The aerosol fine-mode fraction (FMF) is an important
optical parameter of aerosols, and the FMF is difficult to accurately
retrieve by traditional satellite remote sensing methods. In this study, FMF
retrieval was carried out based on the multiangle polarization data of
Polarization and Anisotropy of Reflectances for Atmospheric Science coupled
with Observations from Lidar (PARASOL), which overcame the shortcomings of
the FMF retrieval algorithm in our previous research. In this research, FMF
retrieval was carried out in China and compared with the AErosol RObotic
NETwork (AERONET) ground-based observation results, Moderate Resolution
Imaging Spectroradiometer (MODIS) FMF products, and Generalized Retrieval of
Aerosol and Surface Properties (GRASP) FMF results. In addition, the FMF
retrieval algorithm was applied, a new FMF dataset was produced, and the
annual and quarterly average FMF results from 2006 to 2013 were obtained
for all of China. The research results show that the FMF retrieval results
of this study are comparable with the AERONET ground-based observation
results in China and the correlation coefficient (r), mean absolute error
(MAE), root mean square error (RMSE), and the proportion of results that
fall within the expected error (Within EE) are 0.770, 0.143, 0.170, and
65.01 %, respectively. Compared with the MODIS FMF products, the FMF
results of this study are closer to the AERONET ground-based observations.
Compared with the FMF results of GRASP, the FMF results of this study are
closer to the spatial variation in the ratio of PM2.5 to PM10 near
the ground.
Introduction
Aerosols have a great impact on human production, life, and climate change
(Kaufman et al., 2002; Huang et al., 2014; Shi et al., 2018). Aerosols have
become a research hotspot for scientists from various fields. There are many
methods for monitoring aerosols, among which the large-scale coverage of
remote sensing technology makes it an effective method for monitoring
aerosols. Aerosols produce strong scattering effects in the visible light
band (Kokhanovsky et al., 2015).
Therefore, in current satellite remote sensing, visible light channels are
generally used to observe aerosols and aerosol information can be obtained
on a global scale. At present, in the field of atmospheric environmental
research, aerosol optical depth (AOD) products produced by traditional
satellite remote sensing platforms, such as the Moderate Resolution Imaging
Spectroradiometer (MODIS), are the most commonly used (Bellouin et al.,
2005; Lee et al., 2011; Xie et al., 2015; Zhao et al., 2017; Zhang et al.,
2020). Related scholars have carried out many AOD retrieval studies on
traditional scalar observation platforms, which can achieve high-precision
retrievals and the retrieval of AOD (Li et al., 2013; Kim et al., 2014; Zhang
et al., 2014; Zhong et al., 2017; Ge et al., 2019). However, other new aerosol
optical parameters, such as the fine-mode fraction (FMF), are quite
different in definition from the ground-based observations (Remer et al.,
2005; Levy et al., 2010), which makes them incomparable. The FMF is a
parameter that can reflect the content of human-made aerosols
(Bellouin et al., 2005; Kaufman et al., 2005) and
application requirements have been put forward in many studies. For example,
in the PM2.5 remote sensing (PMRS) model based on the purely physical
approach proposed by Zhang and Li (2015) and Li et al. (2016), the FMF is one of the core input parameters
that determines the final particle concentration retrieval accuracy
(Zhang and Li, 2015; Li et al., 2016). However, the existing publicly
released satellite FMF products have poor accuracy, which severely limits
the retrieval accuracy of the model.
Multiangle polarization observations are a frontier research direction in
the field of aerosol remote sensing. These observations have unique
advantages in the retrieval of aerosol parameters. Related information
analysis work shows that polarization observations can obtain more aerosol
information than scalar observations (Chen et al., 2017a, b; Hou et al., 2018). Therefore, the accurate acquisition of more new
aerosol parameters based on multiangle polarization observations is of great
significance for both atmospheric environmental research and the development
of aerosol basic retrieval algorithms. Although official institutions and
some scholars have carried out retrieval studies of aerosol parameters based
on multiangle polarization observation platforms, such as POLarization and
Directionality of the Earth's Reflectances (POLDER), these studies have
their own limitations. For example, the French Laboratoire d'Optique
Atmospherique (LOA) only provided the fine-mode aerosol optical depth
(AODf) datasets in its operational products over land (Deuzé et al., 2001; Tanré et al., 2011); the total aerosol optical depth
(AODt) was not provided
(Chen et al., 2020). Dubovik
et al. (2011) proposed an optimized retrieval method for polarization observation
platforms that can obtain high-precision aerosol optical parameters. Recently, an operational aerosol
product of Generalized Retrieval of Aerosol and Surface Properties (GRASP)
based on POLDER data was released (Dubovik et al., 2014) and relevant
validation studies show that the product has high retrieval accuracy (Tan
et al., 2019; Wei et al., 2020). However, with regard to this method, its
computational convergence speed is slow, computational resources are
consumed, and a large amount of mathematical statistics is involved.
