STAR GOES-R Algorithm Working Group website National Oceanographic & Atmospheric Administration website NOAA Center for Satellite Applications and Research website

Aerosol Detection


Aerosols, which are suspended particles in the air, are a key component of smog. Such smog reduces air quality. In addition to urban/industrial sources of aerosols, they are also produced by volcanic ash eruptions, dust "storms", and forest fires and other burning to clear agricultural land.

Aerosols are detrimental to human health and the environment. High concentrations of aerosols, when inhaled, lead to upper respiratory diseases including asthma. They decrease visibility and lead to unsafe conditions for transportation. The Environmental Protection Agency (EPA) estimates that more than 106 million people in the United States live in areas of poor air quality, costing about $143 billion dollars per year in hospital expenditures. Aerosols are also a major climate forcing component. They affect the radiative balance of the Earth, cooling or warming the atmosphere (depending on aerosol composition).

Product Description

The Aerosol Detection data product indicates the presence of dust and/or smoke for each pixel in the satellite image area. This can be used to quickly identify locations of dust and smoke plumes.

Improvements and Benefits

GOES-R aerosol products will be more accurate than current GOES products (GOES-R ABI accuracy is ~10% compared to current GOES AOD at ~20%). Additionally, the availability of these products at a 5-minute interval will be beneficial to the user as the products can be tailored to 15-minute or 30-minute composites to fill the data gaps associated with clouds. The use of the near real-time fire and smoke aerosol emissions in operational numerical air quality prediction models will greatly enhance the accuracy of forecast guidance. The combination of numerical forecast guidance and near real-time satellite aerosol imagery will benefit field forecasters in their air quality warnings and alerts. Accumulation of the satellite data over a long time period and extending the current GOES record is also useful for air quality assessment work done by the EPA.

GOES-R Aerosol Detection using MODIS data
GOES-R Aerosol Detection using MODIS data

How does it work? - Algorithm

Since the aerosols scatter and absorb light, when the concentration is high, they are easily visible in the satellite imagery. The challenge is to distinguish aerosols from clouds and from bright surfaces in the background. This is done by comparing values from multiple wavelengths in the visible light and thermal infrared portion of the spectrum. The 2.1 μm channel is transparent to most aerosols, so this channel is used to obtain the contribution of the surface to the signal received at the satellite. Several infrared channels are used to detect clouds.

The presence of aerosols can be detected by comparing differences in the signal (as brightness temperature) between 11 μm and 12 μm and other spectral and spatial variability tests, especially for dust and smoke aerosols.

See the GOES-R ATBD page for all ATBDs.

How are the results compared to existing data? - Calibration and Validation

The primary source of validation data will be ground-based dust/smoke measurements from networks such as IMPROVE (Interagency Monitoring of Protected Visual Environment) and aircraft observations. Secondary validation of the ABI aerosol detection product is also possible by inter- comparison with validated aerosol detection from other satellites. The NOAA/NESDIS Hazard Mapping System (HMS) smoke product can be used for the validation of the ABI smoke aerosol detection product.

The ABI aerosol detection product can be compared with the aerosol model that the ABI aerosol optical depth algorithm selected (generic, urban, smoke, dust) to check if both algorithms are consistent in picking smoke/dust aerosol. Other miscellaneous comparisons could involve manual comparisons of synthetic ABI RGB images to aerosol detection product images to see if plume areas are consistent between the two.

A more technical validation presentation, (PDF, 13.33 MB) is also available.