Developing a spatially continuous 1 km surface albedo data set over North America from Terra MODIS products

Surface albedo is an important factor governing the surface radiation budget and is critical in modeling the exchange of energy, water and carbon between Earth surface and atmosphere. Global satellite observation systems have been providing surface albedo products periodically. However, due to factors associated with weather, sensors and algorithms, albedo products from satellite observations often have many gaps and low quality pixels. Take the North American Moderate Resolution Imaging Spectroradiometer (MODIS) albedo products (MOD43B3) from 2000-2004 as an example, only 31.3% of pixels were retrieved with fully data-driven inversions and labeled with the highest possible mandatory quality flag of "good''. Conversely, 13.3% did not contain any valid retrieval and the remaining values vary in quality from moderate full inversions to poor retrievals utilizing a back-up algorithm. This indicates considerable potential for product improvement through the use of gap-filling algorithms. Our objective is to generate spatially and temporally continuous albedo products through gap filling and filtering based on multiyear observations and high quality neighboring pixels. The resultant albedo data set substantially improves the time series of surface albedo, especially in the winter, when for some areas the MODIS albedo products may have no retrievals. A comparison with field measurements shows that the filtered albedo correlates well with the measured surface albedo with the root mean squared errors (RMSEs) around 0.064 for snow-free pixels and 0.078 overall. The generated spectral albedo, broadband albedo and monthly albedo can be used for climate modeling and data assimilation applications.