Satellite Retrieval of Aerosol Optical Thickness over Arid Region: Case Study over Makkah, Mina and Arafah, Saudi Arabia
M.Z. Mat Jafri,
In this study, we presents the potentiality of retrieving Aerosol Optical Thickness (AOT) in the atmosphere which combined Landsat 7 ETM+ satellite and ground based closures enable the determination of required aerosol characteristics over moderate bright surfaces area of Makkah, Mina and Arafah. A multispectral algorithm was developed by assuming that surface condition of study area was lambertian and homogeneous. In-situ AOT data was calculated using Beer Lambert law from transmittance of atmospheric measured using the FieldSpec handheld spectroradiometer and their locations were determined by a handheld Global Positioning System (GPS). The Digital Number (DN) recorded by satellite imageries were converted to top of the atmosphere (TOA) reflectance which is the sum of the ground reflectance and atmospheric reflectance. Then, the atmospheric correction (ATCOR2) method was used to retrieve the surface reflectance. The reflectance measured from the satellite at the top of the atmosphere (TOA) was subtracted from the amount given by the surface reflectance to obtain the atmospheric reflectance. Measured PM10 and AOT were correlated with atmospheric reflectance value using regression technique. Various types of regression algorithms were then examined by comparing the correlation coefficient (R) values and the Root-Mean-Square Error (RMSE) values. The three band model of algorithm was selected based on the highest R value and the lowest RMSE value. The proposed algorithm added evidence on the correlation found between aerosol optical thickness derived from Landsat 7 ETM+ satellite using multispectral algorithm with Terra Multiangle Imaging SpectroRadiometer (MISR) AOT product.
Received: June 03, 2010;
Accepted: September 15, 2010;
Published: October 14, 2010
Satellite remote sensing has provided quantitative information on aerosols
with accuracy comparable to that of surface measurements. The use of earth observation
to assess and map the atmospheric pollution in different geographical areas
and especially in cities has received considerable attention from researchers
who developed a variety of techniques (Kaufman et al.,
1990; Sifakis and Deschamps, 1992; Retalis
et al., 1999; Wald and Baleynaud, 1999; Wald
et al., 1999; Hadjimitsis, 2009). The key
parameter for assessing atmospheric pollution in photochemical air pollution
studies is the aerosol optical thickness (Kaufman et
al., 1990), which is also the most important unknown parameter in every
atmospheric correction algorithm for solving the Radiative Transfer (RT) equation
and removing atmospheric effects from satellite remotely sensed images (Kaufman
and Tanre, 1996; Kaufman and Sendra, 1988; Hadjimitsis
and Clayton, 2008). Single scattering approximation was used in this study
related to aerosol scattering.
The large-scale distribution of aerosol concentration and characteristics,
aerosol radiative forcing and property changes of clouds interacting with aerosols
are among the important observations that can be provided by satellite remote
sensing (Xingna et al., 2009). Due to the growing
recognition of the importance of aerosol properties for studies of climate and
global change, it is indeed fortunate that a number of very significant and
greatly enhanced satellite systems are being developed for launch in the next
few years. Satellite measurements can also be inverted to yield information
on Aerosol Optical Thickness (AOT), angular scattering properties and size distribution
(Lee et al., 2008). Satellite measurements clearly
have the advantage of being the only set of measurements that provide a wide
The optical characteristics of atmospheric aerosol are needed in order to derive
the AOT and mass burden from path radiance measurements taken from space (Fraser
et al., 1984; Kaufman and Sendra, 1988;
Holben et al., 1992; Martonchik
and Diner, 1992), or the aerosol single-scattering albedo (Kaufman,
1987) and the particle size (Kaufman et al.,
1990). Satellite data are well suited to study aerosol effects on the large
scale of area (Kaufman et al., 2002). The first
applications of satellite remote sensing of aerosols began in the mid-1970s
and concerned the detection of desert particles above the ocean (Fraser,
1976; Norton et al., 1980; Griggs,
1979). They used Land observing satellite (Landsat), Geostationary Operational
Environmental Satellites (GOES) and Advanced Very High Resolution Radiometer
(AVHRR) data, respectively. Crist et al. (1986)
describe the method to normalize Landsat data affected by haze, using the third
feature of the Tasseled Cap transformation. This study shows that atmospheric
scattering decreases in severity with increasing the wavelength and since the
visible bands of the Landsat Multispectral Scanner (MSS) sensor (i.e., band
1 and 2) are highly correlated in their response to surface features, a contrast
of these two bands, as represented in yellowness, could be expected to provide
atmospheric scattering information.
