remote sensing Article
Performance of the NPP-VIIRS and aqua-MODIS Aerosol Optical Depth Products over the Yangtze River Basin Lijie He 1 , Lunche Wang 2, * and Jing Wei 4 ID 1 2 3 4
*
ID
, Aiwen Lin 1, *, Ming Zhang 2 , Muhammad Bilal 3
ID
School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China;
[email protected] Laboratory of Critical Zone Evolution, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China;
[email protected] School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China;
[email protected] Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China;
[email protected] Correspondence:
[email protected] (L.W.);
[email protected] (A.L.); Tel.: +86-027-67883001 (L.W.); +86-027-68778893 (A.L.)
Received: 2 December 2017; Accepted: 13 January 2018; Published: 16 January 2018
Abstract: The visible infrared imaging radiometer suite (VIIRS) environmental data record aerosol product (VIIRS_EDR) and the aqua-moderate resolution imaging spectroradiometer (MYD04) collection 6 (C6) aerosol optical depth (AOD) products are validated against the Cimel sun–photometer (CE318) AOD measurements during different air quality conditions over the Yangtze river basin (YRB) from 2 May 2012 to 31 December 2016. For VIIRS_EDR, the AOD observations are obtained from the scientific data set (SDS) “aerosol optical depth at 550 nm” at 6 km resolution, and for aqua-MODIS, the AOD observations are obtained from the SDS “image optical depth land and ocean” at 3 km (DT3K) and 10 km (DT10K) resolutions, “deep blue aerosol optical depth 550 land” at 10 km resolution (DB10K), and “AOD 550 dark target deep blue combined” at 10 km resolution (DTB10K). Results show that the high-quality (QF = 3) DTB10K performs the best against the CE318 AOD observations, along with a higher R (0.85) and more retrievals within the expected error (EE) ± (0.05 + 15%) (55%). Besides, there is a 10% overestimation, but the positive bias does not exhibit obvious seasonal variations. Similarly, the DT3K and DT10K products overestimate AOD retrievals by 23% and 15%, respectively, all over the year, but the positive biases become larger in spring and summer. For the DB10K AOD retrievals, there is an overestimation (underestimation) in autumn and winter (spring and summer). Compared to the aqua-MODIS AOD products, the VIIRS_EDR AOD retrievals are less correlated (R = 0.73) and only 44% of the retrievals fall within EE. Meanwhile, the VIIRS_EDR shows larger bias than the aqua-MODIS C6 retrievals, and tends to overestimate AOD retrievals in summer and underestimate in winter. Additionally, there is an underestimation for the VIIRS_EDR AOD retrievals over the regions during high aerosol loadings. These indicate that the VIIRS_EDR retrieval algorithm needs to be improved in further applications over the YRB. Keywords: AOD; MODIS; VIIRS; Yangtze River Basin
1. Introduction Aerosols are small suspended particles in the atmosphere of the earth, which have a significant effect on climate change, air quality, visibility and human health [1]. Especially, the particulate matter Remote Sens. 2018, 10, 117; doi:10.3390/rs10010117
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with diameters less than 2.5 µm (PM2.5 ), has the capacity of remaining in the atmosphere for a long time and can be breathed into the lungs, resulting in various diseases [2]. Therefore, in order to meet the requirements of air quality monitoring and climate change assessment, long-term continuous aerosol observations over regional scales are necessary and critical. Many efforts have been paid for constructing global and national aerosol foundation networks, such as the aerosol robotic network (AERONET) and the China aerosol remote sensing network (CARSNET). The ground-level aerosol observations are retrieved directly by sun photometer, thus, avoiding surface reflectance contamination [3–5]. However, there is a sparse distribution of aerosol ground sites all over the world, especially in the remote countryside, due to their high operating costs. Therefore, with the improvement of satellites resolution, satellite remote sensing is expected as an ideal technology for retrieving aerosol optical and radiative properties at global and regional scales. During the past decades, various satellite sensors have been launched, such as the moderate resolution imaging spectroradiometer (MODIS) and the visible infrared imaging radiometer suite (VIIRS). VIIRS is the next-generation polar-orbiting operational environment sensor launched in October 2011, which is expected to continue to provide, in the long run, global aerosol retrievals after MODIS. Although there are some small differences in cloud masking and pixel selection, the VIIRS AOD retrieval algorithm is based on the MODIS heritage [6]. However, compared with MODIS C6 aerosol products, the VIIRS_EDR AOD retrievals showed lower accuracy (−0.009 vs. −0.005) and larger uncertainty (0.130 vs. 0.106) over global land [6,7]. Thus, the robustness of the VIIRS_EDR retrieval algorithm needs to be improved. Recently, these MODIS C6 and VIIRS_EDR aerosol products have been widely validated against ground observations in clear sky over the global land and ocean [8,9], India [10,11], Southeast Asia [12], East Asia [13,14], Greece [15,16] and China [17–19]. However, their performances over frequent haze-fog areas are still unclear [20]. Bail et al. [21] reported that both DT and DB MODIS C6 AOD retrievals were reduced during several random haze events in 2013 over the Beijing–Tianjin–Hebei region. Wang et al. [22] further analyzed that the missing VIIRS AOD retrievals during heavy aerosol loadings over the North China Plain were attributed to cloud masking, ephemeral water body and poor data quality. Additionally, for air quality remote sensing, the relationship between the MODIS AOD retrievals and the corresponding PM2.5 concentrations was estimated over Beijing and China mainland by [23] and [2], respectively. In other words, the evaluations of the MODIS C6 and VIIRS_EDR aerosol products during heavily polluted conditions usually focused on the eastern developed regions of China, such as the Beijing-Tianjin-Hebei [21–25], Hong Kong [26] and Wuhan [27,28]. However, to our knowledge, few related studies were performed over central and western China like the whole Yangtze river basin (YRB). The Yangtze river basin is one of the most developed regions in China, along with intensive industrialization and urbanization activities, causing serious air pollution [18]. Due to lack of ground-level aerosol sites and air quality sites, especially in the middle and upper reaches of the YRB, MODIS and VIIRS are treated as suitable tools to evaluate aerosol properties and monitor air quality over the YRB. However, the various aerosol sources and diverse underlying surfaces over the YRB may amplify the biases in AOD for aerosol products [18]. Thus, before the scientific application of satellite aerosol retrievals, it is necessary to evaluate their performance over the whole YRB. The major objectives of this study are to evaluate the performances of the aqua-MODIS C6 (DT3K, DT10K, DB10K and DTB10K) and VIIRSEDR (6 km, 6 K) AOD products for different quality flags over a frequent haze-fog area like the YRB. The study is organized as follows: Data and methods are described in Sections 2–4 refer to the results and the discussion, respectively, and finally, conclusions are summarized in Section 5.
