Atmospheric Environment 111 (2015) 113e126 Contents lists available at ScienceDirect Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv Intercomparison of MODIS, MISR, OMI, and CALIPSO aerosol optical depth retrievals for four locations on the Indo-Gangetic plains and validation against AERONET data Humera Bibi a, Khan Alam a, *, Farrukh Chishtie b, Samina Bibi a, Imran Shahid b, Thomas Blaschke c a b c Department of Physics, University of Peshawar, Khyber Pakhtunkhwa, Pakistan Department of Space Science, Institute of Space Technology, Islamabad, Pakistan Department of Geoinformatics e Z_GIS, University of Salzburg, Hellbrunnerstrasse 34, Salzburg 5020, Austria h i g h l i g h t s MODISSTDeAERONET AOD comparisons revealed a high degree of correlation. The MODISDBeAERONET AOD comparisons revealed even better correlations. MISR AOD data was more accurate over areas close to the ocean than over other areas. MODISSTD consistently outperformed MODISDB in all of the investigated areas. a r t i c l e i n f o a b s t r a c t Article history: Received 1 February 2015 Received in revised form 4 April 2015 Accepted 7 April 2015 Available online 8 April 2015 This study provides an intercomparison of aerosol optical depth (AOD) retrievals from satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), Ozone Monitoring Instrument (OMI), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) instrumentation over Karachi, Lahore, Jaipur, and Kanpur between 2007 and 2013, with validation against AOD observations from the ground-based Aerosol Robotic Network (AERONET). Both MODIS Deep Blue (MODISDB) and MODIS Standard (MODISSTD) products were compared with the AERONET products. The MODISSTDeAERONET comparisons revealed a high degree of correlation for the four investigated sites at Karachi, Lahore, Jaipur, and Kanpur, the MODISDBeAERONETcomparisons revealed even better correlations, and the MISReAERONET comparisons also indicated strong correlations, as did the OMIeAERONET comparisons, while the CALIPSOeAERONET comparisons revealed only poor correlations due to the limited number of data points available. We also computed figures for root mean square error (RMSE), mean absolute error (MAE) and root mean bias (RMB). Using AERONET data to validate MODISSTD, MODISDB, MISR, OMI, and CALIPSO data revealed that MODISSTD data was more accurate over vegetated locations than over un-vegetated locations, while MISR data was more accurate over areas close to the ocean than over other areas. The MISR instrument performed better than the other instruments over Karachi and Kanpur, while the MODISSTD AOD retrievals were better than those from the other instruments over Lahore and Jaipur. We also computed the expected error bounds (EEBs) for both MODIS retrievals and found that MODISSTD consistently outperformed MODISDB in all of the investigated areas. High AOD values were observed by the MODISSTD, MODISDB, MISR, and OMI instruments during the summer months (AprileAugust); these ranged from 0.32 to 0.78, possibly due to human activity and biomass burning. In contrast, high AOD values were observed by the CALIPSO instrument between September and December, due to high concentrations of smoke and soot aerosols. The variable monthly AOD figures obtained with different sensors indicate overestimation by MODISSTD, MODISDB, OMI, and CALIPSO instruments over Karachi, Lahore, Jaipur and Kanpur, relative to the AERONET data, but underestimation by the MISR instrument. © 2015 Elsevier Ltd. All rights reserved. Keywords: AOD MODIS MISR OMI CALIPSO AERONET * Corresponding author. E-mail address: [email protected] (K. Alam). http://dx.doi.org/10.1016/j.atmosenv.2015.04.013 1352-2310/© 2015 Elsevier Ltd. All rights reserved. 114 H. Bibi et al. / Atmospheric Environment 111 (2015) 113e126 1. Introduction Aerosols are formed in the atmosphere through a variety of natural (e.g., biogenic and volcanic) processes and anthropogenic processes involving the combustion of fossil fuel, urban/industrial processes, and biomass burning; they disperse widely both horizontally and vertically through atmospheric circulation (Ramachandran and Kedia, 2013). Sea salt, dust, and sulfate produced over ocean surfaces dominate the natural global aerosol abundance, but a proportion of the dust in the atmosphere may also be due to anthropogenic activities (Prospero et al., 2002; Habib et al., 2006). Aerosols are short lived in the lower troposphere, lasting only about one week, and large inconsistencies in their spatial and temporal distributions suggest broadly scattered sources (Ramachandran and Kedia, 2013). Atmospheric aerosols are an important component in the earth's climatic system and are likely to affect our understanding of anthropogenic radiative forcing. However, due to our limited understanding of the properties of aerosols there remain significant uncertainties in their temporal and spatial variations and the effects that they have on the earth's climate (IPCC, 2001, 2007). The radiative effects of aerosols (both direct and indirect) remain a significant source of uncertainty in atmospheric studies (Solomon, 2007; Ramachandran et al., 2013). Accurate and reliable measurements of aerosol distributions and properties are required in order to reduce these large uncertainties, since aerosols play a vital role in current analyses and predictions of the global climate (Hansen et al., 2000; Liu et al., 2008a,b). Satellite-based remote sensing measurements allow systematic retrieval of aerosol optical properties on both local and global scales (Kaufman et al., 2005; Kahn et al., 2010). Ground-based measurements of aerosols also fulfill a vital role in characterizing the optical and microphysical properties of aerosols, as well as in determining aerosol loadings and the radiative effects that aerosols have over specific