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Atmos. Meas. Tech., 9, 13771398, 2016 www.atmos-meas-tech.net/9/1377/2016/ doi:10.5194/amt-9-1377-2016 Author(s) 2016. CC Attribution 3.0 License.

Myungje Choi1, Jhoon Kim1, Jaehwa Lee2,3, Mijin Kim1, Young-Je Park4, Ukkyo Jeong1, Woogyung Kim1, Hyunkee Hong5, Brent Holben3, Thomas F. Eck3,6, Chul H. Song7, Jae-Hyun Lim8, and Chang-Keun Song8

1Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of Korea

2Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA

3NASA Goddard Space Flight Center, Greenbelt, MD, USA

4Korea Ocean Satellite Center, Korea Institute of Ocean Science and Technology, Ansan, Republic of Korea

5Department of Spatial Information Engineering, Pukyong National University, Busan, Republic of Korea

6Universities Space Research Association, Columbia, MD, USA

7School of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea

8National Institute of Environmental Research (NIER), Incheon, Republic of Korea

Correspondence to: Jhoon Kim ([email protected])

Received: 22 July 2015 Published in Atmos. Meas. Tech. Discuss.: 15 September 2015 Revised: 16 January 2016 Accepted: 2 March 2016 Published: 1 April 2016

Abstract. The Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorological Satellite (COMS) is the rst 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, ne-mode fraction (FMF) at 550 nm, single-scattering albedo (SSA) at 440 nm, ngstrm 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 CoxMunk ocean bidirectional reectance distribution function (BRDF) model is used over ocean, and an improved minimum reectance 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 coefcient 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.

1 Introduction

Aerosols have an important role in the Earths climate system, inuencing 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 op-

Published by Copernicus Publications on behalf of the European Geosciences Union.

GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign

1378 M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation

tical 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 (SeaW-iFS), 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 Multi-function 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 xed 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 condence 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 rst 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 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 rst to use real GOCI data. The GOCI YAER products are validated with AERONET data from 38 sites during the DRAGON-NE Asia 2012 campaign. Inter-comparison 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.

2 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 owchart 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 owchart. The algorithm uses topof-atmosphere (TOA) reectance (TOA) as input data,

TOA ( ) =

L( )

[notdef]0 E0 ( )

, (1)

where is the wavelength of each GOCI channel (412, 443, 490, 555, 660, 680, 745, and 865 nm), L( ) is the observed radiance from GOCI, [notdef]0 is the cosine of the solar zenith angle ( 0), and E0 is the extraterrestrial solar ux.

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M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation 1379

Table 1. The specication of ocean color sensors.

Sensor CZCS SeaWiFS MERIS MODIS GOCI

Platform Nimbus-7 OrbView-2 Envisat Terra/Aqua COMS

Period 24 Oct 1978 1 Aug 1994

18 Dec 1999 current (Terra)4 May 2002current (Aqua)

26 June 2010 current

1 Aug 199711 Dec 2010

1 Mar 20028 Apr 2012

GEO

Orbit type LEO (sun-synchronous orbit)

LEO (sun-synchronous orbit)

12:00 descending node

10:00 descending node

Local equatorial crossing time (only for LEO), or longitude (only for GEO)

12:00 descending node

10:30 descending node (Terra)13:30 ascending node (Aqua)

128.2 E

Swath (only for LEO) 1600 km 2800 km 1150 km 2230 km

Coverage/cycle Near-global coverage every day

Global coverage everyday

Global coverage in 3 days

Global coverage nearly twice/day (long-wave channels) or once/day (short-wave channels)

Area of 2500 km

2500 km/hourly in daylight (8 times per day)

Spatial resolution 825 m 1100 m 300 m (Europe) 1200 m (global)

1000 m 500 m

No. of ocean color channels

6 8 15 (total 36 channels) 8

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)

Center wavelengths (and band width) of ocean color bands (nm)

443 (20) 520 (20) 550 (20) 670 (20) 750 (100) 1150 (1000)

3. standard deviation of 3 3 pixels TOA(550 nm)

> 0.0025 ! cloud over oceanTOA(412 nm)/TOA(660 nm) > 0.75 ! dust over

ocean (not masked).

