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Evaluation of the MISR fine resolution aerosol product using MODIS, MISR, and ground observations over China
Journal Pre-proof Evaluation of the MISR fine resolution aerosol product using MODIS, MISR, and ground observations over China Yidan Si, Liangfu Chen, Xiaozhen Xiong, Shuaiyi Shi, Letu Husi, Kun Cai PII:
S1352-2310(19)30867-2
DOI:
https://doi.org/10.1016/j.atmosenv.2019.117229
Reference:
AEA 117229
To appear in:
Atmospheric Environment
Received Date: 8 April 2019 Revised Date:
3 December 2019
Accepted Date: 15 December 2019
Please cite this article as: Si, Y., Chen, L., Xiong, X., Shi, S., Husi, L., Cai, K., Evaluation of the MISR fine resolution aerosol product using MODIS, MISR, and ground observations over China, Atmospheric Environment (2020), doi: https://doi.org/10.1016/j.atmosenv.2019.117229. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
1
Evaluation of the MISR fine resolution aerosol product
2
using MODIS, MISR, and ground observations over China
3
Yidan Si1, Liangfu Chen2, Xiaozhen Xiong3, Shuaiyi Shi2, Letu Husi2, Kun Cai4,5* 1
4
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites,
5
National Satellite Meteorological Center, China Meteorological Administration, Beijing 10081,
6
China 2
7
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital
8
Earth, Chinese Academy of Sciences, Beijing 100101, China 3
9 4
10
National Oceanic and Atmospheric Administration, MD 20746, USA
Spatial information processing engineering laboratory of Henan province, Kaifeng 475004,
11 12
China 5
School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
13 14 15
Yidan Si was the first author who designed the experiment, downloaded the data, processed
16
data, wrote the paper.
17
Liangfu Chen and Xiaozhen Xiong helped me re-construct the paper and pointed out
18
unreasonable sentences.
19
Shuaiyi Shi helped me revise full text and gave some useful suggestions about validation
20
part.
21
Letu Husi helped me realized MODIS 3km AOD retrieval algorithm, and pointed out the
22
difference between MISR 4.4km and MODIS 3km AOD.
23
Kun Cai helped me re-construct the organizations, polish the sentences and provided
24
funding supporting.
1
Evaluation of the MISR fine resolution aerosol product
2
using MODIS, MISR, and ground observations over China
3
Yidan Si1, Liangfu Chen2, Xiaozhen Xiong3, Shuaiyi Shi2, Letu Husi2, Kun Cai4,5* 1
4
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites,
5
National Satellite Meteorological Center, China Meteorological Administration, Beijing 10081,
6
China 2
7
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital
8
Earth, Chinese Academy of Sciences, Beijing 100101, China 3
9 10
4
11 12
National Oceanic and Atmospheric Administration, MD 20746, USA
Spatial Information Processing Engineering Laboratory of Henan Province, Kaifeng 475004, China
5
School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
13
Abstract: Recently, NASA’s Multiangle Imaging SpectroRadiometer (MISR) team released the
14
Version 23 (V23) 4.4-km Aerosol Optical Depth (AOD) product, which has better spatial
15
resolution than V22 at 17.6 km. However, its quality has not been validated in China. Here, V23
16
products for different spatiotemporal domains are obtained for validation against Aerosol
17
Robotic NETwork (AERONET) AOD measurements for 29 sites from 2008–2017. Based on the
18
national daily mean, V23 AOD yields a correlation coefficient (R) of 0.902 with AERONET;
19
59.45% of retrievals fall within the expected error (=EE; ±0.05 or ± 0.2 × AOD). A mean error
20
(ME) of −0.0605 with 24.11% of retrievals falling below the EE indicates that MISR data are
21
still underestimated at high AODs. The sample numbers and accuracies of spatially averaged
22
17.6-km and 50-km data are greatly improved relative to V22. The seasonal mean of V23
23
retrievals =EE in fall and winter are highest, followed by those in summer and spring; the
24
validation results at 17.6 km are generally better than those at 50 km. By region, V23 retrievals
25
=EE in the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta
26
(PRD) regions are 68.87%, 61.43%, and 73.13%, respectively, while that of Taiwan is below 60%
27
(55.75%). No V22 records exist in PRD, and V23 products over other regions in all seasons
28
perform well; V22 retrievals in summer are also recommended. Compared with Terra/MODIS
29
3-km AOD in 2016, the V23 product has a slightly higher R value (0.925) with AERONET than
30
MODIS (0.909). The MISR AOD bias is lower, and MODIS AOD is overestimated relative to
31
the
32
(spring>winter>summer>autumn), with the maximum in March and minimum in August. To
33
investigate the spatiotemporal characteristics over long-term AOD, MISR V23 4.4-km AOD can
34
be used in combination with other observation data.
35
Keywords: MISR; V23 4.4 km; AOD; V22; AERONET; MODIS 3 km
36
1 Introduction
ground
truth;
both
present
consistent
seasonality
characteristics
37
Atmospheric aerosol is a general term for solid and liquid particles, with diameters ranging
38
from 0.001~100 µm, suspended in the atmosphere, which plays an important role in terms of
39
their interaction with the surrounding environment; for example, particle scattering directly
40
affects clouds and radiative forcing (Che et al. 2018; Christensen et al. 2017; Garrett et al. 2009;
41
Zhao et al. 2018) and even indirectly influences climate change (Deetz et al. 2018; Glassmeier et
42
al. 2017; Zhao et al. 2012). Additionally, impaired atmospheric visibility and aggregated frequent
43
haze days (Li et al. 2013a; Yang et al. 2016a; Yang et al. 2016b; Zou et al. 2018) have been
44
proven to be major environmental risk factors fundamentally related to human health (Cao et al.
45
2012; Krall et al. 2013). The aerosol optical depth (AOD) is usually used as a quantitative
46
measure of the amount of aerosol particles in the atmospheric column. Some studies have
47
demonstrated that long-term AOD variations over large areas can be obtained from advanced
48
remote sensing technology (Guo et al. 2011; Kumar et al. 2018; Sogacheva et al. 2018; Xiao et al.
49
2016; Yang et al. 2019).
50
The Multiangle Imaging SpectroRadiometer (MISR), at an altitude of 705 km with a swath
51
width of 380 km, is aboard the polar-orbiting NASA EOS Terra platform, which passes over at
52
approximately
53
(±70.5° , ±60.0° , ±45.6° , ±26.1° ,
54
spectral bands (446 nm, 558 nm, 672 nm and 866 nm) and is capable of providing globally
55
continuous retrievals of AOD on the daylit portion of the Earth as well as information on aerosol
56
components and microphysical properties. The limited swath width prolongs its revisit period of
57
2~9 days depending on the latitude of the region. The cross-track IFOV and spacing between
58
centers of each pixel is 275 m for all of the off-nadir cameras and 250 m for the nadir camera.
59
The sample spacing in the downtrack direction is 275 m in all cameras. As the retrieval
60
algorithms were gradually improved, the AOD datasets in different algorithm versions were
61
validated by the global federated observation network (Aerosol RObotic NETwork, AERONET)
62
of ground-based sun photometers, for example, Version 12 (Liu et al. 2004) and Version 15
10:30
local
time.
MISR
has
nine
viewing
angles
) along the direction of the satellite motion in four
63
(Jiang et al. 2007). Since December 2007, the MISR Version 22 (V22) operational aerosol
64
retrieval algorithm has been in production, which has added additional absorbing spherical
65
particles and a selection of multimodal aerosol mixtures to the climatology to improve the
66
retrieval accuracy (Kahn et al. 2010). Many researchers have also examined the quality of the
67
MISR V22 dataset. For example, using the 81 global AERONET stations, Kahn et al. (2010)
68
reported that approximately 70% to 75% of MISR AOD retrievals fall within the greater of 0.05
69
or 20% × AOD of the paired validation data over the regions where the aerosol models are
70
assumed to be urban, continental, biomass burning and maritime, but only 47.56% of retrievals
71
fall within the error ranges over the smoke-dust hybrid dominated regions. Cheng et al. (2012)
72
found approximately 66.82% of the MISR-retrieved AODs falling within ±(0.05 ± 0.15 ×
73
AOD) over four sites located in China during 2001-2011 and found a high correlation of
74
approximately 0.88 for the MISR vs. AERONET data. A correlation coefficient of 0.92, slope of
75
0.64 and intercept of 0.05 for the monthly mean AOD were obtained by Li et al. (2016), who
76
validated the MISR AOD using AERONET observations in China from 2006 to 2009.
77
Meanwhile, many studies have demonstrated that high-accuracy MISR AOD observations can be
78
used to analyze the spatial-temporal distributions of aerosol loadings over some regions and even
79
globally (Kahn et al. 2001; Kahn and Gaitley 2015; Zhao et al. 2017), to establish
80
inter-comparisons of multiple satellite sensors for a better dataset (Kahn et al. 2007; Kahn et al.
81
2009; Kumar et al. 2015). The advantage of having aerosol component information and
82
microphysical properties enables MISR AOD observations to be successfully used to estimate
83
near-surface total PM2.5 in both the eastern and western US (Liu 2013; Liu et al. 2005; Zheng et
84
al. 2017), as well as the sulfate component concentration of PM2.5 (Liu et al. 2007; Liu et al.
85
2011). Many studies have also improved the accuracy of PM2.5 estimations by combining MISR
86
AOD products with other AOD products (Geng et al. 2015). In addition, studies on the
87
inter-comparison of MISR aerosol component datasets (Li et al. 2013b; Li et al. 2016) and
88
microphysical properties such as Angstrom exponent and single scattering albedo (Li et al. 2015;
89
Si et al. 2017) have been gradually emerging.
90
To the best of our knowledge, the MODIS team released a global AOD product at 3-km
91
resolution as part of the Collection 6 (C6) delivery (Remer et al. 2013). By contrast, MISR V22
92
gridded at 17.6 km is too coarse for urban-based studies of air pollution (Gupta et al. 2013).
93
Recently, a higher-resolution MISR AOD product, Version 23 (V23) with a spatial resolution of
94
4.4 km, has been made available by the MISR aerosol team. The initial results showed that V23
95
had an improved correlation (R = 0.957), a smaller root mean square error (RMSE) (0.0768), a
96
reduced bias (−0.0208) and a larger fraction within the greater of 0.05 or 20% × AOD (80.92%)
97
relative to the V22 retrievals in comparisons made between the MISR AOD products and
98
multiple
99
(AERONET-DRAGON) AODs (Garay et al. 2017). Subsequently, Franklin et al. (2017) used
100
total and size-fractionated AOD from the MISR 4.4-km data to generate prediction maps of
101
PM2.5 and PM10 over Southern California. Reconsidering MISR mixtures and fractional AODs in
102
modeling, Meng et al. (2018) developed statistical models with 4.4-km aerosol microphysical
103
properties to predict ground-level concentrations of major PM2.5 chemical components, including
104
sulfate, nitrate, OC and EC, in Southern California. Franklin et al. (2018) validated the MISR
AERONET
Distributed
Region
Aerosol
Gridded
Observations
Network
105
V23 AOD retrievals against the AOD retrievals interpolated to 550 nm from the Dalanzadgad
106
AERONET site and found good agreement, with R = 0.845 and RMSE = 0.0712. To date, though,
107
there have been no studies on the performance of the MISR 4.4 km AOD dataset in China.
108
The objective of this study is to provide the first evaluation of the newly released MISR 4.4
109
km aerosol retrievals for China. This paper is organized as follows. In Section 2, the whole study
110
domain and the AERONET observations are described in detail, as well as the operational MISR
111
products. In Section 3, the satellite daily mean 4.4 km retrievals and the old 17.6 km products are
112
compared with the AERONET measurements by different spatial average strategies and by
113
season. Additionally, the MODIS 3 km AOD products are introduced to further investigate the
114
performance of MISR high-resolution products. Finally, we discuss the uncertainties of the
115
MISR AOD retrievals and provide a summary of the findings.
116
2 Data and Methods
117
2.1 Study region
118
China, with an area of 9,600,000 km2, lies between latitudes 18° and 54°N and longitudes
119
73° and 135°E. The landscapes in China vary significantly across its vast width, as shown in
120
Figure 1. The northwest region is mainly composed of barren land and desert; desert/rock/sand
121
composes more than 90% of the Tibetan Plateau region. The North China Plain (NCP) and
122
northeast China are covered with forest and cultivated crops on dry land, and southern China is
123
covered with grassland and cultivated crops on both dry land and wet land. Based on the
124
different terrains, China is characterized by higher elevations in the western regions and lower
125
elevations in the eastern regions, as described by Ma et al. (2014). For example, the average
126
elevation of the Tibetan Plateau is nearly 5 km, while that of most of eastern China is 0.5-1 km.
127
The plains, basins, plateau, hills and mountains contribute to the highly complex topographies
128
across China; thus, the climate differs from region to region.
129 130
Figure 1 Locations of AERONET sites (black solid points) and AERONET-DRAGON sites (red
131
triangles) in China. The land classification data are provided by the Multiresolution Land
132
Characteristics Consortium (http://www.mrlc.gov/nlcd06_data.php).
133
Currently, Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD) and the Pearl
134
River Delta (PRD), three typical regions with high aerosol loadings in China, are the most
135
populated and polluted urbanized areas and have attracted much attention for monitoring and
136
controlling atmospheric pollution. Therefore, the three regions of BTH, YRD and PRD are
137
selected for the validation of the MISR and MODIS aerosol products at regional scales and will
138
be discussed in the coming sections. Taiwan has a dense network of AERONET sites and a large
139
number of observations; therefore, Taiwan is also selected as a typical region in this analysis.
140
2.2 Ground-based measurements
141
The AERONET project is a federation of ground-based remote sensing aerosol networks
142
that provides long-term, continuous and globally distributed observations of the spectral AOD,
143
inversion
144
(https://aeronet.gsfc.nasa.gov/new_web/index.html). Based on empirical knowledge, AERONET
145
retrieval algorithms have gradually evolved from Version 1.0 to Version 2.0 and now to Version
146
3.0 (V3), among which the AOD data of the two latest versions are computed at three data
147
quality levels: Level 1.0 (unscreened), Level 1.5 (automatically cloud-screened and
148
quality-controlled) and Level 2.0 (quality-assured). It is universally acknowledged that AOD
149
retrieval with a low uncertainty of ~0.01 and a high temporal resolution of 15 min can be widely
150
used as “ground truth” in satellite retrieval calibration and aerosol characterization (Holben et al.
151
1998). Since January 2018, some updates of the V3 Level 2.0 AOD products have been released.
152
In this analysis, there are a total of 29 AERONET sites in China, shown as black solid circles in
153
Figure 1, and detailed information on these sites is provided in Table 1. V3 Level 2.0 AOD
154
550-nm data, interpolated on a log-log scale with AOD at 440 nm and 870 nm, are employed to
155
evaluate the satellite retrieval products.
products
and
precipitable
water
data
in
diverse
aerosol
regimes
156
The sequential number, site name, location, region and sampling period of each AERONET
157
site is listed in Table 1. For example, five stations ranging from site No. 1 to 10 in Table 1 are
158
classified in the BTH region, Nos. 6-9 belong to the YRD region, Nos. 10-14 are defined as the
159
PRD region, Nos. 15-20 are located in the northwest region and Nos. 23-29 belong to the Taiwan
160
region. The Tibetan region only has two sites, NAM_CO and QOMS_CAS. This classification
161
information is employed when examining the performance of AOD retrievals by region.
162
2.3 MISR aerosol product
163
Based on the assumption of aerosols being homogeneous within 17.6 km × 17.6 km regions
164
at the surface, the MISR V22 (MISR_v22 hereafter) aerosol retrieval algorithm uses the 256
165
m-1.1 km MISR Level 1B2 pixels as an input. The instrument can collect data in two modes; the
166
global mode is a nominal operational mode that reports data at a 275 m resolution in all nine
167
cameras in the red band and the nadir camera in the blue, green and near-infrared bands while
168
reporting data at a 1.1-km resolution in the off-nadir camera in the blue, green and near-infrared
169
bands (16 pixels × 1.1 km = 17.6 km). The other mode is designed to cover prescribed target
170
areas at full (275 m) resolution in the nine cameras in the four spectral bands, traversing a
171
distance along the track approximately equal in length to the swath width. The latest Version 23
172
AOD (MISR_v23 hereafter) products, with a 4.4-km spatial resolution, are produced by global
173
mode data at a 1.1-km resolution in all bands and cameras. In addition to changes to the
174
resolution, V23 incorporates changes to the product format and content, as well as changes to the
175
cloud screening algorithm relative to V22. These will be described by the MISR operational
176
aerosol team.