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2. Data and Methods 2.1. MODIS C6 Data The aqua-MODIS Level 2 Collection 6 (C6) aerosol products can be obtained from https://ladsweb. modaps.eosdis.nasa.gov/ since 2002. Compared to the previous Collection 5.1 (C51), the MODIS C6 aerosol products present significant improvements on the retrieval algorithm and spatial resolution [8,14,21]. The MODIS C6 product provides AOD observations based on two algorithms: dark target and deep blue. Generally, the dark target algorithm (DT) is suitable for the vegetated surfaces, while the enhanced C6 deep blue algorithm (DB) can retrieve AOD values over bright and vegetated land surfaces but not over oceans. Both algorithms are unsuitable for snow-covered surfaces. The details about the two retrieval algorithms were already reported in previous researches [29–32]. The MODIS C6 10 km aerosol product also provides a set of DT and DB combined database (DTB). The quality flags of the MODIS AOD retrievals are classified as 0, 1, 2 and 3, for unproduced, low, medium, and high quality, respectively. Additionally, for monitoring air quality, C6 also provides AOD observations at 3 km resolution based on the DT algorithm. In this study, the aqua-MODIS C6 DT AOD at 3 km and 10 km, DB at 10 km, and DTB AOD retrievals at 10 km for all quality flags (Table 1) are obtained to compare with ground-level AOD observations. Table 1. Characteristics of aqua-MODIS C6 and VIIRS_EDR AOD products used in this study over the Yangtze river basin (YRB) during 2 May 2012–31 December 2016. Scientific Data Set (SDS) Satellite
Product
Suomi–NPP
VIIRS_EDR
Aerosol Optical Depth_at_550 nm
QF1_VIIRSAEROEDR
aqua
DT10K DB10K DTB10K DT3K
Image_Optical_Depth_Land_And_Ocean Deep_Blue_Aerosol_Optical_Depth_550_Land AOD_550_Dark_Target_Deep_Blue_Combined Image_Optical_Depth_Land_And_Ocean
Land_Ocean_Quality_Flag Deep_Blue_Aerosol_Optical_Depth_550_Land_QA_Flag AOD_550_Dark_Target_Deep_Blue_Combined_QA_Flag Land_Ocean_Quality_Flag
Aerosol Optical Depth
Quality Flag
2.2. VIIRS_EDR Data VIIRS is the next-generation environmental sensor aboard the Suomi National Polar-Orbiting Partnership (S–NPP) satellite, which was launched in October 2011. Similar to aqua-MODIS, the passing time of VIIRS is also approximately at the local time of 13:30. The level 2.0 aerosol VIIRS_EDR product provides AOD values for 11 wavelengths (412–2250 nm) since 2 May 2012 at www.class.noaa. gov. The VIIRS_EDR retrieval is created from 8 × 8 pixel aggregations of the intermediate product (IP) values (0.75 km), and therefore, the spatial resolution of the VIIRS_EDR AOD is about 6 km resolution at nadir. The VIIRS_EDR AOD values are retrieved for the unproduced (QF = 0), low (QF = 1), medium (QF = 2) and high (QF = 3) quality flags. Furthermore, the retrieval algorithm of the VIIRS_EDR product was analyzed in the previous studies over land and water and can be found here [6,7,33]. In this study, the VIIRS_EDR AOD values at 550 nm for different quality flags are obtained for the period of 2 May 2012–31 December 2016 over YRB (Table 1). 2.3. Ground-Based Measurements The AERONET (aerosol robotic network) and the CARSNET (China aerosol remote sensing network) are two common ground-based aerosol networks, which retrieve aerosol optical properties by using the CIMEL sun photometer (CE318). The CE318 sun photometer performs direct solar radiation measurements at eight wavelengths ranging from 340 nm to 1020 nm, but excludes 550 nm. As reported in previous studies, the systemic bias of AERONET AOD values was about 0.01–0.02 [34]. More details about the retrieval algorithm and instrument calibration of the CE318 sun photometer can be found in [4,5]. As shown in Table 2 and Figure 1, in order to verify the performance of satellite AOD retrievals over theYRB, CE318 measurements are obtained for Taihu (TH), Hefei (HF) and Wuhan (WH) from 2 May 2012 to 31 December 2016, whereas for the Kunming (KM), these measurements
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are obtained from 2 May 2012 to 31 December 2013. All the sites are located in urban area, except TH (suburban site).2018, 10, 117 Remote Sens. 4 of 18 Table 2. Statistics of CE318 groundsites sitesover over YRB YRB from used in this study. Table 2. Statistics of CE318 ground from2012 2012toto2016 2016 used in this study. Region YRD
Region YRD
CE318 Site
CE318 Site
Taihu (TH)
Taihu (TH)
Hefei (HF)
Lon(E)/Lat(N)
Lon(E)/Lat(N)
120.12/31.25
120.12/31.25
117.17/31.90
Station Type
Quality Flag
Time
AERONET (suburban)
level 1.5
2012.05.02–2016.12.31
CARANET (urban)
level 2.0
2012.05.02–2016.12.31
Station Type
Quality Flag
AERONET (suburban)
level 1.5
Time
2012.05.02–2016.12.31
117.17/31.90 MidstreamHefei (HF) Midstream 114.21/30.32 WuhanWuhan (WH)(WH) 114.21/30.32
CARANET (urban) CARANET (urban) (urban) CARANET
level 2.0 2012.05.02–2016.12.31 level 2.0 2.0 2012.05.02–2016.12.31 level 2012.05.02–2016.12.31
Source area Kunming 102.42/25.02 Source area Kunming (KM) (KM)102.42/25.02
CARANET (urban) (urban) CARANET
level 1.5 1.5 2012.05.02–2013.12.31 level 2012.05.02–2013.12.31
Figure 1. The location ofofthe sites(triangle (trianglesymbol) symbol) over YRB. Figure 1. The location theaerosol aerosol ground ground sites over YRB.
2.4. Comparison Methods 2.4. Comparison Methods According to previous research [18,35], the comparison procedures of satellite AOD retrievals
According to previous research [18,35], the comparison procedures of satellite AOD retrievals against CE318 AOD observations are performed as follows. Firstly, the CE318 AOD values at 550 nm against AOD observations are performed as follows. Firstly, the CE318 AOD values at 550 nm areCE318 calculated from Equations (1) and (2). are calculated from Equations (1) and (2). AODλi = βλi−α (1) AODλi = βλi−α (1)
(
ln AODλ1 / AODλ2
)
AOD 2
λ α =ln AOD /AOD , β = AOD −α ln(λλ11 / λ2 ) λ2 , β = λ2 λ2 α=
ln(λ1 /λ2 )
λ2−α
(2)
(2)
where α refers to the Ångström exponent, β is the turbidity coefficient, λ1 and λ2 are the respective nm and 870 nm, which arethe notturbidity interferedcoefficient, by water vapor. Secondly, the CE318 wherewavelengths α refers to at the440 Ångström exponent, β is λ1 and λ2 are the respective AOD values at 550 collected only within min of the VIIRS passing wavelengths at 440 nmnm andare 870 nm, which are not±30 interfered byMODIS water (aqua) vapor.and Secondly, the CE318 (13:30 local time). Only when at least two± CE318 observations during the satellite passing time time AOD time values at 550 nm are collected only within 30 min of the MODIS (aqua) and VIIRS passing are available, then the CE318 AOD values are averaged. Thirdly, the satellite AOD retrievals are are (13:30 local time). Only when at least two CE318 observations during the satellite passing time collected only within 3 × 3 pixels centered on the CE318 ground sites. That is to say, for the MODIS available, then the CE318 AOD values are averaged. Thirdly, the satellite AOD retrievals are collected C6 10 km (DT, DB and DTB) products, the sampling area is 30 km × 30 km; for the MODIS C6 3 km only within 3 × 3 pixels centered on the CE318 ground sites. That is to say, for the MODIS C6 10 km (DT, and VIIRS_EDR (6 km) products, the sampling areas are 9 km × 9 km and 18 km × 18 km, respectively. DB and DTB) to products, thesatellite sampling area iserrors, 30 kmit×is30 km; for that the MODIS C6 3pixels km and VIIRS_EDR In order reduce the retrieval necessary at least two of satellite (6 km) products, sampling areas are 9 km × 9 km and 18 × 18 km, respectively. In order retrievals fall the in their corresponding sampling areas. Finally, thekm linear regressions of the MODIS C6 to reduce the satellite retrieval errors,against it is necessary that at least two pixels of satelliteover retrievals fall in and VIIRS_EDR AOD retrievals the CE318 AOD observations are performed the YRB 2 May 2012–31 December The the regression slope,C6 theand y-intercept, their during corresponding sampling areas.2016. Finally, linear parameters regressionsinclude of the the MODIS VIIRS_EDR correlation coefficient (R) and the rootobservations mean square error (RMSE, Equation AODthe retrievals against the CE318 AOD are performed over (3)). the Furthermore, YRB duringthe 2 May uncertainty on the satellite retrieval algorithms is examined based on the expected error (EE, 2012–31 December 2016. The regression parameters include the slope, the y-intercept, the correlation coefficient (R) and the root mean square error (RMSE, Equation (3)). Furthermore, the uncertainty on