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)

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2.1 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:

1. TOA(490 nm) > 0.40 ! cloud over land or ocean

2. standard deviation of 3 3 pixels TOA(412 nm)

> 0.0025 ! cloud over land

1380 M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation

GOCI L1B dataTOA spectral reflectance in 500 m 500 m resolution

Land Ocean

Land-Ocean mask

Cloud masking

Spatial variability test in 3 3 pixels (412 nm) Threshold test at 490 nm

Cloud masking

Spatial variability test in 3x3 pixels (550 nm) Threshold test at 490 nm Dust test from the ratio of 412 and 660 nm

Turbid water detection test

Clear water

Turbid water

Surface reflectance database

The minimum reflectivity technique Rayleigh- and gas-corrected reflectance (RCR) Collecting RCRs of pixels for each month, each hour, and each channel within 6 km 6 km. Select darkest 1 %3 % pixels of RCRs at 412 nm as surface. Interpolation from each month data set according to data

Surface reflectance

Fresnel reflectance according to wind speed and geometry (Cox and Munk) ECMWF reanalysis wind speed data at 10 meters above sea level

LUT

Aerosol models from AERONET level 2.0 (26 types)

Created by quantized square bins over the FMF and SSA domainsAERONET sites in the global area

Inversion

Spectral matching of AOD at 550 nm

Using channels of which surface reflectance is less then 0.15 (land) Using whole eight channels (ocean)

Best three aerosol models are selected for final products.

Final products

AOD (550 nm)

FMF (550 nm)

SSA (440 nm) ngstrm exponent (440870 nm) Aerosol typeat 6 km x 6 km resolution

also has statistical signicance in low AOD range (Levy et al., 2007, 2013).

2.2 Surface reectance over land and ocean

The lack of a 2.1 m channel in GOCI limits the capability of estimating surface reectance in the visible from the 2.1 m TOA reectance as in the MODIS DT algorithm (Levy et al., 2007; Kaufman et al., 1997b). Instead, the GOCI YAER algorithm uses the minimum reectivity technique to determine the surface reectance (SFC) over land and turbid water (Herman and Celarier, 1997; Hsu et al., 2004; Koelemeijer et al., 2003). First, each scenes TOA reectance is corrected for Rayleigh scattering to derive the Rayleigh-corrected reectance (RCR) (Hsu et al., 2013). It is assumed

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Figure 1. Flowchart for GOCI YAER algorithm.

are aggregated to be fed into the retrieval process. In this step, the darkest 20 % and the brightest 40 % of pixels in reference to TOA(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) ag 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 ran

domly retrieved, small negative AOD caused by uncertainty in surface reectance because it is within the expected retrieval error with reference to the MODIS DT algorithm, and

M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation 1381

Table 2. Conditions for determining pixel QA values from 0 to 3.

QA Number of pixels (N) selected Range of retrieved AOD at 550 nm from possible 12 12 pixels

0 6 N 14 0.10 AOD < 0.05, or 3.6 < AOD 5.0

1 15 N 21 0.05 AOD 3.6

2 22 N 35 0.05 AOD 3.6

3 36 N 58 (maximum) 0.05 AOD 3.6

that in a 30-day period, changes in surface reectance are insignicant and there is at least 1 clear day (Lee et al., 2010b).To increase the number of samples to nd clear pixels, it is also assumed that the surface reectance is homogeneous over 12 12 pixels; therefore L1B resolution data are used

for determining the minimum reectance. Thus, the spatial resolution of surface reectance is the same as the aerosol retrieval resolution of 6 km 6 km. To allow for changes of

surface reectance with sunsatellite geometry, RCRs at a given hour during the day are composited for each month.The maximum number of samples available to determine surface reectance at a pixel is 144 pixels 30 days, a to

tal 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 reectance is lower and atmospheric signals such as Rayleigh scattering or aerosol reectance are higher than at longer wavelengths.Thus, the RCR at 412 nm is used to nd clear pixels during the 30-day window. According to Hsu et al. (2004), a surface reectance database can be obtained by nding 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 reectance should not be selected to evaluate surface reectance. For example, Lee et al. (2010b) selected the second minimum value, and Fukuda et al. (2013) used the modied minimum reectance method using rst and second minimum values to avoid cloud shadow effects for determining surface reectance. In the GOCI YAER algorithm, the maximum number of L1B pixel samples for one surface reectance pixel at a given time is 144 pixels 30 days, a total of 4320 samples.

