MODIS Collection 6 MAIAC Algorithm

• Published in: Atmospheric measurement techniques Vol. 11; no. 10; pp. 5741 - 5765
• Main Authors: Lyapustin, Alexei, Wang, Yujie, Korkin, Sergey, Huang, Dong
• Format: Journal Article
• Language: English
• Published: Goddard Space Flight Center Copernicus 18.10.2018; Copernicus GmbH; Copernicus Publications
• Subjects: Aerosol optical depth; Aerosols; Albedo; Albedo (solar); Algorithms; Analysis; Atmospheric correction; Atmospheric particulates; Bidirectional reflectance; Cloud detection; Clouds; Collection; Data; Detection; Distribution functions; Dust; Dust storms; Flags; Geosciences (General); Meteorological research; Meteorological satellites; Methods; MODIS; Numerical weather prediction; Oceans; Optical analysis; Products; Quality assurance; Radiative transfer; Reflectance; Regression coefficients; Resolution; Retrieval; Smoke; Snow; Snow cover; Water vapor; Water vapour; Maiac Algorithm; Modis Collection 6
• ISSN: 1867-8548; 1867-1381; 1867-8548
• DOI: 10.5194/amt-11-5741-2018

This paper describes the latest version of the algorithm MAIAC (Multi-Angle Implementation of Atmospheric Correction) used for processing the MODIS (Moderate-resolution Imaging Spectroradiometer) Collection6 data record. Since initial publication in 2011-2012, MAIAC has changed considerably to adapt to global processing and improve cloud/snow detection, aerosol retrievals and atmospheric correction of MODIS data. The main changes include (1) transition from a 25 to 1 km scale for retrieval of the spectral regression coefficient (SRC) which helped to remove occasional blockiness at 25 km scale in the aerosol optical depth (AOD) and in the surface reflectance, (2) continuous improvements of cloud detection, (3) introduction of smoke and dust tests to discriminate absorbing fine- and coarse mode aerosols, (4) adding over-water processing, (5) general optimization of the LUT (LookUp-Table)-based radiative transfer for the global processing, and others. MAIAC provides an interdisciplinary suite of atmospheric and land products, including cloud mask (CM), column water vapor (CWV), AOD at 0.47 and 0.55 m, aerosol type (background, smoke or dust) and fine-mode fraction over water; spectral bidirectional reflectance factors (BRF), parameters of Ross-thick Lisparse (RTLS) bidirectional reflectance distribution function (BRDF) model and instantaneous albedo. For snow-covered surfaces, we provide subpixel snow fraction and snow grain size. All products come in standard HDF4 (software library) format at 1 km resolution, except for BRF, which is also provided at 500 m resolution on a sinusoidal grid adopted by the MODIS Land team. All products are provided on per-observation basis in daily files except for the BRDF/Albedo product, which is reported every 8 days. Because MAIAC uses a time series approach, BRDF/Albedo is naturally gap-filled over land where missing values are filled-in with results from the previous retrieval. While the BRDF model is reported for MODIS Land bands 1-7 and ocean band 8, BRF is reported for both land and ocean bands 1-12. This paper focuses on MAIAC cloud detection, aerosol retrievals and atmospheric correction and describes MCD19 data products and quality assurance (QA) flags.