There are two major sources of aerosol data: (1) satellite instruments, such
as AVHRR-2, Global Ozone Monitoring Experiment (GOME), Total Ozone Mapping Spectrometer
(TOMS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), POLarization and Directionality
of the Earth's Reflectances (POLDER), Moderate Resolution Imaging Spectroradiometer
(MODIS) and Multiangle Imaging SpectroRadiometer (MISR) and (2) ground-based
instruments, such as a narrow band sun photometer (Schaap
et al., 2008) and spectroradiometer (Brogniez
et al., 2008). Both these equipments can be correlated with the
satellite sensors electromagnetic spectrums. Therefore, these equipments
are able to collect values of the AOT in the same wavelength bands of the satellite
imagery. Those permanent narrow band sun photometer and spectroradiometer used
widely is AErosol RObotic NETwork (AERONET) (Holben et
al., 1998) and RSS-1024 Rotating Shadowband Spectroradiometer (Harrison
et al., 1999), respectively. These equipments also come in handy
as it is mobile and able to collect the optical readings anywhere such as MICROTOPS
II hand-held sunphotometer and FieldSpec handheld spectroradiometer (Lim
et al., 2009).
This study presents a new method of assessing atmospheric pollution in arid
regions using satellite remote sensing technology. The method involves the use
of beer lambert law for determining the aerosol optical thickness from handheld
spectroradiometer data. Therefore the use of aerosol optical thickness was found
in the literature to be a valuable parameter to assess air pollution (Sifakis
and Descamps, 1992; Retalis, 1998). The determined
aerosol optical thickness is therefore with various type of multispectral algorithm
used for assessing atmospheric pollution from satellite imagery. The proposed
method was initially applied to two Landsat 7 ETM+ images over Makkah, Mina
and Arafah. Then, the results were compared with Terra MISR AOT product.
MATERIALS AND METHODS
Study area: Figure 1 shows the selected study area
of Makkah, Mina and Arafah over Saudi Arabia. The Holy City of Makkah was an
arid-urban area (Latitude 21°2519 North Meridian 39°4946)
is at an elevation of 277 m above sea level and approximately 80 km inland from
the Red Sea. The elevations of Makkah AI Mukarramah are a group of mountains
and black rocky masses which are granitic basement rocks (Al-Jeelani,
2009). Mountains are traversed by a group of valleys, such as the Ibrahim
valley. The Kaabah's location is in this valley.
Saudi Arabia is located in a dry area where precipitation rarely occurs and
surface winds are inactive almost all the year round. In Saudi Arabia, dust
plays a primary role in causing air pollution in a country which is than 90%
desert. The desert is the source region of dust (PME, 2007).
The desert is characterized by periodical outbreaks of dust storms that transport
large amounts of desert dust in the troposphere, resulting in enhanced of optical
thickness value that is correlated with the aerosol direct radiative forcing.
Increase in the number of pilgrims is accompanied by the increase of their
daily activities as well as the increase of the demands of transportation means.
Consequently, considerable quantities of either gaseous or solid pollutants
are emitted to the atmosphere. The emitted pollutants could cause many harmful
environmental impacts to the Holy City of Makkah and nearby places. Some studies
about air pollution that had been carried out in the Holy City of Makkah, Saudi
Arabia, focusing on the central area near the Holy Mosque and on the Holy places
(Mina and Arafah) (Al-Jeelani, 2009; Al-Jeelani
and Ramadhan, 2004).
Makkah climate is different from other Saudi Arabian cities, retains its warm
temperature in winter (November to March), which can range from 17°C at
midnight to 25°C in the afternoon. During summer (April to October), temperatures
are considered very hot and break the 40°C mark in the afternoon dropping
to 30°C in the evening. Rain is very rare with an average of 10-33 mm usually
falls in December and January and the humidity ratio is about 45-53%. Winds
are north-eastern most of the year time. Some unusual events often happen during
the year, such as dust storms in summer, coming from the Arabian Peninsula's
deserts or from North Africa (Al-Jeelani, 2009).
|| Location of Makkah, Mina and Arafah
|| Satellite imagery information
The methodology process generally were divided into four major parts: data acquisition, pre-processing, data processing and finally, accuracy and validation of results. All data preprocessing and processing steps were carried out using PCI Geomatica 10.2 software.