Therefore, using only the rst or second minimum threshold is not appropriate for the GOCI YAER algorithm. Instead, darkest 01 % 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 reectance corresponding to the middle of each month (day 15). Finally, linear interpolation according to retrieval date is applied.

Figure 2 shows examples of surface reectance at 443 and 660 nm; the difference in the surface reectance between ocean and land is smaller at 443 nm than 660 nm. The high 660 nm surface reectance near the coast of China in the Bo-

hai Sea and in the northern East China Sea shows turbid water with values comparable to the land surface reectance 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 reectances 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 reectance 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 reectance to remove bright surface reectance pixels. Final selected channels for retrieving aerosol over land are those of which surface reectances are less than 0.15.

On the other hand, it is assumed that ocean surface reectance 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 s1, 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 pixels wind speed to retrieve the AOD over the ocean.

2.3 Turbid water detection

Retrieving aerosol properties over turbid water is challenging due to the variability of the turbid water and high surface reectance. 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 reectance can be underestimated, and thus AOD can be overestimated. The previous GOCI YAER algorithm (Lee et al., 2010b) used the surface reectance ratio for turbid water detection, which is the ratio of surface reectance at 640 and 860 nm. If turbid water pixels are detected, the surface reectance 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

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1382 M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation

Figure 2. Surface reectance 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).

of turbidity. In this study, real-time turbid water detection is applied.

According to Li et al. (2003), TOA at 550, 660, and 865 nm showed higher values over turbid water than over clear water. They used the difference between TOA at 550 nm and the value interpolated to 550 nm from TOA at 470, 1240, 1640, and 2130 nm using a linear t on a loglog scale. In this study, because GOCI does not have infrared (IR) channels, [Delta1]660 is dened as the difference in reectance at 660 nm between the observed TOA at 660 nm and linearly interpolated between TOA at 412 and 865 nm to 660 nm. Increased TOA due to turbid water is stronger at 660 nm than at

412 and 865 nm; therefore [Delta1]660 shows a higher value over turbid water than over clear water.

To determine the threshold of [Delta1]660 for distinguishing turbid and clear water over the ocean, hourly data for the rst and fteenth 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 (115126 E, 30

40 N) and an area of clear water (130140 E, 2530 N), as in Lee et al. (2010b). A strict threshold for dening pixels as clear water is necessary to prevent misdetection of less turbid water as aerosol. Figure 3 shows the cumulative normal distribution of [Delta1]660, where ratios below 0.05 are 99.0 %

and 67.4 % for clear water and Yellow Sea pixels, respec-

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M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation 1383

7104

6104

5104

4104

3104

2104

1104

-0.15 -0.10 -0.05 -0.00 0.05

660

Figure 3. Frequency and cumulative normal frequency of [Delta1]660 over the Yellow Sea and over clear water.

tively. Finally, pixels with [Delta1]660 below 0.05 are not con

sidered as turbid water; consequently, the ocean algorithm is applied. On the contrary, pixels where [Delta1]660 is above 0.05

are considered as turbid water; therefore the land algorithm is applied. Note that the surface reectance of turbid water pixels is adjusted to the minimum turbidity during the 30 days; therefore surface reectance 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 [Delta1]660 is above 0.02 are considered as severely turbid water, and excluded from the retrieval procedure.

