Assessing the land albedo model of the new MODIS Aerosol retrieval over land Algorithm for urban scenes
Eduardo Hernandez, City College of New York, New York, NY; and M. M. Oo, L. Charles, B. Gross, M. Mihailidi, and F. Moshary
Determination of aerosol optical depth over land from satellite remote sensing measurements is extremely complex due to the large variability of aerosol optical properties (i.e. phase function, single scattering albedo etc) as well as the effects of the surface albedo which can vary significantly. In the previous land algorithm, to estimate the VIS ground albedo, a single scene invariant assumption was used to connect the VIS surface albedo to the MIR (2130nm) surface albedos which were directly obtained from the measurements. Based on these principles, the basic approach for the collection 4 algorithms were
However, it was readily understood that any correlations between the MIR channels and the VIS channels should be a function of the land type and the solar-view geometry. The newest land algorithm for MODIS (C-005) has recently been developed and reprocessing of the MODIS streams is ongoing. This algorithm makes an attempt to improve both the aerosol assumptions as well as the ground albedo models. For the aerosol models, cluster analysis based on large scale Aeronet measurements were used to define a set of most likely aerosol models for each AOD for a given geographic location. To improve on the previous surface models, extensive matchups based on atmospherically cleared MODIS TOA reflectences using derived Aeronet atmospheres were analyzed as a function of surface classification and sun-view geometry. The results of this matchup were used to develop a more sophisticated algorithm connecting the surface reflectences in the MIR and VIS channels which depends on surface classification and scattering angle. The classification of scene was obtained by investigation of a Modified Vegetation Index (MVI) which unlike the Vegetation Index which looks at ratios between the Red and NIR channels, uses the 1270 and 2130 nm channels to avoid errors due to inaccurate atmospheric retrieval.
In this paper, we examine the consistency of surface albedo models from high spatial resolution hyperion imagery of the NYC metropolitan area.. In particular, we have examined how the MVI index depends on vegetation and urban regions to see if the urban scene can be classified properly using the MODIS collection 5 algorithm. The pixels were classified into urban and vegetation classes using a supervised classification algorithm after undergoing atmospheric correction based on aeronet AOD measurements which were less than .05 thereby increasing the accuracy of the ground retrieval properties.
RGB Image with Training regions Segmented Image
RGB Image with
Finally, we show that a single static relationship of MVI values for classifying surfaces is not correct and can lead to significant classification errors. This is shown through an analysis of the surface MODIS BRDF product which allows us to calculate the MVI index as a function of the solar and view geometries. In fact, the variability of the MVI index as a function of the observation angle can be on the order of the width of the MVI classification zones and the variability of this index as a function of angular variability needs to be studied more carefully. Error budgets for the surface models based on our findings will be presented.
Extended Abstract (420K)
Session 10, Land and Ocean Observations
Thursday, 18 January 2007, 11:00 AM-2:30 PM, 212B
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