Satellite image: The Earth observing instrument on Landsat 7 ETM+, the Enhanced
Thematic Mapper Plus (ETM+), replicates the capabilities of the highly successful
Thematic Mapper instruments on Landsats 4 and 5 TM. On May 31, 2003, the Scan
Line Corrector (SLC), which compensates for the forward motion of Landsat 7
ETM+, failed. The Landsat 7 ETM+ continues to acquire image data in the SLC-off
mode with the same high radiometric and geometric quality as that of the data
collected prior to the SLC failure. The malfunction of the SLC mirror assembly
resulted in the loss of approximately 22% of the normal scene area (Storey
et al., 2005). Note that the SLC failure has no impact on the radiometric
performance with the valid pixels. All scenes were affected by the failure in
SLC (SLC-off mode) that occurred after 2003, so parts of the data in the scenes
The acquisition dates of the Landsat ETM+ scenes employed in the air quality change detection process within seasonal variation (Hajj season and non-Hajj season) were captured at WRS Path/Row 169/45 on 29th December 2006 and 19th January 2009, respectively. All Landsat 7 ETM+ scenes were downloaded from the United States Geological Survey (USGS) as a Level 8 product based on the minimum percentage of cloud cover (<10%) and the availability of ground truth data prior to acquisition. These imageries were acquired in NLAPS format 30x30 m pixels.
To reduce effect due to zenith angle and surface reflectivity effect, both imageries were selected among the same value for sun zenith angle, azimuth angle. The images and acquisition of Landsat 7 ETM+ scenes analyzed in this study are listed in Table 1.
Ground truth data: The ground truth data were obtained from the field
survey covers all type of ground surface and scattered point throughout Makkah,
Mina and Arafah using FieldSpec handheld spectroradiometer. FieldSpec handheld
spectroradiometer was used to measure the transmittance values at the ground
surface. Atmospheric transmittance at wavelength, Tλ may be
computed as Jensen (1995):
||The solar irradiance reached the ground at wavelength (λ)
||Solar irradiance at the top of atmosphere at wavelength (λ)
||Total atmospheric optical thickness at wavelength (λ)
||Relative optical mass. M = 1 when the sun is directly overhead and is
otherwise approximately equal to sec (z), where z is the solar zenith angle
According to the Bouguer-Lambert law, also known as Beers law (Mather,
2004), the attenuation of light through a medium is proportional to the
distance traversed in the medium and the local flux of radiation. The positions
of each station were determined using GPS equipment. All ground data were taken
less than 3 h within 9.00 a.m. to 12.00 p.m. to reduce the uncertainty due to
wind and solar zenith angle. The measurements time was corresponding to the
time of Landsat 7 ETM+ and Terra which overpass at 10.15 a.m. and 10:30 a.m.
equator crossing time, respectively (Williams, 2009).
The ground truth data of AOT were divided into two groups; half of the numbers
were randomly selected for calibration of algorithm and the other half for accuracy
analysis. Presumption made in this study was that each AOT measurement represents
a locus of 1 pixel of Landsat 7 ETM+ data equal to 30x30 m at ground around
each of the air pollution stations.
Geometric and distortion correction: The Landsat 7 ETM+ satellite
image was rectified using the second order polynomial coordinates transformation
to relate groud control points in the map to their equivalent row and column
positions in the Landsat 7 ETM+ scences. Corrected images were projected to
Universal Transverse Mercator Projection with UTM 37 Q D000 WGS 1984 Datum.
The reference points used to resample the satellite images were taken from 14
Ground Control Point (GCP) collected at study area.
Radiometric and atmospheric correction: Typically, the user of the data
can convert the received DN values into radiances by simple linear formulas
using calibration gains and offset. Radiometric correction is applied by transforming
the values of DN to radiance or reflectance values through the algorithm as
follows given by Chander et al. (2009):
|| Gain and offset value for Landsat 7 ETM+
The value of Grescale (c1) and Brescale (c0) for Landsat 7 ETM+ used in this study can be found in Table 2. Also can be expressed as:
||Spectral Radiance at the sensors aperture in W/m2/sr/μm
||Rescaled gain (the data product gain contained in the Level 1 product
header or ancillary data record) in W/m2/sr/μm/DN
||Rescaled bias (the data product offset contained in the Level 1 product
header or ancillary data record ) in W/m2/sr/μm
||The quantized calibrated pixel value in DN
||The spectral radiance that is scaled to QCALMIN in W/m2/sr/μm
||The spectral radiance that is scaled to QCALMAX in W/m2/sr/μm
||The minimum quantized calibrated pixel value (corresponding to LMINλ)
For relatively clear Landsat scenes, a reduction in between-scene variability
can be achieved through a normalization for solar irradiance by converting spectral
radiance, as calculated above, to planetary reflectance or albedo. This combined
surface and atmospheric reflectance of the Earth also known as top of atmosphere
reflectance (TOA) is computed with the following formula (Mather,
||Unitless planetary reflectance
||Spectral radiance at the sensor's aperture
||Earth-Sun distance in astronomical units (Table 1, Chander
et al., 2009)
||Mean solar exo-atmospheric irradiances (Table
3, Chander et al., 2009)
||Solar zenith angle in degrees (Meta data of Landsat 7 ETM+)
||ETM+ solar spectral irradiances (generated using the Thuillier
Atmospheric correction was carried out using ATCOR2 available with PCI Geomatica
using algorithms developed by Richter (1996a, b,
1997, 2005) and Richter
et al. (2009). It calculates correction for flat areas applying constant
or varying atmosphere accounting for adjacency effect. Atmospheric corrections
widely used in hyper spectral imagery to derive surface reflectance without
Automatic calculate haze and cloud' would be the first run of ATCOR. The output
files containing the haze and cloud mask. This mask can be edited if haze is
not correctly assigned, e.g. defining additional haze areas or deleting wrongly
assigned haze areas (Wen and Yang, 2008). Then ATCOR
could be run again with Load Haze and cloud from file employing this edited
mask, which might yield better results for the haze removal.
