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A consistent aerosol optical depth (AOD) dataset over mainland China by integration of several AOD products
Atmospheric Environment 114 (2015) 48e56
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A consistent aerosol optical depth (AOD) dataset over mainland China by integration of several AOD products H. Xu a, f, J. Guang a, *, Y. Xue a, b, *, Gerrit de Leeuw c, d, Y.H. Che a, f, Jianping Guo e, X.W. He a, f, T.K. Wang b a
Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China Faculty of Life Sciences and Computing, London Metropolitan University, 166e220 Holloway Road, London N78 DB, UK c Department of Physics, University of Helsinki, Helsinki, Finland d Finnish Meteorological Institute, Climate Research Unit, Helsinki, Finland e Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081, China f University of Chinese Academy of Sciences, Beijing 100049, China b
h i g h l i g h t s A fusion method to generate an AOD dataset from five different AOD products. A Consistent AOD Dataset over China by Integrating of Various AOD Products. The merged AOD dataset has high overall accuracy (RMSE ¼ 0.16). The merged AOD estimates are closer to the reference field than others.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 December 2014 Received in revised form 11 May 2015 Accepted 12 May 2015 Available online 14 May 2015
The Moderate Resolution Imaging Spectroradiometer (MODIS), the Multiangle Imaging Spectroradiometer (MISR) and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) provide validated aerosol optical depth (AOD) products over both land and ocean. However, the values of the AOD provided by each of these satellites may show spatial and temporal differences due to the instrument characteristics and aerosol retrieval algorithms used for each instrument. In this article we present a method to produce an AOD data set over Asia for the year 2007 based on fusion of the data provided by different instruments and/or algorithms. First, the bias of each satellite-derived AOD product was calculated by comparison with ground-based AOD data derived from the AErosol RObotic NETwork (AERONET) and the China Aerosol Remote Sensing NETwork (CARSNET) for different values of the surface albedo and the AOD. Then, these multiple AOD products were combined using the maximum likelihood estimate (MLE) method using weights derived from the root mean square error (RMSE) associated with the accuracies of the original AOD products. The original and merged AOD dataset has been validated by comparison with AOD data from the CARSNET. Results show that the mean bias error (MBE) and mean absolute error (MAE) of the merged AOD dataset are not larger than that of any of the original AOD products. In addition, for the merged AOD dataset the fraction of pixels with no data is significantly smaller than that of any of the original products, thus increasing the spatial coverage. The fraction of retrievable area is about 50% for the merged AOD dataset and between 5% and 20% for the MISR, SeaWiFS, MODIS-DT and MODIS-DB algorithms. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Merging Aerosol optical depth MODIS MISR SeaWiFS Albedo
1. Introduction
* Corresponding authors. E-mail address: [email protected] (Y. Xue). http://dx.doi.org/10.1016/j.atmosenv.2015.05.023 1352-2310/© 2015 Elsevier Ltd. All rights reserved.
Atmospheric aerosol particles affect climate directly by scattering and absorption of solar radiation and indirectly by modifying the amount and the microphysical and radiative properties of
H. Xu et al. / Atmospheric Environment 114 (2015) 48e56
clouds (King et al., 1999; Andreae and Rosenfeld, 2008). In addition, aerosol particles also affect the environment, the corrosion and soiling effects on materials, biogeochemical cycles, and human health (Lippmann et al., 2000; Tzanis et al., 2011; Varotsos et al., 2012). Because of its spatial and temporal coverage, satellitederived aerosol optical depth (AOD; Andreae and Rosenfeld, 2008) is the most practical measurement of the amount of aerosol for both regional and global assessments (Anderson et al., 2005). Satellite-based AOD retrieval products have been developed by several research teams. Observations taken by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument are used to disseminate AOD products using the Dark Target (DT; Remer et al., 2005, Levy et al., 2013) and Deep Blue (DB; Hsu et al., 2004, 2006) algorithms, the synergetic retrieval of aerosol properties algorithm (SRAP) for land surface (Xue et al., 2014) and the MultiAngle Implementation of Atmospheric Correction for MODIS (MAIAC) (Lyapustin et al., 2011). Recently, the DT and DB algorithms have been merged into one product in MODIS C6 (Levy et al., 2013). The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) provides an AOD dataset based on the “Deep Blue” algorithm (Hsu et al., 2004, 2006; Sayer et al., 2012). Another relevant AOD product is generated from the Multi-angle Imaging SpectroRadiometer (MISR) using the method described by Diner et al. (2005) and Kahn et al. (2005; 2010). More recently, the Advance Along-Track Scanning Radiometer (AATSR) has been further improved and the AOD quality is now similar in quality to that from the above-mentioned sensors (Holzer-Popp et al., 2013; de Leeuw et al., 2013). However, different satellite AOD products do not always give consistent values of aerosol properties and none of the AOD retrieval algorithm outperforms all others everywhere (de Leeuw et al., 2013). For example, the MODIS-DT algorithm has been designed for application over dark land surfaces, i.e. with a low level of reflectivity in the visible spectrum. The MODIS-DB algorithm employs radiances from the blue channel at 412 nm, among other wavelengths, where the surface reflectance is negligible and hence these wavelengths can be used to estimate the aerosol amount over bright targets. In order to reduce the uncertainty in estimating climate effects
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due to tropospheric aerosols (both anthropogenic and natural aerosols), it is important to produce consistent aerosol products with the best possible quality. This can be achieved by merging aerosol information obtained from the multiple satellite sensors that are currently available, taking advantage of the strengths of individual sensors and constraining the weaknesses of others (Ehlers, 1991; Nirala, 2008; Hsu et al., 2012). Various merging approaches have been developed (Leptoukh et al., 2007; Nirala, 2008; Siddans et al., 2007; Chatterjee et al., 2010; Nguyen et al., 2012). Leptoukh et al. (2007) demonstrated that prior to merging data from different sensors, one needs to assess differences in these images. Aerosol physical parameters have a large important impact on the AOD retrieval. Surface albedo is a key parameter in modeling radiative transfer in the atmosphere (Vermote et al., 2002) and is an important source of the uncertainties in AOD retrieval (Shi et al., 2013). AOD amount is also a key parameter related to the error in the retrieved satellite AOD (Sayer et al., 2012). In this study, these two physical parameters have been chosen to obtain uncertainties in different satellite AOD datasets, and corresponding results have been considered in the merging process. The objective of this study is to merge multiple satellite AOD products to obtain a new consistent AOD dataset. In this study, AOD data used are those retrieved from MISR and SeaWiFS, and the MODIS products provided from the synergetic retrieval of aerosol properties (SRAP; Xue et al., 2014), DB and DT. The focus of this study is to give different weights for each single satellite AOD product during the merging process instead of one constant value over the whole study area. Because each satellite AOD product may have a different bias and accuracy for different surface albedo and AOD values, the biases and merging weights were computed for each range of surface albedo and AOD separately. AErosol RObotic NETwork (AERONET) AOD data is used to calculate the bias of each individual AOD product for different conditions of albedo and AOD. A study period of one year (2007) was chosen to obtain a statistically significant data set of satellite and AERONET match-ups. Using the weight results of root mean square root (RMSE) values, the merged AOD dataset is produced using the maximum likelihood estimate (MLE) method. To investigate the accuracy of this merged
Fig. 1. Study area showing the locations of the 34 AERONET and 8 CARSNET stations used for the evaluation of the satellite-based AOD products.