177
Both MISR_v22 and MISR_v23 aerosol retrievals require a look-up table based on 74
178
aerosol mixtures, which are composed of three aerosol components of eight “pure” particle size
179
distributions (#1, #2, #3, #6, #8, #14, #19, and #21 in the MISR Aerosol Physical and Optical
180
Properties Database). According to their different sizes and absorbing/scattering properties, these
181
eight particles can be classified into three AOD types, including spherical non-absorbing aerosols
182
(#1, #2, #3 and #6), spherical absorbing aerosols (#8 and #14) and non-spherical dust analogs
183
(#19 and #21) (Kahn and Gaitley 2015; Lee et al. 2016). The detailed contributions of these three
184
componential AODs to each mixture are presented in (Kahn and Gaitley 2015).
185
We downloaded the MISR_v22 aerosol data for 2008-2017 from the operational website.
186
The second band dimension of the parameter named “RegBestEstimateSpectralOptDepth”
187
(called MISR_v22 AOD hereinafter) denotes the mean AOD at 558 nm for all mixtures that pass
188
the goodness-of-fit tests. The parameter of “AerRetrSuccFlag” (MISR_v22 Succflag hereinafter)
189
was also extracted to filter invalid values of MISR_v22 AOD. In the latest version, the
190
parameters corresponding to MISR_v22 AOD and SuccFlag are the parameters called
191
“Aerosol_Optical_Depth” (MISR_v23 AOD) in the directory of “4.4_KM_PRODUCTS”
192
because the MISR operational aerosol teams adjusted some parameter names and reconstructed
193
their file storage formats. The daily and monthly average values based on the MISR_v22 Level 2
194
(swath) AOD at 17.6 km and the MISR_v23 Level 2 products at 4.4 km during the study period
195
were calculated to match the corresponding ground-based AOD measurements.
196
2.4 MODIS aerosol product
197
As mentioned above, two MODIS sensors were placed onboard the EOS Terra and EOS
198
Aqua satellites. Because MISR is mounted on the Terra satellite, we selected the Terra/MODIS
199
AOD (MOD04) products with the same overpass time for consistent comparisons. Since 2014,
200
MODIS operational teams have gradually released C6 aerosol retrieval products, which still
201
contain independent dark target (DT) algorithms aimed at retrieving dense vegetation areas
202
(Levy et al. 2013) and deep blue (DB) algorithms for performing AOD retrievals over bright
203
surfaces (Hsu et al. 2013; Sayer et al. 2013). In addition, a new merged dataset named
204
“AOD_550_Dark_Target_Deep_Blue_Combined” in C6 AOD at 10 km was publicized to
205
improve the global coverage (Levy et al. 2013). Notably, C6 only provides AOD observations at
206
3 km over land and ocean based on the DT algorithm. This product is created using a similar
207
structure to that of 10-km DT products, and the differences arise only according to the manner in
208
which pixels are selected and grouped for retrieval (Remer et al. 2013). In the operational
209
products,
210
“Optical_Depth_And_Ocean”,
211
“Image_Optical_Depth_And_Ocean”, ImageAOD). In this analysis, we extracted AODDT at 550
212
nm of from the Level 2 MOD04 product at 3 km during the same period as the MISR. The daily
213
averaged datasets were calculated for evaluation and comparison purposes.
214
2.5 Evaluation methods
the
MOD04_3K
are
classified
AODDT)
and
as
quality-assured
(parameter
named
all
quality
(parameter
named
flags
215
Illustrated by Wang and Zhao (2017), different spatial and temporal scales may contribute to
216
varied accuracies. Through the sensitivity analysis, the average ground-based AOD
217
measurements within ±30 min of the Terra overpass times at each site were calculated.
218
Considering the individual characteristics of the spatial resolution for the MISR_v23 and
219
MISR_v22 AOD retrievals, the ground measurements were first matched with 1 × 1 pixels for
220
the MISR_v23 4.4 km products and then averaged at 17.6 km × 17.6 km for comparison with the
221
MISR_v22 operational AOD products (17.6 km) when each grid contains several ground-based
222
sites. The standard validation domain of 50 km (Hyer et al. 2011; Levy et al. 2013; Liu et al.
223
2004; Sayer et al. 2014; Shi et al. 2013) was also employed to further compare the two versions
224
of the AOD products. For the high-resolution AOD products of MISR (4.4 km) and MODIS (3
225
km), the AOD retrievals were averaged within a sampling window of 3 × 3 pixels for MISR and
226
4 × 4 pixels for MODIS with an approximate area of 12 km2 centered on each AERONET site.
227
To quantitatively evaluate the accuracy of different AOD retrievals, we introduce some
228
relevant evaluation indicators, which are described in detail below (Franklin et al. 2018; Nicholls
229
2014; Wei et al. 2018; Yang et al. 2019). First, the Pearson correlation coefficient (R) based on
230
linear regression is selected to establish the relationship between the AOD retrievals and
231
ground-based measurements for the target datasets. Second, the EE envelope of MISR falls
232
within the greater of 0.05 or 0.2 × AOD of the paired ground-measured data. Therefore, Equation
233
(1) is introduced as an evaluation parameter to describe the performance of the AOD retrievals in
234
this analysis. In addition, we define the fraction of samples falling above the EE as >EE, falling
235
below the EE as
236
satellite-retrieved values. Third, the mean error (ME), RMSE, and mean relative bias (MRB) are
237
selected to describe the average difference between the satellite retrievals and ground AOD, as
238
presented in Equations (2-4), respectively.
239
EE =
±0.05, ≤ 0.25 ±0.2 × , > 0.25
(1)
240 241 242
#
ME ME = ∑$+2#( $
RMSE = 5 #
&'()**+(),+
−
-./0$1,+ ) (2)
F ∑D >GH(6789:;<==>;<,> ?678@ABCDE,> )
MRB = $ ∑$+2#
$
6789:;<==>;<,> ?678@ABCDE,> 678@ABCDE,>
243
3 Results and Discussion
244
3.1 Performance of AOD over China by version
245
3.1.1 Performance of daily AOD by version
(3) (4)
246 247
Figure 2 Scatter plots of satellite-retrieved mean AODs against AERONET AOD measurements
248
over China from 2007–2017. a–c) comparisons of MISR_v23 daily mean AOD vs. AERONET
249
AOD using spatial collocation criteria of 4.4 km, 17.6 km and 50 km, respectively. d–e)
250
Comparisons of MISR_v22 daily mean retrievals vs. AERONET AOD using spatial collocation
251
criteria of 17.6 km and 50 km, respectively.
252
Figure 2 provides the validation for the MISR_v23 and MISR_v22 daily mean AOD against
253
AERONET AOD measurements at different spatial resolutions over China using linear
254
regression. The dotted lines in Figure 2 represent 1:1 lines (y=x), and the dashed lines are the
255
expected error envelope (EE), which are defined in Equation (1). During the whole study period,
256
there are 365 paired validation data, in Figure 2 a), and linear regression yields a correlation
257
coefficient (R) of 0.902, an RMSE of 0.222 and an intercept (0.0999) near 0, showing that the
258
MISR newly released AOD retrievals are very robust. For the MISR_v23 retrievals, 59.45% of
259
the samples are within the EE envelope, while 16.44% of the samples are overestimated. The
260
negative ME (-0.0605), smaller slope (0.575 less than 1) and
261
that the AOD in the newly released MISR products are still underestimated at high AODs (Kahn
262
et al. 2010). However, the 4.4 km products make MISR_v23 a unique and increasingly popular
263
dataset compared to the MISR_v22 AODs, and MISR_v23 is useful in supporting the
264
examination of the spatial aerosol loading levels over urban areas.
265
As displayed in Figure 2 d), only 340 of the AOD retrievals are collected from MISR_v22
266
operational products vs. AERONET, and 50.29% of the records fall within EE. However, the
267
RMSE (0.186) and MRB (0.0965) of MISR_v22 are lower than those of MISR_v23 (RMSE:
268
0.220, MRB: 0.288). 23.79% of 1051 samples of MISR_v23 against AERONET AODs indicates
269
that the effect of underestimation at large AOD on the MRB is heavily less than that of
270
overestimation at small AOD. For MISR_v22, note that the values on the X-axis (from
271
AERONET) range from 0 to 2, while the values on the Y-axis range from 0 to 1.3, resulting in a
272
large underestimation of 35% of the samples falling below the EE. It is actually the number of
273
matches between MISR and AERONET that is three times larger for the V23 product compared
274
to the V22 product; the samples falling within the EE increase by 22.41%, and the
275
underestimated samples distinctly decrease by 32.02% (Figure 2 b). These improvements can be
276
attributed to the fact that the higher-resolution 4.4 km AOD data from MISR_v23 capture more
277
spatial variabilities that the 17.6 km AOD data from the MISR_v22 misses, which can be found
278
in Figure S1.
279
When comparing the MISR_v23 and MISR_v22 AODs at the 50 km scale, we can find the
280
same conclusions derived from Figure 2 b) vs. Figure 2 d), that the new version has significant
281
slight improvements in the total sample size and the number of samples falling within the EE. In
282
addition, the accuracy of the MISR_v23 AOD decreases with the increase in the validated scale,
283
specifically, the MISR_v23 data had a 157% deterioration in the MRB and a 5.50% decrease in
284
the R values relative to those of MISR_v22. However, for MISR_v22, the number falling within
285
the EE increases by 9.74%. Generally, the most robust statistic for these comparisons is =EE,
286
which is nearly identical (~60%) for the MISR_v23 AODs regardless of resolution.
287
Considering that most values span the range from 0.01 to 1, a linear scale tends to
288
overemphasize the apparent importance of high AODs, so we also conduct the validations using
289
a log-log scale for comparison (as shown in Figure S2). All key-value pairs of the
290
AERONET-observed and MISR-retrievals belonging to 0.01~1 are shown in negative values on
291
the bottom left. Clearly, the R and RMSE values using a log-log scale are generally lower than
292
those established by linear regression, especially at 4.4 km and 17.6 km. However, at 50 km,
293
higher R values (Figure S2) may be obtained from the regression using the log-log scale, which
294
is not as sensitive to the scattered distributions of the AERONET and MISR AODs (Figure 2 c)
295
and e)).
296
To eliminate the effect of different sample sizes, Table S1 shows the validations to compare
297
the accuracies of the different versions when the same samples are employed for the two versions.
298
Surprisingly, both AOD retrievals at 17.6 km and 50 km of the former version are comparable to
299
those of the latter one, having ~16.37% (17.6 km: 19.20%; 50 km: 13.54%) fewer retrievals
300
falling within the EE, but the newly released products perform better except for their larger MRB
301
values. The comparison of the statistics using two linear methods is similar to the above
302
descriptions in Figure 2 and Figure 2S. Meanwhile, the different sample size and the sensitivity
303
of some of the metrics to large outliers may better explain the statistical differences. When using
304
the same sample size constraint, the advantages of the MISR_v23 AOD are more pronounced.
305
3.1.2 Performance of seasonal AOD by version
306
307 308
Figure 3 Validation of MISR_v23 and MISR_v22 seasonal mean retrievals against AERONET
309
over 17.6 km (first line) and 50 km (second line) from 2008–2017. The four seasons are defined
310
as spring (March–May), summer (June–August), fall (September–November) and winter
311
(December–February).
312
The seasonal datasets of the two MISR versions on the national scale vs. the AERONET
313
measurements using linear regression are shown in Figure 3, and detailed statistics are described
314
in Table S2 for the 17.6-km spatial resolution data and Table S3 for the 50-km spatial resolution
315
data.
316
For MISR_v23 at 17.6 km, there are 379, 164, 219 and 288 retrievals in spring, summer,
317
fall and winter, respectively. More than 70% of the records in the fall and winter fall within the
318
EE with small RMSE (0.12–0.14), ME (-0.039–−0.027) and MRB (0.23–0.25) errors, which are
319
credited with strong reliability. In summer, 21.95% of the retrievals fall above the EE and 23.78%
320
fall within the EE, resulting in a large MRB error of 0.48. Obvious underestimation in spring is
321
obtained from the largest number of samples falling below the EE (31.66%), which is likely
322
because frequent dust storms that appear in spring and disturb the surface reflectance lead to
323
incomplete distraction of the path radiance from the spectral radiances at the top of the
324
atmosphere (Kahn et al. 2010). In contrast, similar seasonal performances are also found in the
325
MISR_v22 dataset, but the summer is the only season in which various evaluation parameters are
326
superior. Generally, MISR_v22 in all seasons has small uncertainties, but we prefer the
327
MISR_v23 AODs in terms of the percentage of samples falling within the EE.
328
The comparisons in Figures 3 c) and d) show that the seasonal patterns of the 50-km spatial
329
resolution data are similar to those of the 17.6-km spatial resolution data. As the spatial matching
330
scale increases, introduced noise reduces the samples falling within the EE but smooths the
331
uncertainties in spring. For example, in Table S3, the R of MISR_v23 vs. AERONET in winter is
332
0.66, which is much lower than that using spatial collocation criteria of 17.6km (0.87). The linear
333
fit between the MISR_v22 and AERONET datasets indicates that the R values are greater than
334
those of the MISR_v23 dataset, especially in summer (MISR_V23: 0.87; MISR_V22: 0.96) and
335
winter (MISR_V23: 0.66; MISR_V22: 0.84), and the statistical information indicates that the
336
MRB values are positively related to the number of overestimations but negatively associated
337
with the underestimated samples. Overall, at a coarse resolution, the advantages of MISR_v23 in
338
all seasons are less obvious, whereas MISR_v22 performs well.
339 340
Figure 4 Scatter plots of satellite-retrieved daily mean AODs at two resolutions in spring (first
341
column), summer (second column), fall (third column), and winter (fourth column) against
342
AERONET measurements. The first two rows are the correlation between the MISR_v23 or
343
MISR_v22 daily mean AOD and AERONET using spatial collocation criteria of 17.6 km,
344
respectively. The last two rows are the same as the first two rows but using spatial collocation
345
criteria of 50 km.
346
Figure 4 displays the comparison between the two versions using the same samples as a
347
constraint. The advantages of MISR_v23 are quite obvious, especially in the number of retrievals
348
falling within the EE. For example, in the first two rows (different versions at 17.6 km), a
349
seasonal mean improvement of ~18.53% appeared in MISR_v23 compared to MISR_v22, with
350
an increase of 41.67% in spring, 18.19% in summer, 10.26% in fall and 3.98% in winter, which
351
is likely due to the errors in aerosol particle characteristics such as single scattering albedo and
352
asymmetry, because the coal burning in winter makes the regional aerosol optical characteristics
353
more complicated
354
of 33.67% (Spring: 22.22%; Summer: 28.18%; Fall: 38.09%; Winter: 46.17%). In light of the
355
last two rows (different versions at 50 km), MISR_v23 has a mean of 14.57% more retrievals
356
falling within EE and 28.79% fewer records falling below EE. In conclusion, the MISR newly
357
released AOD data have first priority when using the MISR products. Integrated with the RMSE
358
and ME errors, for MISR_v22, all seasons excluding the spring at 17.6 km and summer at 50 km
359
are also worthy of consideration.
(Yang et al. 2019). Meanwhile, underestimations are reduced by an average
360
By comparing the statistical parameters using the two fitting methods, we conclude that the
361
worst result in spring is consistent with the previous conclusion, and the comparisons of the R
362
values between the linear regression and log-log scale (Figure S3) are similar to Table S1.
363
However, the fitting results vary with the different seasons and versions. Generally, the results
364
based on the log-log scale are not sensitive to the differences in the AOD distribution, while
365
those by linear regression are heavily dependent on it. No matter which fitting method is adopted,
366
the statistics are convincing. Only the results using linear regression are shown in the following
367
sections.
368
3.2 Performance of AOD over typical regions by version
369
3.2.1 Performance of daily AOD over typical regions by version
370 371
Figure 5 Scatter plots of satellite-retrieved daily mean AODs using spatial collocation criteria of
372
17.6 km over the BTH, YRD, PRD and Taiwan regions from top to bottom. The two columns are
373
the correlation between the MISR_v23 or MISR_v22 daily mean AOD and AERONET,
374
respectively.