locations (Jethva et al., 2007; More et al., 2013). The ability to examine aerosol optical properties (such as AOD) in the atmosphere using automatic, ground-based remote sensing techniques, has improved significantly over recent decades with the development of the AERONET Aerosol Robotic Network (Holben et al., 1998). Continuous AOD time series with a very high temporal resolutions are now available for selected stations through this global network of ground-based radiometers (Fotiadi et al., 2006). For global coverage, however, polar-orbiting sun-synchronous satellite sensors are used to achieve a seasonal characterization of aerosols (Chu et al., 2003; Santese et al., 2007). As well as providing global coverage, observations from satellites have the additional advantage of allowing complete mapping of large areas in a single snapshot (Kosmopoulos et al., 2008). Over the last decade satellite sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multiangle Imaging Spectroradiometer (MISR), the Ozone Monitoring Instrument (OMI), and the CloudAerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) instrument have investigated the atmosphere by characterizing physical and chemical properties of aerosols using observations and retrieval algorithms (Kaufman et al., 1997; Martonchik et al., 1998; Zhang and Christopher, 2003; Winker et al., 2006). Global aerosol properties from satellite observations are very useful for constraining aerosol parameterization in atmospheric models. The reliability of different datasets, however, becomes an important question in the interpretation of global and regional aerosol variability (Li et al., 2014a,b). In order to determine the spatial and temporal variability of aerosol parameters, and to validate the models used, satellite-based measurements of aerosol parameters need to be checked against ground-based measurements (Li et al., 2014a,b). Our understanding of the distribution of aerosols in the atmosphere has been greatly enhanced by the availability of satellite data (Myhre et al., 2005), which has allowed a global coverage to be achieved. Emphasis has been placed on retrieving aerosol properties (e.g., optical depth) from satellite data, with data from the MODIS and MISR dedicated satellite sensors being widely used in aerosol research (Qi et al., 2013). In this study we have analyzed AERONET, MODIS, MISR, OMI and CALIPSO AOD retrievals over four sites on the Indo-Gangetic plains (at Karachi, Lahore, Jaipur and Kanpur) between 2007 and 2013. The satellite AOD retrievals were independently validated by comparing them with the relevant ground-based AERONET AOD observations. 2. Data and methods 2.1. Instrumentation 2.1.1. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) The CALIPSO satellite was launched on April 28, 2006, with equator crossing times of about 13:30 and 01:30 and a 16-day repeating cycle (Winker et al., 2009, 2010). Observations from the CALIPSO satellite have resulted in a remarkable improvement in our understanding of the radiative effects of aerosols. Unlike other satellite-based passive remote sensing instruments, CALIPSO can detect aerosols both in clear sky conditions and beneath thin cloud layers, as well as over bright surfaces (Huang et al., 2007a,b; Winker et al., 2007; Liu et al., 2008a,b; Geng et al., 2011; Ma and Yu, 2014). The main advantage of the CALIPSO data is, however, that it is able to provide vertical profiles of atmospheric aerosol distributions on both global and regional scales. The CALIPSO satellite carries a Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument that operates at two wavelengths (532 nm and 1064 nm) and provides continuous observation with attenuated backscatter, covering the entire globe. The CALIOP instrument provides unique vertical profile of aerosols and clouds (Mishchenko et al., 1999; Winker et al., 2003). To date, CALIPSO data have been mainly used to analyze dust aerosols, and in particular, to study the presence and distribution of aerosols associated with Asian dust plumes (Huang et al., 2007a,b). 2.1.2. Aerosol Robotic Network (AERONET) AERONET is ground-based remote sensing aerosol network established by NASA. It uses CIMEL sun/sky radiometers that take measurements of the direct sun and diffuse sky radiances within the 340e1020 nm and 440e1020 nm spectral ranges, respectively (Holben et al., 1998). AERONET data is available at three levels: Level 1.0 (unscreened), Level 1.5 (cloud screened; Smirnov et al., 2000), and Level 2.0 (cloud screened and quality assured; Holben et al., 1998). The data can be downloaded from the AERONET website (http://aeronet.gsfc.nasa.gov/). AERONET provides columnar aerosol optical depths over both land and ocean but is restricted to point observations (Alam et al., 2011, 2014a). Although such ground-based aerosol remote sensing has a limited spatial coverage, wide angular and spectral measurements of solar and sky radiation provide reliable and continuous data on aerosol optical properties at particular locations (Dubovik et al., 2002). In this study we used Level 2.0 data for the period from 2007 to 2013. 