The atmospheric correction algorithm calculates the surface reflectance using
the default scale factor 4, i.e., the percent reflectance range 0-100% is multiplied
with the factor 4 in the output file. So an output value of DN = 200 corresponds
to a surface reflectance of 200/4 = 50% (or 0.5 for 0-1 reflectance range) and
the output is coded as 8 bit/pixel. Therefore, the maximum output value is 255,
representing a surface reflectance of 255/4 = 63.75% (PCI
Geomatics Enterprises Inc., 2005). Larger values will be truncated at 255.
ATCOR2 is based on a database of atmospheric correction functions stored in
look-up tables. The database consists of a broad range of elevation information
setup, sensor information, atmospheric information and correction parameter
as in Table 4. All meteorological data used in this study
were taken from Weather Underground (2009) webpage. Cogliani
(2001) and Rodriguez et al. (2009) also used
Weather Underground (2009) in their research. The result
of ATCOR2 is a ground or surface reflectance image in each spectral band with
a relative error of approximately 10% (Lehner et al.,
Data processing: After undergo radiometric correction, the reflectance
measured from the satellite (reflectance at the top of atmospheric, TOA) was
subtracted by the amount given by the surface reflectance to obtain the atmospheric
|| Input parameter for ATCOR2
The atmospheric reflectance satellite data were related with AOT in-situ data
using the regression algorithm analysis. PCI Geomatica EASI modeling was used
to input the developed multispectral algorithm. AOT maps were generated using
proposed algorithm based on the highest R and lowest RMSE values. The final
results were in color coded image of AOT.
Algorithm model: The approach of this study begins with the assumption
of a Lambertian surface in algorithm development, so that the surface reflective
property will not be affected by the observation geometry or terrain effect.
In reality, surface condition over study area is not Lambertian, which implies
that the results of the retrieved AOT would be influenced by the surface canopy,
observation geometry or terrain effects. The Mie (aerosol) scattering theory
was applied to compute the aerosol phase function and spectral optical depth,
based on size distribution, real and imaginary index (King
et al., 1999; Fukushima et al., 2000).
||Atmospheric reflectance/path radiance
||Path radiance due to aerosol scattering
||Aerosol scattering phase function
||Solar zenith angle
||Viewing zenith angle
||Relative azimuthal angles
||Single scattering albedo
||Cosines of the view directions
||Cosines of the illumination directions
Semi-infinite cloud was now abandoned and turn to the more realistic case of
cloud layer with finite optical depth, τ. Then, that case is initially
consider where scattering is conservative; i.e., ωo ≈
1. This assumption sounds drastic, but in fact the single scatter albedo of
cloud droplets is very close to one over most of the visible band and absorption
by clouds is indeed negligible for most purposes within that band. By neglecting
molecule scattering due to Rayleigh (Paronis and Hatzopoulos,
1997), Eq. 5 becomes:
|| ρa (θs, θv, φ)
So, the algorithm of AOT for single band or wavelength (λ) is simplified
Equation 7 is rewrite into two and three band equation as
Eq. 8 and 9:
where, Rλi is the atmospheric reflectance (i = 1, 2 and 3 corresponding to wavelength for satellite) and aj is the algorithm coefficient (j = 0, 1 and 2) are empirically determined.
Accuracy and validation of results: The accuracy assessment and validation
of results obtained was performing with ground truth and other satellite product
data. Accuracy and validation analysis of results were perform using the new
algorithm with AOT ground truth values retrieved using handheld spectroradiometer.