375
Figure 5 presents the daily mean validation of the MISR_v23 or MISR_v22 against
376
AERONET AOD measurements over four typical regions in China from 2008–2017. The AOD
377
performances using the same samples are presented in Table S4. The variabilities in each region
378
are described as follows:
379
1) BTH
380
There are 530 and 144 records matched by the MISR_v23 and MISR_v22 17.6 km data,
381
respectively. Similar to comparisons on the national scale, the sampling sizes of the newly
382
released version are greater than those of the old version. From the ranges of ground truth, the
383
daily mean observations over the BTH region are as high as 2~3, significantly larger than the
384
definition of haze day (AOD > 1.0), indicating severe pollution in this region. The MISR_v23
385
products show good agreement with the ground measurements with a high R = 0.896 and low
386
intercept = 0.0915 and ME = -0.103, as well as a large number of retrievals (68.87%) falling
387
within the EE. Only 3.96% of the retrievals fall above the EE, whereas the underestimation of
388
the high AODs is severe for the retrievals larger than 1.0, resulting in a negative value of the
389
MRB (-0.065). In MISR_v22, the statistical results of the R, RMSE, slope and intercept are
390
slightly improved, but 11.81% of the 144 samples fall above the EE. When using the same
391
samples (Table S4), the accuracy of the AOD data from MISR_v22 with R=0.91 and =EE of
392
65.74% are comparable to those of MISR_v23 data (R=0.93, =EE: 75.93%), both of them are
393
recommended for AOD analysis at a regional scale.
394 395
2) YRD As shown in the Figure 5 c) and d), only 70 and 28 retrievals are collected from MISR_v23
396
and MISR_v22 respectively. The two datasets show that the air quality in the YRD region is
397
better than that in BTH. Although the R, slope and intercept values of MISR_v23 are worse than
398
those of MISR_v22, the number of retrievals falling within the EE in the latest version (61.43%)
399
is significantly higher than that in the old version (39.29%), and the improvement in the
400
MISR_v23 retrievals falling below the EE is remarkable (28.57% compared with the 57.14% for
401
MISR_v22). These variabilities are also captured by limiting the number of samples. Thus, we
402
recommend only the MISR_v23 AOD dataset over the YRD region.
403
3) PRD
404
As expected, the air quality of the PRD is much better than that of the YRD and BTH.
405
Under a comparison at a high resolution, in Figure 5 e), the MISR_v23 AODs are very consistent
406
with the AERONET AOD measurements (R = 0.908), and 73.13% of the 67 records meet the EE
407
requirement with a low ME, RMSE and MRB. Due to the lack of MISR_v22 retrievals at 17.6
408
km, there are no matching points between the MISR_v23 and MISR_v22 datasets for a clearer
409
comparison. We further compared the MISR_v23 and MISR_v22 at 50 km and found that there
410
were 92 and 11 matching points, respectively. Linear regression with R=0.84, =EE of 67.39%
411
and MRB=-0.0825 are yielded from MISR_v23 against the AERONET AODs, whereas the
412
values are 0.59, 36.36% and -0.140, respectively, for MISR_v22 vs. AERONET (data not shown).
413
As displayed in Table S4, the MISR_v23 data are significantly better than those of MISR_v22
414
when the same sample points are used at a 50-km scale.
415 416
4) Taiwan There are 7 AERONET sites distributed in Taiwan, which has an area of 36,000 km2. From
417
the ranges of the X-axis and Y-axis, the AOD loadings in Taiwan are similar to those in the PRD,
418
and over 70% of the points range from 0 to 0.5. For the data in Figure 5 g), MISR_v23 performs
419
well, with the highest R (~0.927) and slope (0.715) and the lowest RMSE (0.109) and ME
420
(-0.0327) among the four typical regions. Meanwhile, Taiwan has the largest overestimation,
421
with 15.52% of 174 retrievals falling above the EE. Although MISR_v22 correlated well with the
422
ground truths, with R=0.83, ME=-0.069, MRB=-0.022, the number of samples falling within the
423
EE limits its accuracy. Table S4 also highlights the advantage of MISR_v23 in Taiwan.
424
3.2.2 Performance of seasonal AOD over typical regions by version
425
Table 2 Statistics for the mean AOD of MISR_v23 against AERONET by region and season
426
using spatial collocation criteria of 17.6 km from 2008–2017 season
N
r
RMSE
MAE
MRE
=EE
>EE
spring
190
0.84
0.39
-0.17
-0.12
56.32
5.79
37.89
summer
89
0.97
0.21
-0.095
-0.026
65.17
4.49
30.34
fall
116
0.96
0.18
-0.068
-0.081
78.45
1.72
19.83
winter
135
0.86
0.15
-0.039
0.0071
80.74
2.96
16.3
spring
29
0.87
0.21
-0.042
0.013
62.07
17.24
20.69
summer
13
0.79
0.48
-0.22
-0.13
46.15
7.69
46.15
fall
5
0.99
0.03
-0.014
-0.049
100
0
0
winter
23
0.94
0.13
-0.072
-0.11
60.87
4.35
34.78
spring
13
0.98
0.25
-0.14
-0.13
46.15
7.69
46.15
summer
4
1.00
0.02
0.0075
0.082
100
0
0
fall
11
0.95
0.06
-0.043
-0.11
81.82
0
18.18
winter
39
0.90
0.07
-0.029
-0.073
76.92
5.13
17.95
spring
75
0.95
0.13
-0.059
0.045
50.67
13.33
36
summer
26
0.86
0.09
0.033
0.36
53.85
34.62
11.54
fall
38
0.92
0.08
-0.027
-0.034
65.79
10.53
23.68
winter
35
0.92
0.09
-0.032
-0.11
57.14
11.43
31.43
BTH
YRD
PRD
Taiwan
427
428
Table 3 Statistics for the mean AOD of MISR_v22 against AERONET by region and season
429
using spatial collocation criteria of 17.6 km from 2008–2017 Region
season
N
r
RMSE
MAE
MRE
=EE
>EE
BTH
spring
44
0.92
0.30
-0.10
0.070
43.18
25
31.82
summer
26
0.98
0.09
-0.02
0.14
76.92
11.54
11.54
fall
45
0.92
0.25
-0.11
-0.071
71.11
0
28.89
winter
29
0.91
0.10
-0.028
0.080
62.07
10.34
27.59
spring
15
0.70
0.19
-0.10
-0.14
46.67
6.67
46.67
summer
5
1.00
0.14
-0.097
-0.21
60
0
40
fall
5
0.93
0.19
-0.14
-0.25
20
0
80
winter
3
1.00
0.14
-0.13
-0.34
0
0
100
spring
64
0.82
0.19
-0.099
-0.11
32.81
17.19
50
summer
20
0.75
0.12
0.0096
0.59
30
40
30
fall
33
0.82
0.12
-0.069
-0.18
42.42
6.06
51.52
winter
20
0.94
0.11
-0.056
-0.081
50
10
40
YRD
Taiwan
430 431
Table 2 and Table 3 respectively describe the performances of the MISR_v23 and MISR_v22 seasonal AOD datasets over four typical regions using AERONET.
432
1) BTH
433
Regardless of the version or season, the number of samples falling within the EE are higher
434
than the corresponding national averages. Specifically, for the MISR_v23 17.6 km data, 80.74%
435
of the 135 retrievals in the winter fall within the EE and 78.45% of 116 samples fall within the
436
EE in the fall, followed by an =EE of 65.17% and r = 0.97 in summer and an =EE of 56.32% and
437
r = 0.84 in the spring. As shown in Tables 2 and 3, severe underestimations with retrievals falling
438
below the EE appeared in this region, especially in the spring. By contrast, the AOD dataset in
439
the summer for MISR_v23 at 17.6 km can be substituted for the same season in the MISR_v22
440
dataset, which has good agreement with r = 0.98 and quite small RMSE, ME and MRB values.
441
Moreover, 76.92% of the retrievals meeting the accuracy requirement. In the spring, the AOD
442
retrievals in this region are easily affected by sandstorms from Xinjiang province in the
443
northwest of China, and thus, the number of samples falling within the EE are less than 60%
444
(Levy et al. 2013).
445
2) YRD
446
Compared with the BTH region, the YRD region has much fewer matched points in each
447
season. At 17.6 km, the satellite retrievals in the spring and winter of MISR_v23 show good
448
consistency, with greater than 60% of the retrievals falling within the EE, high r values
449
(0.87~0.94) and low uncertainties for the ground measurements, which are certainly
450
recommended relative to those of MISR_v22. In the fall, there are only 5 samples at 17.6 km,
451
and all the records of MISR_v23 fall with the EE, but only 20% of the MISR_v22 retrievals meet
452
the accuracy requirements, which is just a reference due to the lack of retrievals. A similar
453
condition is found in the summer, but the MISR_v22 dataset performs better than the MISR_v23
454
dataset. Further validations will be needed to determine which version is better for analysis if the
455
satellite observations and ground measurements are available, especially at a higher resolution.
456
3) PRD
457
Due to the severe lack of sample points, we only display the comparison of MISR_v23 with
458
AERONET in the PRD region. The AODs in the spring have many retrievals falling below the
459
EE, which is approximately equal to the number falling within the EE (46.15%). However, up to
460
81.82% and 76.92% of the samples fall within the EE, with r values in the range of 0.95~0.90, in
461
the fall and winter at 17.6 km, respectively, demonstrating that the latest MISR products are
462
promising. The higher accuracy in the summer is just a reference because the summer has fewer
463
samples.
464
4) Taiwan
465
Over the Taiwan region, the retrievals falling within the EE in the four seasons are greater
466
than 50%, and the largest number of 65.79% at 17.6 km is in the fall. Among these typical
467
regions, Taiwan is the only region where the validations of the satellite-retrieved MISR_v23
468
AODs against AERONET in all seasons at a high resolution are better than those of MISR_v22
469
against AERONET for almost all evaluation parameters, such as the RMSE, ME and MRB, as
470
well as the number of retrievals falling below the EE. However, in general, the accuracy in
471
Taiwan is not as good as that in the BTH region.
472
3.3 Comparison of MISR and Terra/MODIS AOD retrievals
473
3.3.1 General comparison of MISR and MODIS AOD
474
475 476
Figure 6 Comparisons of MISR and MODIS AOD validated by AERONET observations in 2016,
477
a) the overall match-up trends over three datasets. b–c) Difference between MISR and MODIS
478
AOD as a function of AERONET AOD. The green lines and the blue lines are the EE envelopes
479
of MISR and MODIS, respectively, for consistent comparison.
480
We further compared the performances of MISR 4.4 km AOD retrievals and MODIS 3 km
481
DT products based on AERONET daily observations in 2016. Figure 6 a) displays the variation
482
trends in the AODs when there are available data over three datasets, marked in black, red and
483
blue points for ground truth, MISR and MODIS values, respectively. Overall, there were 104
484
daily records that matched in 2016. Both MISR and MODIS AOD products were significantly
485
consistent with the changes in ground measurements, and their correlation coefficients were
486
0.925 and 0.909, respectively. However, for the ranges, the MODIS retrievals were substantially
487
larger than the AERONET observations at some points, with an ME of 0.0949; for example, the
488
MODIS AOD of 1.44 at the 72nd sample point was larger than the ground truth of 0.897. The ME
489
of -0.0863 indicated that the MISR AOD was underestimated relative to the ground truth, and the
490
absolute value of the MRB (0.21) was closer to that of MODIS (0.23).
491
Numerous comparisons and validations of MISR and MODIS with AERONET have been
492
performed (Cheng et al. 2012; He et al. 2010; Qi et al. 2013); it is universally acknowledged that
493
the MISR AOD retrievals perform well, as demonstrated in Nanjing (Kang et al. 2016), at four
494
sites in China (Cheng et al. 2012), over Durban in South Africa (Kumar et al. 2015), over
495
Karachi in Pakistan (Alam et al. 2011), and over Europe (de Leeuw et al. 2015). For the MODIS
496
AOD, Remer et al. (2013) noted that over land, the 3-km product appears to be globally 0.01 to
497
0.02 larger than the 10-km product.
498
The difference between MISR or MODIS and AERONET (MISR-AERONET or
499
MODIS-AERONET hereinafter) as a function of AERONET AOD is shown in Figures 6 b) and
500
c), respectively, which are overlaid with the EE envelopes of MISR and MODIS for consistent
501
comparison marked with green lines and blue lines, respectively. Notably, the underestimation of
502
MISR AOD was more serious, and the values of MISR-AERONET decreased with the increase
503
in AERONET observations, meaning that the deviations of MISR relative to the ground truth
504
were gradually prominent, particularly in the decrease in the ground truth. Table 4 describes the
505
sample distributions of MISR-AERONET and MODIS-AERONET. For MISR, 66 points were
506
lower than the ground truth, and more than half of the 66 records (N=36) were concentrated in
507
the AERONET AOD range of 0~0.5; among all overestimated samples (N=38), 30 points
508
appeared in the AOD range from 0 to 0.25. Of the 104 samples, 59.62% fell within the MISR EE
509
envelope and 80.77% fell within the MODIS EE envelope (±0.05 ± 0.15τ), which demonstrated
510
that the MISR retrievals have high accuracies. Xiao et al. (2009) also indicated that the MISR
511
AOD retrievals agreed well with the ground-based observations for AOD < 0.5 but were
512
systematically underestimated for AOD > 0.5 in China. For MODIS-AERONET, most samples
513
had a disorderly distribution above the y=0 line, meaning that 88 AOD points were biased high,
514
with nearly half of the samples concentrated in 0~0.25, and the biases gradually decreased with
515
the increase in AERONET AOD. Qi et al. (2013) concluded that when AOD is small (0~0.5),
516
MODIS retrievals are higher than the AERONET observations, but when the AOD is large
517
(0.5~1.0), the MODIS retrievals are lower than the AERONET observations. In terms of the
518
underestimated parts, when the AERONET observations ranged from 0 to 1, the differences
519
showed a positive trend with AOD; for example, the ground truth value of 0.992 was far higher
520
than the MODIS value of 0.17. Of the 104 records, 43.27% and 71.15% fell within the MISR EE
521
and MODIS EE envelopes, respectively, and were lower than the MISR records. According to
522
Remer et al. (2013), both the validations of retrieved AOD at 3 km against AERONET
523
measurements over land and ocean were slightly lower than those at 10 km, and the former ones
524
have more samples falling above the EE. Additionally, Remer et al. (2013) proposed that
525
expected errors associated with the 3-km land product are determined to be greater than those of
526
10-km product (±0.05 ± 0.20τ).
527
Table 4 Sample distributions of the differences between the MISR or MODIS data and
528
AERONET observations Dataset MISR_Diff
MODIS_Diff
N
0~0.25
0.25~
0.50~
0.75~
1.0~
1.25~
1.5~
MISR/MO
>0
38
30
6
2
0
0
0
0
62/84
<0
66
19
17
12
7
4
5
2
>0
88
45
19
11
4
3
4
2
<0
16
4
4
3
3
1
1
0
529
1
530
MISR-defined EE and MODIS-defined EE, respectively.
45/74
MISR/MODIS EE indicates the number of retrievals of MISR_Diff or MODIS_Diff that fall within the
531
3.3.2 Spatial comparison of MISR and MODIS AOD
532
533 534
Figure 7 MISR true color images at nadir a), Af b), Bf c), Cf d) and Df e), complete overpass f)
535
and Terra/MODIS true color image g), as well as their combined coverage h) on February 13,
536
2018.
537
We compared the true color images of MISR and MODIS on 13 February 2018. As
538
mentioned above, MISR images are required with normal view angles relative to the surface
539
reference ellipsoid of 0°, 26.1°, 45.60°, 60.00° and 70.50° for An, Af/Aa, Bf/Ba, Cf/Ca, and
540
Df/Da, respectively. Figures 7 a)-e) display images at nadir and four angles along with the
541
scanning direction. The values of radiation brightness are gradually larger as the observation
542
angle increases, illustrating that multiple angles enable MISR to provide abundant spectral
543
information. Compared to identification characteristics in true color images, both MISR and
544
MODIS captured the cloud/snow area in northeastern China and the slight haze in Henan
545
Province. For the coverage, as depicted in Figure 7 f), MISR only covered a part of eastern China
546
with the origin from Harbin, Changchun, and Shenyang and then passed through Bohai Sea and
547
adjacent areas to Jiangxi, Fujian and Guangdong Provinces and traversed one zone, including
548
Urumqi, Qinghai Province and the Tibetan region. However, in Figure 7 g), except for a part of
549
Guizhou and Yunnan Provinces, MODIS released an operational true color image almost
550
covering the whole domain of China, and its width was obviously larger than that of MISR
551
(Figure 7 h). As previously discussed, the sensors’ widths often determine their own overpass
552
periods; MODIS onboard the Terra passes over each day, whereas MISR varies with latitude and
553
requires 2~9 days to revisit the same place, which makes MISR not as popular as MODIS for
554
retrieving aerosol properties.
