The aerosol quantity determined by most instruments is the aerosol optical depth (AOD). This is related to the amount of light aerosols scatter or absorb in a column through the atmosphere (specifically, it is the vertically-integrated aerosol extinction), and is also sometimes referred to as aerosol optical thickness (AOT). AOD depends on wavelength; a common reference wavelength reported by satellite data products is 550 nm.
This map from the SeaWiFS Deep Blue Version 4 dataset shows the annual cycle of AOD. Intense, darker colors indicate more aerosols. Areas with persistent snow/ice/cloud cover or polar night are shown in grey.
The Ångström Exponent (denoted as AE or α) is a measure of how the AOD changes relative to the various wavelength of light (known as 'spectral dependence'.) This is related to the aerosol particle size. Roughly speaking, values less than 1 suggest an optical dominance of coarse particles (e.g. dust, ash, sea spray), while values greater than one 1 dominance of fine particles (e.g. smoke, industrial pollution).
[Here, the Ångström Exponent is denoted α, the AOD τ, and wavelength light λ]
The Ångström exponent is a measure of the wavelength-dependence of AOD, which is related to aerosol particle size. For most aerosol types, AOD is higher at shorter wavelengths (e.g. blue light) than longer (e.g. red light).
For health purposes, people are often interested in the mass of aerosols of a certain size at ground level. This is often referred to by the term 'particulate matter' (PM) and separated by size. Terms such as PM1, PM2.5 and PM10 indicate the mass of aerosol particles with diameters smaller than 1, 2.5, and 10 microns respectively. PM1 and PM2.5 are therefore dominated by fine particles, while PM10 includes coarse particles such as dust.
Note that PM is difficult to determine from current satellite instruments because they are sensitive to the total column rather than just the surface concentration. There are, however, techniques to estimate ground-level PM from satellite AOD.
Visualization of the size of PM10 and PM2.5 particles relative to grains of sand. Emitted mineral dust particles are typically much smaller than the grains of sand you see on the beach.
The single scattering albedo (SSA) is the fraction of light that is scattered compared to the total extinction (scattering plus absorption) optical depth. A value of 1 indicates that an aerosol only scatters light, and values closer to 0 indicate that an aerosol mostly absorbs light. This is important as it affects how much of a local heating or cooling effect the aerosols have. SSA also depends on wavelength; in the visible region, most aerosols have SSA in the range 0.7 to 1. SSA is difficult to determine from space.
Typical values of single scattering albedo of different aerosols at various wavelengths. Aerosols such as sea spray are essentially non-absorbing, while aerosols like smoke can vary depending on the source.
Deep Blue has been used to process data from several similar sensors on different satellites. By using multiple satellites, with overlap in time, we can move toward a long-term aerosol data record and check for consistency between the different satellite instruments. Currently, Deep Blue algorithms have been applied to AVHRR, SeaWiFS, MODIS (on both the Terra and Aqua satellites), and VIIRS. These sensors are all broad-swath passive multispectral imaging radiometers, and can be considered to a first approximation as cameras in space.
The Advanced Very High Resolution Radiometers (AVHRR) are a series of instruments which have flown on multiple satellites since the late 1970s. These early instruments allowed many advances in remote sensing of the Earth's atmosphere and surface, in a variety of scientific disciplines, and paved the way for later missions.
SeaWiFS, which stands for Sea-Viewing Wide Field-of-View Sensor, was a satellite-borne sensor intended to collect measurements to improve our understanding of global ocean biology. The main objective of SeaWiFS was to quantify chlorophyll produced by marine phytoplankton, but the measurements are also useful for monitoring aerosols. SeaWiFS was launched aboard the SeaStar satellite on August 1, 1997, and lasted until December 11, 2010, much longer than its five-year design life.
MODIS stands for Moderate Resolution Imaging Spectroradiometer. Two MODIS instruments orbit the Earth on board the Terra and Aqua satellites, which are part of NASA’s primary Earth Observing System (EOS). Terra was launched on December 18th, 1999, while Aqua was launched on May 4th, 2002. Note that it takes a short time for launch for the spacecraft to reach stable orbit and the instruments to undergo commissioning, so MODIS Terra data are not available until early 2000. Both satellites carry a MODIS sensor, as well as an array of other instruments for Earth Science applications.
While using two of the same sensor in space may seem redundant, differences in Terra's and Aqua's orbits result in different viewing and cloud-cover conditions for each given location; thus, each can collect different information about the same scene. In particular, each satellite has a different local equatorial crossing time: 10:30 AM for Terra, and 1:30 PM for Aqua for the daytime nodes. Additionally, the satellites orbit in opposite directions.
A model of the MODIS instrument, on display at NASA GSFC. Phototaken by A. Sidel.
The Visible Infrared Imaging Radiometer Suite (VIIRS) collects visible and infrared imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans. The VIIRS sensor is a component of the Suomi National Polar-orbiting Partnership (S-NPP) satellite, and has similar capabilities and applications to MODIS. S-NPP was launched on October 28, 2011, and a follow-on mission with a similar instrument payload is expected to launch in 2017.
