Improving Land Surface Pixel Level Albedo Characterization Using Sub-Pixel Information Retrieved from Remote Sensing

Surface albedo plays an important role in climate model simulations. Current climate models usually use simplified approaches to calculate albedo and can not take sub-grid heterogeneity into account. For heterogeneous land areas, retrieving large scale surface albedo by using current albedo characterization schemes can cause considerably spatial scaling bias. The scaling biases in the albedo estimation processes mainly result from overlooking sub-pixel variability of land surface characteristics and non-linear relationships between albedo and related parameters. The objective is to establish a new methodology to further reduce spatial scaling bias of surface albedo at coarse resolution. Contexture-based and texture-based methods were applied to remove spatial scaling bias. In addition, a new method, dealing with spatial variation of between and within land cover type, was proposed and applied. The results indicate that lumped albedo value can be considerably biased from the distributed albedo (about 20% on average). New proposed corrective algorithm is generally effective for heterogeneous boreal forest pixels.