1. No category

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: khanalam@upesh.edu.pk (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
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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.
Acknowledgments
We are grateful to the Multi-sensor Aerosol Product Sampling
System (MAPSS) teams at NASA for the provision of satellite data.
We would also like to thank NASA and the Institute of Space
Technology's Karachi office for providing the AERONET data (http://
aeronet.gsfc.nasa.gov/).
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