555 556
Figure 8 Spatial distributions of the daily MISR (left) and MODIS (right) AOD on February 13,
557
2018.
558
Figure 8 displays the spatial distribution of MISR 4.4 km and MODIS 3 km AOD on
559
February 13, 2018. As shown by the true-color images, the two sensors were affected by cloud
560
contamination in Guangxi, Guizhou, western China and Liaoning provinces. Compared to valid
561
MISR retrievals, MODIS was capable of clearly identifying the boundary of cloud pixels near
562
Guangdong and Hubei provinces but had no inversion results when light haze appeared in the
563
BTH region. By contrast, MISR had stronger ability in areas where clouds and clean underlying
564
surfaces were mixed in the west and northeast China regions. From valid retrievals of both MISR
565
and MODIS, MISR AOD was less biased than MODIS; for example, for Anhui province,
566
MODIS AOD was ~0.35, whereas MISR AOD was only ~0.2. Although the results of the 3-km
567
product mirror the 10-km retrievals, Remer et al. (2013) found an increase in noisy artifacts in
568
the finer resolution product, which unfortunately occurred most frequently over urban surfaces.
569
3.3.3 Seasonal comparison of MISR and MODIS AOD
570
571 572
Figure 9 Comparison of monthly average MISR a) and MODIS b) AOD in 2016 and seasonal
573
average MISR c) and MODIS d) AOD in 2016.
574
Figures 9 a) and b) show a comparison of the monthly average MISR and MODIS AOD in
575
2016. The percentiles of 90% and 10% of all sorted samples are employed as the upper boundary
576
and lower boundary, respectively, to lessen the impacts of possible outliers, and the mean value
577
in each month is shown as a red dot. Both MISR and MODIS mean AODs displayed the highest
578
value in March and lowest value in August over the whole year, and even the inflections of
579
variation were exactly consistent. However, many discrepancies were found in the variation
580
trends between MISR and MODIS; for example, MODIS was better at capturing more details
581
than MISR, with wider ranges in all months; the maximum in 2016 for MODIS was ~0.45 higher
582
than that for MISR, and the monthly means of MODIS AOD were 0.042~0.094 higher than those
583
of MISR AOD. These findings validated that some overestimations were found in MODIS; by
584
contrast, the MISR retrievals were biased lower.
585
On the seasonal scale, Figures 9 c) and d) show that AODs in spring were largest, followed
586
by winter and summer, and smallest in fall. A handful of studies have reported that frequent dust
587
storm events appeared in spring in China, especially in the northwest region covered with deserts
588
and barren lands, and explained that high AOD levels were mainly from coarse particles (Tao et
589
al. 2017). Meanwhile, the southwestern part of China, such as Yunnan and Guangxi provinces,
590
tends to be affected by the transmission of pollutants from Southeast Asia in spring (Liu et al.
591
1999); and the Hongkong tends to be affected by the transmission of pollutants from the Pearl
592
River Delta region (Yang et al. 2018). In winter, human activity was dominant in the northern
593
Yangtze River area, and high levels of SO2 and NOx emissions from large amounts of coal
594
heating were the main cause of winter pollution (Krotkov et al. 2016). Under favorable weather
595
conditions, for example, the boundary layer heights in summer and fall are relatively large,
596
which is beneficial for pollutant diffusion. These findings can be well captured by MISR and
597
MODIS instruments (Kang et al. 2016; Li et al. 2016). In general, both datasets performed well
598
when investigating the characteristics of long-term variation in AOD, but when quantitatively
599
using their values, more attention should be paid to the overestimation of MODIS and
600
underestimation of MISR.
601
3.4 Uncertainty analyses
602
In general, the accuracy of AOD retrievals heavily depends on the removal of cloud
603
contamination, the assumption of surface reflectance and the selection of aerosol models, which
604
are adaptive to most satellites/sensors (Kahn et al. 2010; Levy et al. 2010; Yoon et al. 2012).
605
Each contributing factor for MISR is discussed as follows:
606
1) Cloud screening methods. The MISR cloud team has developed three independent
607
cloud
detection
methods:
Radiometric
Camera-by-Camera
Cloud
Mask
(RCCM),
608
Stereoscopically Derived Cloud Mask (SDCM), and Angular Signature Cloud Mask (ASCM).
609
More details can be found in the Data Products Specifications manual (Mike Bull 2011). In the
610
operational products, the Retrieval Applicability Mask flag (=0) is used to identify pixels free of
611
clouds, glint, and other factors. The Cloud Screening Parameter is used to indicate how many
612
pixels belong to clear flags versus a total of 16×16×9 flags. Witek et al. (2013) investigated
613
MISR AOD over oceans using the Maritime Aerosol Network (MAN) and found that MISR
614
overestimates AOD by 0.04 on average compared to MAN, and the R and RMSE were 0.95 and
615
0.06, respectively. Subsequently, setting a clear flag fraction threshold to 0.6 reduces the bias to
616
below 0.02, improving the overestimation of AOD over ocean areas (Witek et al. 2013). As
617
reported by Remer et al. (2005), MISR lacks channels in the near and far-infrared regions where
618
cirrus clouds are most easily detected. Therefore, considering the wider spectral information of
619
MODIS, Shi et al. (2014) used MODIS-based cloud screening methods to examine the impacts
620
of cloud contamination on the MISR AOD products and showed that on average globally, thin
621
cirrus cloud contamination introduces a possible ~0.01 high bias for the overwater MISR AOD
622
retrievals, and the bias increases to 0.015–0.02 over the mid- to high-latitude oceans and
623
Southeast Asia. Kahn et al. (2010) also noted some overestimations of AOD retrievals possibly
624
affected by neighboring pixels of scattered or broken clouds. These investigations demonstrated
625
that an accurate cloud screening method directly determines the retrieval uncertainty.
626
2) Surface reflectance assumption. In this analysis, general underestimation of MISR
627
AOD was found, meaning that the assumed surface reflectance was heavily overstated. Figures 7
628
a–e) show that the signal received by MISR was brightest when the angle increased to 70.5°,
629
which makes balancing the reflection signals from all angles a difficult challenge. MISR
630
retrievals are robust in bright surface landscapes filled with deserts, snow or other landscapes
631
(Alam et al. 2011); Figure 9 a) captured this situation well. Si et al. (2017) found that the single
632
scattering albedo (SSA) values released by the MISR team in 74 mixtures ranges from 0.8 to 1.0,
633
and SSA is equal to 1 in the first thirty mixtures. On the basis of the reference materials (David J.
634
Diner March 10, 2008; Kahn and Gaitley 2015), the MISR team defined the SSA of all spherical
635
particles as 1, indirectly showing some bias between determining scattering and absorbing
636
properties. All of the above descriptions indicate that the observation signal process method and
637
identification over bright pixels have strong impacts on the overestimation of surface reflectance.
638
When the aerosol loading is low, the influence of surface reflectance underestimation is
639
dominant in the aerosol derivation, leading to a large overestimation of AOD in clean conditions.
640
For example, in Table 4, there are 30 pairs (total of 38 pairs) in which biases corresponded to
641
AERONET AOD 0~0.25 ranges, thus addressing the importance of appropriate surface
642
reflectance.
643
3) Aerosol model selection. When the aerosol loading is high, the role of the appropriate
644
aerosol model should be emphasized. MODIS C6 and VIIRS are divide into five classifications,
645
including continental model, moderate absorbing model, non-absorbing model, strong absorbing
646
model and non-spherical model, whereas the MISR team predefined 74 mixtures (Figure 10).
647
Each mixture is made up of three components: a spherical non-absorbing component, a spherical
648
absorbing component and a dust component. Each component consists of two or three particles.
649
More information on mixed situations can be referenced in Kahn and Gaitley (2015) and is
650
depicted in Figure 10. In the first ten mixtures, #1 and #6 mutually vary at an interval of 10%
651
with a total amount of 100%; the 51st mixture is made up of 72% #2, 8% #6 and 20% #19. The
652
MISR EOF algorithm sets chi-square values to determine whether a mixture fits the tests and
653
defines two scenarios for AOD retrieval: one is described in Section 2.3, and the other treats the
654
AOD corresponding to only one mixture passing through the chi-square test as the “lowest
655
Aerosol Optical Depth” (lowAOD). The bestAOD parameter was used in this analysis, which
656
means that more aerosol models are likely to be considered in the retrievals. However, when only
657
one mixture succeeds, even 74 combinations of MISR are meaningless. Meanwhile, the constant
658
interval limits the selection of more dynamic or close to real atmospheric conditions; for example,
659
dust storms occur frequently in spring, affected by high wind speed and southwesterly wind, and
660
a large number of coarse particles accumulate in the central and eastern regions of China where
661
the Nos. 51~74 mixtures should be added. In addition, MODIS C6 distinguishes aerosol models
662
from different seasons and regions, whereas MISR does not.
663 664
Figure 10 MISR operational team predefined 74 mixtures and componential AOD
665
666
percentage (%) in each mixture.
4 Conclusions
667
In this analysis, AERONET measurements from 29 sites in China from 2008–2017 were
668
obtained and used to validate the newly released MISR Level 2 AOD products at 4.4 km. First,
669
on a national scale, we compared MISR-observed daily and seasonal AOD from different
670
versions at spatially averaged resolutions of 17.6 km and 50 km, respectively. Next, the
671
performances over typical regions, including BTH, YRD, PRD and Taiwan, were analyzed. Then,
672
using Terra/MODIS 3-km products, we investigated the MISR 4.4-km AOD from the true-color
673
images and retrieved spatial distribution, monthly variation trend, and seasonal patterns. Finally,
674
the possible uncertainties of AOD retrievals were discussed. The results show the following:
675
1) The MISR 4.4 km retrievals are well correlated with the AERONET measurements (R:
676
0.902, =EE: 59.45%) with little underestimation (ME: -0.0605;
677
versions or spatial validation domains, the number of matching samples of the latter version is
678
2~3 times larger than those of the former version, and the MISR V23 retrievals falling within the
679
EE increase by 22.41% at 17.6 km and 8.46% at 50 km. The number of retrievals falling within
680
the EE in fall and winter are larger than those in summer and spring for both versions, and the
681
performance of the high-resolution averaged domain is generally better than that at 50 km.
682
2) The daily dataset over BTH (68.87%), YRD (61.43%) and PRD (73.13%) falling within
683
the EE exceed 60%, except Taiwan (55.75%). By season, the number of retrievals falling within
684
the EE for MISR V23 and V22 over the BTH in spring is slightly worse than that in other
685
seasons but performs better than that in other regions. Based on few paired values, the number of
686
V23 retrievals falling within the EE in fall and winter exceeds 60%. Accompanied by the
687
improvement of spatial resolution, there is a small number of sample points in PRD, and the
688
number of retrievals falling within the EE in all seasons is greater than 76%, except in spring. We
689
only recommend the AOD data in all seasons of V23 in Taiwan. Integrated with the RMSE and
690
ME errors, for V22, the retrievals over the YRD in the summer and the BTH in the summer and
691
winter are recommended.
692
3) Compared with Terra/MODIS 3-km AOD in 2016, the MISR V23 4.4-km product has a
693
slightly higher R value (0.925) with AERONET than with MODIS (0.909), and the MISR AOD
694
generally has a lower bias; however, MODIS AOD is overestimated relative to the ground truth.
695
Among the 104 matched points, 62 and 84 of the MISR retrievals fall into the MISR-defined EE
696
and MODIS-defined EE, respectively, higher than the 45 and 74 points, respectively, of the
697
MODIS AOD. In addition, consistent seasonality characteristics for the two datasets are as
698
follows: spring>winter>summer>autumn.
699
Notably, the retrieval algorithm does not appear to be significantly improved, meaning that
700
cloud screening methods, surface reflectance and aerosol models using the same data as MISR
701
V22 have similar impacts on the uncertainties of AOD retrievals. On the basis of a preliminary
702
and first comparison of the newly released MISR products, in the future, the matching
703
uncertainties from spatial resolution and time average will be discussed in details, more complex
704
and appropriate aerosol models will be examined to retrieve AODs with higher accuracies.
705 706
Table 1 Detailed information on the AERONET sites in China No.
Site name
Lon
Lat
Region
Period
1.
Beijing
116.38
39.977
BTH
2007.01-2017.05
2.
Beijing-CAMS
116.31
39.933
2012.08-2017.01
3.
PKU_PEK
116.18
39.593
2007.01-2008.08
4.
Xianghe
116.96
39.754
2007.01-2017.05
5.
Xinglong
117.57
40.396
2007.01-2012.05
6.
Hangzhou-ZFU
119.72
30.257
7.
Hefei
117.16
31.905
YRD
2007.08-2007.11 2007.01-2008.11
8.
Shouxian
116.78
32.558
2008.05-2008.12
9.
Taihu
120.21
31.421
2007.01-2016.08
10.
Hong_Kong_Hok_Tsui
114.25
22.21
11.
Hong_Kong_PolyU
114.18
22.303
2007.01-2017.03
12.
Hong_Kong_Sheung
114.11
22.483
2012.02-2016.05
13.
Kaiping
112.53
22.315
2008.10-2008.11
14.
Zhongshan_Uni
113.39
23.06
2011.11-2012.07
15.
Dunhuang_LZU
94.955
40.492
16.
Jingtai
104.1
37.333
2008.02-2008.05
17.
Lanzhou_City
103.85
36.048
2009.10-2010.03
18.
Minqin
102.95
38.607
2010.05-2010.06
19.
SACOL
104.13
35.946
2007.01-2012.08
20.
Zhangye
100.27
39.079
2008.04-2008.06
21.
NAM_CO
90.962
30.773
22.
QOMS_CAS
86.948
28.365
23.
Chen-Kung_Univ
120.21
23
24.
Chiayi
120.49
23.496
2013.09-2017.04
25.
Douliu
120.54
23.712
2015.09-2017.06
26.
EPA-NCU
121.18
24.968
2007.01-2017.10
27.
Heng-Chun
120.7
22.055
2013.03-2015.05
28.
Lulin
120.87
23.469
2007.01-2017.07
29.
NCU_Taiwan
121.19
24.967
2007.01-2013.07
PRD
Northwest
Tibetan
2007.10-2010.07
2012.03-2012.05
2007.01-2016.08 2010.09-2015.11
Taiwan
2007.01-2016.06
707
708
Acknowledgements
709
This work was supported by the National Natural Science Foundation of China (Grant No.
710
41801281). We appreciate the NASA Langley Research Center Atmospheric Sciences Data
711
Center (https://eosweb.larc.nasa.gov/) for providing the AOD data. Meanwhile, we appreciate the
712
four anonymous reviewers for precious suggestions.
713
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1
Evaluation of the MISR fine resolution aerosol product
2
using MODIS, MISR, and ground observations over China
3
Yidan Si1, Liangfu Chen2, Xiaozhen Xiong3, Shuaiyi Shi2, Letu Husi2, Kun Cai4,5* 1
4
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites,
5
National Satellite Meteorological Center, China Meteorological Administration, Beijing 10081,
6
China 2
7
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital
8
Earth, Chinese Academy of Sciences, Beijing 100101, China 3
9 4
10
National Oceanic and Atmospheric Administration, MD 20746, USA
Spatial information processing engineering laboratory of Henan province, Kaifeng 475004,
11 12
China 5
School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
13
14
Highlights
15
Nationally, MISR V23 data are still underestimated with R=0.902, EE=59.45% and
16
MAE=-0.0605.
17
V23 retrievals =EE in the BTH, YRD and PRD regions are respectively greater than 55.75%
18
in Taiwan.
19
The V23 retrievals =EE in fall and winter are highest, followed by those in summer and
20
spring.
21
V23 products over BTH, YRD, Taiwan in all seasons perform well; that of V22 in PRD
22
summer are better.
