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Impact of the Aqua MODIS Band 6 Restoration on Cloud/Snow Discrimination

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  • 1 NOAA/CREST, City College of the City University of New York, New York, New York
  • | 2 Cooperative Institute for Meteorological Satellite Studies, Madison, Wisconsin
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Abstract

Distinguishing between clouds and snow is an intrinsically challenging problem because both have similar high albedo across many bands. The 1.6-μm channel (band 6) on the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument provides an essential tool for distinguishing clouds from snow, since snow typically has a much lower albedo in this band. Unfortunately, this band is severely damaged on the MODIS/Aqua platform and is typically not used in either snow or cloud products. An algorithm was previously introduced for quantitative image restoration (QIR) that can restore missing pixels of band 6 using multilinear regression with input from a spatial-spectral window in other bands. Also previously demonstrated was the effectiveness of this restoration for snow products over cloud-free pixels only. The focus of the authors’ previous work was to evaluate the impact of this restoration on the snow product, and they had relied on the current cloud mask, which does not use any information from band 6. In this work the authors propose to apply the QIR-corrected band 6 to directly create a new cloud mask. They demonstrate that this new cloud mask is much more consistent with the one produced using the undamaged MODIS band 6 on Terra. The restoration of MODIS band 6 on the Aqua platform and its impact on the discrimination of clouds, snow, and clear sky is also examined. A comprehensive evaluation was conducted on a global set of granules. The method shows great promise and should be considered for use in NASA’s next reprocessing of Aqua MODIS data.

Corresponding author address: Irina Gladkova, NOAA/CREST, City College of the City University of New York, 160 Convent Avenue, New York, NY 10031. E-mail: gladkova@cs.ccny.cuny.edu

Abstract

Distinguishing between clouds and snow is an intrinsically challenging problem because both have similar high albedo across many bands. The 1.6-μm channel (band 6) on the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument provides an essential tool for distinguishing clouds from snow, since snow typically has a much lower albedo in this band. Unfortunately, this band is severely damaged on the MODIS/Aqua platform and is typically not used in either snow or cloud products. An algorithm was previously introduced for quantitative image restoration (QIR) that can restore missing pixels of band 6 using multilinear regression with input from a spatial-spectral window in other bands. Also previously demonstrated was the effectiveness of this restoration for snow products over cloud-free pixels only. The focus of the authors’ previous work was to evaluate the impact of this restoration on the snow product, and they had relied on the current cloud mask, which does not use any information from band 6. In this work the authors propose to apply the QIR-corrected band 6 to directly create a new cloud mask. They demonstrate that this new cloud mask is much more consistent with the one produced using the undamaged MODIS band 6 on Terra. The restoration of MODIS band 6 on the Aqua platform and its impact on the discrimination of clouds, snow, and clear sky is also examined. A comprehensive evaluation was conducted on a global set of granules. The method shows great promise and should be considered for use in NASA’s next reprocessing of Aqua MODIS data.

Corresponding author address: Irina Gladkova, NOAA/CREST, City College of the City University of New York, 160 Convent Avenue, New York, NY 10031. E-mail: gladkova@cs.ccny.cuny.edu
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