Table 2.

Basic information on land-cover products.

Dataset	Producer/Institution	Primary data sources	Classification method	Resolution
ESRI-GLC10	Impact Observatory and Esri7	Sentinel-2 optical imagery and manually dense markup	U-Net CNN	10 m
ChinaCropland10m	Fuzhou University11	Sentinel-1 SAR data and Sentinel-2 optical imagery	VSPS algorithm and MILS method	10 m
SinoLC-1	Wuhan University10	1 m Google imagery; three 10 m land-cover maps (weak labels); OpenStreetMap	L2HNet framework	1 m

Note: Abbreviations used in this Table 2 include U-Net CNN (U-Net Convolutional Neural Network), VSPS (Vegetation–Soil–Pigment–SAR), MILS (Mapping cropping Intensity based on combined consideration of vegetative and productive Stages), and L2HNet (Low-to-High Network).