5 Spatially and Temporally Complete Global Snow-free MODIS Albedo and Reflectance Anisotropy Products

Wednesday, 30 May 2012
Rooftop Ballroom (Omni Parker House)
Qingsong Sun, Boston University, Boston, MA; and Z. Wang, Q. Zhang, F. Zhao, C. B. Schaaf, A. H. Strahler, M. D. King, S. Platnick, and E. Moody

Operational albedo and reflectance anisotropy or BRDF (Bidirectional Reflectance Distribution Function) products have been produced from the MODIS sensors aboard NASA's Terra and Aqua satellites for the past decade. These global products have been used to create spatially and temporally complete snow-free reflectance anisotropy models, surface albedo quantities and Nadir BRDF-Adjusted Reflectances (NBAR) to support climate and biogeochemical modeling efforts. These V005 global 30 arc-sec products (CMG or climate modeling grid products in geographic lat-lon) are provided at a 8 day time-step (based on a 16-day window). The focus of this effort has been to realistically bridge gaps due to persistent cloud cover and ephemeral snow cover in the high quality operational MODIS BRDF products. This has been accomplished through the application of rigorous temporal interpolation techniques based on per-pixel vegetation development curves computed with the Timesat methodology. Any original high quality MODIS retrievals are fully replicated and the new data sets are accompanied by quality fields describing the methods used to fill the temporal and spatial gaps. These higher spatial and temporal resolution products are being prepared to replace and extend the earlier V004 gap-filled albedo products produced in collaboration with the MODIS Atmosphere Team (Moody et al., 2008; 2005). The availability of the underlying BRDF information also allows modelers to use MODIS albedo and NBAR data at solar illumination angles other than the local solar noon results traditionally provided. The gap filled NBAR data, traditionally used for vegetation phenology, land cover mapping, disturbance monitoring and biomass estimates, is of particular interesting to regional modeling, monitoring and forecasting researchers.
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