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Proceedings Paper

Ingesting MODIS land surface classification into AOD retrievals
Author(s): Adam A. Atia; Ana Picon; Nabin Malakar; Barry Gross; Fred Moshary
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Paper Abstract

As urbanization continues to grow, retrieval of aerosols in higher urbanized areas becomes more important and successful unbiased retrievals in urban areas will become more important both for air quality and in climate applications. However, retrieval of AOD by satellite remote sensing measurements over land is complicated by the fact that the Top of Atmosphere (TOA) reflectance is a combination of the desired atmospheric path reflectance as well as the ground reflectance. To avoid this problem, AOD retrieval with the MODIS instrument attempts to isolate “dark” pixels such as dense dark vegetation. To account for land surface reflection properties, the latest MODIS retrieval algorithm over land tries to improve on surface albedo modeling using collocated MODIS and AERONET sky radiometer data to improve the VIS - SWIR ratios. However, the matchup data taken on a global scale still was heavily concentrated over vegetated areas since there are very few urban AERONET sites and the resulting surface models are incapable of describing the urban surface reflection ratios properly. In addition, as we move to higher spatial resolution such as Collect 6 3km retrievals, the ability to isolate dark pixels is significantly reduced and the ability to independently characterize the surface using MODIS land classification data is crucial to avoid biases and improve on retrievals. The purpose of this presentation is to demonstrate that the C005 surface models trained on a global scale and used by the MODIS algorithm to estimate the ground reflectance is not appropriate for urban areas and that the use of Urban based surface models together with a land classification flag can remove biases and improve retrieval.

Paper Details

Date Published: 16 October 2013
PDF: 11 pages
Proc. SPIE 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV, 888707 (16 October 2013); doi: 10.1117/12.2029335
Show Author Affiliations
Adam A. Atia, The City College of New York (United States)
Ana Picon, U.S. Patent and Trademark Office (United States)
Nabin Malakar, The City College of New York (United States)
Barry Gross, The City College of New York (United States)
Fred Moshary, The City College of New York (United States)

Published in SPIE Proceedings Vol. 8887:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XV
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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