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the satellite retrieval algorithms is examined based on the expected error (EE, Equation (4)), the mean absolute error (MAE, Equation (5)) and the relative mean bias (RMB, Equation (6)). r RMSE =
1 n ( AOD(satellite)i − AOD(CE318)i )2 n ∑ i =1
EE = ±(0.05 + 0.15AODCE318 ) 1 n MAE = ∑ AOD(satellite)i − AOD(CE318)i n i =1 RMB = ( AODsatellite /AODCE318 )
(3) (4) (5) (6)
The value of RMB > 1 and RMB < 1 indicate over- and under-estimation in the satellite AOD observations, respectively. 3. Results 3.1. Comparison of VIIRS and aqua-MODIS C6 AOD vs. CE318 AOD Figure 2 shows the comparison of the DT3K AOD and the collocated CE318 AOD at different quality flags (QF) over YRB for the period of 2 May 2012 to 31 December 2016. There are 803, 680 and 671 successful DT3K–CE318 matchups for QF > 0, QF > 1 and QF = 3, respectively. Note that QF > 0 includes AOD retrievals with all quality flags, and QF > 1 includes those of QF = 2 and QF = 3. Overall, filtering by quality flags (QF > 1), the comparison has been improved for all statistics, i.e., RMSE is decreased by 11%, the R and the percentage of retrievals within the EE are increased up to 3% and 5%, respectively. However, constraining to only high-quality flag (QF = 3) does not improve the overall agreement any further. The reason is likely that there are too few AOD retrievals (only nine points over 4.5 years) for QF = 2. These issues need to be further studied site by site. In terms of the site-by-site comparison over YRB (Table 3), filtering by quality flags (QF > 1), the improvement can be observed at TH, HF and WH. However, for the KM site, the matchup statistics are poor, and increasing QF does not help, probably due to the very low number of samples (67–77) [28]. In Figure 2, the correlation coefficient (R) is in the range of 0.78–0.81, along with 43–46% of DT3K AOD retrievals falling into the expected error (EE) envelope. This indicates that although the correlation is good, the DT3K still does not meet the requirements of the EE as the percentage within the EE is less than one standard deviation (i.e., 68%). Also, it has large RMB (1.23–1.28) which indicates significant overestimation in the algorithm. This overestimation is likely due to the underestimation in the estimated surface reflectance in the visible channels over urban and suburban areas [8]. Besides, obviously in Table 3, the best performance of DT3K AOD retrievals appears at WH with all statistical metrics (R = 0.84–0.86, RMB = 1.06–1.11, MAE = 0.05–0.07 and 59–63% of retrievals within the EE). Similar comparison results at WH were also reported by [28]. Approximately 72% of the DT3K-CE318 AOD matchups fell into the EE with a high R value (0.87) and nearly 7% of the DT3K AOD retrievals (RMB = 1.07) were overestimated [28]. Figure 3 also reports the comparison results of the DT10K and CE318 AOD retrievals. A total of 1213, 942 and 823 matchups are successfully obtained for QF > 0, QF > 1 and QF = 3, respectively. The quality flag analysis results for DT10K are similar to those for the DT3K. Using higher-quality DT10K AOD retrievals lead to the better performance at the TH, HF and WH sites, but it is not suitable at the KM site (Table 4). Besides, compared to the DT3K, the more numbers of DT10K-CE318 AOD matchups may be due to their different sampling criteria used in the AOD retrievals [36,37].The DT10K retrievals are well matched with ground observations by high R values (0.81–0.82), but nearly 42–55% of the DT10K AOD retrievals fall above the EE, resulting in 15–32% overestimation (RMB = 1.15–1.32) compared to the CE318 observations. From the site-by-site comparison in Table 4, there are also 37–51%, 25–31% and 5–19% overestimations for the DT10K, respectively at TH, HF and WH. However, DT10K
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AOD retrievals at10, KM Remote Sens. 2018, 117 are lower than the corresponding CE318 observations by 2–17%, probably 6 of due 18 Remote 2018,values 10, 117 (nearly 70% of the total CE318 observations less than 0.3). Similar result 6 of was 18 to its lowSens. AOD probably duei.e., to its low values (nearly 70%toofslightly the total CE318 observations less than 0.3). also17%, reported by [8], that theAOD DT10K product tends underestimate AOD retrievals in the 17%, probably due also to itsreported low AOD values (nearly 70% of theproduct total CE318 observations less than 0.3). Similar result was by [8], i.e., that the DT10K tends to slightly underestimate condition with low aerosol loadings (AOD < 0.3) over global land. Similar result was alsocondition reported with by [8], i.e., that the DT10K(AOD product tends slightly underestimate AOD retrievals in the low aerosol loadings < 0.3) overtoglobal land. AOD retrievals in the condition with low aerosol loadings (AOD < 0.3) over global land.
Figure 2. 2. Comparison productsagainst againstCE318 CE318 AOD observations the YRB Figure ComparisonofofDT3K DT3K AOD AOD products AOD observations overover the YRB for thefor 2. Comparison of DT3K AOD products against CE318 AOD observations over the YRB for the theFigure period of 2 May 2012–31 December 2016. The DT3K AOD retrievals are classified by all > 0, period of 2 May 2012–31 December 2016. The DT3K AOD retrievals are classified by all (QF > (QF 0, (a)), period of 2 May 2012–31 December 2016. The DT3K AOD retrievals are classified by all (QF > 0, (a)), (a)),medium medium(QF (QF > 1, (b)) and high 3, (c)) quality flags. straight the 1:1the line; the > 1, (b)) and high (QF(QF = 3,=(c)) quality flags. The The blackblack straight line isline theis1:1 line; red (QF 1,the (b)) and highline (QF =and 3,the (c)) quality flags. The black straight line is the 1:1 line; the red redmedium straightline lineis>isthe regression line the dot lines the expected (EE) envelopes. straight regression and dot lines areare the expected errorerror (EE) envelopes. straight line is the regression line and the dot lines are the expected error (EE) envelopes. Table 3. ComparisonofofDT3K DT3KAOD AODproducts products (QF (QF >> 0, 0, QF QF > 1 and Table 3. Comparison and QF QF == 3)3) against againstCE318 CE318AOD AOD Table 3. Comparison ofWH DT3K AOD products (QFofof>220, QF2012–31 > 1 andDecember QF = 3) against observations at TH, HF, and KM for theperiod period May 2012–31 December 2016. observations at TH, HF, WH and KM for the May 2016. CE318 AOD observations at TH, HF, WH and KM for the period of 2 May 2012–31 December 2016. Station QF Station Station QFQF TH THTH HF HFHF WH WH WH KM KM KM
QF > 0 QF QF > 0> 01 QFQF > 1>= 13 QFQF = 3=> 30 QF > 01 QF > 0 QFQF > 1>= 13 QFQF = 3=> 30 QF > 01 QF > 0 QFQF > 1>= 13 QFQF = 3=> 30 QF > 01 QF > 0 QFQF > 1>= 13 QF QF = 3= 3
N NN
Slope Slope Slope
236 236 182 236 182 182 182 178 182 178 159 178 159 159 159 312 159 312 273 312 273 270 273 270 77 270 77 69 77 69 67 69 67 67
0.84 0.84 0.79 0.84 0.79 0.79 0.79 1.06 1.06 1.03 1.06 1.03 1.03 1.03 0.97 1.03 0.97 0.95 0.97 0.95 0.96 0.95 0.96 1.01 0.96 1.01 0.91 1.01 0.91 0.90 0.91 0.90 0.90
y-int R MAE y-int MAE y-int RRCE318 MAE DT3K vs. DT3Kvs. vs. CE318 DT3K CE318 0.42 0.75 0.32 0.42 0.75 0.32 0.45 0.79 0.31 0.42 0.75 0.32 0.45 0.45 0.79 0.79 0.31 0.31 0.45 0.20 0.79 0.82 0.23 0.79 0.31 0.31 0.20 0.82 0.23 0.19 0.85 0.20 0.20 0.82 0.23 0.19 0.19 0.85 0.85 0.20 0.20 0.19 0.09 0.85 0.84 0.07 0.19 0.85 0.20 0.20 0.09 0.07 0.08 0.84 0.85 0.09 0.84 0.04 0.07 0.08 0.07 0.85 0.86 0.05 0.08 0.85 0.04 0.04 0.07 0.17 0.86 0.78 0.17 0.07 0.86 0.05 0.05 0.17 0.17 0.19 0.78 0.75 0.16 0.17 0.78 0.17 0.19 0.20 0.75 0.74 0.19 0.75 0.16 0.16 0.20 0.20 0.74 0.74 0.16 0.16
RMB RMSE Within EE% RMB RMSE RMSE Within WithinEE% EE% RMB 1.53 1.53 1.45 1.53 1.45 1.45 1.45 1.41 1.45 1.41 1.35 1.41 1.35 1.35 1.35 1.11 1.35 1.11 1.06 1.11 1.06 1.06 1.06 1.48 1.06 1.48 1.42 1.48 1.42 1.42 1.42 1.42
0.31 0.31 0.26 0.31 0.26 0.26 0.26 0.24 0.26 0.24 0.20 0.24 0.20 0.20 0.20 0.22 0.20 0.22 0.20 0.22 0.20 0.20 0.20 0.19 0.20 0.19 0.18 0.19 0.18 0.18 0.18 0.18
26 26 2926 2929 29 3029 30 3230 3232 32 5932 59 6359 6363 63 3263 32 31 32 3131 3131
0
0
Figure 3. As in Figure 2, but comparison of the DT10K AOD products. The DT10K AOD retrievals Figure As in QF Figure 2, 2, but comparison ofofthe AOD TheDT10K DT10KAOD AODretrievals retrievals Figure 3.>As in Figure but the DT10K AOD products. products. The with3.QF 0, > 1 and QF = 3comparison are presented in DT10K (a–c), respectively. with QFQF > 0,> QF > 1> and with 0, QF 1 andQF QF==33are arepresented presented in in (a–c), (a–c), respectively. respectively. Table 4. As in Table 3, but comparison of the DT10K AOD products. Table 4. As in Table 3, but comparison of the DT10K AOD products.