2.1.3. Moderate Resolution Imagining Spectroradiometer (MODIS) MODIS is a key instrument that is carried on both the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the H. Bibi et al. / Atmospheric Environment 111 (2015) 113e126 afternoon. The MODIS instrument has 36 spectral bands that provide abundant information on atmospheric, terrestrial, and oceanic environments. MODIS uses different methods for data retrieval et al., 1997). over land (Kaufman et al., 1997) and over oceans (Tanre In order to improve the accuracy and quality of retrieved data, the MODIS algorithms have been updated to make use of improved cloud-masking processes, aerosol models, and the surface reflectance database (Remer et al., 2005; Levy et al., 2007). The introduction of the Deep-Blue algorithm has improved the MODIS Level 2 observations over bright land surfaces such as the Sahara Desert. The MODIS instruments provide observations at moderate spatial resolutions (1e250 km) and temporal resolutions (1e2 days), over different portions of the electromagnetic spectrum. Detailed information on algorithms for the retrieval of aerosol and cloud parameters is available from http://modisatmos. gsfc.nasa.gov. 2.1.4. Ozone Monitoring Instrument (OMI) The OMI satellite (EOS-Aura) was launched in July 2004 by the Netherlands Agency for Aerospace Programs (NIVR), in collaboration with the Finnish Meteorological Institute (FMI). The OMI has a nadir-viewing imaging spectrometer that measures the top of the atmosphere (TOA) upwelling radiances in the ultraviolet and visible regions of the solar spectrum (270e500 nm), with a spatial resolution of approximately 0.5 nm (Levelt et al., 2006). The OMI was originally designed to retrieve data on trace gases such as O3, NO2, SO2, etc., but it also provides valuable information on atmospheric aerosols. It has a wavelength range around 400 nm that can be used to detect elevated layers of absorbing aerosols such as those resulting from biomass burning and desert dust plumes. An improved (Level-2) OMI aerosol product is now available from http://disc.gsfc.nasa.gov/Aura/OMI/omaero_v003.shtml. 2.1.5. Multi-angle Imaging Spectroradiometer (MISR) The MISR instrument was launched in 1999 on a polar orbiting sun-synchronous satellite (TERRA), which has an altitude of 705 km. The MISR has a temporal resolution of 16 days and nominal spatial resolutions of 250 m, 275 m, and 1 km, but radiances at 1.1 km resolution are processed to yield the standard Level-2 MISR aerosol product at a 17.6 km 17.6 km pixel size. A heterogeneous land algorithm was developed by Martonchik et al. (1998). The MISR instrument continuously acquires daytime data over most parts of the world, but with a frequency that is dependent on latitude. Due to the overlap of the swathes (paths) near the poles and their broad separation at the equator, coverage intervals vary between 2 and 9 days, respectively. The MISR Level-2 global data product is available on a daily basis from https://www-misr.jpl. nasa.gov/ (Alam et al., 2011; Wong et al., 2013). 2.2. AOD retrieval algorithms: the Dark Target and Deep Blue approaches Since 1999 there have been several product updates to both the Aqua and the Terra MODIS AOD retrieval algorithms. Two significant new approaches have been introduced. The first approach involves the “Dark Target” (DT) retrieval algorithm (Kaufman et al., 1997; Remer et al., 2005), which is limited to surface reflectances up to 0.15 and assumes transparency of aerosols in the mid-IR spectral range. The first step in the DT approach uses empirical relationships in the visible and mid-IR parts of the spectra and calculates surface reflectance at 470 and 660 nm. The aerosol is then classified using the ratios between the path radiance at the two wavelengths upon which the surface reflectance is based. In the final step of the algorithm (following corrections that include removing cloud screening effects, gas absorption effects, and radiances obtained from the satellite data together with those 115 simulated from the ground data) the radiative transfer is determined and then final AOD values are determined using a look-up et al., 1997; Remer et al., 2005). The table (LUT) method (Tanre DT approach breaks down at surface reflectance above 0.15 or with coarse size particles; these problems were first addressed in a major product update by Levy et al. (2007). Using the DT approach, this improved product takes polarization into account when computing the radiative transfer, thereby improving the AOD retrieval for desert-like surfaces and for coarser particles. The second approach involves the “Deep Blue” (DB) algorithm (Hsu et al., 2004), which differs from the DT algorithm. As the name implies, the major physical assumption is that there is lower surface reflectance in the blue part of the visible spectra than in the red part, and this is used to retrieve AOD values for geographical regions with surface reflectances greater than 0.15. Such reflectances typically occur in desert, arid, semi-arid, and urban geographical regions. For this study, we use the Aqua Collection 005 Deep Blue AOD data. 2.3. Methodology for comparisons between satellite-based and ground-based AODs Comparisons between satellite-based and ground-based AODs are crucial for radiative forcing calculations, for estimating uncertainties in satellite measurements, for data assimilation, and for the development of improved algorithms. In this study we have compared data from MODIS, MISR, OMI, and CALIPSO satelliteborne sensors with ground-based (AERONET) AOD data for four different locations in India and Pakistan (Karachi, Lahore, Jaipur, and Kanpur) between 2007 and 2013. For such a comparison it is necessary to adjust the AOD values from each sensor to a common wavelength. The AERONET AOD wavelength was therefore converted to the MODISSTD, MODISDDB, OMI, MISR, and CALIPSO AOD wavelengths using: AODa ¼ AODb a a b (1) where a ¼ 550 nm, 558 nm, and 532 nm for MODIS (Standard and Deep Blue products), MISR, and CALIPSO, respectively, b ¼ 500 nm for AERONET, and a is the (440e870 nm) Angstrom exponent (Prasad et al., 2007; Liu et al., 2008a,b; Alam et al., 2011, 2014a). Therefore, for comparison of the AERONET AOD with that of satellite AOD at the same wavelength above interpolation (Equation (1)) is required (Eck et al., 1999; Zhao et al., 2002; Remer et al., 2005). The investigation into the correlation between satellitebased remote sensing AOD data and ground-based AOD data have suggested that correlation between retrieved