Compared with the traditional lookup table (LUT) method, this method is more
difficult to implement. Although other scholars are conducting related
research (Chen et al., 2018; Frouin et al., 2019; Schuster et al., 2019; Li
et al., 2020), it is still seldom used in actual engineering applications.
The research of other scholars on the retrieval of new aerosol parameters
based on the LUT method, although the results produced by the algorithm have
high retrieval accuracy, generally only focus on a specific
area, and the spatial scale is not large (Cheng et al., 2012; Xie et al.,
2013; Wang et al., 2015; Qie et al., 2015; Wang et al., 2018). There are also
fewer studies on the production of long-term aerosol optical parameter
datasets. In 2016, we proposed a method for retrieving the FMF based on
satellite multiangle scalar and polarization observations (Zhang et
al., 2016), mainly based on multiangle scalar observations to obtain
AODt and multiangle polarization observations to obtain AODf. The
ratio of the two is the FMF. Compared with the existing MOIDS FMF products, the
accuracy of the FMF results obtained by this method is significantly
improved, which shows the feasibility of the method. However, there are
still some problems that need to be solved if this method is to be applied
in large spaces. For example, the empirical parameters of surface
reflectance estimation during scalar retrieval vary greatly with region and
high-precision AODt retrieval results can only be obtained in specific
regions. In polarization retrieval, the problem of a low AODf retrieval
value for high aerosol loading exists (Chen et al., 2015; Zhang et al.,
2018). In response to these problems, we have also carried out follow-up
research, made certain improvements to the above problems, and have
achieved more accurate AODt and AODf in a large space
(Zhang et al., 2017, 2018). Then, in theory, it is
possible to achieve the goal of FMF in a large space. Although Yan et al. (2017, 2019)
achieved high-precision FMF retrieval based on the LUT-SDA method, their method is mainly oriented to traditional
multispectral scalar sensors. To apply this method to multiangle
polarization sensors, it is necessary to perform a series of algorithm
adjustments. In previous research, we have achieved high-precision retrieval
of AODt and AODf in a large space. The retrieval method and
results can be directly used to obtain the FMF without additional algorithmic
adjustments.
This paper is mainly based on the POLDER-3 multiangle polarization sensor on
the Polarization and Anisotropy of Reflectances for Atmospheric Science
coupled with Observations from a Lidar (PARASOL) satellite and the existing
research foundation, and it carried out the retrieval and validation of the
FMF in the land area of China. The second section of the study briefly
introduces the FMF retrieval algorithm based on multiangle polarization
observation, AErosol RObotic NETwork (AERONET) data, and the data validation
method. The third section mainly compares the retrieval results based on the
AERONET ground-based observation data. At the same time, it was also
compared with the operational aerosol products of MODIS and GRASP. Section 4
summarizes the full text and proposes future work prospects.
MethodologyIntroduction to the FMF retrieval method
The technical framework of FMF retrieval in this research is shown in Fig. 1. Overall, the FMF retrieval in this study consists of two parts: using the multiangle scalar and polarization data of POLDER-3 to obtain
AODt and AODf, and the final ratio of the two is FMF. This method
is the same as the retrieval method proposed in our 2016 study (Zhang
et al., 2016). However, our previous method is limited by semiempirical
parameters on the surface and can only obtain better FMF results on the
urban scale. To obtain stable and accurate results in a large space, we have
made major changes to the retrieval methods of AODt and AODf. For
the specific retrieval method, please refer to the research we published in
2017 and 2018; here, only a brief introduction is given.
FMF retrieval technology framework of this research.
For the retrieval of AODt, we introduced the empirical orthogonal
function (EOF) to estimate the surface reflection contribution under
multiangle observations to solve the regional limitation of the
semiempirical parameters of the surface in the original method.
Subsequently, this is combined with the retrieval lookup table and
substituted into the forward model for simulation calculation and, finally,
AODt can be obtained through the cost function. The correlation
coefficient (r) and root mean square error (RMSE) between the obtained
AODt and AERONET ground-based observations are 0.891 and 0.097,
respectively. The EOF method has previously been used for the retrieval of
land aerosols on Multi-angle Imaging SpectroRadiometer (MISR); we
transplanted this method into POLDER based on the MISR approach. For more
details, please refer to our 2017 study (Zhang et al., 2017).