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AOD dataset, the original and merged AOD data sets were compared with ground-based AOD observations from the China Aerosol Remote Sensing Network (CARSNET). 2. Data Multiple satellite AOD products, ground-based AOD measurements and surface albedo data have been used. The study area covers the area from 15 N to 60 N in latitude and from 65 E to 145 E in longitude (see Fig. 1). The time period chosen is the whole year 2007. 2.1. Satellite AOD data Five satellite AOD products were used as the source AOD products, three of them have been retrieved from MODIS, one from SeaWiFS and one from MISR data. The data sets used are publicly available from the internet as indicated below. The detailed description of these satellite data sets are as follows. The MODIS instrument aboard the polar satellites Terra and Aqua observes the Earth with a total swath of more than 2300 km. The DT and DB algorithms are the mainstream algorithms for MODIS AOD retrieval. The DT algorithm is applicable over dark surfaces such as water bodies and dense vegetation and the DB method expands MODIS AOD coverage to bright desert surfaces. The MOD04\MYD04 aerosol product which includes AOD data both from the DT and DB algorithms is available on the website “http:// ladsweb.nascom.nasa.gov”. The SRAP algorithm has been developed based on the synergetic use of the data from MODIS on both the Terra and Aqua satellites (Xue and Cracknell, 1995). For the SRAP algorithm, only cloudy pixels are eliminated. The China Collection 2.0 AOD datasets, retrieved using the SRAP algorithm, have a better coverage over the mainland China and East Asian region than the MODIS-DT and MODIS-DB AOD products (Xue et al., 2014). Comparing the AOD results with AERONET data shows that 62% of China Collection 2.0 AOD values are within an expected error (EE) range of ± (0.05 þ 20%) and that 56% are within an EE range of ± (0.05 þ 15%) when compared with AERONET-observed values. Comparison with CARSNET data shows that 60% of China Collection 2.0 AOD values are within an expected error (EE) range of ± (0.05 þ 20%) and that 53% are within an EE range of ± (0.05 þ 15%) (Xue et al., 2014). In this paper, MODIS Collection 5.1 Level-2 aerosol products (MOD04_L2) data and MODIS-SRAP AOD data are used with quality assurance flag values from 1 to 3. MISR aboard the polar satellite Terra captures upwelling shortwave radiance at nine viewing angles embracing the forward and backward directions along the flight path, that is, 70.5 , 60.0 , 45.6 , 26.1, and nadir (Diner et al., 1998). The width of the overlapping swath of MISR (i.e., the swath seen by all nine cameras) is 360 km. MISR uses multi-angle observations of the same ground scene (Diner et al., 2005). This makes it possible to accurately account for directional surface scattering in the retrieval procedure. In this paper, the best estimate of AOD at 558 nm which is the data field RegBestEstimateSpectralOptDepth from the Level 2 MISR aerosol product (MIL2ASAE) of version 22 is used. SeaWiFS aboard the SeaStar platform was in operation between September 1997 and December 2010. The SeaWiFS AOD dataset over-land is retrieved using the “Deep Blue” algorithm (Hsu et al., 2004, 2006; Sayer et al., 2012), providing AOD at the commonlyused reference wavelength of 550 nm. The updated algorithm is applied over both vegetated land surfaces and bright arid surfaces (Sayer et al., 2012). The current version (v004) of the SeaWiFS AOD dataset is freely available from “http://disc.gsfc.nasa.gov/dust/”. We used the SeaWiFS field name AOD_550_Best_Estimate.
2.2. Ground-based AOD data AERONET is a federation of ground-based remote sensing aerosol network providing a long-term, continuous and readily accessible public domain database of globally distributed observations of spectral AOD (Holben et al., 1998; Dubovik and King, 2000). AOD is provided for spectral bands centered at 340, 380, 440, 500, 675, 870, and 1020 nm, with an accuracy of ±0.02. For the level 2.0 products, the AOD uncertainties are of the order of 0.01 in the visible and near-infrared channels (Eck et al., 1999). In this paper, L2 data from thirty-four AERONET sites in the study area have been used to establish the biases of each individual satellite AOD product for different ranges of surface albedo and AOD values. CARSNET, established in 2002, is an operational network for the study of aerosol optical properties and for the validation of satellite aerosol retrievals (Che et al., 2009). The instrument deployed by CARSNET is the same CE-318 sunphotometer as used in AERONET. The long-term CARSNET measurements provide an unprecedented opportunity to study aerosol properties and validate MODIS retrieved AODs over various terrestrial regions in China. CARSNET AOD measurements are highly correlated with those from AERONET and have a similar accuracy, as verified over the Chinese Yangtze Delta region (Pan et al., 2010). In this paper, data from Eight CARSNET sites in the study area are selected not only for establishing the biases of each individual satellite AOD product for different ranges of surface albedo and AOD values but also for evaluating the performances of both each individual satellite AOD product and the merged AOD dataset. The locations of the thirty-four AERONET and the eight CARSNET sites are shown in the map displayed in Fig. 1. Tables 1 and 2 report the latitude, longitude and elevation of the AERONET and CARSNET
Table 1 Location and elevation of the 34 AERONET sites used in this study. AERONET site name
Latitude ( )
Longitude( )
Elevation (m)
Anmyon Bac_Giang Beijing Chen-Kung_Univ Chiang_Mai Dalanzadgad EVK2-CNR Gandhi_College Gosan_SNU Gwangju_K-JIST Hangzhou-ZFU Hefei Hong_Kong_Hok_Tsui Hong_Kong_PolyU Irkutsk Issyk-Kul Kanpur Karachi Lulin Mukdahan NAM_CO NCU_Taiwan Noto Osaka Pimai Pune Shirahama Sinhgad Taihu Taipei_CWB Tomsk Ussuriysk XiangHe Xinglong
36.54 21.29 39.98 23.00 18.81 43.58 27.96 25.87 33.29 35.23 30.26 31.91 22.21 22.30 51.80 42.62 26.51 24.87 23.47 16.61 30.77 24.97 37.33 34.65 15.18 18.54 33.69 18.37 31.42 25.03 56.48 43.70 39.75 40.40
126.33 106.23 116.38 120.22 98.99 104.42 86.81 84.13 126.16 126.84 119.73 117.16 114.26 114.18 103.09 76.98 80.23 67.03 120.87 104.68 90.96 121.19 137.14 135.59 102.56 73.81 135.36 73.75 120.22 121.50 85.05 132.16 116.96 117.58
47 15 92 50 324 1470 5050 60 72 52 14 36 80 30 670 1650 123 49 2868 166 4740 171 200 50 220 559 10 1450 20 26 130 280 36 970
H. Xu et al. / Atmospheric Environment 114 (2015) 48e56 Table 2 Location and elevation of the 8 CARSNET sites used in this study. CARSNET site name
Latitude ( )
Longitude ( )
Elevation (m)
Chengdu Gucheng Longfengshan Lin'an Lasa Shangdianzi Xilinhaote Zhengzhou
30.65 39.13 44.73 30.3 29.67 40.65 43.95 34.78
104.04 115.8 127.6 119.73 91.13 117.12 116.07 113.68
553 11 330.5 138.6 3648.9 293.3 989.5 110
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which is borrowed from the climatology constructed using seven years (2004e2010) of MODIS black-sky visible albedo. In particular, we use the MCD43 product from collection 5.0, which is obtained by inverting multi-date, multi-angular, cloud-free, atmospherically corrected, surface reflectance observations acquired by the MODIS instruments on board the Terra and the Aqua satellites over a 16day period. The accuracy of the high quality MODIS operational albedo is well less than 5% at the majority of the validation sites studied by Cescatti et al. (2012). In addition, only albedo values with a high confidence index were used to build the albedo climatology used in the present article. The latter was built at a spatial resolution of 0.05 0.05 .
sites used in this study. 3. Methods 2.3. Surface albedo data 3.1. Preprocessing The biases and accuracies of the source satellite AOD products are evaluated according to the daily-averaged surface albedo (a)
In this paper, the wavelength at 550 nm is chosen as the
Fig. 2. The AOD spatial distribution over the study area aggregated from the original and merged AOD datasets in March 2007: (a) Terra MODIS-DT AOD, (b) Terra MODIS-DB AOD, (c) Terra MODIS-SRAP AOD; (d) MISR AOD, (e) SeaWiFS AOD; (f) merged AOD.