Deep Blue is an algorithm used to calculate aerosol optical depth (AOD) and Ångström Exponent (α or AE) over land using data from the aforementioned satellite instruments. By making use of measurements at different wavelengths, with different contrast between surface and atmospheric features, Deep Blue estimates AOD. This process is known as a 'retrieval'. At many wavelengths of visible light, the contrast between aerosols and the surface is difficult to discern, but in the 412 nm band, aerosol signals tend to be bright and surface features dark. The 412 nm band is sometimes referred to as the "deep blue" band, from which the algorithm gets its name. AOD retrieval in the 412 nm band allows for increased spatial coverage relative to tthe Dark Target algorithm, which makes different assumptions which are not valid over bright surfaces such as deserts.
On the left, a true color image from MODIS Terra. Center, the same area viewed using a 650 nm (red) band. On the right, the same area viewed using a 412 nm (Deep Blue) band. Aerosol features stand out more strongly in the Deep Blue band than the red band.
Over land, Deep Blue uses different bands for bright land (primarily 412, 470/490 nm) and dark land (primarily 470/490, 650 nm). Over water, the Deep Blue algorithm is not used. However, Deep Blue data sets also contain aerosol data over oceans, derived from a different algorithm, based on a multispectral inversion approach. Some of the solar bands measured by these instruments are illustrated below; note that MODIS and VIIRS possess additional bands not shown.
The VIIRS, MODIS, and SeaWiFS instruments all measure light at multiple wavelengths. As they observe at similar wavelengths, Deep Blue can use similar techniques to monitor aerosols with all of them.
The latest version of Deep Blue, provided in MODIS Collection 6, SeaWiFS version 4, and the forthcoming VIIRS release brings a massive expansion in spatial coverage by extending the retrieval algorithm to include dark (e.g. vegetated) surfaces as well as the previously covered bright (e.g. arid) surfaces. So, Deep Blue now has spatial coverage spanning most of the world’s land surfaces. Aerosol and surface reflectance models have also been improved, decreasing uncertainty and increasing correlation with AERONET. Significant improvements have been made in cloud screening as well. You can read more about these improvements in our published peer-reviewed journal articles.
Deep Blue, and all algorithms from similar sensors, still lack spatial coverage in cloudy, snow-covered or ice-covered scenes, and areas of water with strong sun glint.
Satellite aerosol data, such as AOD from Deep Blue, are typically compared to 'ground truth' data to assess their reliability. This process is often referred to as validation. The most widely-used validation data sources are the Aerosol Robotic Network (AERONET) and Maritime Aerosol Network (MAN), which use groud-based sun photometers. Satellite-based aerosol retrievals can be compared with data from the hundreds of AERONET sites around the world to determine an algorithm's performance in a variety of locations and conditions. By this process, it has been determined that MODIS and VIIRS Deep Blue have an uncertainty (one-standard-deviation confidence interval) of ±(0.03+20%) over land, and SeaWiFS Deep Blue has an uncertainty of ±(0.05+20%), which is comparable with other aerosol datasets. Uncertainties over oceans are lower. Ångström Exponent information should be considered qualitative, unless aerosol loading is high. See our publications page for references with more detailed information.
Aerosol Robotic Network (AERONET) is a global network of ground-based sun photometers. These sun photometers are able to calculate the AOD and amount of water vapor in the atmosphere by comparing the amount of light they detect with the amount of solar radiation which would be observed in an aerosol-free atmosphere. AERONET also takes sky brightness measurements which can be used to infer aerosol size distribution, refractive index, and SSA.
When inactive, a sun photometer faces downwards. When active, the sun photometer will turn towards the sun and begin collecting data.
Data from AERONET are much more accurate than most data derived from current satellite instruments orbiting the Earth. AERONET is very useful in the validation of global-scale satellite datasets, and for many other applications. By comparing data from AERONET with the satellites' data, the accuracy of the satellites can be determined, and systematic inaccuracies can be corrected. However, AERONET coverage is point-based and very sparse, especially in areas of the globe without a lot of people, such as the Arctic or Saharan Africa. So, we still need satellites to get a global picture of aerosols in the atmosphere.
Sun photometers on the roof of NASA GSFC turn towards the sun to collect data. Video taken by A. Sidel.
Microtops, which contribute to the Maritime Aerosol Network (MAN), are handheld, user-friendly portable sun photometers. As land-based sun photometers need a stable static platform to operate, and so cannot be used on ships, Microtops are mainly used to fill in gaps in data over oceans. MAN is part of AERONET.
Microtops sun photometers can use GPS to record the coordinates of where they make their measurements, making them great for portable aerosol measurement, such as when on board a ship. Photo taken by B. Howl.
Light Detection and Ranging (LIDAR), uses laser pulses to measure ranges of atmospheric constituents such as aerosols and clouds to the Earth. LIDAR is capable of measuring vertical profiles of the atmosphere, rather than just the total column. Some aerosol LIDAR systems are based on land while others are mounted on aircraft.
Multisensor comparisons are another method of evaluation. By understanding why data products derived from different sensors or algorithms are consistent or inconsistent, improvements can be made.
Airborne or ground-based measurements made during field campaigns, such as by NASA's SMARTLabs, also often allow detailed characterization of atmospheric composition for particular case studies of interest. Typically, a large variety of instrumentation are operated together during these field campaigns.
SMARTLabs-ACHIEVE trailer and team during instrument calibration at Wallops Flight Facility, VA, USA.
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This page was prepared by A. Chen, B. Howl, and A. Sidel during their summer 2015 internship with the Deep Blue group at NASA GSFC.