23
MISR 4.4-km AOD biased lower and MODIS 3-km data overestimated relative to
24
AERONET observations.
25
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
S1352-2310(19)30867-2
DOI:
https://doi.org/10.1016/j.atmosenv.2019.117229
Reference:
AEA 117229
To appear in:
Atmospheric Environment
Received Date: 8 April 2019 Revised Date:
3 December 2019
Accepted Date: 15 December 2019
Please cite this article as: Si, Y., Chen, L., Xiong, X., Shi, S., Husi, L., Cai, K., Evaluation of the MISR fine resolution aerosol product using MODIS, MISR, and ground observations over China, Atmospheric Environment (2020), doi: https://doi.org/10.1016/j.atmosenv.2019.117229. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
1
Evaluation of the MISR fine resolution aerosol product
2
using MODIS, MISR, and ground observations over China
3
Yidan Si1, Liangfu Chen2, Xiaozhen Xiong3, Shuaiyi Shi2, Letu Husi2, Kun Cai4,5* 1
4
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites,
5
National Satellite Meteorological Center, China Meteorological Administration, Beijing 10081,
6
China 2
7
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital
8
Earth, Chinese Academy of Sciences, Beijing 100101, China 3
9 4
10
National Oceanic and Atmospheric Administration, MD 20746, USA
Spatial information processing engineering laboratory of Henan province, Kaifeng 475004,
11 12
China 5
School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
13 14 15
Yidan Si was the first author who designed the experiment, downloaded the data, processed
16
data, wrote the paper.
17
Liangfu Chen and Xiaozhen Xiong helped me re-construct the paper and pointed out
18
unreasonable sentences.
19
Shuaiyi Shi helped me revise full text and gave some useful suggestions about validation
20
part.
21
Letu Husi helped me realized MODIS 3km AOD retrieval algorithm, and pointed out the
22
difference between MISR 4.4km and MODIS 3km AOD.
23
Kun Cai helped me re-construct the organizations, polish the sentences and provided
24
funding supporting.
1
Evaluation of the MISR fine resolution aerosol product
2
using MODIS, MISR, and ground observations over China
3
Yidan Si1, Liangfu Chen2, Xiaozhen Xiong3, Shuaiyi Shi2, Letu Husi2, Kun Cai4,5* 1
4
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites,
5
National Satellite Meteorological Center, China Meteorological Administration, Beijing 10081,
6
China 2
7
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital
8
Earth, Chinese Academy of Sciences, Beijing 100101, China 3
9 10
4
11 12
National Oceanic and Atmospheric Administration, MD 20746, USA
Spatial Information Processing Engineering Laboratory of Henan Province, Kaifeng 475004, China
5
School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
13
Abstract: Recently, NASA’s Multiangle Imaging SpectroRadiometer (MISR) team released the
14
Version 23 (V23) 4.4-km Aerosol Optical Depth (AOD) product, which has better spatial
15
resolution than V22 at 17.6 km. However, its quality has not been validated in China. Here, V23
16
products for different spatiotemporal domains are obtained for validation against Aerosol
17
Robotic NETwork (AERONET) AOD measurements for 29 sites from 2008–2017. Based on the
18
national daily mean, V23 AOD yields a correlation coefficient (R) of 0.902 with AERONET;
19
59.45% of retrievals fall within the expected error (=EE; ±0.05 or ± 0.2 × AOD). A mean error
20
(ME) of −0.0605 with 24.11% of retrievals falling below the EE indicates that MISR data are
21
still underestimated at high AODs. The sample numbers and accuracies of spatially averaged
22
17.6-km and 50-km data are greatly improved relative to V22. The seasonal mean of V23
23
retrievals =EE in fall and winter are highest, followed by those in summer and spring; the
24
validation results at 17.6 km are generally better than those at 50 km. By region, V23 retrievals
25
=EE in the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta
26
(PRD) regions are 68.87%, 61.43%, and 73.13%, respectively, while that of Taiwan is below 60%
27
(55.75%). No V22 records exist in PRD, and V23 products over other regions in all seasons
28
perform well; V22 retrievals in summer are also recommended. Compared with Terra/MODIS
29
3-km AOD in 2016, the V23 product has a slightly higher R value (0.925) with AERONET than
30
MODIS (0.909). The MISR AOD bias is lower, and MODIS AOD is overestimated relative to
31
the
32
(spring>winter>summer>autumn), with the maximum in March and minimum in August. To
33
investigate the spatiotemporal characteristics over long-term AOD, MISR V23 4.4-km AOD can
34
be used in combination with other observation data.
35
Keywords: MISR; V23 4.4 km; AOD; V22; AERONET; MODIS 3 km
36
1 Introduction
ground
truth;
both
present
consistent
seasonality
characteristics
37
Atmospheric aerosol is a general term for solid and liquid particles, with diameters ranging
38
from 0.001~100 µm, suspended in the atmosphere, which plays an important role in terms of
39
their interaction with the surrounding environment; for example, particle scattering directly
40
affects clouds and radiative forcing (Che et al. 2018; Christensen et al. 2017; Garrett et al. 2009;
41
Zhao et al. 2018) and even indirectly influences climate change (Deetz et al. 2018; Glassmeier et
42
al. 2017; Zhao et al. 2012). Additionally, impaired atmospheric visibility and aggregated frequent
43
haze days (Li et al. 2013a; Yang et al. 2016a; Yang et al. 2016b; Zou et al. 2018) have been
44
proven to be major environmental risk factors fundamentally related to human health (Cao et al.
45
2012; Krall et al. 2013). The aerosol optical depth (AOD) is usually used as a quantitative
46
measure of the amount of aerosol particles in the atmospheric column. Some studies have
47
demonstrated that long-term AOD variations over large areas can be obtained from advanced
48
remote sensing technology (Guo et al. 2011; Kumar et al. 2018; Sogacheva et al. 2018; Xiao et al.
49
2016; Yang et al. 2019).
50
The Multiangle Imaging SpectroRadiometer (MISR), at an altitude of 705 km with a swath
51
width of 380 km, is aboard the polar-orbiting NASA EOS Terra platform, which passes over at
52
approximately
53
(±70.5° , ±60.0° , ±45.6° , ±26.1° ,
54
spectral bands (446 nm, 558 nm, 672 nm and 866 nm) and is capable of providing globally
55
continuous retrievals of AOD on the daylit portion of the Earth as well as information on aerosol
56
components and microphysical properties. The limited swath width prolongs its revisit period of
57
2~9 days depending on the latitude of the region. The cross-track IFOV and spacing between
58
centers of each pixel is 275 m for all of the off-nadir cameras and 250 m for the nadir camera.
59
The sample spacing in the downtrack direction is 275 m in all cameras. As the retrieval
60
algorithms were gradually improved, the AOD datasets in different algorithm versions were
61
validated by the global federated observation network (Aerosol RObotic NETwork, AERONET)
62
of ground-based sun photometers, for example, Version 12 (Liu et al. 2004) and Version 15
10:30
local
time.
MISR
has
nine
viewing
angles
) along the direction of the satellite motion in four
63
(Jiang et al. 2007). Since December 2007, the MISR Version 22 (V22) operational aerosol
64
retrieval algorithm has been in production, which has added additional absorbing spherical
65
particles and a selection of multimodal aerosol mixtures to the climatology to improve the
66
retrieval accuracy (Kahn et al. 2010). Many researchers have also examined the quality of the
67
MISR V22 dataset. For example, using the 81 global AERONET stations, Kahn et al. (2010)
68
reported that approximately 70% to 75% of MISR AOD retrievals fall within the greater of 0.05
69
or 20% × AOD of the paired validation data over the regions where the aerosol models are
70
assumed to be urban, continental, biomass burning and maritime, but only 47.56% of retrievals
71
fall within the error ranges over the smoke-dust hybrid dominated regions. Cheng et al. (2012)
72
found approximately 66.82% of the MISR-retrieved AODs falling within ±(0.05 ± 0.15 ×
73
AOD) over four sites located in China during 2001-2011 and found a high correlation of
74
approximately 0.88 for the MISR vs. AERONET data. A correlation coefficient of 0.92, slope of
75
0.64 and intercept of 0.05 for the monthly mean AOD were obtained by Li et al. (2016), who
76
validated the MISR AOD using AERONET observations in China from 2006 to 2009.
77
Meanwhile, many studies have demonstrated that high-accuracy MISR AOD observations can be
78
used to analyze the spatial-temporal distributions of aerosol loadings over some regions and even
79
globally (Kahn et al. 2001; Kahn and Gaitley 2015; Zhao et al. 2017), to establish
80
inter-comparisons of multiple satellite sensors for a better dataset (Kahn et al. 2007; Kahn et al.
81
2009; Kumar et al. 2015). The advantage of having aerosol component information and
82
microphysical properties enables MISR AOD observations to be successfully used to estimate
83
near-surface total PM2.5 in both the eastern and western US (Liu 2013; Liu et al. 2005; Zheng et
84
al. 2017), as well as the sulfate component concentration of PM2.5 (Liu et al. 2007; Liu et al.
85
2011). Many studies have also improved the accuracy of PM2.5 estimations by combining MISR
86
AOD products with other AOD products (Geng et al. 2015). In addition, studies on the
87
inter-comparison of MISR aerosol component datasets (Li et al. 2013b; Li et al. 2016) and
88
microphysical properties such as Angstrom exponent and single scattering albedo (Li et al. 2015;
89
Si et al. 2017) have been gradually emerging.
90
To the best of our knowledge, the MODIS team released a global AOD product at 3-km
91
resolution as part of the Collection 6 (C6) delivery (Remer et al. 2013). By contrast, MISR V22
92
gridded at 17.6 km is too coarse for urban-based studies of air pollution (Gupta et al. 2013).
93
Recently, a higher-resolution MISR AOD product, Version 23 (V23) with a spatial resolution of
94
4.4 km, has been made available by the MISR aerosol team. The initial results showed that V23
95
had an improved correlation (R = 0.957), a smaller root mean square error (RMSE) (0.0768), a
96
reduced bias (−0.0208) and a larger fraction within the greater of 0.05 or 20% × AOD (80.92%)
97
relative to the V22 retrievals in comparisons made between the MISR AOD products and
98
multiple
99
(AERONET-DRAGON) AODs (Garay et al. 2017). Subsequently, Franklin et al. (2017) used
100
total and size-fractionated AOD from the MISR 4.4-km data to generate prediction maps of
101
PM2.5 and PM10 over Southern California. Reconsidering MISR mixtures and fractional AODs in
102
modeling, Meng et al. (2018) developed statistical models with 4.4-km aerosol microphysical
103
properties to predict ground-level concentrations of major PM2.5 chemical components, including
104
sulfate, nitrate, OC and EC, in Southern California. Franklin et al. (2018) validated the MISR
AERONET
Distributed
Region
Aerosol
Gridded
Observations
Network
105
V23 AOD retrievals against the AOD retrievals interpolated to 550 nm from the Dalanzadgad
106
AERONET site and found good agreement, with R = 0.845 and RMSE = 0.0712. To date, though,
107
there have been no studies on the performance of the MISR 4.4 km AOD dataset in China.
108
The objective of this study is to provide the first evaluation of the newly released MISR 4.4
109
km aerosol retrievals for China. This paper is organized as follows. In Section 2, the whole study
110
domain and the AERONET observations are described in detail, as well as the operational MISR
111
products. In Section 3, the satellite daily mean 4.4 km retrievals and the old 17.6 km products are
112
compared with the AERONET measurements by different spatial average strategies and by
113
season. Additionally, the MODIS 3 km AOD products are introduced to further investigate the
114
performance of MISR high-resolution products. Finally, we discuss the uncertainties of the
115
MISR AOD retrievals and provide a summary of the findings.
116
2 Data and Methods
117
2.1 Study region
118
China, with an area of 9,600,000 km2, lies between latitudes 18° and 54°N and longitudes
119
73° and 135°E. The landscapes in China vary significantly across its vast width, as shown in
120
Figure 1. The northwest region is mainly composed of barren land and desert; desert/rock/sand
121
composes more than 90% of the Tibetan Plateau region. The North China Plain (NCP) and
122
northeast China are covered with forest and cultivated crops on dry land, and southern China is
123
covered with grassland and cultivated crops on both dry land and wet land. Based on the
124
different terrains, China is characterized by higher elevations in the western regions and lower
125
elevations in the eastern regions, as described by Ma et al. (2014). For example, the average
126
elevation of the Tibetan Plateau is nearly 5 km, while that of most of eastern China is 0.5-1 km.
127
The plains, basins, plateau, hills and mountains contribute to the highly complex topographies
128
across China; thus, the climate differs from region to region.
129 130
Figure 1 Locations of AERONET sites (black solid points) and AERONET-DRAGON sites (red
131
triangles) in China. The land classification data are provided by the Multiresolution Land
132
Characteristics Consortium (http://www.mrlc.gov/nlcd06_data.php).
133
Currently, Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD) and the Pearl
134
River Delta (PRD), three typical regions with high aerosol loadings in China, are the most
135
populated and polluted urbanized areas and have attracted much attention for monitoring and
136
controlling atmospheric pollution. Therefore, the three regions of BTH, YRD and PRD are
137
selected for the validation of the MISR and MODIS aerosol products at regional scales and will
138
be discussed in the coming sections. Taiwan has a dense network of AERONET sites and a large
139
number of observations; therefore, Taiwan is also selected as a typical region in this analysis.
140
2.2 Ground-based measurements
141
The AERONET project is a federation of ground-based remote sensing aerosol networks
142
that provides long-term, continuous and globally distributed observations of the spectral AOD,
143
inversion
144
(https://aeronet.gsfc.nasa.gov/new_web/index.html). Based on empirical knowledge, AERONET
145
retrieval algorithms have gradually evolved from Version 1.0 to Version 2.0 and now to Version
146
3.0 (V3), among which the AOD data of the two latest versions are computed at three data
147
quality levels: Level 1.0 (unscreened), Level 1.5 (automatically cloud-screened and
148
quality-controlled) and Level 2.0 (quality-assured). It is universally acknowledged that AOD
149
retrieval with a low uncertainty of ~0.01 and a high temporal resolution of 15 min can be widely
150
used as “ground truth” in satellite retrieval calibration and aerosol characterization (Holben et al.
151
1998). Since January 2018, some updates of the V3 Level 2.0 AOD products have been released.
152
In this analysis, there are a total of 29 AERONET sites in China, shown as black solid circles in
153
Figure 1, and detailed information on these sites is provided in Table 1. V3 Level 2.0 AOD
154
550-nm data, interpolated on a log-log scale with AOD at 440 nm and 870 nm, are employed to
155
evaluate the satellite retrieval products.
products
and
precipitable
water
data
in
diverse
aerosol
regimes
156
The sequential number, site name, location, region and sampling period of each AERONET
157
site is listed in Table 1. For example, five stations ranging from site No. 1 to 10 in Table 1 are
158
classified in the BTH region, Nos. 6-9 belong to the YRD region, Nos. 10-14 are defined as the
159
PRD region, Nos. 15-20 are located in the northwest region and Nos. 23-29 belong to the Taiwan
160
region. The Tibetan region only has two sites, NAM_CO and QOMS_CAS. This classification
161
information is employed when examining the performance of AOD retrievals by region.
162
2.3 MISR aerosol product
163
Based on the assumption of aerosols being homogeneous within 17.6 km × 17.6 km regions
164
at the surface, the MISR V22 (MISR_v22 hereafter) aerosol retrieval algorithm uses the 256
165
m-1.1 km MISR Level 1B2 pixels as an input. The instrument can collect data in two modes; the
166
global mode is a nominal operational mode that reports data at a 275 m resolution in all nine
167
cameras in the red band and the nadir camera in the blue, green and near-infrared bands while
168
reporting data at a 1.1-km resolution in the off-nadir camera in the blue, green and near-infrared
169
bands (16 pixels × 1.1 km = 17.6 km). The other mode is designed to cover prescribed target
170
areas at full (275 m) resolution in the nine cameras in the four spectral bands, traversing a
171
distance along the track approximately equal in length to the swath width. The latest Version 23
172
AOD (MISR_v23 hereafter) products, with a 4.4-km spatial resolution, are produced by global
173
mode data at a 1.1-km resolution in all bands and cameras. In addition to changes to the
174
resolution, V23 incorporates changes to the product format and content, as well as changes to the
175
cloud screening algorithm relative to V22. These will be described by the MISR operational
176
aerosol team.