For the retrieval of AODf, our research and that of other scholars has
shown that the AODf results obtained by using the operational LOA
algorithm have a certain deviation compared with ground-based observations.
To improve the retrieval accuracy of AODf, we proposed the grouped
residual error sorting (GRES) method in 2018 to solve the problem of an
inaccurate evaluation function caused by error accumulation under multiangle
observation. Based on this method, combined with a bidirectional polarization distribution function (BPDF) model to estimate the polarized surface
reflectance (Nadal and Bréon, 1999), we have obtained
higher-precision AODf results in eastern China, and the r and RMSE
between the results and the AERONET ground-based observations are 0.931 and
0.042, respectively. More method details can be found in our research
published in 2018 (Zhang et al., 2018).
Results of the FMF seasonal average spatial distribution
of China. Panels (a)–(d) are the results of spring, summer, autumn, and winter, respectively, from 2006 to 2013.
Based on the new retrieval method, we have obtained higher-precision
AODt and AODf retrieval results on a large spatial scale, which
also provides the possibility of obtaining accurate FMF results on a large
spatial scale. Figure 2 shows the seasonal average spatial distribution
results of the FMF in China from 2006 to 2013 obtained in this study. In the
figure, spring is from March to May, summer is from June to August, autumn
is from September to November, and winter is from December to February. As
seen in the figure, for the eastern area of the “Hu Line”, the overall FMF
reached its highest value in winter, mainly concentrated in the range of
0.7–0.8. The FMF of southern China still has a relatively high value in the
spring, and the overall value is approximately 0.6, while in northern China,
the plain area is lower, generally between 0.4–0.5. The North China Plain in
summer is similar to that in spring, but there is a significant decline in
southern China, where the value is generally between 0.3–0.5. In autumn,
the overall value begins to rise, with a value of approximately 0.6. The
Sichuan–Chongqing economic zone maintains a relatively high value in all
four seasons and the value in some areas in winter is close to 0.8, while
the three northeastern provinces also have high values in winter, with an
overall value between 0.4–0.7. For the area west of the “Hu line”, the
northern Xinjiang area is higher in autumn and winter; it can reach 0.7
in some areas in winter. The southern Xinjiang area also shows a
significant increase in winter, with some high values close to 0.6, whereas
the Qinghai–Tibet Plateau maintains a low value in all seasons, and the
value is mainly concentrated between 0.1–0.3.
Next, we will validate the FMF retrieval results based on the AERONET
ground-based observation results. Note that since the EOFs during the
AODt retrieval need to be constructed with the observation results of
the POLDER 3 × 3 window, the resolution of the final FMF retrieval result is
also the size of the POLDER 3 × 3 window (approximately 18 km).
AERONET data
At present, aerosol ground-based products of AERONET have been developed to
version V3 and the data of version V2 are no longer available for download.
Among these products, there are two products that can be used to validate
the results of satellite FMF retrieval: one is the FMF product based on the
spectral deconvolution (SDA) method (O'Neill et al., 2001a, b, 2003) and the other is based on the size
distribution (SD) retrieval product (Dubovik and King, 2000). Generally,
SDA products can provide more FMF ground-based results. At present, most
ground-based stations in China provide SDA products with level 2.0 data
quality. Therefore, SDA products are the first choice for the FMF comparison in
this study. However, it is worth pointing out that the Beijing site lacks
the SDA product with level 2.0 data quality, so we used the SD product
instead. Finally, this study selected the level 2.0 products of 16 AERONET
sites in China during 2006–2013 (POLDER on-orbit time) to validate the FMF
retrieval results of this study. The specific spatial locations of AERONET
sites are shown in Fig. 3, and the specific site information is shown in
Table 1. However, note that not all AERONET sites have long-term
observational data. The sites with long-term observational data are the
Beijing, Xianghe, Taihu, and Hong_Kong_PolyU
sites.
The spatial distribution of AERONET sites selected in this
study.
AERONET site information employed in this study. The land cover
types are from the MODIS MCD12 land cover product.
