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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.
Data sets

MCD19A1 MODIS/Terra+Aqua Land Surface BRF Daily L2G Global 500m and 1km SIN Grid V006 A. Lyapustin and Y. Wang https://doi.org/10.5067/MODIS/MCD19A1.006

MCD19A2 MODIS/Terra+Aqua Land Aerosol Optical Depth Daily L2G Global 1km SIN Grid V006 A. Lyapustin and Y. Wang https://doi.org/10.5067/MODIS/MCD19A2.006

MCD19A3 MODIS/Terra+Aqua BRDF Model Parameters 8-Day L3 Global 1km SIN Grid V006 A. Lyapustin and Y. Wang https://doi.org/10.5067/MODIS/MCD19A3.006

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...
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