The AOT maps generated using developed algorithm also compared with aerosol
product of AOT using Terra MISR MIL3DAElarc.004 level 3 component global aerosol
product covering daily statistical summary 0.5x0.5 degree aerosol product over
selected individual locations which is Makkah, Mina and Arafah with respect
to the Landsat 7 ETM+ subset for that particular day. The AOT are retrieved
from the Terra MISR daytime data at wavelength 555 nm. Knowing that MISR satellite
data has been well calibrated using ground truth data of AERONET (Lyapustin
et al., 2007). The validation is reasonable because the different
time between Landsat 7 ETM and Terra is about ±30 min, the Landsat 7
ETM+ equatorial crossing time from north to south on a descending orbital node
between 10:00 a.m. to 10:15 a.m. on each pass Williams (2009)
while, Terra MISR daily visit in sun-synchronous polar orbit with an equator
crossing time of 10:30 a.m. (Diner et al., 1998,
Image analysis: A total of two dataset had been used for the development of multispectral algorithm in this study. The AOT values measured using FieldSpec handheld spectroradiometer from the ground truth data were correlated with atmospheric reflectance from Landsat 7 ETM+ in red, green and blue band. Distribution of AOT with respect to reflectance of atmosphere for red band (Rλ3), green band (Rλ2) and blue band (Rλ1) shows as in Fig. 2, for combination dataset of 29th December 2006 and 19th January 2009. In this study, we used the regression equation to correlate the AOT measured by FieldSpec handheld spectroradiometer were correlated with Landsat 7 ETM+ red band (Rλ3), green band (Rλ2) and blue band (Rλ1).
Table 5 shows the comparison value for R and RMSE values
for various type of algorithm using regression analysis for combination dataset
on 29th December 2006 and 19th January 2009. Thus, when applying the algorithm
to the entire of Landsat 7 ETM+ image, the proposed regression algorithm as
stated in Table 5 was used based on the highest R and lowest
Graph of AOT data versus atmospheric reflectance for three
bands, Rλ1, Rλ2 and Rλ3
for blue, green and red band, respectively on 29th December 2006 (S1)
and 19th January 2009 (S2)
|| Table of regression algorithm of PM10 combined datasets on
26th December 2006 and 19th January 2009
|Rλ1, Rλ2 and Rλ3
are the reflectance values for blue, green and red band, respectively
|| AOT colour-coded maps on 29th December 2006
|| AOT colour coded image on 19th January 2009
A wide-coverage AOT map can be obtained conveniently. Figure 3 shows the distribution of AOT on 29th December 2006
which is Hajj season. Figure 4 is the distribution of AOT
on 19th January 2009 which is non-Hajj season.
The AOT concentrations are indicated through the color of red for high AOT
value and blue for low AOT value. The summarization the data percentage for
each range of the AOT values by corresponding color coded classification for
five days on 29th December 2006 and 19th January 2009 was presented in Table
|| Percentage of AOT color coded maps on 26th December 2006
and 19th January 2009
||Graph of measured AOT versus calculated AOT for 29th December
||Graph of measured AOT versus calculated AOT for 19th January
It clearly showed the Hajj season is more polluted than non-Hajj season. The
higher AOT distributed mainly at Makkah and Mina area where there was construction
site generated a lot of dust and the values were relatively lower in rural area.
Validation of AOT algorithm
Ground truth data: Figure 5 shows the measured
AOT and calculated AOT for 29th December 2006, where the measured AOT were the
AOT measured using Fieldspec handheld spectroradiometer and calculated AOT were
the AOT calculated using developed multispectral algorithm. Figure
6 shows measured AOT and calculated AOT concentration for 19th January 2009.
||Terra MISR AOT daily product at 555 nm on 29th December 2006
The validation of the results generated using multispectral algorithm of AOT
shows that all dataset gives the accuracy >0.85 of R coefficient value and
low RMSE. It clearly shows that the developed multispectral algorithm is worked
excellently in determining AOT value within arid region of Makkah.
Satellite data-terra MISR: A close-up view of the typical temporal distribution
of AOT can be compared with AOT distributions pattern mapped using multispectral
algorithm retrieved using Landsat 7 ETM+ satellite images by examining the daily
product AOT of Terra MISR over selected individual locations which is Makkah,
Mina and Arafah as marked in red rectangular shape area with respect to the
Landsat 7 ETM+ subset image. AOT concentrations are indicated through the color
of red for high AOT value and blue for low AOT value. Figure 7
shows the image of Terra MISR AOT product where the subset of Landsat 7 ETM+
was located on 29th December 2006. Figure 8 shows the image
of Terra MODIS AOT daily product on 19th January 2009.
||Terra MISR AOT daily product at 555 nm on 19th January 2009
The resolution of MISR MIL3DAElarc.004 AOT is 0.5x0.5 degree.