The FMF retrieved in this study is the FMF at 550 nm. Neither the SDA
product nor the SD product directly provides the FMF result at this
wavelength. Therefore, the AERONET FMF needs to be wavelength converted. For
SDA products, the products include AODt and AODf at 500 nm and the
corresponding Ångström exponent (AE) so the FMF of SDA products can
be converted to 550 nm by Eq. (1):
FMF550,SDA=τf500⋅(500/550)αfτt500⋅(500/550)αt,
where FMF550,SDA is the FMF of the SDA product at 550 nm
after conversion, τf500 is the AODf at 500 nm, τt500 is the AODt at 500 nm, αf is the fine-mode AE,
and αt is the coarse and fine-mode AE.
The SD products provide AODt and AODf at 440 and 675 nm,
respectively. Equations (2)–(4) can be used to obtain FMF results at 550 nm:
2αt=-lnτt675/τt440ln(675/440),3αf=-lnτf675/τf440ln(675/440),4FMF550,SD=τf440⋅(440/550)αfτt440⋅(440/550)αt,
where FMF550,SD is the SD product FMF at 550 nm after conversion,
τf675 is AODf at 675 nm, τt675 is AODt at 675 nm, τf440 is AODf at 675 nm, and τt440 is AODt
at 675 nm.
Validation method
In this study, the average value of ground-based observation results within
±30 min of the satellite's transit was used for comparison with the
satellite retrieval results. The satellite retrieval result used for
comparison is the effective retrieval result centred on the location of the
AERONET site within the closest distance in the 3 × 3 window (about 54 km).
Note that when the retrieved AODf is greater than the retrieved
AODt, we consider this situation as a failure of the FMF retrieval and
the results of this part were not involved in the comparison. These results
account for about 10 %.
The statistical indicators used in the validation include the correlation
coefficient (r), mean absolute error (MAE), bias, RMSE, and expected error
(EE). The specific statistical evaluation index definitions are shown in Eqs. (5)–(10):
5r=CovFMFretrieval,FMFAERONETDFMFretrievalDFMFAERONET,6MAE=1n∑i=1nFMFi,retrieval-FMFi,AERONET,7Bias=1n∑i=1nFMFi,retrieval-FMFi,AERONET,8RMSE=1n∑i=1nFMFi,retrieval-FMFi,AERONET2,9EEFMF=±0.2,10EEAOD=±0.05±0.15AODAERONET,
where Cov represents the covariance, D represents the variance,
FMFretrieval represents the FMF retrieval value, FMFAERONET
represents the value of AERONET FMF, AODAERONET represents the value
of AERONET AOD, i is the matched data points, and n is the number of
validation points.
Validation and comparisonValidation against AERONET ground-based data
Figure 4 is a scatter plot of the comparison between the retrieved and
AERONET ground-based FMFs. Figure 4a–n lists the verification
results at the corresponding sites where the number of matching results is
greater than 2. The figure shows that the FMF results obtained in this study
have an overall high correlation with the AERONET ground-based observations.
Among the 14 AERONET sites, r is between 0.508 (Taihu site) and 0.902
(Lanzhou City site). The ranges of MAE and RMSE are 0.096
(Hangzhou_City site) to 0.160 (QOMS_CAS site)
and 0.095 (Hangzhou_City site) to 0.184 (QOMS_CAS site). Except for the QOMS_CAS site, the proportion of
results that fell within the EE accounted for approximately 65 %. The
statistical indicators of the QOMS_CAS site are all poor. The
specific reason is that the site is located at the southern edge of the
Qinghai–Tibet Plateau. It is a high-altitude site and has very little
aerosol content. In the AERONET SDA products of 2009–2013, the 5-year
average values of AODt and AODf (500 nm) are only 0.052 and 0.038,
respectively. Under the combined influence of the aerosol model and the
surface reflectance estimation error in the retrieval process, it is
difficult to accurately retrieve a low AOD value for satellite observations,
resulting in a large deviation of the FMF at this site.
FMF results comparison at 14 AERONET sites. Panels (a)–(n) are the validation results for the Beijing, Hangzhou_city, Hongkong_PolyU, Kaiping, Lanzhou_city, NAM_CO, NUIST, QOMS_CAS, SACOL, Taihu, Taipei, Xianghe, Xinglong, Zhongshan_Univ sites, respectively.
We have counted the FMF validation results for different surface types and
the specific information is shown in Table 2. The r, MAE, and RMSE at all
sites in this study are 0.770, 0.143, and 0.170, respectively; Within EE
is 65.01 %, again indicating that the FMF satellite retrieval results of
this study are comparable with the ground-based observation results. All the
validation results of this study cover six surface types: urban, barren,
grasslands, wetlands, croplands, and forests. Overall, since the validation
data for the barren type mainly come from the QOMS_CAS site,
the validation results for this surface type are poor. Although the r for the
other five surface types has a certain change, 0.508 (barren) and 0.831
(forests), in terms of the three indicators of MAE, RMSE, and Within EE,
the differences in the five surface types are relatively small, especially
Within EE, which is concentrated at approximately 65 % and similar to the
site-by-site results. The uncertainty of the FMF retrieval results in this
study are relatively stable for these five surface types.
