A 10-m annual grazing intensity dataset in 2015-2021 for the largest temperate meadow steppe in China.

Mapping grazing intensity (GI) using satellites is crucial for developing adaptive utilization strategies according to grassland conditions. Here we developed a monitoring framework based on a paired sampling strategy and the classification probability of random forest algorithm to produce annual grazing probability (GP) and GI maps at 10-m spatial resolution from 2015 to 2021 for the largest temperate meadow in China (Hulun Buir grasslands), by harmonized Landsat 7/8 and Sentinel-2 images. The GP maps used values of 0-1 to present detailed grazing gradient information. To match widely used grazing gradients, annual GI maps with ungrazed, moderately grazed, and heavily grazed levels were generated from the GP dataset with a decision tree. The GI maps for 2015-2021 had an overall accuracy of more than 0.97 having significant correlations with the statistical data at city (r = 0.51) and county (r = 0.75) scales. They also effectively captured the GI gradients at site scale (r = 0.94). Our study proposed a monitoring approach and presented annual 10-m grazing information maps for sustainable grassland management.