177
Both MISR_v22 and MISR_v23 aerosol retrievals require a look-up table based on 74
178
aerosol mixtures, which are composed of three aerosol components of eight “pure” particle size
179
distributions (#1, #2, #3, #6, #8, #14, #19, and #21 in the MISR Aerosol Physical and Optical
180
Properties Database). According to their different sizes and absorbing/scattering properties, these
181
eight particles can be classified into three AOD types, including spherical non-absorbing aerosols
182
(#1, #2, #3 and #6), spherical absorbing aerosols (#8 and #14) and non-spherical dust analogs
183
(#19 and #21) (Kahn and Gaitley 2015; Lee et al. 2016). The detailed contributions of these three
184
componential AODs to each mixture are presented in (Kahn and Gaitley 2015).
185
We downloaded the MISR_v22 aerosol data for 2008-2017 from the operational website.
186
The second band dimension of the parameter named “RegBestEstimateSpectralOptDepth”
187
(called MISR_v22 AOD hereinafter) denotes the mean AOD at 558 nm for all mixtures that pass
188
the goodness-of-fit tests. The parameter of “AerRetrSuccFlag” (MISR_v22 Succflag hereinafter)
189
was also extracted to filter invalid values of MISR_v22 AOD. In the latest version, the
190
parameters corresponding to MISR_v22 AOD and SuccFlag are the parameters called
191
“Aerosol_Optical_Depth” (MISR_v23 AOD) in the directory of “4.4_KM_PRODUCTS”
192
because the MISR operational aerosol teams adjusted some parameter names and reconstructed
193
their file storage formats. The daily and monthly average values based on the MISR_v22 Level 2
194
(swath) AOD at 17.6 km and the MISR_v23 Level 2 products at 4.4 km during the study period
195
were calculated to match the corresponding ground-based AOD measurements.
196
2.4 MODIS aerosol product
197
As mentioned above, two MODIS sensors were placed onboard the EOS Terra and EOS
198
Aqua satellites. Because MISR is mounted on the Terra satellite, we selected the Terra/MODIS
199
AOD (MOD04) products with the same overpass time for consistent comparisons. Since 2014,
200
MODIS operational teams have gradually released C6 aerosol retrieval products, which still
201
contain independent dark target (DT) algorithms aimed at retrieving dense vegetation areas
202
(Levy et al. 2013) and deep blue (DB) algorithms for performing AOD retrievals over bright
203
surfaces (Hsu et al. 2013; Sayer et al. 2013). In addition, a new merged dataset named
204
“AOD_550_Dark_Target_Deep_Blue_Combined” in C6 AOD at 10 km was publicized to
205
improve the global coverage (Levy et al. 2013). Notably, C6 only provides AOD observations at
206
3 km over land and ocean based on the DT algorithm. This product is created using a similar
207
structure to that of 10-km DT products, and the differences arise only according to the manner in
208
which pixels are selected and grouped for retrieval (Remer et al. 2013). In the operational
209
products,
210
“Optical_Depth_And_Ocean”,
211
“Image_Optical_Depth_And_Ocean”, ImageAOD). In this analysis, we extracted AODDT at 550
212
nm of from the Level 2 MOD04 product at 3 km during the same period as the MISR. The daily
213
averaged datasets were calculated for evaluation and comparison purposes.
214
2.5 Evaluation methods
the
MOD04_3K
are
classified
AODDT)
and
as
quality-assured
(parameter
named
all
quality
(parameter
named
flags
215
Illustrated by Wang and Zhao (2017), different spatial and temporal scales may contribute to
216
varied accuracies. Through the sensitivity analysis, the average ground-based AOD
217
measurements within ±30 min of the Terra overpass times at each site were calculated.
218
Considering the individual characteristics of the spatial resolution for the MISR_v23 and
219
MISR_v22 AOD retrievals, the ground measurements were first matched with 1 × 1 pixels for
220
the MISR_v23 4.4 km products and then averaged at 17.6 km × 17.6 km for comparison with the
221
MISR_v22 operational AOD products (17.6 km) when each grid contains several ground-based
222
sites. The standard validation domain of 50 km (Hyer et al. 2011; Levy et al. 2013; Liu et al.
223
2004; Sayer et al. 2014; Shi et al. 2013) was also employed to further compare the two versions
224
of the AOD products. For the high-resolution AOD products of MISR (4.4 km) and MODIS (3
225
km), the AOD retrievals were averaged within a sampling window of 3 × 3 pixels for MISR and
226
4 × 4 pixels for MODIS with an approximate area of 12 km2 centered on each AERONET site.
227
To quantitatively evaluate the accuracy of different AOD retrievals, we introduce some
228
relevant evaluation indicators, which are described in detail below (Franklin et al. 2018; Nicholls
229
2014; Wei et al. 2018; Yang et al. 2019). First, the Pearson correlation coefficient (R) based on
230
linear regression is selected to establish the relationship between the AOD retrievals and
231
ground-based measurements for the target datasets. Second, the EE envelope of MISR falls
232
within the greater of 0.05 or 0.2 × AOD of the paired ground-measured data. Therefore, Equation
233
(1) is introduced as an evaluation parameter to describe the performance of the AOD retrievals in
234
this analysis. In addition, we define the fraction of samples falling above the EE as >EE, falling
235
below the EE as
236
satellite-retrieved values. Third, the mean error (ME), RMSE, and mean relative bias (MRB) are
237
selected to describe the average difference between the satellite retrievals and ground AOD, as
238
presented in Equations (2-4), respectively.
239
EE =
±0.05, ≤ 0.25 ±0.2 × , > 0.25
(1)
240 241 242
#
ME ME = ∑$+2#( $
RMSE = 5 #
&'()**+(),+
−
-./0$1,+ ) (2)
F ∑D >GH(6789:;<==>;<,> ?678@ABCDE,> )
MRB = $ ∑$+2#
$
6789:;<==>;<,> ?678@ABCDE,> 678@ABCDE,>
243
3 Results and Discussion
244
3.1 Performance of AOD over China by version
245
3.1.1 Performance of daily AOD by version
(3) (4)
246 247
Figure 2 Scatter plots of satellite-retrieved mean AODs against AERONET AOD measurements
248
over China from 2007–2017. a–c) comparisons of MISR_v23 daily mean AOD vs. AERONET
249
AOD using spatial collocation criteria of 4.4 km, 17.6 km and 50 km, respectively. d–e)
250
Comparisons of MISR_v22 daily mean retrievals vs. AERONET AOD using spatial collocation
251
criteria of 17.6 km and 50 km, respectively.
252
Figure 2 provides the validation for the MISR_v23 and MISR_v22 daily mean AOD against
253
AERONET AOD measurements at different spatial resolutions over China using linear
254
regression. The dotted lines in Figure 2 represent 1:1 lines (y=x), and the dashed lines are the
255
expected error envelope (EE), which are defined in Equation (1). During the whole study period,
256
there are 365 paired validation data, in Figure 2 a), and linear regression yields a correlation
257
coefficient (R) of 0.902, an RMSE of 0.222 and an intercept (0.0999) near 0, showing that the
258
MISR newly released AOD retrievals are very robust. For the MISR_v23 retrievals, 59.45% of
259
the samples are within the EE envelope, while 16.44% of the samples are overestimated. The
260
negative ME (-0.0605), smaller slope (0.575 less than 1) and
261
that the AOD in the newly released MISR products are still underestimated at high AODs (Kahn
262
et al. 2010). However, the 4.4 km products make MISR_v23 a unique and increasingly popular
263
dataset compared to the MISR_v22 AODs, and MISR_v23 is useful in supporting the
264
examination of the spatial aerosol loading levels over urban areas.
265
As displayed in Figure 2 d), only 340 of the AOD retrievals are collected from MISR_v22
266
operational products vs. AERONET, and 50.29% of the records fall within EE. However, the
267
RMSE (0.186) and MRB (0.0965) of MISR_v22 are lower than those of MISR_v23 (RMSE:
268
0.220, MRB: 0.288). 23.79% of 1051 samples of MISR_v23 against AERONET AODs indicates
269
that the effect of underestimation at large AOD on the MRB is heavily less than that of
270
overestimation at small AOD. For MISR_v22, note that the values on the X-axis (from
271
AERONET) range from 0 to 2, while the values on the Y-axis range from 0 to 1.3, resulting in a
272
large underestimation of 35% of the samples falling below the EE. It is actually the number of
273
matches between MISR and AERONET that is three times larger for the V23 product compared
274
to the V22 product; the samples falling within the EE increase by 22.41%, and the
275
underestimated samples distinctly decrease by 32.02% (Figure 2 b). These improvements can be
276
attributed to the fact that the higher-resolution 4.4 km AOD data from MISR_v23 capture more
277
spatial variabilities that the 17.6 km AOD data from the MISR_v22 misses, which can be found
278
in Figure S1.
279
When comparing the MISR_v23 and MISR_v22 AODs at the 50 km scale, we can find the
280
same conclusions derived from Figure 2 b) vs. Figure 2 d), that the new version has significant
281
slight improvements in the total sample size and the number of samples falling within the EE. In
282
addition, the accuracy of the MISR_v23 AOD decreases with the increase in the validated scale,
283
specifically, the MISR_v23 data had a 157% deterioration in the MRB and a 5.50% decrease in
284
the R values relative to those of MISR_v22. However, for MISR_v22, the number falling within
285
the EE increases by 9.74%. Generally, the most robust statistic for these comparisons is =EE,
286
which is nearly identical (~60%) for the MISR_v23 AODs regardless of resolution.
287
Considering that most values span the range from 0.01 to 1, a linear scale tends to
288
overemphasize the apparent importance of high AODs, so we also conduct the validations using
289
a log-log scale for comparison (as shown in Figure S2). All key-value pairs of the
290
AERONET-observed and MISR-retrievals belonging to 0.01~1 are shown in negative values on
291
the bottom left. Clearly, the R and RMSE values using a log-log scale are generally lower than
292
those established by linear regression, especially at 4.4 km and 17.6 km. However, at 50 km,
293
higher R values (Figure S2) may be obtained from the regression using the log-log scale, which
294
is not as sensitive to the scattered distributions of the AERONET and MISR AODs (Figure 2 c)
295
and e)).
296
To eliminate the effect of different sample sizes, Table S1 shows the validations to compare
297
the accuracies of the different versions when the same samples are employed for the two versions.
298
Surprisingly, both AOD retrievals at 17.6 km and 50 km of the former version are comparable to
299
those of the latter one, having ~16.37% (17.6 km: 19.20%; 50 km: 13.54%) fewer retrievals
300
falling within the EE, but the newly released products perform better except for their larger MRB
301
values. The comparison of the statistics using two linear methods is similar to the above
302
descriptions in Figure 2 and Figure 2S. Meanwhile, the different sample size and the sensitivity
303
of some of the metrics to large outliers may better explain the statistical differences. When using
304
the same sample size constraint, the advantages of the MISR_v23 AOD are more pronounced.
305
3.1.2 Performance of seasonal AOD by version
306
307 308
Figure 3 Validation of MISR_v23 and MISR_v22 seasonal mean retrievals against AERONET
309
over 17.6 km (first line) and 50 km (second line) from 2008–2017. The four seasons are defined
310
as spring (March–May), summer (June–August), fall (September–November) and winter
311
(December–February).
312
The seasonal datasets of the two MISR versions on the national scale vs. the AERONET
313
measurements using linear regression are shown in Figure 3, and detailed statistics are described
314
in Table S2 for the 17.6-km spatial resolution data and Table S3 for the 50-km spatial resolution
315
data.
316
For MISR_v23 at 17.6 km, there are 379, 164, 219 and 288 retrievals in spring, summer,
317
fall and winter, respectively. More than 70% of the records in the fall and winter fall within the
318
EE with small RMSE (0.12–0.14), ME (-0.039–−0.027) and MRB (0.23–0.25) errors, which are
319
credited with strong reliability. In summer, 21.95% of the retrievals fall above the EE and 23.78%
320
fall within the EE, resulting in a large MRB error of 0.48. Obvious underestimation in spring is
321
obtained from the largest number of samples falling below the EE (31.66%), which is likely
322
because frequent dust storms that appear in spring and disturb the surface reflectance lead to
323
incomplete distraction of the path radiance from the spectral radiances at the top of the
324
atmosphere (Kahn et al. 2010). In contrast, similar seasonal performances are also found in the
325
MISR_v22 dataset, but the summer is the only season in which various evaluation parameters are
326
superior. Generally, MISR_v22 in all seasons has small uncertainties, but we prefer the
327
MISR_v23 AODs in terms of the percentage of samples falling within the EE.
328
The comparisons in Figures 3 c) and d) show that the seasonal patterns of the 50-km spatial
329
resolution data are similar to those of the 17.6-km spatial resolution data. As the spatial matching
330
scale increases, introduced noise reduces the samples falling within the EE but smooths the
331
uncertainties in spring. For example, in Table S3, the R of MISR_v23 vs. AERONET in winter is
332
0.66, which is much lower than that using spatial collocation criteria of 17.6km (0.87). The linear
333
fit between the MISR_v22 and AERONET datasets indicates that the R values are greater than
334
those of the MISR_v23 dataset, especially in summer (MISR_V23: 0.87; MISR_V22: 0.96) and
335
winter (MISR_V23: 0.66; MISR_V22: 0.84), and the statistical information indicates that the
336
MRB values are positively related to the number of overestimations but negatively associated
337
with the underestimated samples. Overall, at a coarse resolution, the advantages of MISR_v23 in
338
all seasons are less obvious, whereas MISR_v22 performs well.
339 340
Figure 4 Scatter plots of satellite-retrieved daily mean AODs at two resolutions in spring (first
341
column), summer (second column), fall (third column), and winter (fourth column) against
342
AERONET measurements. The first two rows are the correlation between the MISR_v23 or
343
MISR_v22 daily mean AOD and AERONET using spatial collocation criteria of 17.6 km,
344
respectively. The last two rows are the same as the first two rows but using spatial collocation
345
criteria of 50 km.
346
Figure 4 displays the comparison between the two versions using the same samples as a
347
constraint. The advantages of MISR_v23 are quite obvious, especially in the number of retrievals
348
falling within the EE. For example, in the first two rows (different versions at 17.6 km), a
349
seasonal mean improvement of ~18.53% appeared in MISR_v23 compared to MISR_v22, with
350
an increase of 41.67% in spring, 18.19% in summer, 10.26% in fall and 3.98% in winter, which
351
is likely due to the errors in aerosol particle characteristics such as single scattering albedo and
352
asymmetry, because the coal burning in winter makes the regional aerosol optical characteristics
353
more complicated
354
of 33.67% (Spring: 22.22%; Summer: 28.18%; Fall: 38.09%; Winter: 46.17%). In light of the
355
last two rows (different versions at 50 km), MISR_v23 has a mean of 14.57% more retrievals
356
falling within EE and 28.79% fewer records falling below EE. In conclusion, the MISR newly
357
released AOD data have first priority when using the MISR products. Integrated with the RMSE
358
and ME errors, for MISR_v22, all seasons excluding the spring at 17.6 km and summer at 50 km
359
are also worthy of consideration.
(Yang et al. 2019). Meanwhile, underestimations are reduced by an average
360
By comparing the statistical parameters using the two fitting methods, we conclude that the
361
worst result in spring is consistent with the previous conclusion, and the comparisons of the R
362
values between the linear regression and log-log scale (Figure S3) are similar to Table S1.
363
However, the fitting results vary with the different seasons and versions. Generally, the results
364
based on the log-log scale are not sensitive to the differences in the AOD distribution, while
365
those by linear regression are heavily dependent on it. No matter which fitting method is adopted,
366
the statistics are convincing. Only the results using linear regression are shown in the following
367
sections.
368
3.2 Performance of AOD over typical regions by version
369
3.2.1 Performance of daily AOD over typical regions by version
370 371
Figure 5 Scatter plots of satellite-retrieved daily mean AODs using spatial collocation criteria of
372
17.6 km over the BTH, YRD, PRD and Taiwan regions from top to bottom. The two columns are
373
the correlation between the MISR_v23 or MISR_v22 daily mean AOD and AERONET,
374
respectively.