High values of AOT were observed during Hajj season in 29th December 2006,
while low values of AOT on 19th January 2009 is the consequence of non-Hajj
season which reducing activities around study area. Physical geography and topography
of the area corresponding to the surrounding area of rocky mountain known as
valley area where enables a weak air flow contribute significantly to the high
value of AOT around valley of Mina and Makkah. In addition, the construction
activities were actively concentrated at Makkah and Mina produced a lot of dust
pollution at nearby area. These variation could be attributed the metrological
conditions, despite the little variation between the two successive years and
Hajj circumstances. These studies showed that there were high concentrations
of air pollutants in the atmosphere, exceeding the standards that are attributed
to traffic emission during Hajj season, where about three million people gathered
in these limited areas (Al-Jeelani and Ramadhan, 2004;
Al-Jeelani, 2009). Also there was very high value of
AOT near the Holy Mosque which showed the contribution of pollution by traffic
and hajj pilgrimage (Al-Jeelani, 2009; Al-Raddadi,
1996). According to the results, both AOT data retrieved using multi temporal
Landsat 7 ETM+ using the multispectral algorithm gives highly correlation with
Terra MISR data at particular day. This is clearly seen where average AOT value
retrieved using Landsat 7 ETM+ images located in the range of AOT product of
Terra MISR value over Makkah, Mina and Arafah are low in non-Hajj season compared
to Hajj season.
The number of ground stations present in urban air quality monitoring networks is usually not sufficient to produce reliable maps of particulate distribution over a considerable area. This study indicated that the feasibility of AOT retrieval technique by using a FieldSpec handheld spectroradiometer which shows that all dataset gives the accuracy >0.85 of R coefficient value. From the perspective of the AOT retrieved in this study, the AOT concentrations retrieved and mapped using developed multispectral algorithm in study area have correlation with data product form Terra MODIS and MISR at particular range. The Terra MISR is compatible to be used for the wide range of area with resolution of 0.5x0.5 degree aerosol product where Landsat 7 ETM+ has resolution of 30x30 m more suitable for small range of study area. The proposed technique can be used for the determination of the AOT values from the FieldSpec handheld spectroradiometer with a reasonable accuracy compared to Terra MISR data. The technique is a cheaper alternative to obtaining remotely sensed data for AOT studies.
These researches were supported by USM-RU-PRGS PFIZIK/1001/831020 and FRGS (IPTA) 203/PFIZIK/ 6711107 (Hajj Research). Appreciations are extended to USM and all who involved in this research project. The images and data used in this study were acquired using the GES-DISC Interactive Online Visualization and analysis Infrastructure (Giovanni) as part of the NASA's Goddard Earth Sciences (GES) Data and Information Services Center (DISC).
1: Al-Jeelani, H.A. and M.H. Ramadhan, 2004. Assessment of air quality in Mina and Makkah City during Hajj season. First year report submitted to the Institute of Research and Consultations, King Abdulaziz University.
2: Al-Jeelani, H.A., 2009. Evaluation of air quality in the Holy Makkah during Hajj season 1425 H. J. Applied Sci. Res., 5: 115-121.
Direct Link |
3: Al-Raddadi, A.S., 1996. Study the air quality inside the souk alsagheer tunnel. First Report Submitted to Hajj Research Center, Aum Alqura University.
4: Brogniez, C., V. Buchard and F. Auriol, 2008. Validation of UV-visible aerosol optical thickness retrieved from spectroradiometer measurements. Atmospheric Chem. Phys., 8: 4655-4663.
Direct Link |
5: Chander, G., B.L. Markham and D.L. Helder, 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+ and EO-1 ALI sensors. Remote Sens. Environ., 113: 893-903.
6: Cogliani, E., 2001. Air pollution forecast in cities by an air pollution index highly correlated with meteorological variables. Atmospheric Environ., 35: 2871-2877.
7: Crist, E.P., R. Laurin and R.C. Cicone, 1986. Vegetation and soils information contained in transformed thematic mapper data. Proceedings of International Geoscience and Remote Sensing Symposium, (IGARSS`86), European Space Agency, Paris, pp: 1465-1470.
8: Diner, D.J., J.C. Beckert, T.H. Reilly, C.J. Bruegge and J.E. Conel et al., 1998. Multiangle imaging spectroradiometer (MISR) instrument description and experiment overview. Geosci. Remote Sens., 36: 1072-1087.