AODs would be enhanced by taking into account surface reflectance and aerosol properties (Ramachandran and Kedia, 2013). In order to calculate the correlation coefficient we used datasets from MODIS (Standard and Deep Blue products) MISR, OMI, and CALIPSO sensors that matched the AERONET data at same wavelength. These datasets were used to compare AOD values obtained from MODIS (Standard and Deep Blue products), MISR, OMI, and CALIPSO sensors with the corresponding values from AERONET. Linear regression analysis was performed for MODISSTD, MODISDB, MISR, OMI and CALIPSO AODs with respect to AERONET AODs using: AODsatellite ¼ m AODAERONET þ c (2) where m (slope), c (intercept), AODAERONET represents AERONET AOD and AODsatellite represents AODs from MODIS, MISR, OMI and CALIPSO satellites. The regression coefficient (R2), which is the 116 H. Bibi et al. / Atmospheric Environment 111 (2015) 113e126 square of the correlation coefficient, indicates the correlation between MODIS and AERONET AODs (Hyer et al., 2011). All of these quantities (m, c, and R2) serve as useful indicators of the local spatial characteristics of the aerosol parameter (AOD) at a particular location and time (Ichoku et al., 2002). The slope (m) of the linear regression equation (Equation (2)) reveals how close the assumed aerosol model over a particular region is to the local aerosol type, and the intercept indicates the error caused by surface reflectance (Tripathi et al., 2005; Hyer et al., 2011). The linear regression equation therefore provides information concerning the factors that affect the correlation (Misra et al., 2012). If there was a perfect correlation between ground-based AOD and satellite-based measurements then the value of c would be 0 and of m would be 1 (Tripathi et al., 2005). Large intercepts are due to large errors in surface reflectance and at ground surface reflection the retrieval algorithm is biased towards low AOD values, which are indicated by non-zero intercepts that may be associated with an inappropriate assumption or with calibration error (Chu et al., 2002; Tripathi et al., 2005). In contrast to real situations, where the slope in the retrieval algorithm is other than unity this may indicate some irregularities between the optical properties and the aerosol microphysical properties used in the retrieval algorithm (Zhao et al., 2002). In addition to using linear regression, we also computed the root mean square error (RMSE) and mean absolute error (MAE) between the satellite and AERONET observations. The RMSE is defined as vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n 2 u1 X AODðsatelliteÞi AODðAERONETÞi RMSE ¼ t n i¼1 (3) and the MAE as MAE ¼ n 1X AODðsatelliteÞi AODðAERONETÞi n i¼1 (4) where n is the number of observations. Overestimation or underestimation of retrievals can be quantified by calculating the root mean bias (RMB), which is defined as: RMB ¼ AODsattelite AODAERONET (5) If RMB < 1, then this represents an underestimation, and if RMB > 1 this indicates overestimation. The accuracy of algorithms used in this study, in particular the MODIS algorithms, can be further assessed using the expected error (EE) measure, which is determined as confidence envelopes for each retrieval algorithm over land and are used in particular to evaluate the quality of Deep Blue Collection 5 (C005) AOD product. The expected error has been defined (Remer et al. (2008); Levy et al. (2010)) as: EE ¼ ±ð0:05 þ AODAERONET Þ (6) For good quality matches the MODIS-retrieved AOD is expected to fall within the expected error bound (EEB), which is: AODAERONET jEEj AODsatellite AODAERONET þ jEEj (7) where jEEj is the absolute value of the expected error defined previously. 3. Results and discussion 3.1. Intercomparison of satellite and ground-based AOD The regression approach revealed the correlation between MODISSTD and AERONET at 550 nm over Karachi, Lahore, Jaipur, and Kanpur. Fig. 1 shows a very good over all agreement between MODISSTD data and AERONET data, with m ~1.14, 1.09, 1.17 and 1.06, c ~0.02, 0.09, 0.07 and 0.08, and R2 ~ 0.71, 0.67, 0.76 and 0.61 over Karachi, Lahore, Jaipur and Kanpur, respectively. The best correlation was for Jaipur (R2 ~ 0.76), which is consistent with the correlation (R2 ~ 0.72) found by Tripathi et al. (2005) over the Ganga Basin in India. Previous studies have also used AERONET-measured AODs to validate MODIS derived AODs. Alam et al. (2011) found the best correlation between MODIS and AERONET AODs over Lahore (R2 ¼ 0.72), with relatively poor correlation over Karachi (R2 ¼ 0.58). Gupta et al. (2013) also compared MODIS-derived AODs with AERONET-derived AODs over Lahore and found a similarly good correlation between the two (R2 ¼ 0.72). More et al. (2013) compared AOD data from MODIS and MICROTOPS with AERONET data over Pune, India, and found good correlations (0.62e0.93) on the seasonal scale. Excellent agreement has been found between MODIS and AERONET measurements over the ocean (R2 ~ 0.84), while MODIS performed less well over land (R2 ~ 0.53) due to surface variability (Ichoku et al., 2002). Chu et al. (2002) reported a good correlation between MODIS and AERONET (R2 ¼ 0.88 at 470 nm and R2 ¼ 0.72 at 660 nm). Prasad et al. (2007) found a good correlation (R2 ¼ 0.47) between MODIS and AERONET over Kanpur during winter but a poor correlation (R2 ¼ 0.29) in summer. Alam et al. (2014a) compared the MODIS and AERONET AODs over Lahore in pre-monsoon and post-monsoon seasons, obtaining correlation coefficients of 0.66 and 0.68, respectively. Similarly, Alam et al. (2014b) compared the MODIS and AERONET AODs over Lahore during dust storm and found a highest correlation (R2 > 0.92) between the two datasets. Choudhry et al. (2012) provided a comparison between MODIS and AERONET AODs over Kanpur, Gandhi College, and Nainital, and