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H. Xu et al. / Atmospheric Environment 114 (2015) 48e56
common spectral wavelength for the merging process and validation of the merged AOD dataset. While AOD products derived from MODIS-DT, MODIS-DB and SeaWiFS are already disseminated at this wavelength, AOD estimates derived from AERONET and CARSNET are converted for each pixel from the original wavelength € m exponent for the (440 nm) to 550 nm using the Ångstro € m, 1929). The MISR-derived 440e870 nm wavelength pair (Ångstro AOD values are not transformed because of the spectral proximity of the retrieval wavelength (558 nm) to 550 nm and some likely € m coefficients. uncertainty in the AERONET and CARSNET Ångstro Prior to merging, all original satellite-retrieved AOD products are produced on a common 50 km grid. In the merging process, grid cells in the MODIS-DT, MODIS-DB and MODIS-SRAP AOD products are only considered when each of the original datasets contains at least five retrievals. For the MISR and SeaWiFS AOD products, at least three and four retrievals in the 50 km grid respectively are considered. 3.2. Merging method The MLE method represents a weighted average and takes into account information on the statistical uncertainties in the data using metrics such as mean, standard deviation, and number of counts (Chu and Aggarwal, 1993; Nirala, 2008). In this paper, we used the MLE method to calculate the weight for each original satellite AOD product at each grid point using:
tMLE ¼ i
N X k¼1
R2 i;k t ; PN 2 i;k k¼1 Ri;k
(1)
where tMLE represents the merged AOD values, ti;k represents the i mean AOD value at grid point i from the original satellite AOD product k and N is the number of all the original AOD products. The coefficients Ri, k is the RMSE at grid point i for the original satellite AOD product k. The reason for choosing RMSE as the statistics for the MLE method is that RMSE can be used to measure the average dispersion of the AOD uncertainty and provide an estimate of the expected error in AOD in the absence of information about specific sources of errors. 3.3. Weight calculating
In order to obtain the RMSE value for each original satellite AOD product at any point whether there is AERONET AOD observation or not, it is necessary to find several parameters which are available regionally or globally and establish the relationship between RMSE and these parameters. In this paper, surface albedo and AOD values are chosen as two parameters to calculate RMSE for each original satellite AOD product. For this propose, four surface albedo ranges (0e5%, 5e8%, 8e11%, and 11e25%) and four AOD ranges (0.00e0.25, 0.25e0.5, 0.5e0.8, and 0.8e5.0) are considered. RMSEs are calculated using Eq. (2) for these different surface albedo and AOD ranges. For each original satellite AOD product, there is one RMSE value for each surface albedo range and each AOD range and 16 RMSE values for four surface albedo ranges and four AOD ranges. The RMSE values are discussed in detail in section 4.2. 3.4. Validation method Two methods proposed by Ichoku et al. (2002) and Taylor (2001) are used in this paper for the validation of both the original AOD products and the merged AOD dataset. In order to compare the original and merged AOD dataset with CARSNET AOD data, the spatial mean of the original or merged AOD datasets and the temporal mean of the CARSNET observations are used. A valid matchup is defined when there is at least one CARSNET measurement within ±30 min of the satellite overpass, and at least one original or one merged AOD value in a grid of 50 50 km2 with the CARSNET station in the middle. Statistics calculated for the comparison include the correlation (R) and RMSE between each original/merged satellite AOD product and the AERONET/CARSNET value for each site. 4. Results and analysis 4.1. Merging result By assessing the biases of the original satellite AOD products for each of the four aggregated surface albedo ranges and each of the four AOD value ranges, the RMSE for each original AOD product in each surface albedo range and each AOD value range were computed using Eq. (2). Using the merging method mentioned in section 3.2 (Eq. (1)), the merged AOD dataset for the whole year of
The RMSE value used in this study is defined as follows:
Ri; k
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 PM i¼1 si;k gi ¼ M
(2)
where Ri; k represents the RMSE value at grid point i for the original satellite AOD product k, si,k represents the mean AOD values at grid point i from the original satellite AOD product k. gi represents the mean ground-based AOD value at grid point i from the AERONET counterparts. The si, k is spatial mean and gi is temporal mean. M is the number of pairs of si, k and gi. In order to find the pairs of si, k and gi, a spatial-temporal approach based on Ichoku et al. (2002) was used. Instantaneous AOD values from original satellite-derived AOD datasets are evaluated against temporally coincident AERONET ground-based counterparts resulting from averaging all available AERONET observations within a period of ±30 min centered at the overpass time of each satellite. The RMSE is needed at each point for each original satellite AOD product. However, it can only be calculated for retrieval areas for which AERONET-derived AOD data is available. AERONET stations exist at only a finite number of locations. Where there is no AERONET AOD observation, RMSE cannot be calculated.
Fig. 3. The fraction of pixels with AOD data for original AOD products and the merged AOD dataset in 2007.
H. Xu et al. / Atmospheric Environment 114 (2015) 48e56
2007 was obtained. In this section, we will analyze the merged AOD in three aspects: spatial coverage, evaluation result and time series analysis.
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4.1.1. Spatial coverage analysis Fig. 2 presents the distribution of AODs over the study area averaged from the original and merged AOD datasets in March 2007. Fig. 2f demonstrates that combining multiple AOD products can significantly reduce the fraction of pixels with no data, thus
Fig. 4. Scatter plots illustrating the comparison between the five original and the merged satellite AOD products with the AOD data from the 8 CARSNET stations for the whole year of 2007. The dashed and solid lines are the 1-1 lines and the linear regression for the data, respectively. Text at the top describes: the regression curve, correlation (R), the number of collocations (N), and the root mean square error (RMSE) of the fit.
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Table 3 MBE and MAE of the original AOD products and merged AOD dataset. AOD product
MBE
MAE
RMSE
No. of plot data
MODIS-DT MODIS-DB MODIS-SRAP MISR SeaWiFS Merged
0.03 0.03 0.09 0.26 0.08 0.03
0.12 0.23 0.14 0.29 0.16 0.12
0.16 0.33 0.21 0.40 0.20 0.16
174 45 191 53 105 213
increasing the spatial coverage. The fractions of retrievable pixels for the source AOD products and the merged AOD dataset in 2007 are shown in Fig. 3. The Fig. clearly shows the better coverage with fewer data gaps over the study region for the merged datasets than for the source AOD products. The fraction of retrievable area is about 50% for the merged AOD dataset and between 5% and 20% for the MISR, SeaWiFS, MODIS-DT and MODIS-DB algorithms. 4.1.2. Evaluation using CARSNET AOD data In order to examine the accuracy of the merged AOD dataset, all original satellite AOD products and merged AOD estimates should be evaluated using independent and accurate ground-based observations. Fig. 4 shows the validation results for both the five original satellite AOD products used in this paper and the merged AOD dataset, using CARSNET-derived AOD data within the study area, for the whole year of 2007. Note that MODIS-SRAP, MISR and SeaWiFS AOD products and the merged AOD dataset are underestimated, as the slopes resulting from linear regression (see Fig. 4) range from 0.61 for the SeaWiFS AOD product to 0.91 for the merged AOD dataset. Compared with the five original satellite AOD
products, the merged AOD estimates greatly improve the coverage with an RMSE (R ¼ 0.91) which is only slightly lower than the MODIS-DT (R ¼ 0.93), but larger than the SeaWiFS RMSE (R ¼ 0.61), the MISR RMSE (R ¼ 0.87), and the MODIS-DB RMSE (R ¼ 0.81) and the MODIS-SRAP RMSE (R ¼ 0.85). The number of collocated data points reaches 213 for the merged AOD dataset and only 53 for MISR, 105 for SeaWiFS, 174 for MODIS-DT, 56 for MODIS-DB, and 191 for MODIS-SRAP AOD products. The mean bias error (MBE) and mean absolute error (MAE) of the original AOD products and merged AOD datasets are shown in Table 3. The MBEs are 0.03, 0.03, 0.09, 0.26, 0.08, for the MODIS-DT, MODIS-DB, MODIS-SRAP, MISR and SeaWiFS AOD products, respectively, while the MBE for the merged AOD dataset is 0.03. The MAEs are 0.12, 0.23, 0.14, 0.29, 0.16 for the MODIS-DT, MODIS-DB, MODIS-SRAP, MISR and SeaWiFS AOD products, respectively, while the that of the merged AOD dataset is 0.12. Hence, the MBE and MAE of the merged AOD dataset is no larger than that of any original AOD products.