To conrm whether [Delta1]660 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 reectance at 412 and 443 nm (Hsu et al., 2004, 2006; Ciren and Kondragunta, 2014). Note that DAI and [Delta1]660 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. [Delta1]660 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 wa

ter ( 1.6). Although heavy aerosol plumes can have [Delta1]660

above 0.05 over clear water, this does not cause a signi-

cant issue because the land algorithm is applied instead, not affecting spatial coverage.

An additional role of [Delta1]660 is to detect the remaining cloud-contaminated pixels after cloud masking. There are in-

1.0

Yellow sea Clear water

0.8

Frequency

Cumulative normal frequency

0.6

0.4

0.2

0.0

-0.15 -0.10 -0.05 -0.00 0.05

660

homogeneous 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 [Delta1]660 above 0.05 (red color). This is a similar to the visible reectance anomaly of the VIIRS aerosol algorithm (Jackson et al., 2013). Because pixels with [Delta1]660 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 [Delta1]660 above 0.02 after cloud masking over total available ocean pixels is about 2 % during the campaign.

2.4 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 reect those properties well from the perspective of optical properties for aerosol retrieval. Assumed aerosol models play an important role in the retrieval accuracy. To reect global climatological properties, AERONET inversion data (Dubovik and King, 2000) are used for creating aerosol models to be used in the retrieval process. A classication method for AERONET inversion data using ne-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 GOCIs rst 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 ob-

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1384 M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation

Figure 4. [Delta1]660 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.

servation 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 classied 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 0.00.5, 0.50.8, and0.83.6 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.

2.5 LUT calculation and inversion procedure

Table 4 shows the node points for calculating TOA reectances using a discrete ordinate radiative transfer (DISORT) code of the libRadtran software package (http://libradtran.org

Web End =http://

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M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation 1385

http://libradtran.org

Web End =(a) (b)

http://libradtran.org

Web End = http://libradtran.org

Web End = Figure 5. 25 March 2012, 13:30 LST (a) true color image and (b) [Delta1]660.

Table 3. 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.

http://libradtran.org

Web End =FMF (550 nm)

http://libradtran.org

Web End =0.10.2 0.20.3 0.30.4 0.40.5 0.50.6 0.60.7 0.70.8 0.80.9 0.91.0

http://libradtran.org

Web End =0.850.90

http://libradtran.org

Web End =H1 H2 H3 H4 H5 H6 H7 H8 H9 3298 4309 1960 1360 1151 1256 2145 3420 19330.0940.184 0.3360.366 0.5630.632 0.6740.855 0.8321.065 1.1401.239 1.2301.430 1.3051.569 1.5701.6170.1560.173 0.2430.247 0.3390.345 0.4470.448 0.5410.553 0.6470.652 0.7560.758 0.8520.857 0.9280.9340.8830.886 0.8800.881 0.8710.881 0.8740.877 0.8760.879 0.8770.882 0.8760.879 0.8800.881 0.8800.884

http://libradtran.org

Web End =0.900.95

http://libradtran.org

Web End =M1 M2 M3 M4 M5 M6 M7 M8 M9 5699 6111 2396 1606 1185 1431 2344 5520 66410.1320.182 0.2780.366 0.4210.638 0.4080.868 0.7651.070 1.0821.270 1.2031.452 1.2761.623 1.5631.6480.1650.174 0.2270.246 0.3400.350 0.4450.447 0.5480.552 0.6490.652 0.7540.755 0.8560.863 0.9340.9460.9180.920 0.9200.921 0.9210.922 0.9220.922 0.9170.923 0.9150.923 0.9190.926 0.9200.927 0.9270.930

http://libradtran.org

Web End =0.95-1.00

http://libradtran.org

Web End =N1 N2 N3 N4 N5 N6 N7 N8 558 366 289 279 382 845 2643 75850.2760.380 0.4640.645 0.4520.877 0.7111.065 1.0321.275 1.1911.464 1.2581.652 1.4261.7440.2300.248 0.3440.350 0.4410.448 0.5460.555 0.6540.658 0.7560.759 0.8600.869 0.9410.9560.9580.965 0.9610.965 0.9590.967 0.9570.965 0.9610.967 0.9590.968 0.9620.969 0.9670.970