FMF validation results for different surface types.
Land coverNrMAERMSEWithintypeEEOverall result11860.7700.1430.17065.01 %Urban4210.7330.1390.16366.98 %Barren630.7110.1580.18255.55 %Grasslands1130.7770.1370.17066.37 %Wetlands1500.5080.1450.17663.33 %Croplands3940.6510.1460.17464.21 %Forests450.8310.1330.15968.88 %
FMF retrieval error distribution results. Panel (a) is for all results and panel (b) is for the results with AODf greater than 0.2.
We further counted the error distribution of the FMF retrieval results, and
the statistical results are shown in Fig. 5a. The figure shows that the
FMF error of this research is mainly distributed between -0.3 and 0.3. This
part of the data accounts for approximately 86 %, but the part less than
the AERONET ground-based FMF observation value accounts for approximately
75 %, indicating that the retrieval result of this study is lower than
that of the ground-based observations. We further screened out the points
with AODf greater than 0.2, and the corresponding FMF error
distribution results are shown in Fig. 5b. Comparing the two figures, it
can be found that after screening the proportion of FMF error ranging from
-0.4 to -0.3 decreased by approximately 7 % and the proportion of FMF
error ranging from -0.1 to 0.1 increased by approximately 6 %, which shows
that when the AOD is higher, our FMF retrieval method is more sensitive.
AOD results comparison for six surface types. Panels (a), (c), (e), (g), (i), and (k) are the AODt validation results for
the barren, croplands, forests, grasslands, urban, and wetlands types,
respectively. Panels (b), (d), (f), (h), (j), and (l) are the AODf validation results for the barren, croplands, forests, grasslands, urban, and wetlands types, respectively.
Since our FMF is obtained from the ratio of AODf and AODt
retrieval results, and the retrieval accuracy of the two parameters directly
determines the retrieval accuracy of the FMF, we further compared the retrieved
AODs for the six different surface types with those of the ground-based data
from 2006 to 2013, and the statistical results are shown in Fig. 6 and
Table 3. It can be seen from Fig. 6 that for the comparison results of
AODf, except for the barren type, the AODf for all surface types
are in good agreement with the ground-based observation results and the r
is greater than 0.7. Because the data for the barren type mainly come from
the QOMS_CAS site, the AODf value at this site is low
and the r is not suitable for evaluating the retrieval performance. Most of
the retrieval results for the barren type fall within the EE, which can indicate
that the retrieval results for this type have a good accuracy. For the
comparison results of AODt, the retrieval results for the barren type are
obviously positively shifted. This is due to the low aerosol loading at the
QOMS_CAS site; the inaccurate estimation of the surface
reflectance can easily magnify the errors in the retrieval results. It
indicates that the EOF method used to retrieve AODt in this study still
needs further improvement. However, it is difficult to analyse the reasons
for the negative bias of most FMF retrieval results from the scatter plot,
so we further counted the biases of AODt and AODf. Table 3 shows
that the bias of the retrieved AODf and AODt for the six different
surface types. It can be seen from Table 3 that the proportion of positive
bias is greater than the proportion of negative offset for most AODt
retrieval results, while AODf is the opposite. For the overall result, the bias of AODf is -0.037, where the proportion of negative bias is 58.68 % and the bias of AODt is 0.063, where the proportion of positive bias is 68.29 %, indicating that the AODf retrieval result has a negative bias and the AODt retrieval result has a positive bias;
that is, the numerator is small and the denominator is large, eventually
leading to a negative bias of the FMF.
Comparison with MODIS products
MODIS aerosol products also include FMF datasets, but this FMF has a
different definition. In fact, the FMF of MODIS refers to the “fine-model
fraction”, which is the proportion of the bimodal fine-dominated aerosol model,
but not the pure fine mode (Levy et al., 2007). Because the FMF
results obtained by MODIS are different in definition from the ground-based
results (Levy et al., 2009), the retrieval results are quite
different from the ground-based observation results, which limits the
research that depends on the FMF parameter. We compared the retrieved and
MODIS FMF with the AERONET ground-based observations to further evaluate the
significance of our results. The MODIS FMF results were derived from the
MYD04 product of collection 6.1. Figure 7 shows the comparison between the
two results and the AERONET ground-based observation results from 2011 to
2013, which are the results where both MODIS and POLDER match the
ground-based observations. As seen from the figure, compared with
ground-based observations, the r of the FMF obtained in this study is 0.812,
while that of MODIS is 0.302. The correlation coefficient of the results
obtained in this study is much higher than that of MODIS. At the same time,
notice that there are many 0 values in the MODIS results. These 0 values are
not meaningless but correspond to the situation where there is no fine-dominated aerosol model.