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Table 4. As in Table 3, but comparison of the DT10K AOD products. Remote Sens. 2018, QF 10, 117 Station
TH
HF
WH
KM
N
Station
QF
N
TH
QF > 1
277
HF
QF > 1
281
WH
QF > 1
299
KM
QF > 1 QF = 3
85 68
QF > 0 358 QF > 1 277 QF = QF 3 > 0237358
y-int
Slope
y-int R MAE RMB RMSE Within EE% 0.39 0.78 1.51 0.26 18 DT10K vs. CE318 0.32 0.35 0.85 0.25 1.39 0.20 21 0.39 18 27 0.38 0.78 0.85 0.320.24 1.51 1.370.26 0.25 0.35 0.85 0.25 1.39 0.20 21 0.26 0.81 0.20 1.31 0.24 33 0.38 0.85 0.24 1.37 0.25 27 0.23 0.83 0.17 1.27 0.22 37 0.26 33 42 0.21 0.81 0.84 0.200.15 1.31 1.250.24 0.21 0.23 0.83 0.17 1.27 0.22 37 0.15 0.81 0.14 1.19 0.24 51 0.21 0.84 0.15 1.25 0.21 42 59 0.11 0.81 0.05 1.07 0.22 0.15 51 63 0.10 0.81 0.84 0.140.03 1.19 1.050.24 0.20 0.11 0.81 0.05 1.07 0.22 59 −0.02 0.88 −0.01 0.98 0.13 70 0.10 0.84 0.03 1.05 0.20 63 62 −0.01 0.83 −0.05 0.85 0.12 −0.02 70 49 − 0.02 0.88 0.85 −0.01 −0.070.98 0.830.13 0.09 −0.01 0.83 −0.05 0.85 0.12 62 −0.02 0.85 −0.07 0.83 0.09 49
0.89 0.85 0.89 0.81
0.85
QF > 0 321 QF = 3 237 QF > 1 281 QF = QF 3 > 0253321
0.90 0.81 0.90 0.90 0.89
QF > 0 409 QF = 3299253 QF > 1 QF = QF 3 > 0261409
0.99 0.89 0.91 0.99 0.89
QF > 0 125 QF > QF 1 = 3 85261 QF = QF 3 > 0 68125
0.90
0.91
1.03 0.89 0.80 1.03 0.75
0.80 0.75
R
MAE
RMB
RMSE
7 of 18 Within EE%
Slope
DT10K vs. CE318
As shown in Figure 4, there are 1350, 947 and 825 DB10K-CE318 matchups, respectively for QF > 0, QF > 1 and = 3ininFigure a total of four HF, WH and KM). matchups, Similar torespectively the DT3Kfor and As QF shown 4, there aresites 1350, (TH, 947 and 825 DB10K-CE318 QFDT10k > products, by =QF > a1,total there a significant improvement in Similar the DB10K the R is 0, QFfiltering > 1 and QF 3 in of is four sites (TH, HF, WH and KM). to theretrievals, DT3K andi.e., DT10k increased up tofiltering 12% and isisdecreased byimprovement 24% and thein increase of the percentage within products, by the QF >RMSE 1, there a significant the DB10K retrievals, i.e., the R is the increased up to 12% and the RMSE is decreased by 24% and the increase of the percentage within the does EE remains within 5%. However, constraining to only high-quality flag (QF = 3), the improvement EE remains within 5%. However, constraining to only high-quality flag (QF = 3), the improvement not occur. As seen in Table 5, filtering by QF > 1, the site-by-site comparison except KM supports this does not occur. seen in Table 5, filtering by QF decreases > 1, the site-by-site comparison except KM supports recommendation (RAs and %EE increase and RMSE with increasing QF). Besides, in Figure 4c, this recommendation (R and %EE increase and RMSE decreases with increasing QF). Besides, in the good comparison of high-quality DB10K-CE318 retrievals (R = 0.84, MAE = −0.05) over the YRB Figure 4c, the good comparison of high-quality DB10K-CE318 retrievals (R = 0.84, MAE = −0.05) over is consistent with research over global land (R = 0.92, MAE = −0.01) and Southeast Asia (R = 0.88, the YRB is consistent with research over global land (R = 0.92, MAE = −0.01) and Southeast Asia (R = MAE 0.88, = −0.06) However, approximately 40% 40% of the high-quality DB10K below the MAE [30]. = −0.06) [30]. However, approximately of the high-quality DB10Kretrievals retrievals fall fall below EE, leading to a 12% underestimation (RMB = 0.88) compared to the CE318 observations. For the EE, leading to a 12% underestimation (RMB = 0.88) compared to the CE318 observations. For QFQF > >0 (Figure 4a), the DB10K became slightly overestimated with a positive MAE (0.01), which is similar 0 (Figure 4a), the DB10K became slightly overestimated with a positive MAE (0.01), which is similar to to the(0.03) MAEover (0.03)global over global In terms of the evaluationsover overindividual individual sites (Table the MAE land land [30].[30]. In terms of the evaluations sitesofofYRB YRB (Table 5), 5), the tend DB10K to underestimate retrievals quality flagsexcept exceptthe theTH TH site. site. the DB10K totend underestimate AODAOD retrievals for for all all quality flags
Figure in Figure 2, but comparisonofofthe theDB10K DB10K AOD AOD products. DT10K AOD retrievals Figure 4. As4.inAsFigure 2, but comparison products.The The DT10K AOD retrievals with QF > 0, QF > 1 and QF = 3 are presented in (a–c), respectively. with QF > 0, QF > 1 and QF = 3 are presented in (a–c), respectively.