found good correlations for Kanpur and Nainital (R2 > 0.7, 0.68, respectively) and a poor correlation for Gandhi College (R2 ~ 0.5). Fig. 2 shows the regression results for MODISDB AOD vs. AERONET AOD at 550 nm over Karachi, Lahore, Jaipur and Kanpur. The R2 values for MODISDBeAERONET are 0.54, 0.64, 0.73 and 0.61 over Karachi, Lahore, Jaipur and Kanpur, respectively, with corresponding intercepts of 0.13, 0.06, 0.21 and 0.15 (as shown in Table 1). The intercepts for the MODISDBeAERONET comparisons are thus negative for all sites, implying a slight overcorrection for surface reflectance. The R2 value for the MODISDBeAERONET comparison over Jaipur is relatively high (0.73) compared to the other sites. Misra et al. (2014) compared MODIS AOD values derived from the DB algorithm with ground-based sun photometer (MICROTOPS) observations over Ahmadabad for the period from 2002 to 2005 and reported only a poor correlation (R ¼ 0.43). Hoelzemann et al. (2009) carried out a validation of MODIS AODs using AERONET data over numerous sites in South America and found a correlation between the two of R2 > 0.5. The MISR-derived AOD was compared to the AERONET AOD at 558 nm over the four investigated sites: the R2 values for the MISReAERONET comparison at 558 nm were 0.79, 0.57, 0.63 and 0.73 over Karachi, Lahore, Jaipur and Kanpur, respectively, as shown in Fig. 3. The poor MISR retrievals over Lahore and Jaipur can be attributed to the scarcity of matched data points during the study period. The high R2 value over Karachi (0.79) indicates a significant H. Bibi et al. / Atmospheric Environment 111 (2015) 113e126 117 Fig. 1. Scatter plots for AERONET AOD vs. MODIS Standard product over different cities (Karachi, Lahore, Kanpur and Jaipur). Fig. 2. Scatter plots for AERONET AOD vs. MODIS Deep Blue product over different cities (Karachi, Lahore, Kanpur and Jaipur). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Table 1 Parameters of AERONET derived AOD vs. MODISSTD, MODISDB, MISR, OMI and CALIPSO AODs over four locations in India and Pakistan. Sites AERONET vs. MODISDB MODISSTD Karachi Lahore Jaipur Kanpur MISR OMI CALIPSO N R2 m c N R2 m c N R2 m c N R2 m c N R2 m c 635 597 400 887 0.71 0.67 0.76 0.61 1.14 1.09 1.17 1.06 0.02 0.09 0.07 0.08 635 597 400 876 0.54 0.64 0.73 0.61 0.91 1.09 1.55 1.36 0.13 0.06 0.21 0.15 122 89 90 175 0.79 0.57 0.63 0.73 0.78 0.54 0.68 0.60 0.04 0.11 0.06 0.12 757 277 182 511 0.55 0.51 0.62 0.45 1.11 0.77 1.05 0.85 0.09 0.04 0.13 0.08 81 59 33 51 0.47 0.23 0.23 0.36 0.97 0.72 1.20 0.83 0.03 0.08 0.01 0.07 correlation between the MISR and AERONET retrievals. Alam et al. (2011) observed good correlation (R2 ¼ 0.67) between MISR and AERONET data over Karachi and poor correlation over Lahore (R2 ¼ 0.10). Prasad et al. (2007) found a relatively good MISReAERONET correlation over Kanpur during summer (R2 ¼ 0.64) and a poor correlation in winter (R2 ¼ 0.33). Results obtained by 118 H. Bibi et al. / Atmospheric Environment 111 (2015) 113e126 Christopher et al. (2008) confirmed that the MISR sensor is a reliable sensor for the retrieval of AOD data in desert regions and indicated a high correlation (R2 ¼ 0.89) for comparisons between MISR and AERONET AOD data over different sites. AOD data retrieved from the MISR instrument over the Beijing urban area between 2002 and 2004 were compared with AERONET AOD measurements by Jiang et al. (2007) who noted that, over all, the correlation between MISR and AERONET AOD was very good (R2 ¼ 0.86). Cheng et al. (2012) compared MODIS, MISR and GOCART aerosol products over China with AERONET data and found that MISR AODs showed higher correlation (R2 ¼ 0.77) with AERONET measurements, which can be attributed to its better viewing and spectral capabilities. In highly reflective areas AOD retrievals from the MISR sensor are better than those from other sensors because of its multiangular capability, which enables it to differentiate between sunlight reflected at the earth's surface and sunlight reflected by aerosols (Qi et al., 2013). Ramachandran and Kedia (2013) compared MISR and MODIS satellite-based AOD data with ground-based MICROTOPS and AERONET sun photometer AOD data from between 2006 and 2008 over Karachi, Kanpur, Ahmadabad, Gurushikhar, and Gandhi College. They found that the correlations were not strong (R2 ¼ 0.25) due to the differences between satellite-retrieved AODs and ground-based measurements. Liu et al. (2010) noted that AODs retrieved from MISR showed a good correlation (R2 > 0.86) with those from ground-based measurements in most northern, southwestern and eastern and parts of China. Fig. 4 shows scatter plots of OMI AOD vs. AERONET AOD at 500 nm over Karachi, Lahore, Jaipur and Kanpur, where the R2 values were 0.55, 0.51, 0.62 and 0.45, respectively. The best correlation (R2 ¼ 0.62) is observed for Jaipur. Curier et al. (2008) compared AOD retrievals from OMI over western Europe with ground measurement data and observed a correlation (R2) of 0.37 and 0.41 for sites such as Paris and Lille, respectively in France, 0.39 for El Arenosillo in Spain, and 0.17 for Ispra (Italy); very poor correlation (R2 < 0.08) was also observed for the other sites. Ahn et al. (2014) compared satellite retrievals by the MODISDB, MISR, OMAERUV, and OMAERO algorithms to AERONET AOD retrievals over 44 selected locations and found that the retrieval algorithms had a correlation (R2) of 0.65 with AERONET data over all sites. Fig. 5 shows scatter plots of CALIPSO retrievals against AERONET data at 532 nm, for the four stations in this study. The plots reveal R2 values of 0.47, 0.23, 0.23 and 0.36 for Karachi, Lahore, Jaipur and Kanpur, respectively, with the best correlation being for Karachi. The poor correlation between CALIPSO and AERONET AODs over these sites is due to an insufficient number of data points. The low