Satellite Measurements of Atmospheric Aerosols
RUDOLF B. HUSAR a
a
Center for Air Pollution Impact and Trend Analysis, CAPITA, Department of Energy, Environment and Chemical Engineering,
Washington University, Saint Louis, MO 63130– rhusar@wustl.edu
1. INTRODUCTION
Radiation sensors on Earth-orbiting satellites offer a way to observe atmospheric aerosols at about kilometer resolution,
near-real time over much of the globe. The aerosol back-scattering of solar radiation allows us to observe the pattern of
atmospheric aerosols, may that be from major pollution events, dust storms, forest fires and other aerosol events. In fact, the
patches of bluish smoke-haze and yellow dust plumes are prominent features of the Earth as viewed from space. Satellite
aerosol measurements have also shown great potential to contribute to Earth science, to the assessment of air quality and to
the management of some disasters. The global-scale aerosol monitoring has the further potential to improve our
understanding of aerosol-induced climate effects. Thus, it is expected that satellite aerosol observing and measuring
systems will continue to expand their utility.
Unfortunately, the quantitative measurement of aerosol properties has proven to be a challenge since the beginning of the
satellite era in the 1960s. This is no surprise, since satellite-based measurement of is among the most complex aerosol
measuring techniques. In principle, atmospheric aerosols are well suited for satellite remote sensing. The sun provides a
stable light source, the aerosol scattering covers the entire solar spectrum and the radiation is easily detectable by highresolution sensors. The difficulties for quantitative aerosol measurements include: (a) Aerosol scattering is just one of the
four components of the detectable radiation (Figure 30-1a.) and separation of the weak aerosol signal form the other signal
components is error-prone; (b) Associating the angular back-scattering by aerosols with intrinsic aerosol properties is
burdened with ambiguity; (c) The downward-looking satellite sensors measure the total back-scattering, which is the
integral over the entire atmospheric column.
This chapter is a brief summary of satellite aerosol detection and measurement. The chapter begins with a background on
aerosol remote sensing followed by a summary of aerosol optical parameters relevant to satellite detection. The bulk of this
chapter is devoted to the physical principles and the challenges of satellite aerosol detection. Following a section on satellite
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data dissemination, illustrative application examples are given for Earth science and air quality management. The chapter is
concluded with a list in desirable developments in satellite aerosol measurements. For more details, the reader is guided to
the excellent textbooks on atmospheric aerosols (Friedlander, 2000; Seinfeld and Pandis, 1998), the review of atmospheric
aerosol measurements by McMurry (2000), and atmospheric visibility measurements and science by Watson (2002). For
more details on satellite aerosol measurement systems see the authoritative article by King et al. (1999), the recent broad
review by Hoff and Christopher (2009) and the comments by Hidy et al., (2009).
2. BACKGROUND ON SATELLITE REMOTE SENSING
Atmospheric aerosol characterization using optical techniques have a rich history, vigorous present and bright prospects for
the future. The abundance of atmospheric optical data prompted early investigators to seek information about the aerosol
properties via the optical data. Following the Krakatoa eruption, Kiessling (1884) concluded that the volcanic dust with
characteristic size between 1 and 2 μm caused the spectacular optical phenomena worldwide. Wegener (1911) used a
simple but most elegant reasoning to infer that the characteristic size of the atmospheric haze particles is comparable to the
wavelength of visible light. With the advent of computers, numerical inversion procedures were developed to infer aerosol
size distributions from optical, i.e. spectral and angular aerosol scattering measurements. For example, based on the
inversion of spectral extinction data Penndorf (1957) concluded that the peculiar blue sun and blue moon phenomena in
Europe were due to aged Canadian forest fire smoke with characteristic size of 0.6 to 0.8 μm. Recently a more sophisticated
inversion scheme was offered by Dubovik and King (2000) for the extraction of columnar aerosol size distribution data
from the AERONET (Holben et al., 1998) sun-sky photometer network data.
Satellite aerosol detection begun in the mid-1960’s with the observation of ‘anomalous gray shades’ in Defense
Meteorological Satellite Program (DMSP) satellite images. By the 1970’s, these gray shades were identified as forest fire
smoke over Central America (Parmenter, 1971), Sahara dust (Carlson and Prospero, 1972), and other major aerosol events.
The application to air pollution was pioneered by Randerson (1968). Quantitative aerosol detection begun in the early
1970’s with the pioneering work of Griggs (1975) who observed that over dark water, the excess radiance detected by a
satellite was correlated with the optical depth (AOD) of the atmospheric aerosol column. Later Fraser (1976) pointed out
that the relationship between radiance and AOD depends on the type of aerosol, such as windblown dust or industrial haze.
The potential of geostationary satellites to augment surface-based particulate air pollution measurements was illustrated by
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(Lyons and Husar, 1976). By the 1980’s, a significant number of satellites were deployed that yielded increasingly better
aerosol characterization. In another seminal paper, Fraser et al. (1984), laid out a procedure for satellite-based measurement
of regional sulfate haze over the Eastern U.S., including the estimation of the aerosol mass transport rates out of the region.
The remote sensing of the Earth surface and of atmospheric composition is significantly perturbed by the presence of
atmospheric aerosols. Much of the early work was therefore focused on devising aerosol correction algorithms for
removing the spurious aerosol signal. The earliest quantitative global scale aerosol measurements were obtained as byproducts arising from aerosol correction of AVHRR sea surface temperature (Stowe, 1991) and yielded a multi-year global
climatology of aerosol optical depth over the oceans (Husar et al., 1997). The columnar ozone detection by the TOMS
ozone sensor was also distorted by absorbing aerosols and the subsequently extracted aerosol data yielded the global
absorbing aerosol maps over land as well over oceans (Herman et al., 1997). The ocean color and chlorophyll detection by
the SeaWiFS sensor (Gordon and Wang, 1994) also contributed significant advances through aerosol corrections
algorithms. By the 1990’s a wide array of sensors were deployed for the measurement of atmospheric aerosols. These
sensors use backscattered solar radiation in multiple narrow wavelength channels for MODIS (King et al., 1992), SeaWiFS,
(Gordon and Wang, 1994), polarization of backscattered radiation (POLDER) (Deuzé et al., 2001), combined angular and
spectral scattering (MISR) (Diner et al., 1998). It is fair to say, however, that none of the sensors can by itself fully
characterize the complex, dynamic, multi-dimensional aspects of the atmospheric aerosol system. For an extensive review
of the recent satellite aerosol measurements, see Hoff and Christopher (2009).
Current Earth-observing satellites are circulating the Earth either in geostationary or polar orbit, representing two different
aerosol sensing and monitoring opportunities. (Figure 30-1b.) The fixed geostationary perspective and data every 30
minutes, allows the monitoring of dynamic meteorological phenomena such as, major dust clouds, smoke plumes or
regional pollution events as they evolve throughout the day. Geostationary meteorological satellite data are routinely
distributed through the Internet, minutes after they are taken and are used in forecasting and other decision support
applications. Polar orbiting platforms at about 100 km elevation scan the Earth once a day observing always the ‘sunny
side’. The low orbit permits polar platform sensors to have higher spatial resolution and geo-location accuracy as well as
sensors at multiple narrow wavelengths. Most quantitative aerosol datasets and climatologies are derived from polar
orbiting satellites carrying a variety of sensors.
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3. AEROSOL PHYSICAL AND OPTICAL PROPERTIES
This section reviews key characteristics of atmospheric aerosols that are relevant to satellite remote sensing. Emphasis is
placed on the observed regularities in size distribution, light scattering efficiency, angular scattering pattern and vertical
distribution. A distinguishing characteristic of atmospheric aerosols is the high dimensionality of the ‘data space’. At any
location and time (x,y,z,t), aerosols are distributed over particle diameter, D, which spans at least two orders of magnitude
(0.1 and 10 μm) in the optically active subrange. Atmospheric particles are also composed of discrete chemical species
with composition, C. Each size class for a given chemical species may have a unique shape, S. A subtle, but for aerosol
optics significant dimension is the nature of aerosol mixing, X. Some of the aerosol species are mixed externally, while
other species are mixed internally in a single particle and their radiative effects are additive, while the optical effect of
internally mixed particles is much less predictable (White, 1986). For a formal treatment of the aerosol size-composition
distribution functions, see Friedlander (1970) and Mc Murry (2000).
The full characterization of an aerosol system requires eight orthogonal dimensions (x, y, z, t, D, C, S, X). Satellite sensors
have high spatial resolution (x,y) but they detect the optical effect of a vertical column which is an integral over five
dimensions (z, D, C, S, X). The de-convolution of this integral is a notoriously ill-posed mathematical problem that does
not possess a unique solution. In other words, the number of unknown aerosol parameters is much larger than the known
entities from optical measurements. This inversion problem is made extra uncertain by the fact that the aerosol optical
signal at the top of the atmosphere is weak compared to the noise from the surface reflectance, interference from clouds and
from the complications of a meta-stable aerosol system.
A strategy toward the solution of the inversion problem is to reduce the dimensionality of the aerosol system. Such
reduction can be achieved by observing the regularities of atmospheric aerosol behavior and applying those regularities to
satellite aerosol measurements. The key aerosol microphysical parameters relevant to the optical detection of aerosols are
shown in Figure 30-2a.
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The aerosol volume (or mass) is typically distributed (Figure 30-2a) between the fine particle mode in the diameter range
0.1 to about 2 m range and the coarse particle mode above 2 m. In a nutshell, the model introduced by Whitby et al.
(1972), Whitby (1978) states that each aerosol mode has a characteristic size distribution, chemical composition and optical
properties. More details on size distribution measurements are found in John (2010). The multi modal concept has been
widely adopted as the standard size distribution model by the aerosol remote sensing community (Remer and Kaufman,
1998; Kahn et al., 2001; Dubovik et al., 2002).
Fine mode particles (below 1-2 m) are mostly formed by combustion and condensation processes, resulting in spherical
liquid droplets that are frequently meta-stable. Hence, the size distribution and optical properties of fine particles is highly
dynamic and responsive to environmental conditions such as gas particle conversion, relative humidity and chemical
interactions among particles. Over urban industrial regions the fine particle mode is composed of sulfate, organic and
nitrate species, each having somewhat different size distribution (Seinfeld and Pandis, 1998). Fine particles tend to be
internally mixed, but may exhibit multiple mass modes. Sulfate particles for instance, occur in two distinctly different mass
modes depending whether the formation mechanism is photochemical gas particle conversion (mass mode ≈ 0.2 μm) or
through liquid phase reactions (mass mode ≈ 0.5 μm) (McMurry et al., 1996). Fine particles exhibit another regularity of
increased mass median diameter with increasing concentration. This is significant because it allows the formulation of
dynamic aerosol models that directly relate the spectral and angular scattering characteristics to aerosol concentration
(Remer and Kaufman, 1998; Dubovik and King, 2000). Coarse mode atmospheric particles tend to originate from primary
emissions, e.g. windblown dust, sea salt, fly ash, road dust and they are irregular in shape. The radiative impact of coarse
particle mixtures are additive. An exception to this rule is when ambient air is ingested into cloud droplets and the result is
internal mixing of all particles in the cloud droplets. Sea salt particles may also grow at high humidity.
The most critical aerosol optical property for satellite aerosol retrieval is the angular scattering since satellites detect the
cumulative scattering of sunlight in the view-path of the sensor (Figure 30-1a). The directional dependence of scattering is
defined by the phase function P(θ) which is the ratio of the energy scattered into direction, θ, to the average energy
scattered into all directions (see Sorenson, 2010). The scattering angle, θ, is the angle between the sun, the aerosol volume
and the sensor (Figure 30-1a). Unlike the symmetric Rayleigh scattering by pure air, the scattering phase function for
aerosols is elongated in the direction of the incoming light. Barteneva (1960), observed a remarkable dependency of the
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phase function shape on the aerosol extinction coefficient in the former Soviet Union. With increasing extinction
coefficient, the ‘cigar’ shaped aerosol phase function becomes increasingly shifted toward the forward direction (Figure 302c). This pattern is consistent with the notion that at higher extinction coefficient, the characteristic particle size increases
and the scattering is more and more forward directed. The broader applicability of the Barteneva’s observations was
confirmed through airborne polar nephelometer measurements in the U.S. by Johnson (1981). However, the nature of the
phase function varies considerably (Sakunov et al., 1996). A pixel-level comparison of AODs derived from MODIS and
MISR satellite aerosol instruments sensors found a significant deviation as a function of the scattering angle between the
two satellite aerosol measuring system (Mishchenko et. al., 2009). This further highlights the need for increased attention to
the aerosol phase function.
Another feature of atmospheric aerosols is that at any given region, there is a high correlation between the total light
scattering coefficient and the mass concentration of fine particles, below 2.5 μm in size. The slope of the correlation
corresponds to scattering efficiency, α ≈ 3-5 m2/g (Malm and Hand, 2007), which is generally applicable for sulfates,
nitrates and organics in the fine particle mode with a peak in extinction efficiency in the 0.3-0.8 μm size range (Fig 30-2b).
For atmospheric dust particles, the corresponding extinction efficiency per unit mass is much less (α ≈ 0.6 m2/g), as
documented by White (1986). The observed regularity and ubiquity of this relationship allows the estimation of fine
particle columnar mass concentration Mfc[g/m2] from measured optical thickness, τ through Mfc=τ/α .
The vertical layering of the atmospheric aerosols also requires special consideration. It is known that aerosols tend to reside
in distinct layers of the atmosphere, as illustrated schematically in Figure 30-3. The vertical distribution can be subdivided
into three layers, each layer having different aerosol types, atmospheric residence time, and mixing characteristics. The
stratospheric or Junge layer (Figure 30-3a) is composed primarily of volcanic sulfuric acid aerosols that circle the Earth at
10-15 km elevation for a year or two after volcanic eruptions. Below the stratosphere is the tropospheric layer which is the
carrier of dust, smoke or occasional industrial haze ejected from the boundary layer. Tropospheric aerosols are thin
pancake-like layers that may cover 1000 km size areas and tend to circle the Earth during their 1-2 week residence time.
The individual layers of tropospheric dust, smoke and haze do not interact, hence their optical effects are additive. The
bottom layer is the planetary boundary layer (PBL) that contains a large variety of aerosols from anthropogenic and natural
sources. (Figure 30-3). Precipitation and other removal mechanisms restricts the aerosol residence time in the PBL to 3-5
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days. Each aerosol type has its own sources, transport and optical characteristics and therefore deserves to be treated
separately with specific aerosol physical and optical parameterization. In Figure 30-3a, the aerosol types that are in separate
boxes are externally mixed and their respective radiative effects are additive. A group of aerosol types in the boundary layer
that are in adjacent boxes represent aerosol types that are generally internally mixed and their physical, chemical and
optical properties and behaviors need to be considered jointly.
The above discussion is a simplified description of the characteristics of atmospheric aerosols. A challenge of satellite
aerosol measurements is to clearly delineate which aspects of aerosol characterization can be supported by satellite sensors
and their retrieval algorithms.
4. PRINCIPLES OF SATELLITE AEROSOL DETECTION AND MEASUREMENTS
The science of satellite aerosol measurements is based on well-understood fundamental physical principles: Mie theory of
light scattering and the theory of radiative energy transfer through scattering and absorbing media. The very same physical
laws that govern satellite aerosol detection are applied to atmospheric visibility studies. In fact, satellite aerosol detection
and day-time vision by the human eye operate on the same general principle of passive remote sensing (Figure 30-1a): The
radiation source is the sun; the target is the Earth’s surface; the detector is an array of color sensitive sensors, may that be
the retina in the human eye or the satellite radiation sensor. In between the reflecting surface and the light sensor is the
radiatively active atmosphere consisting of aerosols, air, and clouds. However, the two fields have different objectives and
evolved rather independently from each other. The objective of visibility studies is to measure and model the ambient
aerosol microphysics and chemistry and to evaluate the resulting effects on the optical environment. Satellite aerosol
remote sensing on the other hand intends to solve the much more difficult inverse problem of deriving the properties of
atmospheric aerosols from the measured optical effects at the top of the atmosphere. The inherent problem is that the
retrieval of the unknown aerosol properties requires knowledge of those same properties. The current practice for resolving
the inversion paradox is to prepare a discrete set of ‘aerosol models’, and to choose the model that best fits the satellite
optical data in form of spectral or angular scattering. The aerosol models and the surface reflectance models are typically
tailored to the characteristics of the particular sensor.
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The radiative signal received by a satellite is the combined contribution of surface reflectance and aerosol reflectance. For
the sake of illustration, a single scatter radiative transfer theory is used here to show the aerosol interactions with solar
radiation. The separation of the aerosol signal from the surface reflectance can be achieved by the solution of the
appropriate radiation transfer equation as derived in (Husar and White, 1976). The resulting relationship between the
surface reflectance and aerosol optical parameters is shown in Figure 30-4.
The aerosol perturbation of the upwelling radiation consists of two competing effects. On one hand, light scattering
particles tend to add reflectance to bright surfaces. This excess reflectance depends both on the aerosol optical thickness, τ,
as well as the aerosol scattering phase function, P. The other role of aerosols is to act as a filter which exponentially
diminishes the upwelling radiation from a reflecting surface and from aerosol scattering itself. The net result of these two
competing aerosol effects is illustrated in Figure 30-5d, where the apparent reflectance is plotted against the aerosol optical
thickness. The bundle of curves corresponds to different values of surface reflectance R0. The simplest case is when the
surface is black, R0=0 and the received radiation is purely due to aerosol scattering along the path, i.e. the path radiance
PR= (1-e-τ)P (Kaufman, 1993). For optically thin aerosol layer, τ<1, PR is proportional to the optical thickness since selfextinction of the aerosol layer is minimal. As the aerosol layer approaches τ>2, self-extinction becomes more significant
and by τ >4, the apparent reflectance asymptotically approaches the value of P which is the case for radiative equilibrium.
Good examples for the strong path radiance are dark surfaces such as ocean or vegetation at blue wavelength where R0 is
small (<0.05), compared to P≈0.3 (Figure 30-5b and 5c).
The opposite case occurs when the surface reflectance R0 is high compared to P. In this case, the addition of aerosol optical
thickness actually diminishes the initial high surface reflectance since the filter term dominates over the aerosol reflectance
term. As the aerosol layer above the bright surface becomes optically thick, τ >4, the apparent reflectance asymptotically
approaches the value of aerosol reflectance, P. For example, an aerosol layer on top of bright clouds will tend to decrease
the brightness of clouds. Figure 30-5a is an example when a layer of yellow dust on top of white clouds reduces the cloud
reflectance. The next interesting case is when the aerosol phase function has the same value as the surface reflectance
function, P= R0. in the direction of sensor’s line of sight. In this case adding aerosol will not change the apparent
reflectance. At P≈R0, e.g. soil and vegetation at 0.8 μm, the reflectance is unchanged by typical haze aerosols and aerosol
detection is not possible.
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The critical measure, whether aerosol will increase or decrease the apparent reflectance is the ratio of the aerosol and
surface reflectances, P/R0. Figure 30-5d also shows that with increasing aerosol optical thickness, the apparent reflectance
of bright and dark surfaces converge toward the value of the aerosol scattering function, P. In other words, with increasing
AOT the contrast between dark and bright surfaces diminishes and they become indistinguishable as τ exceeds 4. In this
case of an optically thick aerosol layer, the filtering and scattering effects of aerosols are balanced, i.e. the aerosol system
reaches radiative equilibrium. The implication is that the retrieval of τ for optically thick aerosol layers (τ>4) is not
possible. Aerosol retrieval is also limited when P≈R0, which occurs over the bright surfaces of deserts and rocky terrains.
The optical thickness, τ of the aerosol column is derived from the excess reflectance, i.e. the difference between the
apparent reflectance of the top of the atmosphere. Through the simple radiative transfer theory it can be shown that the
excess reflectance and the aerosol optical thickness are related by the following simple expression, τ =-ln(R-P)/(R0- P). The
value of P is obtained from fitting the observed and retrieved surface reflectance spectra. For example in light haze at 412
nm it is found that P=0.38.
The color Figure 30-6 provides a graphic illustration of the key steps in satellite aerosol retrieval. The hazy SeaWiFS image
(Fig. 30-6a) shows the composite signal received by the sensor on July 16, 2009. The image was synthesized from the blue
(0.412 μm), green (0.555 μm), and red (0.67 μm) channels. The aerosol-free surface reflectance (Fig. 30-6b) was derived
from time series data. The aerosol optical depth (Fig. 30-6c) was calculated from the excess radiance, i.e. the difference
between the hazy and haze-free reflectances at the blue (412 nm) wavelength. The resulting aerosol pattern shows
qualitatively the hazy patches (red) over the ocean as well as in the vicinity of cloud systems. The black areas are blocked
out by the cloud mask.
Figures 30-6 a, b and c show the received signal at three wavelengths. At blue wavelength (412 nm) Figure 30-6d, the
apparent reflectance, R, reaching the satellite sensor is dominated by the haze since the surface reflectance is low over the
ocean and vegetation. The aerosol patterns are clearly discernable, while all the surface features are obscured by haze.
Hence, the blue wavelength is well suited for aerosol detection over land (Hsu et al., 2006) but surface characterization is
difficult (see Figure 30-5c). At red wavelength (670 nm) Figure 30-6e, the apparent reflectance is contributed significantly
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by both surface reflectance and aerosol reflectance. However, the magnitude of the surface reflectance differs greatly for
vegetated and solid surfaces, such as urban centers. Hence, at the red wavelength aerosol detection over land is difficult
because it is very sensitive to the type of underlying surfaces. Over the ocean the aerosol reflectance is high compared to
the water reflectance, and therefore quantitative aerosol detection is possible. The near IR wavelength (865 nm), Figure 306f is uniquely suitable for aerosol detection over the ocean since the ocean reflectance is below 1%. Hence, virtually all the
apparent reflectance is due to aerosol scattering. Over land, the reflectance of both vegetated and solid surfaces is high and
comparable to the aerosol reflectance P, i.e., P/Ro ~ 1 and aerosol detection is limited.
5.
CHALLENGES OF SATELLITE AEROSOL MEASURING SYSTEMS
Practical satellite aerosol measurements face a number of additional challenges beyond the dynamic and multi-dimensional
aerosol system. Clouds obscure the detection of both underlying surfaces and atmospheric aerosols. The areas delineated by
a ‘cloud mask’ are unavailable for aerosol detection. Since large portions of the Tropics and also at Northern latitudes are
cloudy throughout the year, cloudiness is a major limitation of satellite remote sensing of both aerosols and surfaces. The
cloud edges are not easily discernable, particularly during humid conditions and hygroscopic (e.g. sulfate) haze. Thin cirrus
clouds are also difficult to identify. Consequently, improper cloud masks are a source of error in satellite-derived aerosol
measurements. Cloud shadows on the Earth’s surface also constitute a complication for aerosol retrieval.
Air or Rayleigh scattering of blue light always contributes to the radiation detected by the satellite sensor. The magnitude of
air scattering can be corrected by rigorous calculations based on the known optical properties of air molecules, the elevation
of the surfaces and by application of well established radiative transfer code, such as offered by (Vermote et al., 2002).
Absorption by stratospheric ozone and tropospheric water vapor also attenuate selected bands in the solar spectrum passing
through the atmosphere. The stratospheric ozone layer has an interfering absorption band in the visible range (0.52-0.74
μm). Whenever possible, the satellite sensor wavelength bands are located in transmission windows, i.e. at wavelengths
away from major molecular absorption bands.
From the perspective of aerosol detection, the highly textured and colorful reflection from the Earth’s surface constitutes a
significant background noise that needs to be dealt with. The surface reflectance, i.e. the fraction of the incoming solar
radiation reflected by a surface, depends on many parameters most notably on the nature of the surface itself and the
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geometric and spectral distribution of incoming and reflected radiation. Ocean surfaces reflect the incoming radiation,
mostly by mirror-like, specular reflection. The areas of ‘sun glint’ can be identified from the sun-surface-sensor geometry
and can be masked out just like clouds (Khan et al., 2007). Ocean waves tend to broaden the angular spread of the specular
reflection and may also add spurious reflectance from foaming white caps. Solid land surfaces are mostly diffuse
Lambertian reflectors, with minor specular component. Vegetation is nearly diffuse Lambertian reflector but exhibit an
extra reflection ‘hot spot’ back toward the sun. Over land, the surface reflection is defined by the Bidirectional Diffuse
Reflectance Function (BDRF) (Roujean et al., 1992). It depends on the angle of the incoming radiation, the angle of
reflected radiation as well as on wavelength. Vegetation reflectance has a peak in the green (0.5 μm) and rises sharply to
over 40% in the near IR. Typical soil or rock reflectance gradually increases from about 10-15% in the blue to over 40% in
the near IR. The spectral reflectance of water is low in the visible range and vanishes in the near IR, making it practically
black. The spectral reflectance of many surfaces varies with season. Unfortunately, the a priory quantification of the
spectral BDRF is not available for the ever-changing Earth surfaces at all locations and all times. This necessitates the
extraction of key features of BDRFs from the ‘dirty’ satellite images using spectral reflectance values for the same pixel on
different observation conditions (e.g. Raffuse, 2003).
The performance of satellite aerosol sensors and their respective retrieval algorithms are evaluated primarily against
observations optical thickness data provided by the AERONET (Holben et al., 1998) sun photometer network. The
federated network has strategically located sites that characterize typical aerosol regimes such as dusty desert locations,
biomass smoke areas, marine background, as well as urban industrial sites with complex aerosol mixtures. The validation
results appear to confirm the anticipated retrieval accuracy of MODIS and MISR products. (Remer et al., 2008; Abdou et
al., 2005; Kahn et al., 2005; Kahn et al., 2007). However, a direct comparison of MODIS and MISR aerosol data show
significant disagreements at the pixel level as well as between long-term and spatially averaged aerosol properties,
particularly over land (Liu and Mishchenko, 2008).
6.
SATELLITE DATA AND INFORMATION SYSTEMS
Earth observing satellites are by far the most prolific sources of environmental monitoring data. Literally terabytes of
digital data are collected, transmitted and processed daily. An integral part of Earth observing satellite systems is the data
flow and processing network, i.e. the information system that distributes the relevant processed data to the end users.
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Virtually all the raw and processed satellite products are publicly accessible from their respective agencies. NASA, NOAA
and other agencies have a rich variety of data products relevant to atmospheric aerosols. In fact, atmospheric particulate
matter, i.e. dust, smoke and haze are represented prominently (about 15%) in the NASA product showcases, like the Earth
Observatory. Most of these aerosol products are images from the MODIS sensor on TERRA and AQUA satellites. Other
relevant data include AERONET federated sun photometer data, space lidar data from PICASSO active lidar sensor and the
OMI data from the joint NASA-European Space Agency program.