375
Figure 5 presents the daily mean validation of the MISR_v23 or MISR_v22 against
376
AERONET AOD measurements over four typical regions in China from 2008–2017. The AOD
377
performances using the same samples are presented in Table S4. The variabilities in each region
378
are described as follows:
379
1) BTH
380
There are 530 and 144 records matched by the MISR_v23 and MISR_v22 17.6 km data,
381
respectively. Similar to comparisons on the national scale, the sampling sizes of the newly
382
released version are greater than those of the old version. From the ranges of ground truth, the
383
daily mean observations over the BTH region are as high as 2~3, significantly larger than the
384
definition of haze day (AOD > 1.0), indicating severe pollution in this region. The MISR_v23
385
products show good agreement with the ground measurements with a high R = 0.896 and low
386
intercept = 0.0915 and ME = -0.103, as well as a large number of retrievals (68.87%) falling
387
within the EE. Only 3.96% of the retrievals fall above the EE, whereas the underestimation of
388
the high AODs is severe for the retrievals larger than 1.0, resulting in a negative value of the
389
MRB (-0.065). In MISR_v22, the statistical results of the R, RMSE, slope and intercept are
390
slightly improved, but 11.81% of the 144 samples fall above the EE. When using the same
391
samples (Table S4), the accuracy of the AOD data from MISR_v22 with R=0.91 and =EE of
392
65.74% are comparable to those of MISR_v23 data (R=0.93, =EE: 75.93%), both of them are
393
recommended for AOD analysis at a regional scale.
394 395
2) YRD As shown in the Figure 5 c) and d), only 70 and 28 retrievals are collected from MISR_v23
396
and MISR_v22 respectively. The two datasets show that the air quality in the YRD region is
397
better than that in BTH. Although the R, slope and intercept values of MISR_v23 are worse than
398
those of MISR_v22, the number of retrievals falling within the EE in the latest version (61.43%)
399
is significantly higher than that in the old version (39.29%), and the improvement in the
400
MISR_v23 retrievals falling below the EE is remarkable (28.57% compared with the 57.14% for
401
MISR_v22). These variabilities are also captured by limiting the number of samples. Thus, we
402
recommend only the MISR_v23 AOD dataset over the YRD region.
403
3) PRD
404
As expected, the air quality of the PRD is much better than that of the YRD and BTH.
405
Under a comparison at a high resolution, in Figure 5 e), the MISR_v23 AODs are very consistent
406
with the AERONET AOD measurements (R = 0.908), and 73.13% of the 67 records meet the EE
407
requirement with a low ME, RMSE and MRB. Due to the lack of MISR_v22 retrievals at 17.6
408
km, there are no matching points between the MISR_v23 and MISR_v22 datasets for a clearer
409
comparison. We further compared the MISR_v23 and MISR_v22 at 50 km and found that there
410
were 92 and 11 matching points, respectively. Linear regression with R=0.84, =EE of 67.39%
411
and MRB=-0.0825 are yielded from MISR_v23 against the AERONET AODs, whereas the
412
values are 0.59, 36.36% and -0.140, respectively, for MISR_v22 vs. AERONET (data not shown).
413
As displayed in Table S4, the MISR_v23 data are significantly better than those of MISR_v22
414
when the same sample points are used at a 50-km scale.
415 416
4) Taiwan There are 7 AERONET sites distributed in Taiwan, which has an area of 36,000 km2. From
417
the ranges of the X-axis and Y-axis, the AOD loadings in Taiwan are similar to those in the PRD,
418
and over 70% of the points range from 0 to 0.5. For the data in Figure 5 g), MISR_v23 performs
419
well, with the highest R (~0.927) and slope (0.715) and the lowest RMSE (0.109) and ME
420
(-0.0327) among the four typical regions. Meanwhile, Taiwan has the largest overestimation,
421
with 15.52% of 174 retrievals falling above the EE. Although MISR_v22 correlated well with the
422
ground truths, with R=0.83, ME=-0.069, MRB=-0.022, the number of samples falling within the
423
EE limits its accuracy. Table S4 also highlights the advantage of MISR_v23 in Taiwan.
424
3.2.2 Performance of seasonal AOD over typical regions by version
425
Table 2 Statistics for the mean AOD of MISR_v23 against AERONET by region and season
426
using spatial collocation criteria of 17.6 km from 2008–2017 season
N
r
RMSE
MAE
MRE
=EE
>EE
spring
190
0.84
0.39
-0.17
-0.12
56.32
5.79
37.89
summer
89
0.97
0.21
-0.095
-0.026
65.17
4.49
30.34
fall
116
0.96
0.18
-0.068
-0.081
78.45
1.72
19.83
winter
135
0.86
0.15
-0.039
0.0071
80.74
2.96
16.3
spring
29
0.87
0.21
-0.042
0.013
62.07
17.24
20.69
summer
13
0.79
0.48
-0.22
-0.13
46.15
7.69
46.15
fall
5
0.99
0.03
-0.014
-0.049
100
0
0
winter
23
0.94
0.13
-0.072
-0.11
60.87
4.35
34.78
spring
13
0.98
0.25
-0.14
-0.13
46.15
7.69
46.15
summer
4
1.00
0.02
0.0075
0.082
100
0
0
fall
11
0.95
0.06
-0.043
-0.11
81.82
0
18.18
winter
39
0.90
0.07
-0.029
-0.073
76.92
5.13
17.95
spring
75
0.95
0.13
-0.059
0.045
50.67
13.33
36
summer
26
0.86
0.09
0.033
0.36
53.85
34.62
11.54
fall
38
0.92
0.08
-0.027
-0.034
65.79
10.53
23.68
winter
35
0.92
0.09
-0.032
-0.11
57.14
11.43
31.43
BTH
YRD
PRD
Taiwan
427
428
Table 3 Statistics for the mean AOD of MISR_v22 against AERONET by region and season
429
using spatial collocation criteria of 17.6 km from 2008–2017 Region
season
N
r
RMSE
MAE
MRE
=EE
>EE
BTH
spring
44
0.92
0.30
-0.10
0.070
43.18
25
31.82
summer
26
0.98
0.09
-0.02
0.14
76.92
11.54
11.54
fall
45
0.92
0.25
-0.11
-0.071
71.11
0
28.89
winter
29
0.91
0.10
-0.028
0.080
62.07
10.34
27.59
spring
15
0.70
0.19
-0.10
-0.14
46.67
6.67
46.67
summer
5
1.00
0.14
-0.097
-0.21
60
0
40
fall
5
0.93
0.19
-0.14
-0.25
20
0
80
winter
3
1.00
0.14
-0.13
-0.34
0
0
100
spring
64
0.82
0.19
-0.099
-0.11
32.81
17.19
50
summer
20
0.75
0.12
0.0096
0.59
30
40
30
fall
33
0.82
0.12
-0.069
-0.18
42.42
6.06
51.52
winter
20
0.94
0.11
-0.056
-0.081
50
10
40
YRD
Taiwan
430 431
Table 2 and Table 3 respectively describe the performances of the MISR_v23 and MISR_v22 seasonal AOD datasets over four typical regions using AERONET.
432
1) BTH
433
Regardless of the version or season, the number of samples falling within the EE are higher
434
than the corresponding national averages. Specifically, for the MISR_v23 17.6 km data, 80.74%
435
of the 135 retrievals in the winter fall within the EE and 78.45% of 116 samples fall within the
436
EE in the fall, followed by an =EE of 65.17% and r = 0.97 in summer and an =EE of 56.32% and
437
r = 0.84 in the spring. As shown in Tables 2 and 3, severe underestimations with retrievals falling
438
below the EE appeared in this region, especially in the spring. By contrast, the AOD dataset in
439
the summer for MISR_v23 at 17.6 km can be substituted for the same season in the MISR_v22
440
dataset, which has good agreement with r = 0.98 and quite small RMSE, ME and MRB values.
441
Moreover, 76.92% of the retrievals meeting the accuracy requirement. In the spring, the AOD
442
retrievals in this region are easily affected by sandstorms from Xinjiang province in the
443
northwest of China, and thus, the number of samples falling within the EE are less than 60%
444
(Levy et al. 2013).
445
2) YRD
446
Compared with the BTH region, the YRD region has much fewer matched points in each
447
season. At 17.6 km, the satellite retrievals in the spring and winter of MISR_v23 show good
448
consistency, with greater than 60% of the retrievals falling within the EE, high r values
449
(0.87~0.94) and low uncertainties for the ground measurements, which are certainly
450
recommended relative to those of MISR_v22. In the fall, there are only 5 samples at 17.6 km,
451
and all the records of MISR_v23 fall with the EE, but only 20% of the MISR_v22 retrievals meet
452
the accuracy requirements, which is just a reference due to the lack of retrievals. A similar
453
condition is found in the summer, but the MISR_v22 dataset performs better than the MISR_v23
454
dataset. Further validations will be needed to determine which version is better for analysis if the
455
satellite observations and ground measurements are available, especially at a higher resolution.
456
3) PRD
457
Due to the severe lack of sample points, we only display the comparison of MISR_v23 with
458
AERONET in the PRD region. The AODs in the spring have many retrievals falling below the
459
EE, which is approximately equal to the number falling within the EE (46.15%). However, up to
460
81.82% and 76.92% of the samples fall within the EE, with r values in the range of 0.95~0.90, in
461
the fall and winter at 17.6 km, respectively, demonstrating that the latest MISR products are
462
promising. The higher accuracy in the summer is just a reference because the summer has fewer
463
samples.
464
4) Taiwan
465
Over the Taiwan region, the retrievals falling within the EE in the four seasons are greater
466
than 50%, and the largest number of 65.79% at 17.6 km is in the fall. Among these typical
467
regions, Taiwan is the only region where the validations of the satellite-retrieved MISR_v23
468
AODs against AERONET in all seasons at a high resolution are better than those of MISR_v22
469
against AERONET for almost all evaluation parameters, such as the RMSE, ME and MRB, as
470
well as the number of retrievals falling below the EE. However, in general, the accuracy in
471
Taiwan is not as good as that in the BTH region.
472
3.3 Comparison of MISR and Terra/MODIS AOD retrievals
473
3.3.1 General comparison of MISR and MODIS AOD
474
475 476
Figure 6 Comparisons of MISR and MODIS AOD validated by AERONET observations in 2016,
477
a) the overall match-up trends over three datasets. b–c) Difference between MISR and MODIS
478
AOD as a function of AERONET AOD. The green lines and the blue lines are the EE envelopes
479
of MISR and MODIS, respectively, for consistent comparison.
480
We further compared the performances of MISR 4.4 km AOD retrievals and MODIS 3 km
481
DT products based on AERONET daily observations in 2016. Figure 6 a) displays the variation
482
trends in the AODs when there are available data over three datasets, marked in black, red and
483
blue points for ground truth, MISR and MODIS values, respectively. Overall, there were 104
484
daily records that matched in 2016. Both MISR and MODIS AOD products were significantly
485
consistent with the changes in ground measurements, and their correlation coefficients were
486
0.925 and 0.909, respectively. However, for the ranges, the MODIS retrievals were substantially
487
larger than the AERONET observations at some points, with an ME of 0.0949; for example, the
488
MODIS AOD of 1.44 at the 72nd sample point was larger than the ground truth of 0.897. The ME
489
of -0.0863 indicated that the MISR AOD was underestimated relative to the ground truth, and the
490
absolute value of the MRB (0.21) was closer to that of MODIS (0.23).
491
Numerous comparisons and validations of MISR and MODIS with AERONET have been
492
performed (Cheng et al. 2012; He et al. 2010; Qi et al. 2013); it is universally acknowledged that
493
the MISR AOD retrievals perform well, as demonstrated in Nanjing (Kang et al. 2016), at four
494
sites in China (Cheng et al. 2012), over Durban in South Africa (Kumar et al. 2015), over
495
Karachi in Pakistan (Alam et al. 2011), and over Europe (de Leeuw et al. 2015). For the MODIS
496
AOD, Remer et al. (2013) noted that over land, the 3-km product appears to be globally 0.01 to
497
0.02 larger than the 10-km product.
498
The difference between MISR or MODIS and AERONET (MISR-AERONET or
499
MODIS-AERONET hereinafter) as a function of AERONET AOD is shown in Figures 6 b) and
500
c), respectively, which are overlaid with the EE envelopes of MISR and MODIS for consistent
501
comparison marked with green lines and blue lines, respectively. Notably, the underestimation of
502
MISR AOD was more serious, and the values of MISR-AERONET decreased with the increase
503
in AERONET observations, meaning that the deviations of MISR relative to the ground truth
504
were gradually prominent, particularly in the decrease in the ground truth. Table 4 describes the
505
sample distributions of MISR-AERONET and MODIS-AERONET. For MISR, 66 points were
506
lower than the ground truth, and more than half of the 66 records (N=36) were concentrated in
507
the AERONET AOD range of 0~0.5; among all overestimated samples (N=38), 30 points
508
appeared in the AOD range from 0 to 0.25. Of the 104 samples, 59.62% fell within the MISR EE
509
envelope and 80.77% fell within the MODIS EE envelope (±0.05 ± 0.15τ), which demonstrated
510
that the MISR retrievals have high accuracies. Xiao et al. (2009) also indicated that the MISR
511
AOD retrievals agreed well with the ground-based observations for AOD < 0.5 but were
512
systematically underestimated for AOD > 0.5 in China. For MODIS-AERONET, most samples
513
had a disorderly distribution above the y=0 line, meaning that 88 AOD points were biased high,
514
with nearly half of the samples concentrated in 0~0.25, and the biases gradually decreased with
515
the increase in AERONET AOD. Qi et al. (2013) concluded that when AOD is small (0~0.5),
516
MODIS retrievals are higher than the AERONET observations, but when the AOD is large
517
(0.5~1.0), the MODIS retrievals are lower than the AERONET observations. In terms of the
518
underestimated parts, when the AERONET observations ranged from 0 to 1, the differences
519
showed a positive trend with AOD; for example, the ground truth value of 0.992 was far higher
520
than the MODIS value of 0.17. Of the 104 records, 43.27% and 71.15% fell within the MISR EE
521
and MODIS EE envelopes, respectively, and were lower than the MISR records. According to
522
Remer et al. (2013), both the validations of retrieved AOD at 3 km against AERONET
523
measurements over land and ocean were slightly lower than those at 10 km, and the former ones
524
have more samples falling above the EE. Additionally, Remer et al. (2013) proposed that
525
expected errors associated with the 3-km land product are determined to be greater than those of
526
10-km product (±0.05 ± 0.20τ).
527
Table 4 Sample distributions of the differences between the MISR or MODIS data and
528
AERONET observations Dataset MISR_Diff
MODIS_Diff
N
0~0.25
0.25~
0.50~
0.75~
1.0~
1.25~
1.5~
MISR/MO
>0
38
30
6
2
0
0
0
0
62/84
<0
66
19
17
12
7
4
5
2
>0
88
45
19
11
4
3
4
2
<0
16
4
4
3
3
1
1
0
529
1
530
MISR-defined EE and MODIS-defined EE, respectively.
45/74
MISR/MODIS EE indicates the number of retrievals of MISR_Diff or MODIS_Diff that fall within the
531
3.3.2 Spatial comparison of MISR and MODIS AOD
532
533 534
Figure 7 MISR true color images at nadir a), Af b), Bf c), Cf d) and Df e), complete overpass f)
535
and Terra/MODIS true color image g), as well as their combined coverage h) on February 13,
536
2018.
537
We compared the true color images of MISR and MODIS on 13 February 2018. As
538
mentioned above, MISR images are required with normal view angles relative to the surface
539
reference ellipsoid of 0°, 26.1°, 45.60°, 60.00° and 70.50° for An, Af/Aa, Bf/Ba, Cf/Ca, and
540
Df/Da, respectively. Figures 7 a)-e) display images at nadir and four angles along with the
541
scanning direction. The values of radiation brightness are gradually larger as the observation
542
angle increases, illustrating that multiple angles enable MISR to provide abundant spectral
543
information. Compared to identification characteristics in true color images, both MISR and
544
MODIS captured the cloud/snow area in northeastern China and the slight haze in Henan
545
Province. For the coverage, as depicted in Figure 7 f), MISR only covered a part of eastern China
546
with the origin from Harbin, Changchun, and Shenyang and then passed through Bohai Sea and
547
adjacent areas to Jiangxi, Fujian and Guangdong Provinces and traversed one zone, including
548
Urumqi, Qinghai Province and the Tibetan region. However, in Figure 7 g), except for a part of
549
Guizhou and Yunnan Provinces, MODIS released an operational true color image almost
550
covering the whole domain of China, and its width was obviously larger than that of MISR
551
(Figure 7 h). As previously discussed, the sensors’ widths often determine their own overpass
552
periods; MODIS onboard the Terra passes over each day, whereas MISR varies with latitude and
553
requires 2~9 days to revisit the same place, which makes MISR not as popular as MODIS for
554
retrieving aerosol properties.