Figure 5 also reports the validation of the DTB10K against the CE318 AOD retrievals for QF > 0
Figure 5 also DTB10K againstObviously, the CE318with AOD for QF (N = 1007), QFreports > 1 (N =the 993)validation and QF = 3of (Nthe = 972), respectively. theretrievals improvement of > 0 (N = 1007), QF > 1 (N = 993) and QF = 3 (N = 972), respectively. Obviously, with the improvement the quality flags, the DTB10K AOD retrievals are better matched with the CE318 observations. Forof the quality flags, the retrievals are matched with decrease the CE318 observations. Formore example, example, theDTB10K R values AOD increase from 0.81 to better 0.85, the MAE values from 0.09 to 0.07 and the RAOD values increase 0.81 MAEasvalues decrease from to 0.07 and retrievals fallfrom within the to EE0.85, (55%).the Besides, analyzed in Section 2.1,0.09 the DTB10K AODmore is a setAOD of combined datathe by EE using the DT and DB in Section different2.1, surface reflectance. It can retrievals fall within (55%). Besides, as algorithms analyzed in the DTB10K AOD is abeset of retrieved over vegetated and bright land surfaces. For this reason, compared to other high-quality combined data by using the DT and DB algorithms in different surface reflectance. It can be retrieved
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Remote Sens. 2018, 10, 117 8 of 18 over vegetated and bright land surfaces. For this reason, compared to other high-quality aqua-MODIS C6 products (Figures 2c and 4c), the high-quality DTB10K retrievals perform best, i.e., the R and the aqua-MODIS C6 products (Figures 2c and 4c), the high-quality DTB10K retrievals perform best, i.e., percentage within the EE (Figure 5c) are increased up to 1–5% and 17–22%, respectively, and the RMSE the R and the percentage within the EE (Figure 5c) are increased up to 1–5% and 17–22%, respectively, is decreased by 7%.is Besides, theby bias is small andthe positive an overestimation of an 10%. and the RMSE decreased 7%. Besides, bias is(0.05), small causing and positive (0.05), causing However, larger overestimations were reported by [21] at Beijing_RADI (21%) and Beijing_CAMS overestimation of 10%. However, larger overestimations were reported by [21] at Beijing_RADI (21%) (26%) For the individual comparison at each sitecomparison of YRB (Table 6), approximately 19% of6),the andsites. Beijing_CAMS (26%) sites. For the individual at each site of YRB (Table DTB10K AOD retrievals are underestimated with a negative MAE ( − 0.07) at KM; the best performance approximately 19% of the DTB10K AOD retrievals are underestimated with a negative MAE (−0.07) appears WH. at KM;atthe best performance appears at WH.
Table 5.5.As DB10K AOD AODproducts. products. Table AsininTable Table3,3,but butcomparison comparison of of the the DB10K
Station QFQF Station QF > 0 QF > 0 TH QFQF >1> 1 TH QFQF =3= 3 QF QF > 0 > 0 HF QFQF > 1> 1 HF QFQF = 3= 3 QFQF > 0> 0 > 1> 1 WHWH QFQF QF = 3 QF = 3 QFQF >0> 0 KMKM QF > 1 QF > 1 QF = 3 QF = 3
NN
Slope Slope
418
1.04 1.04 1.04 1.04 1.02 1.02 0.97 0.97 0.96 0.96 1.06 1.06 1.00 1.00 1.07 1.07 1.11 1.11 0.65 0.65 0.46 0.46 0.55 0.55
418 267 267 231 231
329 329 273 273 249 249 462 462 346 346 307 307
141 141 61 61 59
59
y-int R R MAE RMSE Within EE% y-int MAERMB RMB RMSE Within EE% DB10K vs. CE318 DB10K vs. CE318 0.09 0.73 0.11 1.17 0.36 54 0.09 0.73 0.11 1.17 0.36 54 0.03 0.83 0.05 1.07 0.24 60 0.03 0.83 0.05 1.07 0.24 60 0.02 63 63 0.02 0.84 0.84 0.040.04 1.071.07 0.240.24 0.01 49 49 0.01 0.78 0.78 0.000.00 1.001.00 0.290.29 −0.05 0.80 −0.07 0.88 0.26 47 −0.05 0.80 −0.07 0.88 0.26 47 −0.11 0.82 0.82 −0.07 −0.070.880.88 0.240.24 −0.11 49 49 −0.06 41 41 −0.06 0.73 0.73 −0.06 −0.060.930.93 0.340.34 −0.19 0.83 0.83 −0.13 −0.130.810.81 0.260.26 −0.19 42 42 −0.23 0.86 0.86 −0.140.780.78 0.230.23 −0.23 −0.14 42 42 0.02 0.77 0.77 −0.08 −0.080.760.76 0.130.13 0.02 49 49 −0.01 0.87 −0.11 0.41 0.04 39 −0.01 0.87 −0.11 0.41 0.04 39 −0.02 0.88 −0.09 0.44 0.05 39 −0.02 0.88 −0.09 0.44 0.05 39
Figure 5. As in in Figure 2, 2, but products.The TheDT10K DT10KAOD AODretrievals retrievals Figure 5. As Figure butcomparison comparisonof ofthe theDTB10K DTB10K AOD products. with QFQF > 0, QFQF > >1 1and respectively. with > 0, andQF QF==33are arepresented presented in (a–c), respectively.
In Figure a total of 1401 1606,and 1401 and 1133 VIIRS_EDR-CE318 AOD retrievals successfully In Figure 6, a 6, total of 1606, 1133 VIIRS_EDR-CE318 AOD retrievals successfully performed performed over YRB, respectively, for QF > 0, QF > 1 and QF = 3. Different from the results of quality over YRB, respectively, for QF > 0, QF > 1 and QF = 3. Different from the results of quality flag flag analyses of the aqua-MODIS C6 AOD products, filtering by OF > 1, the VIIRS_EDR retrievals are analyses of the aqua-MODIS C6 AOD products, filtering by OF > 1, the VIIRS_EDR retrievals are not significantly improved for all statistical metrics. However, constraining to only high-quality flag not significantly improved for all statistical metrics. However, constraining to only high-quality flag (QF = 3), the significant improvement occurs, i.e., the R and the percentage of retrievals within the EE (QF = 3), the significant improvement occurs, i.e., the R and the percentage of retrievals within the EE are increased up to 16% and 26%, respectively, and the RMSE is decreased by 24%.The same quality are increased up to 16% and 26%, respectively, and the RMSE is decreased by 24%.The same quality flag results can be found at each site over YRB (Table 7). This indicates that the high-quality flagVIIRS_EDR results canproduct be found each site overfor YRB (Table 7). This indicates theBesides, high-quality VIIRS_EDR is at recommended aerosol application over thethat YRB. compared to all product is recommended for aerosol application over the YRB. Besides, compared to all high-quality high-quality aqua-MODIS C6 products (Figures 2c–5c), although the number of the high-quality aqua-MODIS C6 products (Figure 2c, 6c) Figure 3c, Figure Figure 5c), although thethe number of the VIIRS_EDR-CE318 matchups (Figure is increased up 4c, to 16–68%, the R (0.73) and percentage high-quality VIIRS_EDR-CE318 matchups (Figure 6c) is increased up to 16–68%, the R (0.73) and of the retrievals within the EE (44%) are decreased by 10–14% and 4–20%, respectively. The resultsthe percentage of the retrievals within the EE (44%) are decreased 10–14%needs and 4–20%, respectively. demonstrate that the robustness of the VIIRS_EDR retrieval by algorithm to be improved. TheAdditionally, results demonstrate thatofthe of the VIIRS_EDR retrieval algorithm to be improved. nearly 31% therobustness high-quality retrievals fall above the EE, causingneeds 4% overestimation
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Additionally, nearly 31% of the high-quality retrievals fall above the EE, causing 4% overestimation compared to the CE318 observations. The bias (0.03) is consistent with previous research over East compared to the CE318 observations. The bias (0.03) is consistent with previous research over East Asia Asia (0.05) [13], but slightly larger than that over global land (−0.01) [6]. This reason is explained in (0.05)Table [13], but slightly larger thaturban over ground global land −0.01) [6]. This reason is explained in Table 7. 7. HF, WH and KMthan are all sites, (so it is inappropriate to replace the surface HF, WH and KM are all urban ground sites, so it is inappropriate to replace the surface reflectance reflectance of an individual site with the global averaged surface reflectance. In Table 7, there is aof an individual with the of global averaged surface reflectance. In Table 7, better better site performance VIIRS_EDR-CE318 retrievals at WH compared tothere those is at athe otherperformance sites, with of a higher R (0.76), more retrievals within the EE (61%) at and slightly negative (−0.06). Similarmore VIIRS_EDR-CE318 retrievals at WH compared to those thea other sites, with bias a higher R (0.76), results were also [28]aatslightly WH with 52% of retrievals withinSimilar the EE and an underestimation retrievals within the reported EE (61%)byand negative bias (−0.06). results were also reported of at about by [28] WH5%. with 52% of retrievals within the EE and an underestimation of about 5%. Table 6. As in Table 3, but comparison of the DTB10K AOD products.
