Fusion of Satellite Land Surface Albedo Products Across Scales Using a Multiresolution Tree Method in the North Central United States

Land surface albedo is a key factor in climate change and land surface modeling studies, which affects the surface radiation budget. Many satellite albedo products have been generated during the last several decades. However, due to the problems resulting from the sensor characteristics (spectral bands, spatial and temporal resolutions, etc.) and/or the retrieving procedures, surface albedo estimations from different satellite sensors are inconsistent and often contain gaps, which limit their applications. Many approaches have been developed to generate the complete albedo data set; however, most of them suffer from either the persistent systematic bias of relying on only one data set or the problem of subpixel heterogeneity. In this paper, a data fusion method is prototyped using multiresolution tree (MRT) models to develop spatially and temporally continuous albedo maps from different satellite albedo/reflectance data sets. Data from the Multiangle Imaging Spectroradiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Landsat Thematic Mapper/Enhanced Thematic Mapper Plus are used as examples, at a study area in the north central United States mostly covered by crop, grass, and forest, from June to September 2005. Results show that the MRT data fusion method is capable of integrating the three satellite data sets at different spatial resolutions to fill the gaps and to reduce the inconsistencies between different products. The validation results indicate that the uncertainties of the three satellite products have been reduced significantly through the data fusion procedure. Further efforts are needed to evaluate and improve the current algorithm over other locations, time periods, and land cover types.