555 556
Figure 8 Spatial distributions of the daily MISR (left) and MODIS (right) AOD on February 13,
557
2018.
558
Figure 8 displays the spatial distribution of MISR 4.4 km and MODIS 3 km AOD on
559
February 13, 2018. As shown by the true-color images, the two sensors were affected by cloud
560
contamination in Guangxi, Guizhou, western China and Liaoning provinces. Compared to valid
561
MISR retrievals, MODIS was capable of clearly identifying the boundary of cloud pixels near
562
Guangdong and Hubei provinces but had no inversion results when light haze appeared in the
563
BTH region. By contrast, MISR had stronger ability in areas where clouds and clean underlying
564
surfaces were mixed in the west and northeast China regions. From valid retrievals of both MISR
565
and MODIS, MISR AOD was less biased than MODIS; for example, for Anhui province,
566
MODIS AOD was ~0.35, whereas MISR AOD was only ~0.2. Although the results of the 3-km
567
product mirror the 10-km retrievals, Remer et al. (2013) found an increase in noisy artifacts in
568
the finer resolution product, which unfortunately occurred most frequently over urban surfaces.
569
3.3.3 Seasonal comparison of MISR and MODIS AOD
570
571 572
Figure 9 Comparison of monthly average MISR a) and MODIS b) AOD in 2016 and seasonal
573
average MISR c) and MODIS d) AOD in 2016.
574
Figures 9 a) and b) show a comparison of the monthly average MISR and MODIS AOD in
575
2016. The percentiles of 90% and 10% of all sorted samples are employed as the upper boundary
576
and lower boundary, respectively, to lessen the impacts of possible outliers, and the mean value
577
in each month is shown as a red dot. Both MISR and MODIS mean AODs displayed the highest
578
value in March and lowest value in August over the whole year, and even the inflections of
579
variation were exactly consistent. However, many discrepancies were found in the variation
580
trends between MISR and MODIS; for example, MODIS was better at capturing more details
581
than MISR, with wider ranges in all months; the maximum in 2016 for MODIS was ~0.45 higher
582
than that for MISR, and the monthly means of MODIS AOD were 0.042~0.094 higher than those
583
of MISR AOD. These findings validated that some overestimations were found in MODIS; by
584
contrast, the MISR retrievals were biased lower.
585
On the seasonal scale, Figures 9 c) and d) show that AODs in spring were largest, followed
586
by winter and summer, and smallest in fall. A handful of studies have reported that frequent dust
587
storm events appeared in spring in China, especially in the northwest region covered with deserts
588
and barren lands, and explained that high AOD levels were mainly from coarse particles (Tao et
589
al. 2017). Meanwhile, the southwestern part of China, such as Yunnan and Guangxi provinces,
590
tends to be affected by the transmission of pollutants from Southeast Asia in spring (Liu et al.
591
1999); and the Hongkong tends to be affected by the transmission of pollutants from the Pearl
592
River Delta region (Yang et al. 2018). In winter, human activity was dominant in the northern
593
Yangtze River area, and high levels of SO2 and NOx emissions from large amounts of coal
594
heating were the main cause of winter pollution (Krotkov et al. 2016). Under favorable weather
595
conditions, for example, the boundary layer heights in summer and fall are relatively large,
596
which is beneficial for pollutant diffusion. These findings can be well captured by MISR and
597
MODIS instruments (Kang et al. 2016; Li et al. 2016). In general, both datasets performed well
598
when investigating the characteristics of long-term variation in AOD, but when quantitatively
599
using their values, more attention should be paid to the overestimation of MODIS and
600
underestimation of MISR.
601
3.4 Uncertainty analyses
602
In general, the accuracy of AOD retrievals heavily depends on the removal of cloud
603
contamination, the assumption of surface reflectance and the selection of aerosol models, which
604
are adaptive to most satellites/sensors (Kahn et al. 2010; Levy et al. 2010; Yoon et al. 2012).
605
Each contributing factor for MISR is discussed as follows:
606
1) Cloud screening methods. The MISR cloud team has developed three independent
607
cloud
detection
methods:
Radiometric
Camera-by-Camera
Cloud
Mask
(RCCM),
608
Stereoscopically Derived Cloud Mask (SDCM), and Angular Signature Cloud Mask (ASCM).
609
More details can be found in the Data Products Specifications manual (Mike Bull 2011). In the
610
operational products, the Retrieval Applicability Mask flag (=0) is used to identify pixels free of
611
clouds, glint, and other factors. The Cloud Screening Parameter is used to indicate how many
612
pixels belong to clear flags versus a total of 16×16×9 flags. Witek et al. (2013) investigated
613
MISR AOD over oceans using the Maritime Aerosol Network (MAN) and found that MISR
614
overestimates AOD by 0.04 on average compared to MAN, and the R and RMSE were 0.95 and
615
0.06, respectively. Subsequently, setting a clear flag fraction threshold to 0.6 reduces the bias to
616
below 0.02, improving the overestimation of AOD over ocean areas (Witek et al. 2013). As
617
reported by Remer et al. (2005), MISR lacks channels in the near and far-infrared regions where
618
cirrus clouds are most easily detected. Therefore, considering the wider spectral information of
619
MODIS, Shi et al. (2014) used MODIS-based cloud screening methods to examine the impacts
620
of cloud contamination on the MISR AOD products and showed that on average globally, thin
621
cirrus cloud contamination introduces a possible ~0.01 high bias for the overwater MISR AOD
622
retrievals, and the bias increases to 0.015–0.02 over the mid- to high-latitude oceans and
623
Southeast Asia. Kahn et al. (2010) also noted some overestimations of AOD retrievals possibly
624
affected by neighboring pixels of scattered or broken clouds. These investigations demonstrated
625
that an accurate cloud screening method directly determines the retrieval uncertainty.
626
2) Surface reflectance assumption. In this analysis, general underestimation of MISR
627
AOD was found, meaning that the assumed surface reflectance was heavily overstated. Figures 7
628
a–e) show that the signal received by MISR was brightest when the angle increased to 70.5°,
629
which makes balancing the reflection signals from all angles a difficult challenge. MISR
630
retrievals are robust in bright surface landscapes filled with deserts, snow or other landscapes
631
(Alam et al. 2011); Figure 9 a) captured this situation well. Si et al. (2017) found that the single
632
scattering albedo (SSA) values released by the MISR team in 74 mixtures ranges from 0.8 to 1.0,
633
and SSA is equal to 1 in the first thirty mixtures. On the basis of the reference materials (David J.
634
Diner March 10, 2008; Kahn and Gaitley 2015), the MISR team defined the SSA of all spherical
635
particles as 1, indirectly showing some bias between determining scattering and absorbing
636
properties. All of the above descriptions indicate that the observation signal process method and
637
identification over bright pixels have strong impacts on the overestimation of surface reflectance.
638
When the aerosol loading is low, the influence of surface reflectance underestimation is
639
dominant in the aerosol derivation, leading to a large overestimation of AOD in clean conditions.
640
For example, in Table 4, there are 30 pairs (total of 38 pairs) in which biases corresponded to
641
AERONET AOD 0~0.25 ranges, thus addressing the importance of appropriate surface
642
reflectance.
643
3) Aerosol model selection. When the aerosol loading is high, the role of the appropriate
644
aerosol model should be emphasized. MODIS C6 and VIIRS are divide into five classifications,
645
including continental model, moderate absorbing model, non-absorbing model, strong absorbing
646
model and non-spherical model, whereas the MISR team predefined 74 mixtures (Figure 10).
647
Each mixture is made up of three components: a spherical non-absorbing component, a spherical
648
absorbing component and a dust component. Each component consists of two or three particles.
649
More information on mixed situations can be referenced in Kahn and Gaitley (2015) and is
650
depicted in Figure 10. In the first ten mixtures, #1 and #6 mutually vary at an interval of 10%
651
with a total amount of 100%; the 51st mixture is made up of 72% #2, 8% #6 and 20% #19. The
652
MISR EOF algorithm sets chi-square values to determine whether a mixture fits the tests and
653
defines two scenarios for AOD retrieval: one is described in Section 2.3, and the other treats the
654
AOD corresponding to only one mixture passing through the chi-square test as the “lowest
655
Aerosol Optical Depth” (lowAOD). The bestAOD parameter was used in this analysis, which
656
means that more aerosol models are likely to be considered in the retrievals. However, when only
657
one mixture succeeds, even 74 combinations of MISR are meaningless. Meanwhile, the constant
658
interval limits the selection of more dynamic or close to real atmospheric conditions; for example,
659
dust storms occur frequently in spring, affected by high wind speed and southwesterly wind, and
660
a large number of coarse particles accumulate in the central and eastern regions of China where
661
the Nos. 51~74 mixtures should be added. In addition, MODIS C6 distinguishes aerosol models
662
from different seasons and regions, whereas MISR does not.
663 664
Figure 10 MISR operational team predefined 74 mixtures and componential AOD
665
666
percentage (%) in each mixture.
4 Conclusions
667
In this analysis, AERONET measurements from 29 sites in China from 2008–2017 were
668
obtained and used to validate the newly released MISR Level 2 AOD products at 4.4 km. First,
669
on a national scale, we compared MISR-observed daily and seasonal AOD from different
670
versions at spatially averaged resolutions of 17.6 km and 50 km, respectively. Next, the
671
performances over typical regions, including BTH, YRD, PRD and Taiwan, were analyzed. Then,
672
using Terra/MODIS 3-km products, we investigated the MISR 4.4-km AOD from the true-color
673
images and retrieved spatial distribution, monthly variation trend, and seasonal patterns. Finally,
674
the possible uncertainties of AOD retrievals were discussed. The results show the following:
675
1) The MISR 4.4 km retrievals are well correlated with the AERONET measurements (R:
676
0.902, =EE: 59.45%) with little underestimation (ME: -0.0605;
677
versions or spatial validation domains, the number of matching samples of the latter version is
678
2~3 times larger than those of the former version, and the MISR V23 retrievals falling within the
679
EE increase by 22.41% at 17.6 km and 8.46% at 50 km. The number of retrievals falling within
680
the EE in fall and winter are larger than those in summer and spring for both versions, and the
681
performance of the high-resolution averaged domain is generally better than that at 50 km.
682
2) The daily dataset over BTH (68.87%), YRD (61.43%) and PRD (73.13%) falling within
683
the EE exceed 60%, except Taiwan (55.75%). By season, the number of retrievals falling within
684
the EE for MISR V23 and V22 over the BTH in spring is slightly worse than that in other
685
seasons but performs better than that in other regions. Based on few paired values, the number of
686
V23 retrievals falling within the EE in fall and winter exceeds 60%. Accompanied by the
687
improvement of spatial resolution, there is a small number of sample points in PRD, and the
688
number of retrievals falling within the EE in all seasons is greater than 76%, except in spring. We
689
only recommend the AOD data in all seasons of V23 in Taiwan. Integrated with the RMSE and
690
ME errors, for V22, the retrievals over the YRD in the summer and the BTH in the summer and
691
winter are recommended.
692
3) Compared with Terra/MODIS 3-km AOD in 2016, the MISR V23 4.4-km product has a
693
slightly higher R value (0.925) with AERONET than with MODIS (0.909), and the MISR AOD
694
generally has a lower bias; however, MODIS AOD is overestimated relative to the ground truth.
695
Among the 104 matched points, 62 and 84 of the MISR retrievals fall into the MISR-defined EE
696
and MODIS-defined EE, respectively, higher than the 45 and 74 points, respectively, of the
697
MODIS AOD. In addition, consistent seasonality characteristics for the two datasets are as
698
follows: spring>winter>summer>autumn.
699
Notably, the retrieval algorithm does not appear to be significantly improved, meaning that
700
cloud screening methods, surface reflectance and aerosol models using the same data as MISR
701
V22 have similar impacts on the uncertainties of AOD retrievals. On the basis of a preliminary
702
and first comparison of the newly released MISR products, in the future, the matching
703
uncertainties from spatial resolution and time average will be discussed in details, more complex
704
and appropriate aerosol models will be examined to retrieve AODs with higher accuracies.
705 706
Table 1 Detailed information on the AERONET sites in China No.
Site name
Lon
Lat
Region
Period
1.
Beijing
116.38
39.977
BTH
2007.01-2017.05
2.
Beijing-CAMS
116.31
39.933
2012.08-2017.01
3.
PKU_PEK
116.18
39.593
2007.01-2008.08
4.
Xianghe
116.96
39.754
2007.01-2017.05
5.
Xinglong
117.57
40.396
2007.01-2012.05
6.
Hangzhou-ZFU
119.72
30.257
7.
Hefei
117.16
31.905
YRD
2007.08-2007.11 2007.01-2008.11
8.
Shouxian
116.78
32.558
2008.05-2008.12
9.
Taihu
120.21
31.421
2007.01-2016.08
10.
Hong_Kong_Hok_Tsui
114.25
22.21
11.
Hong_Kong_PolyU
114.18
22.303
2007.01-2017.03
12.
Hong_Kong_Sheung
114.11
22.483
2012.02-2016.05
13.
Kaiping
112.53
22.315
2008.10-2008.11
14.
Zhongshan_Uni
113.39
23.06
2011.11-2012.07
15.
Dunhuang_LZU
94.955
40.492
16.
Jingtai
104.1
37.333
2008.02-2008.05
17.
Lanzhou_City
103.85
36.048
2009.10-2010.03
18.
Minqin
102.95
38.607
2010.05-2010.06
19.
SACOL
104.13
35.946
2007.01-2012.08
20.
Zhangye
100.27
39.079
2008.04-2008.06
21.
NAM_CO
90.962
30.773
22.
QOMS_CAS
86.948
28.365
23.
Chen-Kung_Univ
120.21
23
24.
Chiayi
120.49
23.496
2013.09-2017.04
25.
Douliu
120.54
23.712
2015.09-2017.06
26.
EPA-NCU
121.18
24.968
2007.01-2017.10
27.
Heng-Chun
120.7
22.055
2013.03-2015.05
28.
Lulin
120.87
23.469
2007.01-2017.07
29.
NCU_Taiwan
121.19
24.967
2007.01-2013.07
PRD
Northwest
Tibetan
2007.10-2010.07
2012.03-2012.05
2007.01-2016.08 2010.09-2015.11
Taiwan
2007.01-2016.06
707
708
Acknowledgements
709
This work was supported by the National Natural Science Foundation of China (Grant No.
710
41801281). We appreciate the NASA Langley Research Center Atmospheric Sciences Data
711
Center (https://eosweb.larc.nasa.gov/) for providing the AOD data. Meanwhile, we appreciate the
712
four anonymous reviewers for precious suggestions.
713
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1
Evaluation of the MISR fine resolution aerosol product
2
using MODIS, MISR, and ground observations over China
3
Yidan Si1, Liangfu Chen2, Xiaozhen Xiong3, Shuaiyi Shi2, Letu Husi2, Kun Cai4,5* 1
4
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites,
5
National Satellite Meteorological Center, China Meteorological Administration, Beijing 10081,
6
China 2
7
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital
8
Earth, Chinese Academy of Sciences, Beijing 100101, China 3
9 4
10
National Oceanic and Atmospheric Administration, MD 20746, USA
Spatial information processing engineering laboratory of Henan province, Kaifeng 475004,
11 12
China 5
School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
13
14
Highlights
15
Nationally, MISR V23 data are still underestimated with R=0.902, EE=59.45% and
16
MAE=-0.0605.
17
V23 retrievals =EE in the BTH, YRD and PRD regions are respectively greater than 55.75%
18
in Taiwan.
19
The V23 retrievals =EE in fall and winter are highest, followed by those in summer and
20
spring.
21
V23 products over BTH, YRD, Taiwan in all seasons perform well; that of V22 in PRD
22
summer are better.
23
MISR 4.4-km AOD biased lower and MODIS 3-km data overestimated relative to
24
AERONET observations.
25
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: