Forest biomass is an important indicator that can be used to evaluate global carbon-oxygen balance and climate change. At present, the spatial resolution of existing global forest biomass products based on spaceborne large footprint LiDAR data is too coarse to meet the needs of local forest investigation and dynamic monitoring. Therefore, it is necessary to determine a downscaling method to produce high spatial resolution forest biomass products from coarse resolution products. Two areas with different forest distribution patterns in Maryland, USA, were selected in this study to establish statistical relationships between low resolution multispectral data upscaled from TM data and forest aboveground biomass (AGB), which were upscaled from CMS (Carbon Monitoring System) forest AGB products or directly from GEOCARBON AGB products. The statistical relationships were then used as a downscaling model to downscale the forest AGB products from a spatial resolution of 1 km to 30 m. Results showed that the spatial distribution of downscaled 30 m-resolution biomass from simulated forest AGB was roughly the same as that of the CMS biomass. The RMSE was between 59.2 Mg/hm² and 65.5 Mg/hm². The correlation coefficient reached 0.7. Downscaled 30 m-resolution biomass from GEOCARBON forest AGB had a higher RMSE, which was between 75.3 Mg/hm² and 79.9 Mg/hm². Compared with the linear model, the non-linear model showed the relationship between AGB and multispectral data more effectively. In general, there was an AGB underestimation at high values and overestimation at low values.