4.1.3. Time series analysis In order to evaluate the uncertainty of the merged AOD dataset according to its temporal coherence, time series of merged AOD estimates and corresponding CARSNET AOD data are shown in Fig. 5 for the eight CARSNET stations for the whole year of 2007. The Longfengshan site is located in a highly vegetated mountain region. Several AOD peaks are observed over Longfengshan in June (Fig. 5). The merged AOD retrievals compare well with CARSNET AOD data over the Longfengshan station (R ¼ 0.93). Over Shangdianzi, LIN_AN and Chengdu Stations, the merged AOD dataset tends to underestimate the AOD as compared with CARSNET. Over
Fig. 5. Time series of the merged AOD dataset and corresponding CARSNET AOD data at eight CARSNET stations.
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Table 4 Weight matrices for four original satellite AOD products for four albedo ranges (0e5%, 5e8%, 8e11%, and 11e25%) and four AOD ranges (0.00e0.25, 0.25e0.5, 0.5e0.8, and 0.8e5.0). Albedo value 0.00 e 0.05 0.05 e 0.08 0.08 e 0.11 0.11 e 0.25
AOD value
MODIS-DB
0.00e0.25 0.25e0.50 0.50e0.80 0.80e5.00 0.00e0.25 0.25e0.50 0.50e0.80 0.80e5.00 0.00e0.25 0.25e0.50 0.50e0.80 0.80e5.00 0.00e0.25 0.25e0.50 0.50e0.80 0.80e5.00
W e e e e 0.13 0.20 e 0.15 0.07 0.26 0.12 0.14 0.07 0.19 0.27 0.02
MODIS-DT N e e e e 53 21 e 17 65 22 22 44 71 17 20 36
W 0.28 0.27 0.23 0.08 0.43 0.31 0.41 0.58 0.45 0.24 0.37 0.49 0.64 0.38 0.33 0.12
the Zhengzhou station, the merged AOD dataset tends to overestimate the AOD as compared with CARSNET. For the Gucheng and Xinlinhaote stations, the observations of CARSNET AOD data are too limited.
SRAP N 172 67 26 10 117 87 44 37 81 58 36 59 46 26 25 18
W 0.05 0.17 0.41 0.92 0.04 0.17 0.20 0.27 0.01 0.04 0.10 0.37 0.01 0.12 0.14 0.86
MISR N 532 105 37 11 358 196 80 50 353 139 72 74 164 73 65 24
W 0.30 0.26 e e 0.11 0.11 e e 0.05 0.14 0.04 e 0.07 0.12 e e
SeaWiFS N 27 14 e e 41 21 e e 33 34 15 e 33 12 e e
W 0.37 0.30 0.36 e 0.29 0.22 0.39 e 0.42 0.32 0.37 e 0.21 0.19 0.26 e
N 114 22 12 e 97 54 26 e 58 32 23 e 27 28 20 e
original AOD products. It is also shown that the merged AOD dataset combining multiple AOD products significantly reduces the fraction of pixels with no data, thus increasing the spatial coverage. Acknowledgments
4.2. Weights analysis The weights computed for the original satellite AOD products (Table 4) show that the different AOD products provided different contributions to the merged AOD dataset for different albedo and AOD values. MODIS-DT and SeaWiFS AOD products provided the main contributions to the merged AOD dataset for most surface albedo and AOD value ranges (Table 4) because these two AOD products compare with AERONET data better than other original AOD products. The weights of MODIS-DT and SeaWiFS is always high for AOD values greater than 0.5, for all different surface albedos. The weights of MISR are empty or low for AOD values of 0.5e0.8, for all surface albedos. This is because a limitation of the MISR dataset is the smoothing mask used in the current version of over-land retrievals that eliminates high AODs leading to AOD underestimation during periods with high aerosol loading (midvisible AOD is likely to be > 0.5) (Kahn et al., 2010). The weights of MODIS-DB are empty when surface albedo is less than 0.5 and AOD greater than 0.5. The reason could be that MODIS-DB is not designed to retrieve AOD over very dark surfaces. 5. Conclusions Because original satellite AOD products have different accuracies for different surface albedo ranges and different AOD value ranges, different biases for each satellite AOD product were adopted to calculate the RMSE and to create a merged and more accurate AOD dataset by using the synergy of multi-sensor and multialgorithm aerosol information. The satellite AOD products used include MODIS-DT, MODIS-DB, MODIS-SRAP, MISR and SeaWiFS, which were merged using the maximum likelihood approach. The RMSD of each original and the merged AOD products were evaluated considering four albedo ranges (0e5%, 5e8%, 8e11%, and 11e25%) and four AOD ranges (0.00e0.25, 0.25e0.5, 0.5e0.8, and 0.8e5.0). The evaluation result shows that the merged AOD dataset compares well with CARSNET AOD observations from eight CARSNET stations (R ¼ 0.91, RMSE ¼ 0.16). Additionally, the MBE and MAE of the merged AOD dataset are no larger than that of any
This work was supported in part by the Ministry of Science and Technology (MOST), China under grant Nos. 2010CB950803 and 2013AA122801, and by the National Natural Science Foundation of China (NSFC) under grant no. 41101323, and by CAS-RADI Innovation project Y3ZZ15101A and EU/FP7 MarcPolo project (Grant Agreement Number 606953). Part of the work is done in preparation for the Aerosol_cci project (ESA-ESRIN project AO/1e6207/09/ I-LG) and supported by the Centre on Excellence in Atmospheric Science funded by the Finnish Academy of Sciences Excellence (project no. 272041). Surface albedo data is provided by Jean-louis, Roujean. We think him very much. The data for uncertainty analysis and validation came from thirty-four AERONET sites and eight CARSNET data. We thank the PIs, investigators and their staff for establishing and maintaining the forty-two sites used in this study. References € m, A., 1929. On the atmospheric transmission of sun radiation and on dust Ångstro in the air. Geogr. Ann. 11, 156e166. http://dx.doi.org/10.2307/519399. Anderson, T.L., Charlson, R.J., Bellouin, N., Boucher, O., Chin, M., Christopher, S.A., Haywood, J., Kaufman, Y.J., Kinne, S., Ogren, J.A., Remer, L.A., Takemura, T., Tanre, D., Torres, O., Trepte, C.R., Wielicki, B.A., Winker, D.M., Yu, H., 2005. An “A-Train” strategy for quantifying direct climate forcing by anthropogenic aerosols. Bull. Am. Meteorological Soc. 86 (12), 1795e1809. http://dx.doi.org/ 10.1175/BAMS-86-12-1795. Andreae, M.O., Rosenfeld, D., 2008. Aerosol-cloud-precipitation interactions. Part1. The nature and sources of cloud-active aerosols. Earth-Science Rev. 89 (1e2), 13e41. Chatterjee, A., Michalak, A.M., Kahn, R.A., Paradise, S.R., Braverman, A.J., Miller, C.E., 2010. A geostatistical data fusion technique for merging remote sensing and ground-based observations of aerosol optical thickness. J. Geophys. Res. 115, D20207. http://dx.doi.org/10.1029/2009JD013765. Che, H., Zhang, X., Chen, H., Damiri, B., Goloub, P., Li, Z., Zhang, X., Wei, Y., Zhou, H., Dong, F., Li, D., Zhou, T., 2009. Instrument calibration and aerosol optical depth validation of the China aerosol remote sensing network. J. Geophys. Res. 114 (D3), D03206. http://dx.doi.org/10.1029/2008JD011030. Chu, C.C., Aggarwal, J.K., 1993. The integration of image segmentation map using region and edge information. IEEE Trans. Pattern Analysis Mach. Intell. 15 (12), 1241e1252. http://dx.doi.org/10.1109/34.250843. Cescatti, A., Marcolla, B., Santhana Vannan, S.K., Pan, J.Y., Rom an, M.O., Yang, X., Ciais, P., Cook, R.B., Law, B.E., Matteucci, G., Migliavacca, M., Moors, E., Richardson, A.D., Seufert, G., Schaaf, C.B., 2012. Intercomparison of MODIS albedo retrievals and in situ measurements across the global FLUXNET network. Remote Sens. Environ. 121, 323e334. http://dx.doi.org/10.1016/ j.rse.2012.02.019.
