MODIS Collection 6 MAIAC algorithm
| dc.contributor.author | Lyapustin, Alexei | |
| dc.contributor.author | Wang, Yujie | |
| dc.contributor.author | Korkin, Sergey | |
| dc.contributor.author | Huang, Dong | |
| dc.date.accessioned | 2023-07-20T18:51:26Z | |
| dc.date.available | 2023-07-20T18:51:26Z | |
| dc.date.issued | 2018-10-18 | |
| dc.description.abstract | This paper describes the latest version of the algorithm MAIAC used for processing the MODIS Collection 6 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-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 µm, aerosol type (background, smoke or dust) and fine-mode fraction over water; spectral bidirectional reflectance factors (BRF), parameters of Ross-thick Li-sparse (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 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. | en_US |
| dc.description.sponsorship | The research of Alexei Lyapustin, Yujie Wang and Sergey Korkin was funded by NASA Science for Terra, Aqua and SNPP (17-TASNPP17-0116; solicitation NNH17ZDA001NTASNPP). Alexei Lyapustin was additionally supported by the NASA GeoCAPE program. The work of Dong Huang was funded by the NASA DSCOVR program. We appreciate the large amount of work from the MODAPS team on MAIAC integration, in particular the support of Ed Masuoka and Sadashiva Devadiga, and the support of LP DAAC. The lasting support of the NASA Center for Climate Simulations in continental-scale testing and multiple internal releases of MAIAC data has been invaluable. We are grateful to the AERONET team for providing validation data. We appreciate help of Andy Sayer, comments/edits by Jeff Reid and an anonymous reviewer who helped to improve the paper. Lastly, we would like to express gratitude to multiple users and user groups in the land and air quality communities whose continuous analysis of MAIAC MODIS data helped to bring MAIAC to its current level. | en_US |
| dc.description.uri | https://amt.copernicus.org/articles/11/5741/2018/ | en_US |
| dc.format.extent | 25 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.identifier | doi:10.13016/m2hhn1-rnau | |
| dc.identifier.citation | Lyapustin, A., Wang, Y., Korkin, S., and Huang, D.: MODIS Collection 6 MAIAC algorithm, Atmos. Meas. Tech., 11, 5741–5765, https://doi.org/10.5194/amt-11-5741-2018, 2018. | en_US |
| dc.identifier.uri | https://doi.org/10.5194/amt-11-5741-2018 | |
| dc.identifier.uri | http://hdl.handle.net/11603/28819 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | EGU | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC GESTAR II Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Joint Center for Earth Systems Technology (JCET) | |
| dc.rights | This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law. | en_US |
| dc.rights | Public Domain Mark 1.0 | * |
| dc.rights.uri | http://creativecommons.org/publicdomain/mark/1.0/ | * |
| dc.title | MODIS Collection 6 MAIAC algorithm | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0002-5576-6711 | en_US |
| dcterms.creator | https://orcid.org/0000-0003-4690-3232 | en_US |