Comparison between the results of this study and MODIS FMF
with AERONET.
More statistical results of the two are shown in Table 4. The table shows
that the FMF results obtained in this study have an MAE of 0.072, an RMSE of
0.102, and a Within EE of 87.41 %, whereas results of MODIS have an MAE of 0.512, RMSE of 0.574, and Within EE of 19.58 %. The statistical indicators
of the FMF results obtained by our study are closer to the ground-based
observations than the MODIS results. Nevertheless, note that this does not
mean that the FMF of MODIS has a large deviation. As mentioned above, there
is a difference in definition between the FMF of MODIS and the ground-based
observations; consequently, it is difficult to obtain the true deviation of
MODIS FMF based on ground-based observations.
Distribution of FMF for China in 2013 from different
sources. Panel (a) is the normalized results of this study (18 km resolution), panel (b) is the normalized results of MODIS (10 km resolution), panel (c) is the normalized results of GRASP (6 km resolution), and panel (d) is the GRASP results minus the
retrieved results (non-normalized, 18 km resolution).
Figure 8a and b shows the spatial distribution map of the average annual
FMF (550 nm) of China in 2013 obtained by this study and the MODIS product.
To facilitate the comparison of the differences in the spatial distribution
trends of the two, the results are normalized, meaning that they are divided
by the maximum value in the respective FMF image. The figures show that the
results obtained in this study can better reflect the differences in the
level of urbanization in China and are more in line with the “Hu Line”,
reflecting China's population density. That is, in the area to the east of
the “Hu Line” the value of the FMF is higher; the North China Plain,
Sichuan–Chongqing Economic Zone, Pearl River Delta, and Yangtze River Delta
are extremely high value areas. In the area to the west of the “Hu
Line”, the FMF value is small; the high-value area is mainly in the northern
Xinjiang region, while the value in the Qinghai–Tibet Plateau is generally
low. The results of MODIS are quite different from the results of this
study. The MODIS results show that the regions with the highest FMF are
Guizhou, Guangxi, Yunnan, and Hainan. The Three Northeast Provinces and the
central mountainous areas of Taiwan also have high values. For the North
China Plain, Sichuan–Chongqing Economic Zone, and Pearl River Delta, the
results are somewhat similar to this study, while the Yangtze River Delta is
a low-value area.
Comparison with GRASP products
In our previous research, the accuracy of FMFs calculated from the GRASP
“high-precision” product was validated (Wei et al., 2020). The
results of the comparison with eight SONET (Sun-sky radiometer Observation NETwork)
sites show that the r between GRASP FMFs and ground-based observations is
0.77 and Within EE is 62.35 %, which is similar to the results of this
study discussed in Sect. 3.1. However, by comparing the spatial distribution results
of the two, we found some differences. The GRASP product version we
processed is V2.06, which is the latest version that can be obtained from
AERIS/ICARE Data and Services Center (http://www.icare.univ-lille.fr, last access: 27 December 2020).
Figure 8c shows the annual averaged FMF spatial distribution of GRASP in
2013 (also normalized). Compared with Fig. 8a, we can see certain
differences. The relatively high-value area of GRASP results is mainly in
southern China. We subtracted the results of this study from the average
GRASP FMF results and obtained the non-normalized numerical difference
between the two, as shown in Fig. 8d. The figure shows that the
difference between the two in the North China Plain and the southern
Xinjiang region is relatively small. The largest differences are mainly
concentrated in southern and northeastern China and the Qinghai–Tibet
Plateau regions. The GRASP results in these areas are greater than our
results and a small number of pixels can be larger than 0.3. However, these
areas lacked publicly available sunphotometer observations for 2013 or earlier. The PARASOL ended its exploration mission in October 2013 and it is
impossible to compare the subsequent time periods, so it is difficult to
directly compare with ground-based observations to illustrate the
correctness of the spatial distribution of the two.