http://libradtran.org

Web End =libradtran.org ) (Mayer and Kylling, 2005). The input options of this radiative transfer model (RTM) to calculate TOA 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 models ngstrm 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 nal 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 reectance (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 reectances 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 pre-calculated TOA reectance at LUT dimensions to observed TOA reectance according to geometries (solar zenith angle, satellite zenith angle, and relative azimuth angle), assumed aerosol model, wavelength, surface reectance, and terrain height. Then, three aerosol models are selected that minimized the standard deviation () of the different AODs retrieved from each wavelength, dened 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 -weighted average value from three selected models

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1386 M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation

Table 4. LUT dimensions.

Variable Number Entries of entries

Wavelength 8 412, 443, 490, 555, 660, 680, 765, 870 nm (considering spectral response function)

Solar zenith angle 8 0, 10,..., 70 (10 interval)

Satellite zenith angle 8 0, 10,..., 70 (10 interval)

Relative azimuth angle 19 0, 10,..., 180 (10 interval)

AOD 9 0.0, 0.1, 0.3, 0.6, 1.0, 1.5, 2.1, 2.8, 3.6 at 550 nm Aerosol model 26 In Table 2Surface reectance(only for land LUT)

4 0.0, 0.1, 0.2

Terrain height (only for land LUT)

2 0 km, 5 km

6 1, 3, 5, 7, 9, and 20 m s1

Table 5. Output aerosol types for GOCI YAER according to FMF and SSA.

No. Aerosol type FMF (550 nm) SSA (440 nm)

1 Dust 0.0 FMF < 0.4 SSA 0.95

2 Non-absorbing coarse type 0.0 FMF < 0.4 0.95 < SSA < 1.00

3 Mixture 0.4 FMF < 0.6

4 Highly absorbing ne type 0.6 FMF < 1.0 SSA < 0.90

5 Moderately absorbing ne type 0.6 FMF < 1.0 0.90 SSA < 0.95

6 Non-absorbing ne type 0.6 FMF < 1.0 SSA 1.00

as shown in the following equations:

Final AOD at 550 nm=

Wind speed(only for ocean LUT)

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. 6bf. 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 ne-mode dominant aerosol. The retrieved SSAs at those pixels are in the range 0.9550.975, corresponding to non-absorbing aerosol.The detected aerosol type of the haze is therefore classied as non-absorbing ne 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 reectance, higher spatial variability, and higher uncertainty of land surface reectance 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) pix

els.

Another case is a severe dust case on 27 April 2012 as shown in Fig. 7. Heavy yellow dust plumes are evident in the

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Xi=1CModel i Averaged AODModel i

1 Model i

1 Model 1 +

CModel i =

1 Model 2 +

1 Model3

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 classies a total of six aerosol types using the retrieved nal FMF and SSA (Table 5).

3 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 ne-dominated, non-absorbing and ne-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

M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation 1387

Figure 6. 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 ne type), purple (moderately absorbing ne type), and red (highly absorbing ne type).

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 classied as dust, shown in yellow in Fig. 7f.

4 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 inten-

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1388 M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation

Figure 7. As Fig. 6 except for 27 April 2012.

sively 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-theart global aerosol properties, and its aerosol retrieval algo-

rithms 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, intercom-parison using the satellite-based MODIS data set is also performed for evaluating the GOCI product.

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M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation 1389

(a)

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N=9602y= 0.009+ 1.078x R= 0.849RMSE= 0.208 MAE = 0.133 MBE = 0.043% within EE =55.2

2.5

N=8694 y=-0.042+ 1.083x R= 0.881RMSE= 0.178 MAE = 0.121 MBE = -0.005% within EE =57.3

GOCI_AOD_550 (all QA)

GOCI_AOD_550 (QA=3)

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N=712y= 0.032+ 1.145x R= 0.906RMSE= 0.188 MAE = 0.139 MBE = 0.098% within EE =54.2

3.0

Frequency

3.0

Frequency

2.5

N=691y= 0.046+ 0.924x R= 0.876RMSE= 0.127 MAE = 0.089 MBE = 0.015% within EE =71.5

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MODIS_DB_AOD_550 (QA=3)

2.0

1.5

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Figure 8. 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.

