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Remote Sens. 2010, 2(10), 2347-2368; doi:10.3390/rs2102347

Dust and Smoke Detection for Multi-Channel Imagers

1,* , 2
1 National Climatic Data Center, NOAA/NESDIS, Asheville, NC 28801, USA 2 Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin, Madison, WI 53706, USA 3 I. M. System Group, Inc., 3401 Boxhill Place, Kensington, MD 20895, USA
* Author to whom correspondence should be addressed.
Received: 27 August 2010 / Revised: 28 September 2010 / Accepted: 30 September 2010 / Published: 11 October 2010
(This article belongs to the Special Issue Atmospheric Remote Sensing)


A detection algorithm of dust and smoke for application to satellite multi-channel imagers is introduced in this paper. The algorithm is simple and solely based on spectral and spatial threshold tests along with some uniformity texture. Detailed examinations of the threshold tests are performed along with explanations of the physical basis. The detection is performed efficiently at the pixel level and output is in the form of an index (or flag): 0 (no dust/smoke) and 1 (dust/smoke). The detection algorithm is implemented sequentially and designed to run on segments of data instead of pixel by pixel for efficient processing. MODIS observations are used to test the operation and performance of the algorithm. The algorithm can capture heavy dust and smoke plumes very well over both land and ocean and therefore is used as a global detection algorithm. The method can be applied to any multi-channel imagers with channels at (or close to) 0.47, 0.64, 0.86, 1.38, 2.26, 3.9, 11.0, 12.0 μm (such as current EOS/MODIS and future JPSS/VIIRS and GOES-R/ABI) for the detection of dust and smoke. It can be used to operationally monitor the outbreak and dispersion of dust storms and smoke plumes that are potentially hazardous to our environment and impact climate.
Keywords: dust; smoke; detection algorithm; satellite imagers dust; smoke; detection algorithm; satellite imagers
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Zhao, T.-P.; Ackerman, S.; Guo, W. Dust and Smoke Detection for Multi-Channel Imagers. Remote Sens. 2010, 2, 2347-2368.

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