Table 6. As in Table 3, but comparison of the DTB10K AOD products. Station QF N Slope y-int R MAE RMB RMSE Within EE% Station QF N Slope DTB10K y-int R CE318 MAE RMB RMSE Within EE% vs. DTB10K0.78 vs. CE318 QF > 0 312 0.90 0.29 0.23 1.37 0.27 35 TH QF 1.30 0.22 QF>>10 298 312 0.92 0.90 0.25 0.29 0.83 0.78 0.21 0.23 1.37 0.27 35 37 QF=>31 290 298 0.93 0.92 0.26 0.25 0.84 0.83 0.20 0.21 1.30 0.22 37 38 TH QF 1.27 0.21 QF = 3 290 0.93 0.26 0.84 0.20 1.27 0.21 38 QF > 0 280 0.95 0.17 0.82 0.14 1.23 0.23 44 QF > 0 280 0.95 0.17 0.82 0.14 1.23 0.23 44 HF QF > 1 280 0.95 0.17 0.82 0.14 1.23 0.23 44 QF > 1 280 0.95 0.17 0.82 0.14 1.23 0.23 44 HF QF 1.22 0.23 QF==33 276 276 0.94 0.94 0.18 0.18 0.83 0.83 0.14 0.14 1.22 0.23 47 47 QF 1.01 0.24 QF>>00 345 345 1.05 1.05 −0.03 −0.03 0.85 0.85 0.01 0.01 1.01 0.24 67 67 QF>>11 345 345 1.05 1.05 −0.03 −0.03 0.85 0.85 0.01 0.01 1.01 0.24 67 67 WH QF WH 1.01 0.24 QF = 3 336 1.03 −0.02 0.83 0.83 0.00 1.01 0.24 69 QF = 3 336 1.03 −0.02 0.00 1.01 0.24 69 QF > 0 70 0.75 − 0.01 0.85 − 0.07 0.81 0.09 54 54 QF > 0 70 0.75 −0.01 0.85 −0.07 0.81 0.09 QF > 1 70 0.75 −0.01 0.85 −0.07 0.81 0.09 54 KM KM QF 0.09 0.85 −0.07 −0.07 0.81 0.81 0.09 54 54 QF>=13 7070 0.75 0.75 −0.01 −0.01 0.85 QF = 3 70 0.75 −0.01 0.85 −0.07 0.81 0.09 54
Figure 6. As6.inAsFigure 2, but comparison AODproducts. products. The DT10K AOD retrievals Figure in Figure 2, but comparisonofofthe the VIIRS_EDR VIIRS_EDR AOD The DT10K AOD retrievals with QF 0, QF 1 >and QFQF = 3= are respectively. with>QF > 0, > QF 1 and 3 arepresented presented in in (a–c), (a–c), respectively. Table 7. As in Table butcomparison comparison of of the products. Table 7. As in Table 3, 3, but the VIIRS_EDR VIIRS_EDRAOD AOD products. Station
Station
QF
QF
N
N
QF > 0 436 QF > 1 355 QF > QF 0 = 3436271 QF > QF 1 > 0355339 3 > 1271291 HFQF = QF QF QF > 0 = 3339251 QF > QF 1 > 0291574 WH QF = QF 3 > 1251504 QF = 3 434 QF > QF 0 > 0574260 QF > 1 > 1504251 KM QF QF = QF 3 = 3434178 TH
TH
HF
WH
KM
QF > 0 QF > 1 QF = 3
260 251 178
Slope
y-int
R
MAE
RMB
Slope VIIRS_EDR y-int R MAE vs. CE318 0.73 0.48 0.58 VIIRS_EDR vs. 0.71 0.51 0.53 0.73 0.48 0.58 0.79 0.32 0.67 0.71 0.51 0.53 0.72 0.37 0.68 0.79 0.32 0.67 0.67 0.44 0.60 0.78 0.22 0.76 0.72 0.37 0.68 0.72 0.25 0.66 0.67 0.44 0.60 0.70 0.22 0.63 0.78 0.22 0.76 0.72 0.12 0.76 0.72 0.25 0.66 1.12 0.16 0.73 0.70 0.22 0.63 0.85 0.18 0.66 0.72 0.12 0.76 0.71 0.05 0.74
1.12 0.85 0.71
0.16 0.18 0.05
0.73 0.66 0.74
RMSE
RMB
0.33 1.61 0.32 1.52 0.210.33 1.321.61 0.200.32 1.331.52 0.240.21 1.381.32 0.090.20 1.141.33 0.050.24 1.091.38 0.010.09 1.021.14 −0.06 0.90 0.200.05 1.561.09 0.110.01 1.271.02 −0.060.890.90 −0.03
CE318
0.20 0.11 −0.03
1.56 1.27 0.89
Within EE%
RMSE Within EE%
0.32 0.33 0.25 0.32 0.31 0.33 0.33 0.25 0.24 0.31 0.30 0.33 0.31 0.24 0.22 0.29 0.30 0.26 0.31 0.22 0.16
0.29 0.26 0.16
26 28 35 35 30 45 47 41 61 42 42 45
26 28 35 35 30 45 47 41 61 42 42 45
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Figure 7 shows the boxplots of the high-quality AOD biases (AODsatellite-AODCE318) against the Figure 7 shows the boxplots of the high-quality AOD biases (AODsatellite -AODCE318 ) against the CE318 observations over YRB from 2 May 2012 to 31 December 2016. Overall, for DT3K (Figure 7a), CE318 observations over YRB from 2 May 2012 to 31 December 2016. Overall, for DT3K (Figure 7a), DT10K (Figure 7b) and DB10K (Figure 7c) retrievals, the AOD biases are usually greater than zero, DT10K (Figure 7b) and DB10K (Figure 7c) retrievals, the AOD biases are usually greater than zero, and become larger with an increase in CE318 AOD values. This is consistent with previous global and become larger with an increase in CE318 AOD values. This is consistent with previous global and regional evaluations [13,36]. In other words, the MODIS C6 DT and DTB products overestimate and regional evaluations [13,36]. In other words, the MODIS C6 DT and DTB products overestimate the AOD retrievals compared with the CE318 AOD observations over the YRB. However, there is an the AOD retrievals compared with the CE318 AOD observations over the YRB. However, there is an opposite trend for the DB10K AOD bias (Figure 7d). Generally, the DB10K AOD retrievals tend to be opposite trend for the DB10K AOD bias (Figure 7d). Generally, the DB10K AOD retrievals tend to be underestimated at CE318 AOD < 1.0 and slightly overestimated at AOD > 1.0. As for VIIRS_EDR underestimated at CE318 AOD < 1.0 and slightly overestimated at AOD > 1.0. As for VIIRS_EDR AOD AOD (Figure 7e), it matches well with CE318 AOD observations in the range of less than 1.0. But, (Figure 7e), it matches well with CE318 AOD observations in the range of less than 1.0. But, there is an there is an underestimation for CE318 AOD > 1.0. A similar evaluation was also analyzed at WH by underestimation for CE318 AOD > 1.0. A similar evaluation was also analyzed at WH by [28]. [28].
Figure Box plots plots of high-quality aqua-MODIS Figure 7. 7. Box of high-quality aqua-MODIS C6 C6 and andVIIRS_EDRAOD VIIRS_EDRAODretrieval retrievalbiases biases(AOD (AODsatellite satellite-AOD ) against CE318 AOD observations over YRB for the period of 2 May 2012–31 December 2016. AODCE318 CE318) against CE318 AOD observations over YRB for the period of 2 May 2012–31 December 2016. The dot line The dot line is is the the yy == 00 line line and and the the dashed dashed lines lines are are the the EE EE envelopes. envelopes. The The number number above above each each box box refers to the corresponding matchups in the different intervals of CE318 AOD (1–0.3, 0.1–0.5, refers to the corresponding matchups in the different intervals of CE318 AOD (1–0.3, 0.1–0.5, 0.1–0.75 0.1–0.75 and The DT3K, DT3K, DT10K, DT10K, DTB10K, DTB10K, DB10K DB10K and and VIIRS_EDRAOD VIIRS_EDRAOD retrieval retrieval biases biases are are presented and >1.0). >1.0). The presented in in (a–e), respectively. (a–e), respectively.