Direct Link |
9: Diner, D.J., J.C. Beckert, G.W. Bothwell and J.I. Rodriguez, 2002. Performance of the MISR instrument during its first 20 months in Earth orbit. IEEE Trans. Geosci. Remote Sens., 40: 1449-1466.
10: Fraser, R.S., 1976. Satellite measurement of mass of Sahara dust in the atmosphere. Applied Optics, 15: 2471-2479.
11: Fraser, R.S., Y.J. Kaufman and R.L. Mahoney, 1984. Satellite measurements of aerosol mass and transport. Atmospheric Environ., 18: 2577-2584.
12: Fukushima, H., M. Toratani, S. Yamamiya and Y. Mitomi, 2000. Atmospheric correction algorithm for ADEOS/OCTS acean colar data: Performance comparison based on ship and buoy measurements. Adv. Space Res., 25: 1015-1024.
13: PCI Geomatics Enterprises Inc., 2005. Geomatica® Focus. User Guide. Arcadia Publishing, Richmond Hill, Ontario, pp: 211.
14: Griggs, M., 1979. Satellite observations of atmosphere aerosol during the EOMET cruise. J. Atmospheric Sci., 36: 695-698.
15: Hadjimitsis, D.G. and C.R.I. Clayton, 2008. The use of an improved atmospheric correction algorithm for removing atmospheric effects from remotely sensed images using an atmosphere-surface simulation and meteorological data. Meteorol. Appl., 15: 381-387.
16: Hadjimitsis, D.G., 2009. Aerosol Optical Thickness (AOT) retrieval over land using satellite image-based algorithm. Air Quality Atmosphere Health, 2: 89-97.
17: Harrison, L., M. Beauharnois, J. Berndt, P. Kiedron, J.J. Michalsky and Q. Min, 1999. The Rotating Shadowband Spectroradiometer (RSS) at SGP. Geophys. Res. Lett., 26: 1715-1718.
18: Holben, B.N., E. Vermote, Y.J. Kaufman, D. Tanre and V. Kalb, 1992. Aerosol retrieval over land from AVHRR data application for atmospheric correction. IEEE Trans. Geosci. Remote Sens., 30: 212-222.
19: Holben, B.N., T.F. Eck, I. Slutsker, T. Tanre and J.P. Buis et al., 1998. AERONET-A federated instrument network and data archive for aerosol characterization. Remote Sens. Environ., 66: 1-16.
CrossRef | Direct Link |
20: Jensen, J.R., 1995. Introductory Digital Image Processing: A Remote Sensing Perspective. 2nd Edn., Prentice Hall, Upper Saddle River, New Jersy, ISBN: 0132058405, pp: 111-112.
21: Kaufman, Y.J., 1987. Satellite sensing of aerosol absorption. J. Geophys. Res., 92: 4307-4317.
22: Kaufman, Y.J. and C. Sendra, 1988. Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery. Int. J. Remote Sens., 9: 1357-1381.
23: Kaufman, Y.J., R.S. Fraser and R.A. Ferrare, 1990. Satellite measurements of large-scale air pollution: Methods. J. Geophys. Res., 95: 9895-9909.
CrossRef | Direct Link |
24: Kaufman, Y.J. and D. Tanre, 1996. Direct and indirect methods for correcting the aerosol effect on remote sensing. Remote Sens. Environ., 55: 65-79.
25: Kaufman, Y.J., D. Tanre and O. Boucher, 2002. A satellite view of aerosols in the climate system. Nature, 419: 215-223.
26: King, M.D., Y.J. Kaufman, D. Tanre and T. Nakajima, 1999. Remote sensing of tropospheric aerosols from space: Past, present and future. Bull. Am. Meteorol. Soc., 80: 2229-2259.
Direct Link |
27: Williams, D., 2009. Landsat 7 Science Data Users Handbook. Elsevier, USA.
28: Lee, K.H., D.H. Lee, Y.J. Kim and J. Kim, 2008. MODIS 500 × 500-m2 resolution aerosol optical thickness retrieval and its application for air quality monitoring. Adv. Environ. Monitor., 10.1007/978-1-4020-6364-0_17
29: Lehner, S., I. Anders and G. Gayer, 2004. High resolution maps of suspended particulate matter concentration in the german bight. EARSeL eProc., 3: 118-126.
Direct Link |
30: Lim, H.S., M.Z. MatJafri, K. Abdullah, C.J. Wong and M.N. Saleh, 2009. Aerosol optical thickness data retrieval over Penang Island, Malaysia. Proceedings of the Aerospace Conference, March 7-14, Big Sky, MT., pp: 1-6.
