
Reviewed by: three anonymous referees
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