c-values in Fig. 5 (0.03, 0.08, 0.01, and 0.08 for Karachi, Lahore, Jaipur and Kanpur, respectively) indicate that vegetated areas (deciduous forest, evergreen forest, cropland) provide the best estimates of surface reflectance, as has previously been noted by Chu et al. (2002). Parameters (m, c, and R2) for correlations between MODISSTD, MODISDB, MISR, OMI, and CALIPSO measurements of AOD and AERONET-derived AOD are presented in Table 1. Using AERONET AOD as the standard the correlation coefficient between AERONETeMODISSTD and AERONET AODs was found to be relatively high over Lahore and Jaipur and lower over Karachi and Kanpur. In contrast, the MISR data showed a good correlation with AERONET data over Karachi and Kanpur and was less well correlated over Lahore and Jaipur. The CALIPSO sensor yielded too few data points for a statistical analysis, making it difficult to obtain a valid comparison between CALIPSO data and AERONET data. Abdou et al. (2005) reported larger AODs over land from the MODIS instrument than from the MISR instrument. Qi et al. (2013) compared MODIS and MISR AOD values with AERONET measurements over four sites in northern China: their results showed that MISR AODs were more accurate than MODIS AODs at the SACOL (Climate and environmental observatory of Lanzhou University) and Beijing sites but that MODIS AOD retrievals were better than MISR retrievals at the Xianghe and Xinglong sites. Good agreement between MISRretrieved AODs and ground measurements were observed at the desert location in China by Liu et al. (2010). Table 2 shows the computed RMSE and MAE values for the different satellites sensors. The smallest RMSE and MAE values were 0.1051 and 0.0671, respectively, for MISR observations over Karachi, while the largest errors (0.3531 and 0.2624, respectively) were found for CALIPSO retrievals over Jaipur. Table 3 shows the Fig. 3. Scatter plots for AERONET AOD vs. MISR over different cities (Karachi, Lahore, Kanpur and Jaipur). H. Bibi et al. / Atmospheric Environment 111 (2015) 113e126 119 Fig. 4. Scatter plots for AERONET AOD vs. OMI over different cities (Karachi, Lahore, Kanpur and Jaipur). Fig. 5. Scatter plots for AERONET AOD vs. CALIPSO over different cities (Karachi, Lahore, Kanpur and Jaipur). RMB values, which indicate overestimation of MODISSTD retrievals at all sites except Jaipur, and underestimation of MODISDB retrievals at all sites except Kanpur. The percentage of AOD measurements that fall within the expected error bound (EEB) also indicates that MODISSTD consistently outperformed MODISDB at all sites. In summary, our comparisons of MODISSTD, MODISDB, MISR, OMI, and CALIPSO AODs with AERONET AODs indicated that the MISR sensor performed better over Karachi and Kanpur than over the two sites, while the MODISSTD sensor performed better over Lahore and Jaipur. MODISSTD AOD values over Lahore and Jaipur were larger than corresponding AOD values from all other satellite sensors. The MISR data were shown to be in better agreement with AERONET data over Karachi and Kanpur than data from any of the other satellite sensors. 3.2. Monthly AOD variability As part of our study we derived time series of monthly mean AOD values from AERONET, MODIS, MISR, OMI and CALIPSO sensors. The observations in Fig. 6 show that the monthly mean MODISSTD AOD values ranged from 0.22e1.92, 0.18e1.56, 0.21e0.92, and 0.35e1.76 over Karachi, Lahore, Jaipur and Kanpur, respectively, compared to monthly mean AERONET values that ranged from 0.18e1.36, 0.18e0.91, 0.21e0.74, and 0.25e0.97, respectively. These results show an overestimation of MODISSTD AOD relative to AERONET AOD. Fig. 6 shows that, for Karachi, the maximum AERONET AOD value (1.36) was recorded during the month of July 2008 and the maximum MODISSTD AOD value (1.92) was also reported for the 120 H. Bibi et al. / Atmospheric Environment 111 (2015) 113e126 Table 2 Root mean square error, mean absolute error of AERONET, MODIS, MISR, OMI, and CALIPSO. Sites Karachi Lahore Jaipur Kanpur MODISSTD MODISDB MISR OMI CALIPSO RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE 0.1734 0.2624 0.1299 0.2538 0.1168 0.1797 0.0862 0.1693 0.2414 0.2339 0.2122 0.3119 0.2012 0.1764 0.1573 0.2425 0.1015 0.1731 0.1038 0.1850 0.0671 0.1101 0.0682 0.1270 0.3051 0.2555 0.1948 0.2122 0.1899 0.1731 0.1534 0.1573 0.2148 0.3209 0.3531 0.3147 0.1527 0.2309 0.2624 0.2293 Table 3 Root mean bias and % of AOD values within expected error bound (EEB). Sites Karachi Lahore Jaipur Kanpur MODISSTD MODISDB MISR OMI CALIPSO RMB % RMB % RMB % RMB % RMB % 1.1931 1.2454 0.9901 1.2002 59.7 49.4 69 55.9 0.5951 0.9798 0.9908 1.0939 21.4 42 39.5 31.2 0.8974 0.7707 0.8280 0.8024 N/A N/A N/A N/A 1.3098 0.8401 0.7642 0.7290 N/A N/A N/A N/A 1.0522 0.8609 1.1843 0.9475 N/A N/A N/A N/A same month in 2008; for Lahore, the maximum AERONET AOD value (0.91) was observed during the month of October 2010 and the maximum MODISSTD AOD value (1.56) in July 2011; for Jaipur, the maximum AERONET AOD value (0.74) occurred during the month of June 2011 and the maximum MODISSTD AOD value (0.92) was also in June 2011; for Kanpur, the maximum AERONET AOD value (0.97) was recorded in December 2009 and the maximum MODISSTD AOD value (1.76) in July 2011. These results are similar to those obtained by other researchers (Prasad et al., 2007; Ranjan et al., 2007; Singh et al., 2010; Alam et al., 2012). Alam et al. (2011) noted a maximum AERONET AOD value (0.92) in the month of July 2007 over Karachi. Singh et al. (2004) reported the largest AERONET AOD value (1.05) as occurring in July in both 2003 and 2004 over Kanpur. The smallest AOD values are generally observed in winter (Tadros et al., 2002; Singh et al., 2004; Zakey et al., 2004; Alam et al., 2014c, 2015). Prasad et al. (2007) found that MISR yielded better results than MODIS (using AERONET as a