4.1 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 Level2.0 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;ONeill et al., 2003; Dubovik and King, 2000). From the direct sun measurement, AOD and ngstrm 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 xed location, while GOCI ob-serves 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 t on a loglog 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.

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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 reectance over urban in the MODIS DT operational retrievals. Otherwise, the enhanced MODIS DB algorithm (Collection 6) shows the best result, which controls surface reectance 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 %).

Results of intercomparison of AOD between GOCI and MODIS are shown in Fig. 9. Note that ocean pixels near most coastal sites are classied 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 reectance. 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 visiblenear IR channels only. It is more difcult to distinguish the cloud signal clearly over land using only visible characteristics because of bright surface reectance, 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 lin

ear 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 reectance 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 reectance 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

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1390 M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation

4.2 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 xed grid scale over the GOCI observation area, divided into 0.2 0.2 latitudelongitude resolution grid

cells. Therefore, MODIS and GOCI data within the same xed 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.

4.3 Validation of AOD

The validation involves use of the linear regression equation, and validation metrics including the Pearsons linear correlation coefcient (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 coefcient 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

M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation 1391

(a)

(b)

(c)

2.5

2.0

1.5

1.0

0.5

0.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0 MODIS_DT_AOD_550_Land (QA=3)

Figure 9. 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.

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 reectance 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 reectance distribution function (BRDF). Aerosol model constructions of three algorithms are similar as the model considers ne/coarse and absorbing/non-absorbing characteristics. However, the MODIS DB uses reectance at 412 nm for retrieval, similar to GOCI, while MODIS DT does not. Inversion procedures of three algorithms are not signicantly 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 reectance. Hence, the tendency and accuracy of retrieved AOD from GOCI are closer to MODIS DB than DT.

4.4 Validation of ngstrm exponent, ne-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 0.09301.744, 0.1560.956, and 0.8710.970, respectively.

Figure 10a and b show the comparison of AE between

AERONET and GOCI. The correlation coefcient is 0.594 in Fig. 10a, which is signicantly 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 coefcient 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 sufcient 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 reectance with wind speed dependence for the surface reectance, and the surface reectances is similar between GOCI and MODIS DT over, and the surface reectance 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 coefcient is much lower at 0.376.

FMF is provided directly from SDA AERONET, or calculated using the almucantar retrievals of ne 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 coefcients 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 0.91.0, but those of GOCI are grouped at 0.8. GOCI FMF is underestimated compared

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N=62951y= 0.041+ 0.681x R= 0.827 RMSE= 0.284 MAE = 0.217 MBE = -0.128

N=92087y= 0.016+ 0.934x R= 0.866 RMSE= 0.192 MAE = 0.136 MBE = -0.009

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1392 M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation

(a) (b) (c)

N=8510y= 0.100+ 0.601x R= 0.594 RMSE= 0.507 MAE = 0.415 MBE = -0.381

N=5210y= 0.040+ 0.708x R= 0.678 RMSE= 0.439 MAE = 0.356 MBE = -0.316

N=57257y= 0.699+ 0.420x R= 0.376 RMSE= 0.357 MAE = 0.283 MBE = 0.064

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0.0 0.5 1.0 1.5 2.0 MODIS_DT_AE_550_860_Ocean

Figure 10. 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 ngstrm 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.

(a) (b) (c)

0.0 0.2 0.4 0.6 0.8 1.0 MODIS_DT_FMF_550_Ocean

1.0

GOCI_FMF_550 (AERONET_AOD > 0.3)

>20

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N=5491 y=-0.012+ 0.729x R= 0.698RMSE= 0.264 MAE = 0.223 MBE = -0.212

N=647 y=-0.032+ 0.730x R= 0.750RMSE= 0.249 MAE = 0.211 MBE = -0.208

N=57257y= 0.251+ 0.572x R= 0.417 RMSE= 0.182 MAE = 0.141 MBE = -0.055

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0.0 0.2 0.4 0.6 0.8 1.0 SDA_AERONET_FMF_550

0.0 0.2 0.4 0.6 0.8 1.0 Inversion_AERONET_FMF_550

Figure 11. 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.

