Aerosol properties over bright-reflecting source regions

By N. C. Hsu, Si-Chee Tsay, Michael D. King, Jay R. Herman. IEEE Transactions on Geoscience and Remote Sensing, 2004

1,255 citations recorded by the source corpus.

Retrieving aerosol properties from satellite remote sensing over a bright surface is a challenging problem in the research of atmospheric and land applications. In this paper we propose a new approach to retrieve aerosol properties over surfaces such as arid, semiarid, and urban areas, where the surface reflectance is usually very bright in the red part of visible spectrum and in the near infrared, but is much darker in the blue spectral region (i.e., wavelength <500 nm). In order to infer atmospheric properties from these data, a global surface reflectance database of 0.1/spl deg/ latitude by 0.1/spl deg/ longitude resolution was constructed over bright surfaces for visible wavelengths using the minimum reflectivity technique (e.g., finding the clearest scene during each season for a given location). The aerosol optical thickness and aerosol type are then determined simultaneously in the algorithm using lookup tables to match the satellite observed spectral radiances. Examples of aerosol optical thickness derived using this algorithm over the Sahara Desert and Arabian Peninsula reveal various dust sources, which are important contributors to airborne dust transported over long distances. Comparisons of the satellite inferred aerosol optical thickness and the values from ground-based Aerosol Robotic Network (AERONET) sun/sky radiometer measurements indicate good agreement (i.e., within 30%) over the sites in Nigeria and Saudi Arabia. This new algorithm, when applied to Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field of view Sensor (SeaWiFS), and Global Imager (GLI) satellite data, will provide high spatial resolution (/spl sim/1 km) global information of aerosol optical thickness over bright surfaces on a daily basis.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/b3ccdb96a532532e0a601d4a1be836cb48187daf

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Citations in the corpus

Radiative forcing in the ACCMIP historical and future climate simulations (2013, methodology): “We use the standard MODIS AOD product over most of the globe, but obtain greater coverage by infilling missing data with values produced by the Deep Blue algorithm that allows retrievals over bright surfaces such as deserts (Hsu et al., 2004).”
Polarimetric remote sensing of aerosols over land (2009, background): “These particles are usually injected into the atmosphere at high altitudes and can be transported [Hsu et al., 2004] over long distances [Hoff et al., 2005, Hlavka et al., 2005].”
Deep Blue Retrievals of Asian Aerosol Properties During ACE-Asia (2006, methodology): “To improve upon the Lambertian surface reflectance assumption used in [14], we construct a surface reflectance database by binning the reflectance according to the viewing geometry and subsequently determine the minimum reflectivity for the corresponding bin.”
Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust (2006, methodology): “This issue has been analyzed in many studies [e.g., Mishchenko et al., 1995; Kahn et al., 1997; Krotkov et al., 1999; Kalashnikova and Sokolik, 2002; Mishchenko et al., 2003; Kalashnikova and Sokolik, 2004; Kalashnikova et al., 2005; Zhao et al., 2003; Sinyuk et al., 2003; Hsu et al., 2004; Remer et”
THE JOINT AEROSOL- MONSOON EXPERIMENT A New Challenge for Monsoon Climate Research (2008, methodology): “…forcing functions, in situ and surface-based observations are vital for JAMEX, both for providing input and for validating model simulations and for improving satellite algorithms for retrieving aerosol properties over highly reflecting land surface, such as the “Deep Blue” (Hsu et al. 2004).”
Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Edition-4.0 Data Product (2018, background): “Remote Sens.) cloud retrieval code; and aerosol property retrievals from Remer et al. (2008), Levy et al. (2013), and Hsu et al. (2004, 2006).”