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A consistent aerosol optical depth (AOD) dataset over mainland China by integration of several AOD products H. Xu a, f, J. Guang a, *, Y. Xue a, b, *, Gerrit de Leeuw c, d, Y.H. Che a, f, Jianping Guo e, X.W. He a, f, T.K. Wang b a
Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China Faculty of Life Sciences and Computing, London Metropolitan University, 166e220 Holloway Road, London N78 DB, UK c Department of Physics, University of Helsinki, Helsinki, Finland d Finnish Meteorological Institute, Climate Research Unit, Helsinki, Finland e Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081, China f University of Chinese Academy of Sciences, Beijing 100049, China b
h i g h l i g h t s A fusion method to generate an AOD dataset from five different AOD products. A Consistent AOD Dataset over China by Integrating of Various AOD Products. The merged AOD dataset has high overall accuracy (RMSE ¼ 0.16). The merged AOD estimates are closer to the reference field than others.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 December 2014 Received in revised form 11 May 2015 Accepted 12 May 2015 Available online 14 May 2015
The Moderate Resolution Imaging Spectroradiometer (MODIS), the Multiangle Imaging Spectroradiometer (MISR) and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) provide validated aerosol optical depth (AOD) products over both land and ocean. However, the values of the AOD provided by each of these satellites may show spatial and temporal differences due to the instrument characteristics and aerosol retrieval algorithms used for each instrument. In this article we present a method to produce an AOD data set over Asia for the year 2007 based on fusion of the data provided by different instruments and/or algorithms. First, the bias of each satellite-derived AOD product was calculated by comparison with ground-based AOD data derived from the AErosol RObotic NETwork (AERONET) and the China Aerosol Remote Sensing NETwork (CARSNET) for different values of the surface albedo and the AOD. Then, these multiple AOD products were combined using the maximum likelihood estimate (MLE) method using weights derived from the root mean square error (RMSE) associated with the accuracies of the original AOD products. The original and merged AOD dataset has been validated by comparison with AOD data from the CARSNET. Results show that the mean bias error (MBE) and mean absolute error (MAE) of the merged AOD dataset are not larger than that of any of the original AOD products. In addition, for the merged AOD dataset the fraction of pixels with no data is significantly smaller than that of any of the original products, thus increasing the spatial coverage. The fraction of retrievable area is about 50% for the merged AOD dataset and between 5% and 20% for the MISR, SeaWiFS, MODIS-DT and MODIS-DB algorithms. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Merging Aerosol optical depth MODIS MISR SeaWiFS Albedo
1. Introduction
* Corresponding authors. E-mail address: [email protected] (Y. Xue). http://dx.doi.org/10.1016/j.atmosenv.2015.05.023 1352-2310/© 2015 Elsevier Ltd. All rights reserved.
Atmospheric aerosol particles affect climate directly by scattering and absorption of solar radiation and indirectly by modifying the amount and the microphysical and radiative properties of
H. Xu et al. / Atmospheric Environment 114 (2015) 48e56
clouds (King et al., 1999; Andreae and Rosenfeld, 2008). In addition, aerosol particles also affect the environment, the corrosion and soiling effects on materials, biogeochemical cycles, and human health (Lippmann et al., 2000; Tzanis et al., 2011; Varotsos et al., 2012). Because of its spatial and temporal coverage, satellitederived aerosol optical depth (AOD; Andreae and Rosenfeld, 2008) is the most practical measurement of the amount of aerosol for both regional and global assessments (Anderson et al., 2005). Satellite-based AOD retrieval products have been developed by several research teams. Observations taken by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument are used to disseminate AOD products using the Dark Target (DT; Remer et al., 2005, Levy et al., 2013) and Deep Blue (DB; Hsu et al., 2004, 2006) algorithms, the synergetic retrieval of aerosol properties algorithm (SRAP) for land surface (Xue et al., 2014) and the MultiAngle Implementation of Atmospheric Correction for MODIS (MAIAC) (Lyapustin et al., 2011). Recently, the DT and DB algorithms have been merged into one product in MODIS C6 (Levy et al., 2013). The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) provides an AOD dataset based on the “Deep Blue” algorithm (Hsu et al., 2004, 2006; Sayer et al., 2012). Another relevant AOD product is generated from the Multi-angle Imaging SpectroRadiometer (MISR) using the method described by Diner et al. (2005) and Kahn et al. (2005; 2010). More recently, the Advance Along-Track Scanning Radiometer (AATSR) has been further improved and the AOD quality is now similar in quality to that from the above-mentioned sensors (Holzer-Popp et al., 2013; de Leeuw et al., 2013). However, different satellite AOD products do not always give consistent values of aerosol properties and none of the AOD retrieval algorithm outperforms all others everywhere (de Leeuw et al., 2013). For example, the MODIS-DT algorithm has been designed for application over dark land surfaces, i.e. with a low level of reflectivity in the visible spectrum. The MODIS-DB algorithm employs radiances from the blue channel at 412 nm, among other wavelengths, where the surface reflectance is negligible and hence these wavelengths can be used to estimate the aerosol amount over bright targets. In order to reduce the uncertainty in estimating climate effects
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due to tropospheric aerosols (both anthropogenic and natural aerosols), it is important to produce consistent aerosol products with the best possible quality. This can be achieved by merging aerosol information obtained from the multiple satellite sensors that are currently available, taking advantage of the strengths of individual sensors and constraining the weaknesses of others (Ehlers, 1991; Nirala, 2008; Hsu et al., 2012). Various merging approaches have been developed (Leptoukh et al., 2007; Nirala, 2008; Siddans et al., 2007; Chatterjee et al., 2010; Nguyen et al., 2012). Leptoukh et al. (2007) demonstrated that prior to merging data from different sensors, one needs to assess differences in these images. Aerosol physical parameters have a large important impact on the AOD retrieval. Surface albedo is a key parameter in modeling radiative transfer in the atmosphere (Vermote et al., 2002) and is an important source of the uncertainties in AOD retrieval (Shi et al., 2013). AOD amount is also a key parameter related to the error in the retrieved satellite AOD (Sayer et al., 2012). In this study, these two physical parameters have been chosen to obtain uncertainties in different satellite AOD datasets, and corresponding results have been considered in the merging process. The objective of this study is to merge multiple satellite AOD products to obtain a new consistent AOD dataset. In this study, AOD data used are those retrieved from MISR and SeaWiFS, and the MODIS products provided from the synergetic retrieval of aerosol properties (SRAP; Xue et al., 2014), DB and DT. The focus of this study is to give different weights for each single satellite AOD product during the merging process instead of one constant value over the whole study area. Because each satellite AOD product may have a different bias and accuracy for different surface albedo and AOD values, the biases and merging weights were computed for each range of surface albedo and AOD separately. AErosol RObotic NETwork (AERONET) AOD data is used to calculate the bias of each individual AOD product for different conditions of albedo and AOD. A study period of one year (2007) was chosen to obtain a statistically significant data set of satellite and AERONET match-ups. Using the weight results of root mean square root (RMSE) values, the merged AOD dataset is produced using the maximum likelihood estimate (MLE) method. To investigate the accuracy of this merged
Fig. 1. Study area showing the locations of the 34 AERONET and 8 CARSNET stations used for the evaluation of the satellite-based AOD products.
