Abstract
Biomass energy that exists in crop residues can be used for electricity generation and fuel production. However, its spatial distribution has formed a bottleneck in its utilization. This study introduces a data fusion method that uses the Net Primary Productivity (NPP) product of the Moderate-resolution Imaging Spectroradiometer (MODIS) data as a weighting factor to downscale crop statistics from a county scale to a 1 km2 spatial resolution using GIS to accurately map the spatial distribution of cereal bioenergy potential in China. The study demonstrates that the combination of remote sensing and statistical methods improves both spatial resolution and accuracy of the results, and resolves errors and uncertainties stemming from remote sensing processes. The results of the study will allow better decision making for siting biomass power plants, which will in turn reduce the cost of transportation of materials and increase the use of bioenergy.
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