3.2. Seasonal Variation of AOD Retrieval Bias Figure 8 illustrates the AOD seasonal variation of the high-quality satellite retrievals and CE318 observations, from 2 May 2012 to 31 2016. Overall, for observations, respectively respectivelyatatTH, TH,HF, HF,WH WHand andKM KM from 2 May 2012 to December 31 December 2016. Overall, each AOD product at every ground site, site, the higher AODAOD values appear in spring and summer than for each AOD product at every ground the higher values appear in spring and summer the autumn and winter. Similar findings were observed by [35] at WH and by [18] over the YRB. than the autumn and winter. Similar findings were observed by [35] at WH and by [18] over YRB. The reason is likely that there are frequent dust events from North China in spring and local straw burnings in summer. summer. Besides, since the VIIRS retrieval algorithm is based on the joint heritage of the aqua-MODIS DT algorithm [6–8], the DT10K and DT3K AOD retrievals act similarly to those of the VIIRS_EDR However, it doesn’t appear at KM. The reason VIIRS_EDR product product for for all all seasons seasons at TH, HF and WH. However, may be the differences differences of of aerosol aerosol type type and and surface surface reflectance reflectance between betweenKM KMand andthe theother othersites sites[32]. [32].
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Figure 8. Seasonal and annual variation of the aqua-MODIS C6 and VIIRS_EDR AOD retrievals (QF = 3) Figure 8. Seasonal and annual variation of the aqua-MODIS C6 and VIIRS_EDR AOD retrievals (QF and CE318 AOD observations, respectively at TH, HF, WH and KM for the period of 2 May 2012– = 3) and CE318 AOD observations, respectively at TH, HF, WH and KM for the period of 2 May 2012– 31 December 2016. 31 December 2016.
InInorder ordertotohave haveaagood goodknowledge knowledgeofofthe theinfluence influenceofofthe theseasons seasonson onthe thesatellite satelliteretrievals, retrievals, according accordingtoto[32], [32],the thedaily dailymean meanaqua-MODIS aqua-MODISC6 C6(DT, (DT,DB DBand andDTB) DTB)and andVIIRS_EDR VIIRS_EDRretrieval retrievalbiases biases (AOD havebeen beenperformed performed over over TH TH (Figure 9), HF (Figure (AOD satellite-AODCE318 CE318) )have (Figure 10), 10),WH WH(Figure (Figure11) 11)and and satellite KM (Figure 12) for the period of 2 May 2012–31 December 2016. Notice that in order to decrease the KM (Figure 12) for the period of 2 May 2012–31 December 2016. Notice that in order to decrease the interference interferenceofofthe thesurface surfacereflectance, reflectance,the thesatellite-CE318 satellite-CE318retrieval retrievalbiases biasesare areselected selectedonly onlyduring during AOD > AOD CE318 0.4. CE318 As Asillustrated illustratedininthe themiddle middleand andlower lowerreaches reachesofofthe theYRB YRB(TH, (TH,HF HFand andWH), WH),there thereare aresimilar similar seasonal seasonalvariations variationsfor forthe thesame samesatellite satelliteretrieval retrievalbias bias(Figures (Figures1–11). 1–11).Despite Despitefew fewexceptions, exceptions,the the aqua-MODIS C6 DT (3 km and 10 km) retrieval biases are almost positive for the whole year, and aqua-MODIS C6 DT (3 km and 10 km) retrieval biases are almost positive for the whole year, and become contrast, the the DB10K DB10Kretrievals retrievalsare areeasily easilyunderestimated underestimatedin becomelarger largerin inspring spring and and summer. summer. By contrast, inspring springand andsummer, summer,and andoverestimated overestimated in in autumn autumn and winter. winter. Similar Similar seasonal seasonal variations variationsof ofthe the aqua-MODIS C6 DT and DB AOD retrievals were analyzed in Eastern China by [32]. Besides, as for the aqua-MODIS C6 DT and DB AOD retrievals were analyzed in Eastern China by [32].Besides, as for DTB10K retrievals, the positive biasesbiases do notdoexhibit obvious seasonal variations, and are smaller than the DTB10K retrievals, the positive not exhibit obvious seasonal variations, and are smaller the other C6 products. This demonstrates that the combined product is more suitable for than theMODIS other MODIS C6 products. This demonstrates that the DTB combined DTB product is more retrieving AOD values over the YRB compared to the individual DT and DB algorithms. However, the suitable for retrieving AOD values over the YRB compared to the individual DT and DB algorithms. VIIRS_EDR shows larger bias than the MODIS and tends toand overestimate the AOD However, the VIIRS_EDR shows larger bias thanC6 theretrievals, MODIS C6 retrievals, tends to overestimate retrievals summerin and underestimate in winter. Huang et al. [7] also reported similar seasonal the AODin retrievals summer and underestimate in winter. Huang et al. [7] alsoareported a similar variation the VIIRS_EDR bias retrieval over global land. The reasonland. is somewhat attributed to the seasonalofvariation of the retrieval VIIRS_EDR bias over global The reason is somewhat seasonal surface changes [6,7]. attributed to thereflectance seasonal surface reflectance changes [6,7]. As the seasonal variation of of thethe VIIRS_EDR retrieval biasbias at KM, is similar to Asshown shownininFigure Figure1212 the seasonal variation VIIRS_EDR retrieval at KM, is similar those in the middle andand lower reaches of of YRB. However, there is is a notable to those in the middle lower reaches YRB. However, there a notablechange changefor foraqua-MODIS aqua-MODIS C6 C6AOD AODretrievals retrievalsatatKM. KM.Different Differentfrom fromthe theoverestimation overestimationduring duringwinter winterininthe themiddle middleand andlower lower reaches of YRB, the DB10K retrievals show continuously negative biases all the year round in reaches of YRB, the DB10K retrievals show continuously negative biases all the year round inKM. KM. Meanwhile, AOD bias switches from positive at TH andand HF HF to slightly negative at KM Meanwhile,the theDTB10K DTB10K AOD bias switches from positive at TH to slightly negative at for KM the year.year. forwhole the whole
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Figure Seasonalvariation variationofofaqua-MODIS aqua-MODIS C6 and and VIIRS_EDR AOD (QF = 3) only Figure 9. 9. Seasonal AODretrieval retrievalbiases biases (QF only Figure 9. Seasonal variation of aqua-MODISC6 C6 andVIIRS_EDR VIIRS_EDR AOD retrieval biases (QF = =3)3) only during CE318 AOD observations >0.4 at TH site. Notice that y-axis refers to the multi-year daily mean during CE318 AOD observations >0.4 at TH site. Notice that y-axis refers to the multi-year daily mean during CE318 AOD observations >0.4 at TH site. Notice that y-axis refers to the multi-year daily mean AOD bias values theperiod periodofof22May May 2012–31 December December 2016. AOD bias values forfor the AOD bias values for the period of 2 May2012–31 2012–31 December2016. 2016.
Figure 10. As in Figure 9, but for HF. Figure10. 10.As Asin inFigure Figure 9, 9, but but for Figure for HF. HF.
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Figure 11.As Asin Figure9,9,but butfor forWH. WH. Figure 11. As ininFigure Figure 11. but for WH.
Figure12. 12.As AsininFigure Figure 9,but butfor forKM KMduring during 2 May2012–31 2012–31 December2013. 2013. Figure Figure 12. As in Figure 9,9,but for KM during 22 May May 2012–31December December 2013.