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AOD dataset, the original and merged AOD data sets were compared with ground-based AOD observations from the China Aerosol Remote Sensing Network (CARSNET). 2. Data Multiple satellite AOD products, ground-based AOD measurements and surface albedo data have been used. The study area covers the area from 15 N to 60 N in latitude and from 65 E to 145 E in longitude (see Fig. 1). The time period chosen is the whole year 2007. 2.1. Satellite AOD data Five satellite AOD products were used as the source AOD products, three of them have been retrieved from MODIS, one from SeaWiFS and one from MISR data. The data sets used are publicly available from the internet as indicated below. The detailed description of these satellite data sets are as follows. The MODIS instrument aboard the polar satellites Terra and Aqua observes the Earth with a total swath of more than 2300 km. The DT and DB algorithms are the mainstream algorithms for MODIS AOD retrieval. The DT algorithm is applicable over dark surfaces such as water bodies and dense vegetation and the DB method expands MODIS AOD coverage to bright desert surfaces. The MOD04\MYD04 aerosol product which includes AOD data both from the DT and DB algorithms is available on the website “http:// ladsweb.nascom.nasa.gov”. The SRAP algorithm has been developed based on the synergetic use of the data from MODIS on both the Terra and Aqua satellites (Xue and Cracknell, 1995). For the SRAP algorithm, only cloudy pixels are eliminated. The China Collection 2.0 AOD datasets, retrieved using the SRAP algorithm, have a better coverage over the mainland China and East Asian region than the MODIS-DT and MODIS-DB AOD products (Xue et al., 2014). Comparing the AOD results with AERONET data shows that 62% of China Collection 2.0 AOD values are within an expected error (EE) range of ± (0.05 þ 20%) and that 56% are within an EE range of ± (0.05 þ 15%) when compared with AERONET-observed values. Comparison with CARSNET data shows that 60% of China Collection 2.0 AOD values are within an expected error (EE) range of ± (0.05 þ 20%) and that 53% are within an EE range of ± (0.05 þ 15%) (Xue et al., 2014). In this paper, MODIS Collection 5.1 Level-2 aerosol products (MOD04_L2) data and MODIS-SRAP AOD data are used with quality assurance flag values from 1 to 3. MISR aboard the polar satellite Terra captures upwelling shortwave radiance at nine viewing angles embracing the forward and backward directions along the flight path, that is, 70.5 , 60.0 , 45.6 , 26.1, and nadir (Diner et al., 1998). The width of the overlapping swath of MISR (i.e., the swath seen by all nine cameras) is 360 km. MISR uses multi-angle observations of the same ground scene (Diner et al., 2005). This makes it possible to accurately account for directional surface scattering in the retrieval procedure. In this paper, the best estimate of AOD at 558 nm which is the data field RegBestEstimateSpectralOptDepth from the Level 2 MISR aerosol product (MIL2ASAE) of version 22 is used. SeaWiFS aboard the SeaStar platform was in operation between September 1997 and December 2010. The SeaWiFS AOD dataset over-land is retrieved using the “Deep Blue” algorithm (Hsu et al., 2004, 2006; Sayer et al., 2012), providing AOD at the commonlyused reference wavelength of 550 nm. The updated algorithm is applied over both vegetated land surfaces and bright arid surfaces (Sayer et al., 2012). The current version (v004) of the SeaWiFS AOD dataset is freely available from “http://disc.gsfc.nasa.gov/dust/”. We used the SeaWiFS field name AOD_550_Best_Estimate.
2.2. Ground-based AOD data AERONET is a federation of ground-based remote sensing aerosol network providing a long-term, continuous and readily accessible public domain database of globally distributed observations of spectral AOD (Holben et al., 1998; Dubovik and King, 2000). AOD is provided for spectral bands centered at 340, 380, 440, 500, 675, 870, and 1020 nm, with an accuracy of ±0.02. For the level 2.0 products, the AOD uncertainties are of the order of 0.01 in the visible and near-infrared channels (Eck et al., 1999). In this paper, L2 data from thirty-four AERONET sites in the study area have been used to establish the biases of each individual satellite AOD product for different ranges of surface albedo and AOD values. CARSNET, established in 2002, is an operational network for the study of aerosol optical properties and for the validation of satellite aerosol retrievals (Che et al., 2009). The instrument deployed by CARSNET is the same CE-318 sunphotometer as used in AERONET. The long-term CARSNET measurements provide an unprecedented opportunity to study aerosol properties and validate MODIS retrieved AODs over various terrestrial regions in China. CARSNET AOD measurements are highly correlated with those from AERONET and have a similar accuracy, as verified over the Chinese Yangtze Delta region (Pan et al., 2010). In this paper, data from Eight CARSNET sites in the study area are selected not only for establishing the biases of each individual satellite AOD product for different ranges of surface albedo and AOD values but also for evaluating the performances of both each individual satellite AOD product and the merged AOD dataset. The locations of the thirty-four AERONET and the eight CARSNET sites are shown in the map displayed in Fig. 1. Tables 1 and 2 report the latitude, longitude and elevation of the AERONET and CARSNET
Table 1 Location and elevation of the 34 AERONET sites used in this study. AERONET site name
Latitude ( )
Longitude( )
Elevation (m)
Anmyon Bac_Giang Beijing Chen-Kung_Univ Chiang_Mai Dalanzadgad EVK2-CNR Gandhi_College Gosan_SNU Gwangju_K-JIST Hangzhou-ZFU Hefei Hong_Kong_Hok_Tsui Hong_Kong_PolyU Irkutsk Issyk-Kul Kanpur Karachi Lulin Mukdahan NAM_CO NCU_Taiwan Noto Osaka Pimai Pune Shirahama Sinhgad Taihu Taipei_CWB Tomsk Ussuriysk XiangHe Xinglong
36.54 21.29 39.98 23.00 18.81 43.58 27.96 25.87 33.29 35.23 30.26 31.91 22.21 22.30 51.80 42.62 26.51 24.87 23.47 16.61 30.77 24.97 37.33 34.65 15.18 18.54 33.69 18.37 31.42 25.03 56.48 43.70 39.75 40.40
126.33 106.23 116.38 120.22 98.99 104.42 86.81 84.13 126.16 126.84 119.73 117.16 114.26 114.18 103.09 76.98 80.23 67.03 120.87 104.68 90.96 121.19 137.14 135.59 102.56 73.81 135.36 73.75 120.22 121.50 85.05 132.16 116.96 117.58
47 15 92 50 324 1470 5050 60 72 52 14 36 80 30 670 1650 123 49 2868 166 4740 171 200 50 220 559 10 1450 20 26 130 280 36 970
H. Xu et al. / Atmospheric Environment 114 (2015) 48e56 Table 2 Location and elevation of the 8 CARSNET sites used in this study. CARSNET site name
Latitude ( )
Longitude ( )
Elevation (m)
Chengdu Gucheng Longfengshan Lin'an Lasa Shangdianzi Xilinhaote Zhengzhou
30.65 39.13 44.73 30.3 29.67 40.65 43.95 34.78
104.04 115.8 127.6 119.73 91.13 117.12 116.07 113.68
553 11 330.5 138.6 3648.9 293.3 989.5 110
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which is borrowed from the climatology constructed using seven years (2004e2010) of MODIS black-sky visible albedo. In particular, we use the MCD43 product from collection 5.0, which is obtained by inverting multi-date, multi-angular, cloud-free, atmospherically corrected, surface reflectance observations acquired by the MODIS instruments on board the Terra and the Aqua satellites over a 16day period. The accuracy of the high quality MODIS operational albedo is well less than 5% at the majority of the validation sites studied by Cescatti et al. (2012). In addition, only albedo values with a high confidence index were used to build the albedo climatology used in the present article. The latter was built at a spatial resolution of 0.05 0.05 .
sites used in this study. 3. Methods 2.3. Surface albedo data 3.1. Preprocessing The biases and accuracies of the source satellite AOD products are evaluated according to the daily-averaged surface albedo (a)
In this paper, the wavelength at 550 nm is chosen as the
Fig. 2. The AOD spatial distribution over the study area aggregated from the original and merged AOD datasets in March 2007: (a) Terra MODIS-DT AOD, (b) Terra MODIS-DB AOD, (c) Terra MODIS-SRAP AOD; (d) MISR AOD, (e) SeaWiFS AOD; (f) merged AOD.