GRASP products provide AODf and AODt datasets, but do not directly
provide FMF datasets. In this study, the ratio of the two was used to obtain
the GRASP FMF. However, it should be noted that the definition of GRASP
AODf is somewhat different from the AODf in our research, which
may eventually lead to a difference in the definition of FMF. The
AODf in our study is similar to the definition in the ground-based SDA
algorithm; there is no clear cut-off particle size, that is, its definition
is indefinite. This is different from the AODf obtained by calculating
and integrating the size distribution in GRASP. So, the difference in the
spatial distribution results of the two may be caused by the definition
rather than a problem in the retrieval algorithm. In the comparison with AERONET observations by Chen et
al. (2020), the r of AODf is between
0.868 (models approach) and 0.924 (high-precision approach), which is
similar to the r (0.868) of AODf in this study, but their bias is only
-0.02 (models approach) and 0.01 (high-precision approach), which is
different from the bias (-0.037) of AODf in this study. This indicates
that the definition of AODf in GRASP and our study may be different.
To show that the spatial distribution of the FMF in this study is
reasonable, the ground PM2.5 and PM10 in situ results were compared with the
ground-based FMF results. It is expected that the ratio of PM2.5 to
PM10 can be used to analyse the correctness of this study as well as
the GRASP FMF results in the spatial distribution trend. We selected the
2015 Beijing Olympic Sports Center monitoring site (116.407∘ E,
40.003∘ N, straight-line distance of less than 4 km), which was
the closest to the AERONET Beijing site, and compared the hourly averaged
results of the ratio of PM2.5 to PM10 with the FMF results.
Although the definitions of the two are quite different, the ratio of
PM2.5 to PM10 is actually a parameter of particulate matter near
the ground, while the FMF is actually a parameter of the atmospheric column of
aerosols, but the comparison results of the two (Fig. 9) show that there
is a correlation between the ratio of PM2.5 to PM10 and FMF and
the r is 0.709. This result may be because aerosols are mainly distributed
near the ground and PM2.5 and PM10 can represent different
particle modes. Ultimately, the actual difference between the two parameters
is smaller. Since the ratio of PM2.5 to PM10 is comparable to the
ground-based FMF results, if there were more in situ data it could
indirectly verify the spatial distribution trend of this study and the GRASP
results.
Comparison between the ratio of PM2.5 to PM10 and FMF (hourly average).
Forty-seven urban administrative regions in China used to
compare the annual average FMF.
Comparison of the results of the retrieved and GRASP FMF
with the urban average of the ratio of PM2.5 to
PM10 (2013).
Due to the lack of in situ data for particulate matter in China in 2013,
this study can only be based on the 2013 environmental protection key city
air in the China Statistical Yearbook (http://www.stats.gov.cn/tjsj/ndsj/, last access: 26 February 2021).
The annual average value of air quality is used for limited analysis. We
extracted the FMF retrieval results and GRASP results of the corresponding
47 cities in the statistical yearbook and calculated the annual average FMF
of each city for comparison with the ratio of the annual average PM2.5
to PM10 of each city. The spatial distribution of the administrative
regions of these 47 cities is shown in Fig. 10. These cities cover most of
China's provinces and have a wider spatial distribution range than the
AERONET sites in Fig. 1. The comparison results in Fig. 11 show that
although the annual average FMF results of this study in each city are lower
than the annual average results of the ratio of PM2.5 to PM10, the
change trend of the FMF results of this study is better than the results of
GRASP FMF. The r between the FMF of this study and the ratio of PM2.5
to PM10 is 0.778, while GRASP is 0.472, which can provide evidence for
the correctness of the FMF results of this study in the spatial
distribution. The low FMF results in this study are related to the
calculation methods of the annual average values of PM2.5 and PM10
in each city. Generally, most of the in situ monitoring sites for
particulate matter in each city are distributed in urban areas and the
number of sites distributed in rural areas is small (for example, nine of the
12 state-controlled sites in Beijing are in urban areas). When calculating
the average FMF of a city, one pixel may contain the results of multiple
monitoring stations in place, which makes it difficult to achieve accurate
spatial location matching. To facilitate data processing, all pixels within
the urban administrative boundary are directly used to calculate the average
value, and the large number of FMFs in rural areas is generally lower than
that in cities, which ultimately leads to a lower FMF average result.
Based on the validation and comparison results in Sect. 3.1 to 3.3, this
research has obtained FMF satellite retrieval results in
China with good accuracy, which proves the reliability and stability of the retrieval method.
