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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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AMT | Articles | Volume 11, issue 10
Atmos. Meas. Tech., 11, 5741–5765, 2018
https://doi.org/10.5194/amt-11-5741-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 11, 5741–5765, 2018
https://doi.org/10.5194/amt-11-5741-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 18 Oct 2018

Research article | 18 Oct 2018

MODIS Collection 6 MAIAC algorithm

Alexei Lyapustin et al.
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Lorena Grabowski on behalf of the Authors (24 Aug 2018)  Author's response
ED: Referee Nomination & Report Request started (05 Sep 2018) by Jun Wang
RR by Anonymous Referee #1 (05 Sep 2018)
RR by Jeffrey Reid (11 Sep 2018)
ED: Publish subject to minor revisions (review by editor) (12 Sep 2018) by Jun Wang
AR by Alexei Lyapustin on behalf of the Authors (19 Sep 2018)  Author's response    Manuscript
ED: Publish as is (25 Sep 2018) by Jun Wang
Publications Copernicus
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Short summary
MAIAC algorithm used for the MODIS C6 processing is described. MAIAC combines time series analysis and pixel/image-based processing to improve the accuracy of cloud detection, aerosol retrievals and atmospheric correction. MAIAC offers an interdisciplinary suite of atmospheric, land surface and snow products. Due to generally high quality, high resolution and high coverage, MAIAC AOD and surface reflectance/BRDF have been widely used for air quality and land research and applications.
MAIAC algorithm used for the MODIS C6 processing is described. MAIAC combines time series...
Citation