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4.4.Discussion Discussion The Thespatial spatialdistribution distributionofofthe theaqua-MODIS aqua-MODISC6 C6and andVIIRS_EDR VIIRS_EDRAOD AODretrievals retrievals(QF (QF= =3)3)are are performed, 2 May 2012–31 December performed,respectively respectivelyininFigures Figures1313and and1414over overthe theYRB YRBfor forthe theperiod periodofof 2 May 2012–31 December 2016. retrievals. As As clearly clearlyshown shownininFigures Figures1313and and14, 2016.The Thetransparent transparentareas areasrefer referto towhere where there there are no retrievals. 14, thereare aredifferent differentmissing missingpatterns patternsfor for each each AOD product, there product, probably probablydue duetototheir theirdifferent differentretrieval retrieval algorithm. DTBproducts productscan canretrieve retrieve AOD values over inland water algorithm.The Theaqua-MODIS aqua-MODISC6 C6 DT DT and DTB AOD values over inland water with with missing no high-quality retrievals the DB10K and VIIRS_EDR few few missing data,data, whilewhile therethere are noare high-quality retrievals for thefor DB10K and VIIRS_EDR products products overwater inland water such as the Taihu and Dongting Lake. However, over the plateau over inland such as the Taihu Lake andLake Dongting Lake. However, over the plateau areas of the areas ofreaches the upper the YRB, the high-quality VIIRS_EDR provide than betterall upper of thereaches YRB, theofhigh-quality VIIRS_EDR retrievals provide retrievals better performance performance than all aqua-MODIS C6 products. the aqua-MODIS C6the products. AsAsshown shownininFigures Figures1313and and14, 14,there thereisissimilar similarspatial spatialdistribution distributionfor forallallthe thefive fiveaqua-MODIS aqua-MODIS and VIIRS AOD products over the YRB. Generally, the high aerosol loadings (AOD > 0.8) and VIIRS AOD products over the YRB. Generally, the high aerosol loadings (AOD > 0.8)appear appearinin the reaches of ofthe theYRB, YRB,mainly mainlydue duetoto intensive industrial theYRD, YRD,Sichuan SichuanBasin Basinand and the the middle reaches thethe intensive industrial and and urban activities. While values (AOD < 0.4) are usually observed in high mountains the urban activities. While thethe lowlow values (AOD < 0.4) are usually observed in high mountains of theofupper upper reaches the YRB. The result is consistent previous findings China [17,18]. However, reaches of theofYRB. The result is consistent withwith previous findings overover China [17,18]. However, over over areas of the aerosol loadings, VIIRS_EDR retrievals exhibit lower values the the areas of the YRBYRB withwith highhigh aerosol loadings, the the VIIRS_EDR retrievals exhibit lower values than than the aqua-MODIS C6 and DT DTB and products. DTB products. Because the retrievals AOD retrievals of the VIIRS_EDR the aqua-MODIS C6 DT Because the AOD of the VIIRS_EDR product product are limited in the of range of 0.0–2.0, tends to be underestimated over with areashigh withaerosol high are limited only inonly the range 0.0–2.0, it tendsit to be underestimated over areas aerosol loadings, theand YRD and Sichuan basin [6,7,13]. By contrast, over the reaches upper reaches of loadings, such assuch the as YRD Sichuan basin [6,7,13]. By contrast, over the upper of the YRB the YRB with low aerosol loadings, the VIIRS_EDR product provides slightly higher AOD values. with low aerosol loadings, the VIIRS_EDR product provides slightly higher AOD values. Liu et al. [6] Liu al.[6]also reported that theretrievals VIIRS_EDR were likely to beover overestimated over alsoetreported that the VIIRS_EDR wereretrievals likely to be overestimated vegetated surfaces vegetated (e.g., the upper reaches of YRB). (e.g., the surfaces upper reaches of YRB). InInFigure Figure14, 14,the theDT3K DT3Kretrievals retrievalsexhibit exhibitslightly slightlyhigher higherAOD AODvalues valuescompared comparedtotothose thoseofofthe the DT10K DT10Kproduct, product,especially especiallyover overthe theYRD YRDand andSichuan SichuanBasin. Basin.This Thismay maybebebecause becausethe theDT3K DT3Kproduct product has ability toto retrieve small-scale AOD values. On thethe contrary, the small-scale retrievals are likely hasthe the ability retrieve small-scale AOD values. On contrary, the small-scale retrievals are likely smoothed its low lowspatial spatialresolution. resolution.Furthermore, Furthermore,relative relative smoothedby bythe theDT10K DT10Kproduct, product, due to its to to thethe DTDT and and DTB products, there a notable underestimation for the DB10K retrievals, especially the DTB products, there is aisnotable underestimation for the DB10K retrievals, especially overover the YRD, YRD, Sichuan the middle reaches ofYRB. the YRB. The result is consistent the comparison Sichuan BasinBasin and and the middle reaches of the The result is consistent withwith the comparison of the ofDB10K the DB10K retrievals against CE318 observations in Figure retrievals against CE318 observations in Figure 4. 4.
Figure 13. Spatial distribution of the annual high-quality VIIRS_EDR AOD retrievals (QF = 3) over the Figure1. Spatial distribution of the annual high-quality VIIRS_EDR AOD retrievals (QF = 3) over the YRB for the period of 2 May 2012–31 December 2016. The color scale refers to AOD values. YRB for the period of 2 May 2012–31 December 2016. The color scale refers to AOD values.
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Figure 14. As 13,but butfor forthe theaqua-MODIS aqua-MODISC6 C6AOD AODproducts. products. Figure2. As in in Figure Figure1,
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5. Conclusions In this study, a comparison of the aqua-MODIS C6 and VIIRS_EDR AOD products against the CE318 AOD observations was performed during different air quality situations over the YRB for the period of 2 May 2012–31 December 2016. The conclusions are shown as follows. Apart from some exception at KM, quality flag analyses demonstrated that the high-quality (QF = 3) MODIS C6 and VIIRS_EDR AOD products are recommended for the whole YRB. The best performance was for the high-quality DTB10K AOD product, along with a higher R (0.85) and more retrievals within the EE (55%). However, compared to the aqua-MODIS C6 AOD products, although there were more matchups, the high-quality VIIRS_EDR AOD retrievals exhibited poor agreement with the CE318 AOD observations (R = 0.73). This result demonstrated that the retrieval algorithm of the VIIRS_EDR AOD product needs to be further improved. Generally, both DT3K and DT10K AOD products (QF = 3) tend to overestimate the AOD values, respectively with mean overestimations of 23% and 15% over the YRB. The overestimations became larger in spring and summer, probably due to higher aerosol loadings appearing in these seasons. For the DTB AOD product (QF = 3), there was also a slight overestimation of 10% but the positive bias did not exhibit obvious seasonal variations. On the contrary, the DB10K AOD product (QF = 3) showed mean underestimation of 12% over the YRB, but there was a slight overestimation during AODCE318 > 1.0. The seasonal variation analyses exhibited that there was an underestimation (overestimation) in spring and summer (winter and autumn) for the DB product. Different from the MODIS C6 AOD products, the VIIRS_EDR AOD retrievals (QF = 3) exhibited positive biases in summer and negative biases in winter, along with a mean overestimation of 4% over the YRB. There were similar AOD spatial distributions for all the aqua-MODIS C6 and VIIRS_EDR products. The high AOD values appeared in the YRD, Sichuan basin and the middle reaches of the YRB, while the low AOD values were observed in high mountains over the upper reaches of the YRB. However, over the regions with high aerosol loadings, the VIIRS_EDR AOD product retrieved lower AOD values than the DT and DTB products. Meanwhile, the VIIRS_EDR product had no capacity of retrieving AOD values over inland lakes, but it had a better performance than the aqua-MODIS C6 products in the Tibet plateau. Acknowledgments: This work was financially supported by the National Natural Science Foundation of China (No. 41601044), the Special Fund for Basic Scientific Research of Central Colleges, China University of Geosciences, Wuhan (No.CUG15063 and CUGL170401), the Opening Foundation of Key Laboratory of Middle Atmosphere and Global environment Observation (LAGEO), Institute of Atmospheric Physics, Chinese Academy of Sciences and the Natural Science Foundation for Distinguished Young Scholars of Hubei Province of China (No. 2016CFA051). Author Contributions: Lunche Wang, Lijie He and Aiwen Lin designed the research; Lijie He and Ming Zhang performed the experiments and analyzed the data; Lijie He and Lunche Wang wrote the manuscript; Lunche Wang, Wei Jing and Muhammad Bilal revised the manuscript. Conflicts of Interest: The authors declare no conflict of interest.
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