Distinguishing Aerosols from Clouds in Global, Multispectral Satellite Data with Automated Cloud Classification Algorithms
Open Access
- 1 April 2008
- journal article
- Published by American Meteorological Society in Journal of Atmospheric and Oceanic Technology
- Vol. 25 (4), 501-518
- https://doi.org/10.1175/2007jtecha1004.1
Abstract
A new approach is presented to distinguish between clouds and heavy aerosols with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit differences in both spectral and textural signatures between clouds and aerosols to isolate pixels originally classified as cloudy by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask algorithm that in reality contains heavy aerosols. The procedures have been tested and found to accurately distinguish clouds from dust, smoke, volcanic ash, and industrial pollution over both land and ocean backgrounds in global datasets collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This new methodology relies strongly upon data collected in the 0.412-μm bandpass, where smoke has a maximum reflectance in the VIIRS bands while dust simultaneously has a minimum reflectance. The procedures benefit from the VIIRS design, which is dual gain in this band, to avoid saturation in cloudy conditions. These new procedures also exploit other information available from the VIIRS cloud mask algorithm in addition to cloud confidence, including the phase of each cloudy pixel, which is critical to identify water clouds and restrict the use of spectral tests that would misclassify ice clouds as heavy aerosols. Comparisons between results from these new procedures, automated cloud analyses from VIIRS heritage algorithms, manually generated analyses, and MODIS imagery show the effectiveness of the new procedures and suggest that it is feasible to identify and distinguish between clouds and heavy aerosols in a single cloud mask algorithm.Keywords
This publication has 21 references indexed in Scilit:
- Automated cloud detection and classification of data collected by the Visible Infrared Imager Radiometer Suite (VIIRS)International Journal of Remote Sensing, 2005
- Aerosol-cloud interaction-Misclassification of MODIS clouds in heavy aerosolIEEE Transactions on Geoscience and Remote Sensing, 2005
- Cloud detection over desert regions using the 412 nanometer MODIS channelGeophysical Research Letters, 2003
- A satellite view of aerosols in the climate systemNature, 2002
- Cloud top phase determination from the fusion of signatures in daytime AVHRR imagery and HIRS dataInternational Journal of Remote Sensing, 1997
- Validation of automated cloud top phase algorithms: distinguishing between cirrus clouds and snow in a priori analyses of AVHRR imageryOptical Engineering, 1997
- Application of 1-38 μm imagery for thin cirrus detection in daytime imagery collected over land surfacesInternational Journal of Remote Sensing, 1996
- Improved Detection of Optically Thin Cirrus Clouds in Nighttime Multispectral Meteorological Satellite Imagery Using Total Integrated Water Vapor InformationJournal of Applied Meteorology and Climatology, 1995
- On the Temperature and Effective Emissivity Determination of Semi-Transparent Cirrus Clouds by Bi-Spectral Measurements in the 10μm Window RegionJournal of the Meteorological Society of Japan. Ser. II, 1985
- The Large-Scale Movement of Saharan Air Outbreaks over the Northern Equatorial AtlanticJournal of Applied Meteorology, 1972