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H. Xu et al. / Atmospheric Environment 114 (2015) 48e56
common spectral wavelength for the merging process and validation of the merged AOD dataset. While AOD products derived from MODIS-DT, MODIS-DB and SeaWiFS are already disseminated at this wavelength, AOD estimates derived from AERONET and CARSNET are converted for each pixel from the original wavelength € m exponent for the (440 nm) to 550 nm using the Ångstro € m, 1929). The MISR-derived 440e870 nm wavelength pair (Ångstro AOD values are not transformed because of the spectral proximity of the retrieval wavelength (558 nm) to 550 nm and some likely € m coefficients. uncertainty in the AERONET and CARSNET Ångstro Prior to merging, all original satellite-retrieved AOD products are produced on a common 50 km grid. In the merging process, grid cells in the MODIS-DT, MODIS-DB and MODIS-SRAP AOD products are only considered when each of the original datasets contains at least five retrievals. For the MISR and SeaWiFS AOD products, at least three and four retrievals in the 50 km grid respectively are considered. 3.2. Merging method The MLE method represents a weighted average and takes into account information on the statistical uncertainties in the data using metrics such as mean, standard deviation, and number of counts (Chu and Aggarwal, 1993; Nirala, 2008). In this paper, we used the MLE method to calculate the weight for each original satellite AOD product at each grid point using:
tMLE ¼ i
N X k¼1
R2 i;k t ; PN 2 i;k k¼1 Ri;k
(1)
where tMLE represents the merged AOD values, ti;k represents the i mean AOD value at grid point i from the original satellite AOD product k and N is the number of all the original AOD products. The coefficients Ri, k is the RMSE at grid point i for the original satellite AOD product k. The reason for choosing RMSE as the statistics for the MLE method is that RMSE can be used to measure the average dispersion of the AOD uncertainty and provide an estimate of the expected error in AOD in the absence of information about specific sources of errors. 3.3. Weight calculating
In order to obtain the RMSE value for each original satellite AOD product at any point whether there is AERONET AOD observation or not, it is necessary to find several parameters which are available regionally or globally and establish the relationship between RMSE and these parameters. In this paper, surface albedo and AOD values are chosen as two parameters to calculate RMSE for each original satellite AOD product. For this propose, four surface albedo ranges (0e5%, 5e8%, 8e11%, and 11e25%) and four AOD ranges (0.00e0.25, 0.25e0.5, 0.5e0.8, and 0.8e5.0) are considered. RMSEs are calculated using Eq. (2) for these different surface albedo and AOD ranges. For each original satellite AOD product, there is one RMSE value for each surface albedo range and each AOD range and 16 RMSE values for four surface albedo ranges and four AOD ranges. The RMSE values are discussed in detail in section 4.2. 3.4. Validation method Two methods proposed by Ichoku et al. (2002) and Taylor (2001) are used in this paper for the validation of both the original AOD products and the merged AOD dataset. In order to compare the original and merged AOD dataset with CARSNET AOD data, the spatial mean of the original or merged AOD datasets and the temporal mean of the CARSNET observations are used. A valid matchup is defined when there is at least one CARSNET measurement within ±30 min of the satellite overpass, and at least one original or one merged AOD value in a grid of 50 50 km2 with the CARSNET station in the middle. Statistics calculated for the comparison include the correlation (R) and RMSE between each original/merged satellite AOD product and the AERONET/CARSNET value for each site. 4. Results and analysis 4.1. Merging result By assessing the biases of the original satellite AOD products for each of the four aggregated surface albedo ranges and each of the four AOD value ranges, the RMSE for each original AOD product in each surface albedo range and each AOD value range were computed using Eq. (2). Using the merging method mentioned in section 3.2 (Eq. (1)), the merged AOD dataset for the whole year of
The RMSE value used in this study is defined as follows:
Ri; k
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 PM i¼1 si;k gi ¼ M
(2)
where Ri; k represents the RMSE value at grid point i for the original satellite AOD product k, si,k represents the mean AOD values at grid point i from the original satellite AOD product k. gi represents the mean ground-based AOD value at grid point i from the AERONET counterparts. The si, k is spatial mean and gi is temporal mean. M is the number of pairs of si, k and gi. In order to find the pairs of si, k and gi, a spatial-temporal approach based on Ichoku et al. (2002) was used. Instantaneous AOD values from original satellite-derived AOD datasets are evaluated against temporally coincident AERONET ground-based counterparts resulting from averaging all available AERONET observations within a period of ±30 min centered at the overpass time of each satellite. The RMSE is needed at each point for each original satellite AOD product. However, it can only be calculated for retrieval areas for which AERONET-derived AOD data is available. AERONET stations exist at only a finite number of locations. Where there is no AERONET AOD observation, RMSE cannot be calculated.
Fig. 3. The fraction of pixels with AOD data for original AOD products and the merged AOD dataset in 2007.
H. Xu et al. / Atmospheric Environment 114 (2015) 48e56
2007 was obtained. In this section, we will analyze the merged AOD in three aspects: spatial coverage, evaluation result and time series analysis.
53
4.1.1. Spatial coverage analysis Fig. 2 presents the distribution of AODs over the study area averaged from the original and merged AOD datasets in March 2007. Fig. 2f demonstrates that combining multiple AOD products can significantly reduce the fraction of pixels with no data, thus
Fig. 4. Scatter plots illustrating the comparison between the five original and the merged satellite AOD products with the AOD data from the 8 CARSNET stations for the whole year of 2007. The dashed and solid lines are the 1-1 lines and the linear regression for the data, respectively. Text at the top describes: the regression curve, correlation (R), the number of collocations (N), and the root mean square error (RMSE) of the fit.
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H. Xu et al. / Atmospheric Environment 114 (2015) 48e56
Table 3 MBE and MAE of the original AOD products and merged AOD dataset. AOD product
MBE
MAE
RMSE
No. of plot data
MODIS-DT MODIS-DB MODIS-SRAP MISR SeaWiFS Merged
0.03 0.03 0.09 0.26 0.08 0.03
0.12 0.23 0.14 0.29 0.16 0.12
0.16 0.33 0.21 0.40 0.20 0.16
174 45 191 53 105 213
increasing the spatial coverage. The fractions of retrievable pixels for the source AOD products and the merged AOD dataset in 2007 are shown in Fig. 3. The Fig. clearly shows the better coverage with fewer data gaps over the study region for the merged datasets than for the source AOD products. The fraction of retrievable area is about 50% for the merged AOD dataset and between 5% and 20% for the MISR, SeaWiFS, MODIS-DT and MODIS-DB algorithms. 4.1.2. Evaluation using CARSNET AOD data In order to examine the accuracy of the merged AOD dataset, all original satellite AOD products and merged AOD estimates should be evaluated using independent and accurate ground-based observations. Fig. 4 shows the validation results for both the five original satellite AOD products used in this paper and the merged AOD dataset, using CARSNET-derived AOD data within the study area, for the whole year of 2007. Note that MODIS-SRAP, MISR and SeaWiFS AOD products and the merged AOD dataset are underestimated, as the slopes resulting from linear regression (see Fig. 4) range from 0.61 for the SeaWiFS AOD product to 0.91 for the merged AOD dataset. Compared with the five original satellite AOD
products, the merged AOD estimates greatly improve the coverage with an RMSE (R ¼ 0.91) which is only slightly lower than the MODIS-DT (R ¼ 0.93), but larger than the SeaWiFS RMSE (R ¼ 0.61), the MISR RMSE (R ¼ 0.87), and the MODIS-DB RMSE (R ¼ 0.81) and the MODIS-SRAP RMSE (R ¼ 0.85). The number of collocated data points reaches 213 for the merged AOD dataset and only 53 for MISR, 105 for SeaWiFS, 174 for MODIS-DT, 56 for MODIS-DB, and 191 for MODIS-SRAP AOD products. The mean bias error (MBE) and mean absolute error (MAE) of the original AOD products and merged AOD datasets are shown in Table 3. The MBEs are 0.03, 0.03, 0.09, 0.26, 0.08, for the MODIS-DT, MODIS-DB, MODIS-SRAP, MISR and SeaWiFS AOD products, respectively, while the MBE for the merged AOD dataset is 0.03. The MAEs are 0.12, 0.23, 0.14, 0.29, 0.16 for the MODIS-DT, MODIS-DB, MODIS-SRAP, MISR and SeaWiFS AOD products, respectively, while the that of the merged AOD dataset is 0.12. Hence, the MBE and MAE of the merged AOD dataset is no larger than that of any original AOD products.