Recent developments in surface and satellite sensing along with new information technologies now allow real-time, ‘justin-time’ data analysis for the characterization and partial explanation of the of major air pollution events as well as more indepth post-analysis (Husar and Poirot, 2005). However, for the users of satellite data the new developments introduced a
new set of problems. The ‘data deluge’ problem is especially acute for analysts interested in aerosol pollution, since the
aerosol processes are inherently complex, the numerous relevant data range from detailed surface-based chemical
measurements to extensive satellite remote sensing and the integration of these requires the use of sophisticated data
integration and modeling science methods. Unfortunately, neither the current science, nor the tools and methods are
adequate for such sophisticated data integration. As a consequence satellite-derived, Earth observations are severely underutilized in making societal decisions. A remedy is anticipated from the Global Earth Observation System of Systems
(GEOSS), (GEO, 2005) an emerging public information infrastructure for finding, accessing and applying diverse Earth
observations that are useful for science and decision makers.
7.
APPLICATIONS
There is increasing demand for satellite aerosol measurements in Earth science, air quality management, and disaster
management. Satellite-derived global aerosol climatologies have improved our general understanding of global
biogeochemical processes of sulfur, organics, mineral dust and other substances that are vital to the Earth system. Vertical
aerosol column observations are key to the characterization of the atmospheric aerosols since most of the aerosol mass as
well as the atmospheric processes occur aloft. Surface-based measurements characterize only a thin horizontal aerosol
layer. Satellite observations made it possible to identify and derive global-scale aerosol source regions (e.g. Prospero et al.,
2002), global-scale transport pattern (e.g. Husar et. al., 1997) and also crude estimates of atmospheric aerosol residence
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times. These observation-based emission rates and atmospheric residence times, in turn, are improving the performance of
global and regional chemical transport and forecast models.
With increasing concern about human-induced climate change, quantitative global-scale monitoring of radiatively active
atmospheric aerosols has become a major goal. While aerosol back-scattering to space tends to reduce the incoming solar
radiation, aerosol absorption contributes to the heating of the atmosphere. However, the magnitude of these perturbations
and relative contribution of the human induced aerosol burden to heating and cooling effects is not well understood.
Unfortunately, the current satellite-derived aerosol measurements are too uncertain to authoritatively resolve these subtle
aerosol effects. (Mishchenko et al., 2009).
Air quality management is a recent application area of satellite aerosol measurements. In the past, air quality regulations for
aerosols relied on surface-based monitoring networks to enforce the air quality standards. More recently EPA’s Exceptional
Event Rule (EE) (Federal Register, 2008) explicitly encourages, the use of satellites for the documentation of aerosol
contributions, which originate outside the jurisdiction of air quality control agencies. In fact, compelling satellite
observations of smoke and dust events have prompted the introduction of the EE Rule. Figure 30-7 is an example of an
exceptional smoke event where biomass smoke from forest and agricultural fires in southern Mexico and Guatemala was
transported across the entire Eastern US and caused record surface aerosol concentrations through most of the transport
path. The spatial pattern of the bluish smoke plume and white clouds for May 15-16, 1998 was derived from the SeaWiFS
satellite data. The semi-quantitative absorbing aerosol index measured by the TOMS satellite sensor (green overlay)
indicates that the aerosol is light-absorbing smoke, not sulfate haze. The aerosol extinction coefficient (red contour lines),
derived from surface visibility observations, indicates that the smoke was reaching the ground and impacts human health.
Such combination of multi-sensory satellite observations along with surface monitoring data and diagnostic transport
models provides the evidence that the pollution event was due to uncontrollable, extra-jurisdictional causes and not from
urban-industrial sources. Similar integration of satellite and surface observations and models have elucidated the
intercontinental transport of wind-blown dust from the East Asian Gobi Desert and its impact on the West Coast of North
America (Husar et al., 2001).
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Satellite aerosol observations are expected to continue to contribute to several aspects of air quality management, including:
(1) Providing direct observational evidence of regional and long-range aerosol transport; (2) Help improving emission
inventories and tracking emission trends; (3) Evaluation of air quality models; (4) Complementing surface networks
through filling of spatial-temporal gaps. However, before satellite aerosol observations can reach the ‘promised land’ (Hoff
and Christopher, 2009), it will be necessary to better understand their measurement limitations (Hidy et al., 2009) and to
develop science-based methods of integrating satellite data with surface observations, emission inventories and chemical
transport models (NRC 2010).
8.
FUTURE DEVELOPMENTS
Quantitative satellite measurements of aerosol properties are constrained by inherent physical and mathematical limitations.
