  Global forest cover is critical to the provision of certain ecosystem
services. With the advent of the google earth engine cloud platform, fine
resolution global land cover mapping task could be accomplished in a matter of
days instead of years. The amount of global forest cover (GFC) products has
been steadily increasing in the last decades. However, it's hard for users to
select suitable one due to great differences between these products, and the
accuracy of these GFC products has not been verified on global scale. To
provide guidelines for users and producers, it is urgent to produce a
validation sample set at the global level. However, this labeling task is time
and labor consuming, which has been the main obstacle to the progress of global
land cover mapping. In this research, a labor-efficient semi-automatic
framework is introduced to build a biggest ever Forest Sample Set (FSS)
contained 395280 scattered samples categorized as forest, shrubland, grassland,
impervious surface, etc. On the other hand, to provide guidelines for the
users, we comprehensively validated the local and global mapping accuracy of
all existing 30m GFC products, and analyzed and mapped the agreement of them.
Moreover, to provide guidelines for the producers, optimal sampling strategy
was proposed to improve the global forest classification. Furthermore, a new
global forest cover named GlobeForest2020 has been generated, which proved to
improve the previous highest state-of-the-art accuracies (obtained by Gong et
al., 2017) by 2.77% in uncertain grids and by 1.11% in certain grids.
