Development of urban surface models for improved aerosol retrieval Min Oo* a,b , Matthias Jerg a , Ana J. Picon a,b , Eduardo Hernandez a,b , Barry Gross a , Fred Moshary a , Samir Ahmed a a City College of City University of New York, EE Department, 140 th Street & Convent Ave, New York, NY 10031 b Graduate Center of City University of New York, 365 Fifth Avenue, New York, NY 10016 ABSTRACT A combination of CIMEL radiometer and MODIS measurements are used to correct surface albedo models. In particular, we show through an analysis of hyperspectral high resolution Hyperion data that the correlation coefficient assumption underestimates ground albedo resulting in an overestimate of the VIS optical depth and operational collect 5 surface model shows an incorrect trend between the MVI index and the surface correlations. Preliminary radiative transfer calculations based on the same model show that this mechanism can help explain the observed overestimation and the corrected models have been implemented for NYC and Mexico City with significantly improved AOD. Key words: urban ground albedo, aerosol retrieval over land, satellite remote sensing 1. INTRODUCTION It is well known that accurate global characterization of Aerosol Optical Depth (AOD) is essential in quantifying energy balance for climate change studies [1]. On the other hand, regional and local characterization of aerosols at Earth’s surface is essential for monitoring surface air quality [2-5] and effects on human health [6]. While satellite retrieval products are used for both purposes [2, 6], assumptions which are used to derive non-biased global aerosol may induce errors in specific regions [7], including highly urbanized megacities. As a result, using biased AOD measurements directly or through assimilation into Air Quality Forecasts can lead to further errors in PM2.5 monitoring and forecasting. As urbanization continues to grow, retrieval of aerosols within these megacities becomes more important. 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 Moderate Resolution Imaging Spectroradiometer (MODIS) instrument as described in the MODIS Collection 4 (C004) algorithm documentation [7] was focused on using “dark” pixels such as dense dark vegetation in an image as a way of isolating the aerosol contribution [8-9]. In fact, the surface reflectance ratios that connect the visible (VIS) ground albedo to the shortwave infrared (SWIR) albedo were set to values that were suitable mainly for vegetation. These conditions are not realistically met over large urban areas such as the New York City megalopolis as well as other surfaces. In addition, the requirement of dark pixels is a prime reason for the low resolution AOD retrieval (at 10 km) since bright pixels must be removed prior to processing. Therefore, loosening the restriction can ultimately improve the spatial resolution of the AOD product which is especially relevant for urban observations. Several improvements were pursued in trying to improve on the Collection 4 aerosol retrieval over land algorithm in Collection 5 (i.e. C005) [10-12] such as the inclusion of Polarization into the LUT (look up table) generation, [13]. However, special interest was paid to developing surface models that could be connected to land surface types. This effort was based on global comparisons of AERONET (AErosol RObotic NETwork) sky radiometer data and MODIS TOA reflectances. In performing these comparisons, it was found helpful to obtain global aerosol models based on cluster analysis techniques. Once these models were obtained and their climatology examined, selected aerosol models (for the fine mode aerosols) were chosen which were best linked to specific regions. From this analysis, an empirical relationship was constructed to describe the surface VIS-SWIR ratios which were shown to be functions of surface type (as defined by a Modified Vegetation Index (MVI) which is not sensitive to atmospheric uncertainty) and to a lesser extent on scattering angle. However, we find that significant errors occur when this globally trained model is applied to regional urban areas and regional refinements must be implemented. This is best illustrated in figure 3 where MODIS AOD retrieval is compared to collocated AERONET AOD measurements. We note in particular a very strong over-bias in the AOD. On the other hand, MISR inter-comparisons as illustrated in figure 1 show a very different Atmospheric and Environmental Remote Sensing Data Processing and Utilization V: Readiness for GEOSS III, edited by Mitchell D. Goldberg, Hal J. Bloom, Proc. of SPIE Vol. 7456, 74560B · © 2009 SPIE CCC code: 0277-786X/09/$18 · doi: 10.1117/12.826294 Proc. of SPIE Vol. 7456 74560B-1