4.1.3. Time series analysis In order to evaluate the uncertainty of the merged AOD dataset according to its temporal coherence, time series of merged AOD estimates and corresponding CARSNET AOD data are shown in Fig. 5 for the eight CARSNET stations for the whole year of 2007. The Longfengshan site is located in a highly vegetated mountain region. Several AOD peaks are observed over Longfengshan in June (Fig. 5). The merged AOD retrievals compare well with CARSNET AOD data over the Longfengshan station (R ¼ 0.93). Over Shangdianzi, LIN_AN and Chengdu Stations, the merged AOD dataset tends to underestimate the AOD as compared with CARSNET. Over
Fig. 5. Time series of the merged AOD dataset and corresponding CARSNET AOD data at eight CARSNET stations.
H. Xu et al. / Atmospheric Environment 114 (2015) 48e56
55
Table 4 Weight matrices for four original satellite AOD products for four albedo ranges (0e5%, 5e8%, 8e11%, and 11e25%) and four AOD ranges (0.00e0.25, 0.25e0.5, 0.5e0.8, and 0.8e5.0). Albedo value 0.00 e 0.05 0.05 e 0.08 0.08 e 0.11 0.11 e 0.25
AOD value
MODIS-DB
0.00e0.25 0.25e0.50 0.50e0.80 0.80e5.00 0.00e0.25 0.25e0.50 0.50e0.80 0.80e5.00 0.00e0.25 0.25e0.50 0.50e0.80 0.80e5.00 0.00e0.25 0.25e0.50 0.50e0.80 0.80e5.00
W e e e e 0.13 0.20 e 0.15 0.07 0.26 0.12 0.14 0.07 0.19 0.27 0.02
MODIS-DT N e e e e 53 21 e 17 65 22 22 44 71 17 20 36
W 0.28 0.27 0.23 0.08 0.43 0.31 0.41 0.58 0.45 0.24 0.37 0.49 0.64 0.38 0.33 0.12
the Zhengzhou station, the merged AOD dataset tends to overestimate the AOD as compared with CARSNET. For the Gucheng and Xinlinhaote stations, the observations of CARSNET AOD data are too limited.
SRAP N 172 67 26 10 117 87 44 37 81 58 36 59 46 26 25 18
W 0.05 0.17 0.41 0.92 0.04 0.17 0.20 0.27 0.01 0.04 0.10 0.37 0.01 0.12 0.14 0.86
MISR N 532 105 37 11 358 196 80 50 353 139 72 74 164 73 65 24
W 0.30 0.26 e e 0.11 0.11 e e 0.05 0.14 0.04 e 0.07 0.12 e e
SeaWiFS N 27 14 e e 41 21 e e 33 34 15 e 33 12 e e
W 0.37 0.30 0.36 e 0.29 0.22 0.39 e 0.42 0.32 0.37 e 0.21 0.19 0.26 e
N 114 22 12 e 97 54 26 e 58 32 23 e 27 28 20 e
original AOD products. It is also shown that the merged AOD dataset combining multiple AOD products significantly reduces the fraction of pixels with no data, thus increasing the spatial coverage. Acknowledgments
4.2. Weights analysis The weights computed for the original satellite AOD products (Table 4) show that the different AOD products provided different contributions to the merged AOD dataset for different albedo and AOD values. MODIS-DT and SeaWiFS AOD products provided the main contributions to the merged AOD dataset for most surface albedo and AOD value ranges (Table 4) because these two AOD products compare with AERONET data better than other original AOD products. The weights of MODIS-DT and SeaWiFS is always high for AOD values greater than 0.5, for all different surface albedos. The weights of MISR are empty or low for AOD values of 0.5e0.8, for all surface albedos. This is because a limitation of the MISR dataset is the smoothing mask used in the current version of over-land retrievals that eliminates high AODs leading to AOD underestimation during periods with high aerosol loading (midvisible AOD is likely to be > 0.5) (Kahn et al., 2010). The weights of MODIS-DB are empty when surface albedo is less than 0.5 and AOD greater than 0.5. The reason could be that MODIS-DB is not designed to retrieve AOD over very dark surfaces. 5. Conclusions Because original satellite AOD products have different accuracies for different surface albedo ranges and different AOD value ranges, different biases for each satellite AOD product were adopted to calculate the RMSE and to create a merged and more accurate AOD dataset by using the synergy of multi-sensor and multialgorithm aerosol information. The satellite AOD products used include MODIS-DT, MODIS-DB, MODIS-SRAP, MISR and SeaWiFS, which were merged using the maximum likelihood approach. The RMSD of each original and the merged AOD products were evaluated considering four albedo ranges (0e5%, 5e8%, 8e11%, and 11e25%) and four AOD ranges (0.00e0.25, 0.25e0.5, 0.5e0.8, and 0.8e5.0). The evaluation result shows that the merged AOD dataset compares well with CARSNET AOD observations from eight CARSNET stations (R ¼ 0.91, RMSE ¼ 0.16). Additionally, the MBE and MAE of the merged AOD dataset are no larger than that of any
This work was supported in part by the Ministry of Science and Technology (MOST), China under grant Nos. 2010CB950803 and 2013AA122801, and by the National Natural Science Foundation of China (NSFC) under grant no. 41101323, and by CAS-RADI Innovation project Y3ZZ15101A and EU/FP7 MarcPolo project (Grant Agreement Number 606953). Part of the work is done in preparation for the Aerosol_cci project (ESA-ESRIN project AO/1e6207/09/ I-LG) and supported by the Centre on Excellence in Atmospheric Science funded by the Finnish Academy of Sciences Excellence (project no. 272041). Surface albedo data is provided by Jean-louis, Roujean. We think him very much. The data for uncertainty analysis and validation came from thirty-four AERONET sites and eight CARSNET data. We thank the PIs, investigators and their staff for establishing and maintaining the forty-two sites used in this study. References € m, A., 1929. On the atmospheric transmission of sun radiation and on dust Ångstro in the air. Geogr. Ann. 11, 156e166. http://dx.doi.org/10.2307/519399. Anderson, T.L., Charlson, R.J., Bellouin, N., Boucher, O., Chin, M., Christopher, S.A., Haywood, J., Kaufman, Y.J., Kinne, S., Ogren, J.A., Remer, L.A., Takemura, T., Tanre, D., Torres, O., Trepte, C.R., Wielicki, B.A., Winker, D.M., Yu, H., 2005. An “A-Train” strategy for quantifying direct climate forcing by anthropogenic aerosols. Bull. Am. Meteorological Soc. 86 (12), 1795e1809. http://dx.doi.org/ 10.1175/BAMS-86-12-1795. Andreae, M.O., Rosenfeld, D., 2008. Aerosol-cloud-precipitation interactions. Part1. The nature and sources of cloud-active aerosols. Earth-Science Rev. 89 (1e2), 13e41. Chatterjee, A., Michalak, A.M., Kahn, R.A., Paradise, S.R., Braverman, A.J., Miller, C.E., 2010. A geostatistical data fusion technique for merging remote sensing and ground-based observations of aerosol optical thickness. J. Geophys. Res. 115, D20207. http://dx.doi.org/10.1029/2009JD013765. Che, H., Zhang, X., Chen, H., Damiri, B., Goloub, P., Li, Z., Zhang, X., Wei, Y., Zhou, H., Dong, F., Li, D., Zhou, T., 2009. Instrument calibration and aerosol optical depth validation of the China aerosol remote sensing network. J. Geophys. Res. 114 (D3), D03206. http://dx.doi.org/10.1029/2008JD011030. Chu, C.C., Aggarwal, J.K., 1993. The integration of image segmentation map using region and edge information. IEEE Trans. Pattern Analysis Mach. Intell. 15 (12), 1241e1252. http://dx.doi.org/10.1109/34.250843. Cescatti, A., Marcolla, B., Santhana Vannan, S.K., Pan, J.Y., Rom an, M.O., Yang, X., Ciais, P., Cook, R.B., Law, B.E., Matteucci, G., Migliavacca, M., Moors, E., Richardson, A.D., Seufert, G., Schaaf, C.B., 2012. Intercomparison of MODIS albedo retrievals and in situ measurements across the global FLUXNET network. Remote Sens. Environ. 121, 323e334. http://dx.doi.org/10.1016/ j.rse.2012.02.019.
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