Second‐generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance

By Robert C. Levy, Lorraine Remer, Shana Mattoo, Eric F. Vermote, Yoram J. Kaufman. 2007

1,296 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/198f8d5526f1462ae41c644afb6c7d3c8936eb93

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Citations in the corpus

Deep learning in environmental remote sensing: Achievements and challenges (2020, background): “Wolanin et al. (2019) combined process-based modeling with the soil-canopy energy balance RTM (SCOPE) with Sentinel-2 and Landsat 8 optical remote sensing data and NN methods for crop estimation.”
Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling (2016, background): “As the most validated, accessible, and accurate dataset today [28,29], the daily level 2 (L2) AOD products (Collection 5.”
Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application (2010, background): “The near-daily global coverage from the MODIS AOD retrieval (Levy et al. 2007) is advantageous due to frequent measure ments.”
Risk of Nonaccidental and Cardiovascular Mortality in Relation to Long-term Exposure to Low Concentrations of Fine Particulate Matter: A Canadian National-Level Cohort Study (2012, methodology): “Levy, R.C., L.A. Remer, S. Mattoo, E.F. Vermote, and Y.J. Kaufman, Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance, J. Geophys.”
Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations (2010, methodology): “…(14c) where the Fresnel matrixF (αi, n) defined for Stokes parameters referred to directions parallel and perpendicular to the reflection plane can be written as (e.g. see Lenoble et al., 2007): F (αi, n) = 1 2 r2l + r 2 r r 2 l − r 2 r 0 0 r2l − r 2 r r 2 l + r 2 r 0 0 0 0 2rl rr 0 0 0 0 2rl rr .”
Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. (2016, background): “…instead of Red: NDVIMIR = NIR − MidIR/2 NIR + MidIR/2 [4] The advantage of this index is that it is much less sensitive to aerosols because of the use of Mid-IR in lieu of red but still close to the original NDVI as the Mid-IR and red ratio is around 0.5 over most surfaces (See Levy et al. 2007).”
The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 1. Simulation Characteristics With Prescribed SSTs (2018, methodology): “0 with satellite measurement by MODIS (Levy et al., 2007) and MISR (Kahn et al., 2009) instruments.”
The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). (2017, background): “d reflectances fromMODIS on Terra (2000–present) and Aqua (2002–present) (Remer et al. 2005; Levy et al. 2007),”
Global Monitoring and Forecasting of Biomass-Burning Smoke: Description of and Lessons From the Fire Locating and Modeling of Burning Emissions (FLAMBE) Program (2009, background): “These biases are regionally specific due to the tuning performed during product development [65].”