standard) in both summer and winter. Fig. 7 shows that the monthly mean MODISDB AOD values ranged from 0.06e1.3, 0.20e1.64, 0.05e1.07, and 0.06e1.53 over Karachi, Lahore, Jaipur and Kanpur, respectively, compared to monthly mean AERONET AOD values that ranged from 0.18-1.36, 0.18e0.91, 0.21e0.74, and 0.25e0.97, respectively, indicating an overestimation of MODISDB values relative to AERONET values. Fig. 6. Variability in monthly mean AOD values from AERONET and MODIS Standard product over different cities (Karachi, Lahore, Kanpur and Jaipur). H. Bibi et al. / Atmospheric Environment 111 (2015) 113e126 Fig. 7 shows that the maximum AERONET and MODISDB AOD values during the study period were 0.91 (recorded in October 2010) and 1.64 (recorded in August 2009), respectively. The figure shows that the maximum AERONET and MODISDB AOD values for Jaipur were 0.74 (June 2011) and 1.07 (July 2011). The maximum AERONET and MODISDB AOD values for Kanpur in December 2009 were 0.97 and 1.53, respectively, which are similar to the results obtained by Lyamani et al. (2006) during a period of high temperatures. High AOD values have been recorded in almost all major cities of Pakistan during the summer months (Alam et al., 2010). Wang and Yang (2008) found that the AOD in northern and eastern China increased during spring and summer and decreased in autumn and winter. The high AOD in summer was found by Balakrishnaiah et al. (2011) to be associated with coarse particles and the lower AOD in winter with fine particles. A seasonal pattern of higher AOD values in spring and summer and lower AOD values in autumn has also been reported by Jiang et al. (2007). Gupta et al. (2013) found a seasonal cycle of higher AOD values in summer and lower AOD values in winter. In contrast, Kaskaoutis et al. (2012) reported high AOD values during winter and the post-monsoon season, with low AOD values during the pre-monsoon and monsoon seasons over Kanpur. Alam et al. (2014a) reported that AOD values for MODIS were compatible with AERONET values during the pre-monsoon and post-monsoon seasons. High premonsoon MODIS AOD values were observed in Pune, Visakhapatnam, and Hyderabad by Balakrishnaiah et al. (2012). Fig. 8 shows that the monthly mean MISR AOD values ranged from 0.08e0.86, 0.08e0.72, 0.20e0.56, and 0.18e0.78 over Karachi, Lahore, Jaipur and Kanpur, respectively, compared to monthly mean AERONET values that ranged from 0.07e0.82, 0.18e1.25, 0.22e0.70, and 0.15e1.17, respectively. The MISR values indicate an underestimation of the AOD relative to the AERONET AOD values. The maximum AERONET and MISR AOD values for Karachi were found to 121 be 0.82 and 0.86, respectively, in May 2009. During May 2009, which are similar to the values reported by Liu et al. (2008a,b) from atmospheric dust particles in China following dust events. The maximum AERONET and MISR AOD values for Lahore were 1.25 and 0.72, respectively, in March 2008; the maximum AERONET and MISR AOD values for Jaipur were 0.70 in November 2011 and 0.72 in May 2007, respectively; and finally, the maximum AERONET and MISR AOD values for Kanpur were 1.17 and 0.78 in June 2010, respectively. MISR AOD values over Beijing were found by Liu et al. (2010) to be lower than AERONET AOD values. Qi et al. (2013) noted that MISR yielded more accurate results than MODIS at the SACOL site and over Beijing but that, in contrast, MODIS AOD retrievals were more accurate than MISR retrievals at Xianghe and Xinglong. High AOD values in India have been found to be related to dust events (Sarkar et al., 2006). High AOD values were noted in spring and autumn by Fotiadi et al. (2006) in the Eastern Mediterranean Basin, but Alam et al. (2011) found that AOD values over various cities in Pakistan were higher in summer than in spring, autumn, or winter. Fig. 9 shows that monthly average OMI AOD values ranged from 0.25e2.01, 0.18e1.83, 0.11e0.92, and 0.15e1.73 over Karachi, Lahore, Jaipur and Kanpur, respectively, compared to monthly average AERONET AOD values that ranged from 0.23-1.07, 0.23e1.00, 0.20e0.87, and 0.32e1.24, respectively. The figure shows that the maximum AERONET and OMI AOD values were 1.07 and 2.01, respectively, over Karachi during July 2011. The maximum AERONET AOD values for Lahore, Jaipur, and Kanpur were 1.0, 0.87, and 1.24, respectively, during the study period, while the maximum OMI AOD values were 1.83, 0.92, and 1.73, respectively. El-Metwally et al. (2010) reported that maximum AOD values in April and October were due to a combination of natural processes and human activities over Cairo. Using data from MODIS and OMI, Marey et al. (2011) observed high AODs in April and May and low AODs in December and January over Niledelta. AODs in southeast Asia have Fig. 7. Variability in monthly mean AOD values from AERONET and MODIS Deep Blue product over different cities (Karachi, Lahore, Kanpur and Jaipur). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 122 H. Bibi et al. / Atmospheric Environment 111 (2015) 113e126 Fig. 8. Variability in monthly mean AOD values from AERONET and MISR over different cities (Karachi, Lahore, Kanpur and Jaipur). been reported to be high in spring due to biomass burning (Liu et al., 1999; Tang et al., 2003). The AOD over India has been reported to be increasing rapidly since 2000 (Prasad et al., 2004). Frank et al. (2007) noted high AOD values in summer and low AOD values in winter over the Mojave Desert in southern California. AOD is reported to be higher in pre-monsoon seasons that post monsoon due to dust storms and convective activity over Pune, India (Devara et al., 2005). Fig. 9. Variability in monthly mean AOD values from AERONET and OMI over different cities (Karachi, Lahore, Kanpur and Jaipur). H. Bibi et al. / Atmospheric Environment 111 (2015) 113e126 Fig. 10 shows the variability in AERONET