Global tree cover percentage is an important parameter used to understand the global environment. However, the available global percent tree cover products are few, and efforts to validate these maps have been limited. Therefore, producing a new broad-scale percent tree cover dataset is valuable. Our study was undertaken to map tree cover percentage, on a global scale, with better accuracy than previous studies. Using a modified supervised regression tree algorithm from Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2008, the tree cover percentage was estimated at 500 m resolution in Eurasia. Training data were created by simulation using reference data interpreted from Google Earth. We collected approximately 716 high-resolution images from Google Earth. The regression tree model was modified to fit those images for improved accuracy. Our estimation result was validated using 307 points. The root mean square error (RMSE) between estimated and observed tree cover was 11.2%, and the weighted RMSE between them, in which five tree cover strata (0%–20%, 21%–40%, 41%–60%, 61%–80%, and 81%–100%) were weighted equally, was 14.2%. The result was compared to existing global percent-scale tree cover datasets. We found that existing datasets had some pixels with estimation error of more than 50% and each map had different characteristics. Our map could be an alternative dataset and other existing datasets could be modified using our resultant map.