However, both the scientific understanding and the tools and methods of satellite aerosol sensing are maturing. Therefore, it
may be appropriate to express desirable developments as perceived by this writer and the need for more inter-disciplinary
collaboration. Much of the knowledge on ‘aerosol models’, including aerosol size distribution dynamics, chemical
composition and optical properties has been generated through atmospheric aerosol and visibility research and it is directly
applicable to the improvement of satellite aerosol detection. Conversely, satellite aerosol monitoring can significantly
enhance the understanding of atmospheric aerosols, including emissions, transport and spatio-temporal pattern. Thus,
dynamic models for different aerosol types could be co-developed by aerosol scientists and remote sensing specialists.
More robust and continually updated surface reflectance models could help aerosol retrieval as well as surface
characterization. Iterative co-retrieval of aerosol and surface properties could be one possible approach.
Future aerosol retrievals could utilize complementary and redundant information from multiple sensors, i.e. through pixel
level fusion of multiple sensor’s data. Feature-level data fusion, e.g. TOMS/OMI data with MODIS/MISR, could
strengthen aerosol type detection. Fusion of geostationary and polar satellite data could yield a better characterization of
aerosol dynamics throughout the day. An opportunity for the integration of multiple sensors is being pursued by the ‘ATrain’, a synchronized constellation of eight satellites that fly on near-identical orbits as a pack (Stephens et al., 2002).
A more difficult issue is: What comprehensive, integrated system is needed for improving air quality management using
satellite and ground-based observations along with evolving modeling and emission inventories (Hidy et. al., 2009). Lastly,
14
the concept of ‘aerosol retrieval’ could well be expanded beyond the explicit use of the optical aerosol signal processing
arising from single instruments. A more complete effort that is directed toward the full characterization of the entire eightdimensional aerosol system could produce dramatic improvements in our understanding and also provide benefit to many
application areas, such as air quality and human health, climate change and disaster management.
ACRONYMS
AERONET, AErosol RObotic NETwork
AOD, Aerosol Optical Depth
AQUA, Earht observing NASA satellite
AVHRR , Advanced Very High Resolution Radiometer
BDRF, Bidirectional Diffuse Reflectance Function
EPA, Environmental Protection Agency
GEOSS, Global Earth Observation System of Systems
IR, Infra Red
GEOSS, Global Earth Observation System of Systems
MODIS, Moderate Resolution Imaging Spectroradiometer
MISR, Multiangle Imaging SpectroRadiometer
NASA. National Aeronautics and Space administration
NOAA, National Oceanic and Atmospheric Administration
OMI, Ozone Monitoring Instrument
PICASSO, Pathfinder Instrument for Cloud and Aerosol Spaceborne Observations
TERRA Earth observing NASA satellite
TOMS, Total Ozone Mapping Spectrometer
SeaWiFS, Sea-viewing Wide Field of View Sensor
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ACKNOWLEDGEMENTS
This work was partially supported by the NASA Grants NNL05AA00A and NNX08BA33G as well as EPA Grant
XA83228301. Erin Robinson and Janja Husar were instrumental in the preparation of the manuscript and their help is
greatly appreciated.
FIGURE CAPTIONS
Figure 30-1a. Radiation components detected by a satellite aerosol sensor. 1b. Polar and geostationary satellite orbits
.
Figure 30-2. Aerosol physical properties relevant to optical detection. 2a. Aerosol mass distribution. 2b. Light scattering
and absorption per unit aerosol volume (or mass). 2c. Change in the phase function as a function of extinction coefficient.
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Figure 30-3a. Schematic representation of the aerosol vertical distribution. 3b. Astronaut photo of stratospheric aerosol
layers. 3c. Complex physical and radiative interaction between aerosols and clouds.
Figure 30-4. Illustrative radiative model for aerosol remote sensing.
Figure 30-5a. Spectral reflectance with and without aerosol clouds. 5b. Vegetation. 5c. Ocean. 5d. Apparent surface
reflectance as a function of P/R0 and AOT.
Figure 30-6a. True-color composite of SeaWiFS satellite data for July 15, 1999. b. Surface reflectance. c. Derived aerosol
optical depth, AOD. 6b. Surface reflectance in the absence of aerosols and clouds. 6c. Derived aerosol optical depth, AOD.
Figures 6d, e, f. Measured apparent reflectance at 412, 670 and 870 nm wavelength respectively showing increasing
atmospheric transparency at higher wavelenghts.
Figure 30-7. Mexican forest fire smoke plume transport over Eastern U.S. observed through the SeaWiFS and TOMS
satellite sensors and surface visibility. 7a. May 15, 1998, 7b. May 16, 1998.
21