Improved MODIS aerosol retrieval in urban areas using a land classification approach and empirical orthogonal functions
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Improved MODIS aerosol retrieval in urban areas using a land classification approach and empirical orthogonal functions

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Details:

  • Journal Title:
    Remote Sensing Technologies and Applications in Urban Environments
  • Personal Author:
  • NOAA Program & Office:
    CREST (Center for Earth System Sciences and Remote Sensing Technologies and Tech Center at City College of CUNY)
  • Description:
    New, high-resolution aerosol products are required in urban areas to improve the spatial coverage of the products, in terms of both resolution and retrieval frequency. These new products will improve our understanding of the spatial variability of aerosols in urban areas and will be useful in the detection of localized aerosol emissions. Urban aerosol retrieval is challenging for existing algorithms because of the high spatial variability of the surface reflectance, indicating the need for improved urban surface reflectance models. This problem can be stated in the language of novelty detection as the problem of selecting aerosol parameters whose effective surface reflectance spectrum is not an outlier in some space. In this paper, empirical orthogonal functions, a reconstruction-based novelty detection technique, is used to perform single-pixel aerosol retrieval using the single angular and temporal sample provided by the MODIS sensor. The empirical orthogonal basis functions are trained for different land classes using the MODIS BRDF MCD43 product. Existing land classification products are used in training and aerosol retrieval. The retrieval is compared against the existing operational MODIS 3 KM Dark Target (DT) aerosol product and co-located AERONET data. Based on the comparison, our method allows for a significant increase in retrieval frequency and a moderate decrease in the known biases of MODIS urban aerosol retrievals.
  • Source:
    Remote Sensing Technologies and Applications in Urban Environments (2016)
  • DOI:
  • ISSN:
    0277-786X
  • Format:
    PDF
  • Publisher:
    SPIE
  • Document Type:
    Journal Article
  • Rights Information:
    Other
  • Compliance:
    Library
  • Main Document Checksum:
  • Download URL:
  • File Type:

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