Compared with the MODIS FMF products, the r, MAE, RMSE, and Within EE of the
results of this study are all higher than the results of MODIS. Compared
with the GRASP FMF, the results of this study are closer to the results of
the ratio of PM2.5 to PM10 in terms of the spatial distribution
of the entire region of China. The above results all illustrate the
effectiveness and advantages of the FMF retrieval method used in this study.
Compared with our original FMF retrieval method, which can only be used on
the urban area scale, this research has achieved FMF retrieval in a large
space.
Summary
In this study, the multiangle polarization data of PARASOL were used to
perform FMF retrieval and the retrieval results were compared with the
AERONET ground-based observations, MODIS results, and GRASP results. Based
on the above work, the conclusions of this research are described as
follows:
There is good agreement between the FMF results obtained in this study
and the AERONET ground-based observation results. The overall r, MAE, RMSE,
and Within EE between the two are 0.770, 0.143, 0.170, and 65.01 %,
respectively.
The FMF results obtained in this study were more practical than the
MODIS FMF products. The r, MAE, RMSE, and Within EE between the FMF results
and the ground-based observations are 0.812 versus 0.302, 0.072 versus
0.512, 0.102 versus 0.574, and 87.41 % versus 19.58 %, respectively.
Compared with the GRASP FMF, the FMF results obtained in this study are
closer to the ratio of PM2.5 to PM10 in terms of the spatial
distribution trend. Compared with the annual average ratio of PM2.5 to
PM10 in 47 Chinese cities in 2013, the r of this study is 0.778, and
GRASP is 0.472.
The FMF retrieval method in this study has significance for the development
of aerosol polarization satellite remote sensing algorithms, and the FMF
results obtained in China also have good practical value for application
research in the field of atmospheric environments. China has launched the
Gaofen-5 (GF-5) satellite equipped with a new multiangle polarization
sensor. With the release of GF-5 satellite data in the future, the results
of this study can also provide algorithmic support for the application of
its multiangle polarization sensor in the field of atmospheric environmental
monitoring and are expected to produce subsequent FMF datasets. However,
there are some shortcomings in this research. For example, the retrieval of the
FMF still depends on the accuracy of the two parameters AODf and
AODt. In our previous research, although higher-precision results of
AODf and AODt have been obtained, the FMF error is related to the
error of the two retrieval parameters. The transmission of the error will
eventually amplify the retrieval error of the FMF. Compared with the individual
retrieval of AODf and AODt, the retrieval of the FMF is still
difficult. In the future, it is still necessary to further improve the
retrieval accuracy of AODf and AODt to obtain more accurate FMF
results. In this way, some applications that rely on FMF (such as using the
PMRS model to estimate PM2.5 concentration) can perform better. In addition, due to the limitation of the validation data, we
are temporarily unable to further discuss the correctness of the spatial
distribution trend of the FMF in this study and GRASP, and only the results
of the ratio of PM2.5 to PM10 were used for indirect comparison.
In the future, we can try to perform FMF retrieval in other regions with
many ground-based observations around the world to further compare the
findings of the two results.
Data availability
The FMF datasets produced in this study
can be requested from the corresponding author (lizq@radi.ac.cn).
Author contributions
ZheL conceived and designed the study. YZ and LQ collected and processed the remote sensing data. YZ and YW
performed the FMF retrievals. YZ and YX compared the retrieval results with
the AERONET, MODIS, and GRASP products. WH and LL analysed the spatio-temporal
trends of FMF in China. ZhiL and YW collected and processed the in situ data. YZ and ZheL prepared the paper with contributions from all coauthors.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Satellite and ground-based remote sensing of aerosol optical, physical, and chemical properties over China”. It is not associated with a conference.
Acknowledgements
This work was supported by the National
Natural Science Fund of China (41901294, 41771535), the National Natural
Science Foundation of Chongqing, China (cstc2019jcyj-msxm0726), the
Scientific Research Foundation of CUIT (KYTZ201909), the Science and
Technology Department of Sichuan Province Foundation (2019YFS0470), and the
Chengdu Science and technology project (2018-ZM01-00037-SN).
Financial support
This research has been supported by the National Natural Science Foundation of China (grant no. 41901294 and 41771535), the National Natural Science Foundation of Chongqing, China (grant no. cstc2019jcyj-msxm0726), the Scientific Research Foundation of CUIT (grant no. KYTZ201909), the Science and Technology Department of Sichuan Province Foundation (grant no. 2019YFS0470), and the Chengdu Science and technology project (grant no. 2018-ZM01-00037-SN).
Review statement
This paper was edited by Linlu Mei and reviewed by two anonymous referees.
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