31: Lyapustin, A., Y. Wang, R. Kahn, J. Xiong and A. Ignatov et al., 2007. Analysis of MODIS-MISR calibration differences using surface albedo around AERONET sites and cloud reflectance. Remote Sens. Environ., 107: 12-21.
32: Martonchik, J.V. and D.J. Diner, 1992. Retrieval of aerosol optical properties from multi-angle satellite imagery. IEEE Trans. Geosci. Remote Sens., 30: 223-230.
33: Mather, P.M., 2004. Computer Processing of Remotely-Sensed Images. An Introduction. 3rd Edn., John Wiley and Sons, New York, pp: 111-119.
34: Norton, C.C., F.R. Mosher, B. Hinton, D.W. Martin, D. Santik and W. Kuhlow, 1980. A model for calculating desert aerosol turbidity over ocean from geostationary satellite data. J. Applied Meteorol., 19: 633-644.
Direct Link |
35: Paronis, D.K. and J.N. Hatzopoulos, 1997. Aerosol optical thickness and scattering phase function retrieval from solar radiances recorded over water: A revised approach. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Aug. 3-8, Singapore, United States, pp: 1920-1922.
36: PME, 2007. Sources of Pollution. PME., Saudi Arabia.
37: Schaap, M., R.M.A. Timmermans, R.B.A. Koelemeijer, G. de Leeuw and P.J.H. Builtjes, 2008. Evaluation of MODIS aerosol optical thickness over Europe using sun photometer observations. Atmospheric Environ., 42: 2187-2197.
38: Storey, J.C., P. Scaramuzza and G. Schmidt, 2005. Landsat 7 scan line corrector-off gap filled product development. Proceedings of the 16th Conference on Global Priorities in Land Remote Sensing, October 23-27, 2005, Sioux Falls, South Dakota, pp: 23-27.
39: Retalis, A., 1998. Study of atmospheric pollution in large cities with the use of satellite observations: Development of an atmospheric correction algorithm applied to polluted urban areas. Ph.D. Thesis, Department of Applied Physics, University of Athens.
40: Retalis, A., C. Cartalis and E. Athanassiou, 1999. Assessment of the distribution of aerosols in the area of Athens with the use of landsat thematic mapper data. Int. J. Remote Sensing, 20: 939-945.
CrossRef | Direct Link |
41: Richter, R., 1996. A spatially adaptive fat atmospheric correction algorithm. Int. J. Remote Sensing, 17: 1201-1214.
Direct Link |
42: Richter, R., 1996. Atmospheric correction of satellite data with haze removal including a haze/clear transition region. Comput. Geosci., 22: 675-681.
CrossRef | Direct Link |
43: Richter, R., 1997. Correction of atmospheric and topographic effects for high spatial resolution satellite imagery. Int. J. Remote Sensing, 18: 1099-1111.
CrossRef | Direct Link |
44: Richter, R., 2005. Atmospheric/Topographic Correction for Satellite Imagery. Version 6.1, Remote Sensing Data Center, Wessling, Germany.
45: Richter, R., T. Kellenberger and H. Kaufmann, 2009. Comparison of topographic correction methods. Remote Sensing, 1: 184-196.
46: Rodriguez, R., A. Cortes and T. Margalef, 2009. Injecting dynamic real-time data into a DDDAS for forest fire behavior prediction. Proceedings of the 9th International Conference on Computational Science, May 25-27, Baton Rouge, LA, USA., pp: 489-499.
47: Wald, L. and J.M., Balleynaud, 1999. Observing air quality over the city of Nantes by means of landsat thermal infrared data. Int. J. Remote Sensing, 20: 947-959.
Direct Link |
48: Weather Underground, 2009. Detailed history and climate. WunderMap new. web page
49: Sifakis, N. and P.Y., Deschamps, 1992. Mapping of air pollution using SPOT satellite data. Photogrmmetric Eng. Remote Sensing, 58:: 1433-1437.
Direct Link |
50: Wald, L., L. Basly and J.M. Balleynaud, 1999. Satellite data for the air pollution mapping. Proceedings of the 18th EARseL Symposium on Operational Sensing for Sustainable Development, May 11-14, Enschede, The Netherlands, pp: 133-139.
51: Wen, X. and X. Yang, 2008. An operational improvement of haze/clear line identification from satellite imagery based on multi-resolution segmentation. Proceedings of the 2nd International Symposium on Intelligent Information Technology Application, Dec. 20-22, Shanghai, pp: 571-575.
52: Xingna, Y.U., Z.H.U. Bin, F.A.N. Shuxian, Y.I.N. Yan and B.U. Xiaoli, 2009. Ground-based observation of aerosol optical properties in Lanzhou, China. J. Environ. Sci., 21: 1519-1524.