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 coefcient 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 coefcient between AERONET and OMI SSA is 0.406. They also showed that 44.91 and 70.29 % of OMI SSA data are within differ-

ences 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 overesti

mated 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 signicant 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 classied 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.

GOCI AE and SSA product qualities could also be compared with other previous studies while the region and period are different. Global MODIS DT ngstrm exponent validation results with AERONET were presented in Levy et al. (2010) and Levy et al. (2013) over land and ocean,

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M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation 1393

1.00

>10

Frequency

racy than AOD. Nevertheless, these values can be useful for qualitative studies, although not for quantitative studies.

5 Error analysis of GOCI YAER AOD

Uncertainties in surface reectance, 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 reectance and assumptions about aerosol micro-physical properties, respectively (Sayer et al., 2013). Levy et al. (2010) also described that systematic bias for low AOD results from overestimating the surface reectance in the visible channels. Therefore, the minimum reectivity technique can overestimate surface reectance 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 insignicant 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 difcult 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 xed. 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 difcult 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 uctuating 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 uctuation tendency of error as underestimation at scattering angles

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0.95

GOCI_SSA_440

0.90

0.85

N=635y= 0.783+ 0.173x R= 0.353 RMSE= 0.036 MAE = 0.028 MBE = 0.017 Q_0.03=69.0 % Q_0.05=86.9 %

0.80

0.80 0.85 0.90 0.95 1.00 Inversion_AERONET_SSA_440

Figure 12. 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.

respectively. Levy et al. (2010) compared the MODIS DT Collection 5 ngstrm 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 ngstrm 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 ngstrm exponent (over land) shows similar accuracy with GOCI YAER ngstrm 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 ngstrm exponent and single-scattering albedo retrieved from the Polarization and Directionality of Earths Reectance (POLDER) instrument on-board the Polarization and Anisotropy of Reectances 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 accu-

1394 M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation

(a) (b)

(e) (f)

ned as (TOA(865 nm) TOA(660 nm)) / (TOA(865 nm) + TOA(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 (00.05) 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 limi

tation in minimum reectivity 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 signicantly 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

Atmos. Meas. Tech., 9, 13771398, 2016 www.atmos-meas-tech.net/9/1377/2016/

Figure 13. 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.

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 uctuant 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 xed 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 reectance using the minimum reectivity technique.

The method for determining surface reectance 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, de-

M. Choi et al.: GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation 1395

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.

6 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 us

ing 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 reectance assumptions and channels are applied for the land and ocean. Turbid water is detected according to [Delta1]660, and the land algorithm is applied to it for better performance. In addition, nonsphericity and dynamical properties of aerosol are reected 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 coefcient 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 ex

pected error for GOCI is 57.3 %, which is worse than MODIS DB (71.5 %) but similar to MODIS DT (54.2 %). In the inter-comparison 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 agree

ment 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 = 0.5940.750) and

tends to underestimate (MBE = 0.381 to 0.208). Over

ocean, the comparison of size parameters between GOCI and MODIS DT shows signicantly poorer agreement (R =

0.3760.417), 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 (0.900.95) 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 reectance over vegetated areas for the next version, and possibly account for the elevation of forested mountains relative to the aerosol vertical prole.

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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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.

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Title

GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign

Author

Eck, Thomas F; Song, Chul H

Pages

1377-1398

Publication year

2016

Publication date

2016

Publisher

Copernicus GmbH

ISSN

18671381

e-ISSN

18678548

Source type

Scholarly Journal

Language of publication

English

DOI

https://doi.org/10.5194/amt-9-1377-2016

ProQuest document ID

1788551425

GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign

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