and CALIPSO AOD values. Monthly averaged AERONET AOD values ranged from 0.14e1.32, 0.24e0.85, 0.11e0.66 and 0.27e1.20 over Karachi, Lahore, Jaipur and Kanpur, respectively, while monthly average CALIPSO AOD values ranged from 0.07e1.53, 0.10e1.07, 0.07e1.13 and 0.01e1.46, respectively. The maximum monthly average AERONET AOD values over Karachi, Lahore, Jaipur, and Kanpur were 1.32, 0.85, 0.66 and 1.20, respectively, while the maximum monthly average CALIPSO AOD values were 1.53, 1.07, 1.13, and 1.46, respectively. Ma et al. (2014) noted that CALIPSO AOD values were considerably lower than MODIS AOD values over dusty regions during their study period. The seasonal variations in AOD values that we noted in our study (high in summer and low in winter) are similar to those noted in previous studies by other authors (e.g., Pandithurai et al., 1997). Choudhry et al. (2012) found increases in AOD values over various sites in India during the pre-monsoon season, and decreases during the post-monsoon season. Alam et al. (2010) and Munir and Zareen (2006) also noted high AOD values over Karachi and Lahore in summer, due to fact that both are industrialized, urbanized locations. Our own results are in strong agreement with those obtained by Ramachandran and Kedia (2013) who found that AOD values over Kanpur were high during winter (December) because of the dominance of fine aerosols from the burning of fossil fuels and biomass, while the higher AOD values during summer (July) than winter were attributed to the dominance of coarse dust and sea salt particles. El-Metwally et al. (2008) reported maximum AOD values over Cairo (>0.2) in October due to farmers burning residues following the rice harvest, with plumes associated with biomass-burning leading to increased AOD values. The AOD values retrieved over Kanpur by Ramachandran et al. (2013) were mostly in the range 0.5e1. The highest AOD values obtained over Kanpur by Dey et al. (2004) and Singh et al. (2004) occurred during the summer months. 123 Our analysis shows seasonal variations in AOD values, with maximum values in June/July from all of the sensors. Alam et al. (2011) suggested that AOD is increasing as a result of regular and ongoing anthropogenic activities (such as industrial activity, traffic, and cooking). Higher air temperatures also tend to hold more water vapor, which in turn encourages the growth of aerosols (Masmoudi et al., 2003). Sarkar et al. (2006) and Ranjan et al. (2007) analyzed variations in AOD and reported high AOD values during the summer, increasing from March, and reaching a maximum in June, with high AOD values persisting until August over India, neighboring Pakistan. The atmospheric boundary layer is narrow during the postmonsoon (October and November) and winter (December, January and February), which restricts pollutants to a smaller volume close to the earth's surface, resulting in increased AOD values (Ramachandran and Kedia, 2013). Dust activity has been found to increase in spring (MarcheMay) over the Indian subcontinent (Prospero et al., 2002). AOD values in general depend on seasonal cycles and are higher during the pre-monsoon due to higher winds speeds from the southwest (March, April, and May) and monsoon (June, July, August), which increase the sea salt and dust concentrations in the atmosphere (Christopher et al., 2008; Ramachandran and Kedia, 2013). The monthly averaged AOD values and standard deviations for AERONET, MODIS, MISR, OMI, and CALIPSO are given in Table 4. 4. Conclusion We have compared AOD values derived from MODISBD, MODISSTD, MISR, OMI and CALIPSO satellite-borne instruments with those from four AERONET ground-based sites in order to validate the satellite retrievals against ground measurements. Over all, the MODISSTD retrievals showed an excellent agreement with AERONET Fig. 10. Variability in monthly mean AOD values from AERONET and CALIPSO over different cities (Karachi, Lahore, Kanpur and Jaipur). 124 H. Bibi et al. / Atmospheric Environment 111 (2015) 113e126 Table 4 Monthly average AOD values of AERONET, MODIS, MISR, OMI, and CALIPSO. Sites AERONET Avg ± SD Karachi Lahore Jaipur Kanpur 0.47 0.66 0.48 0.66 ± ± ± ± 0.27 0.31 0.23 0.31 MODISSTD N Avg ± SD 1456 599 781 1382 0.51 0.76 0.38 0.70 ± ± ± ± 0.34 0.43 0.27 0.36 MODISDB N Avg ± SD 1095 1851 1278 1388 0.59 0.57 0.46 0.73 ± ± ± ± 0.41 0.39 0.39 0.51 observations over bright surfaces such as desert or coastal sites (e.g., over Jaipur or Karachi), while MISR retrievals showed a high degree of correlation with AERONET observations close to the ocean (e.g., over Karachi and Kanpur). MODISDB retrievals showed a reasonable agreement with AERONET observations over all sites, as did OMI retrievals. There are numerous important aerosol sources in southern Asia, including human activities that emit both fine and coarse particles, smoke from biomass burning, and sea salt from the ocean. High AOD values have been recorded by MODISSTD, MODISDB, MISR, and OMI over Karachi, Lahore, Jaipur and Kanpur, although there are also seasonal variations recorded by CALIPSO. AOD values were observed to be higher in summer (June to August) than during the rest of the year due a predominance of coarse dust and sea salt particles and possibly also due to the higher water vapor content of the atmosphere due to high summer temperatures, which encourages the growth of aerosols. It was also noted that high AOD values occurred in October, associated with the harvesting of crops and subsequent plumes associated with biomass-burning. High AOD values in southern Asia during the month of December may be due the predominance of fine aerosol emissions from smoke and fossil fuels. In March and April high wind speeds cause increased dust activity which also leads to higher AOD values. 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