Temperate savanna shares a wide geographical distribution. It plays a crucial role in supporting a substantial portion of rangelands and livestock, thereby contributing significantly to human livelihoods.We develop a new temperate savanna identification method for mapping the temperate savanna in Northeastern China within a minimum identifiable area of 5000 m2 by integrating Unmanned Aerial Vehicle(UAV) imagery with high-resolution satellite imagery of Sentinel-1 and Sentinel-2 by using Random Forest regression and Classification and Regression Tree algorithms. The training and validation datasetswere collected from UAV imagery.The proposed method achieved with the overall accuracy of 0.82 for savanna identification.Temperate savanna in Northeastern China covering a total area of 170,000 km2.This map enhances our understanding of the spatial extent and area of temperate savanna. The derived method provides a new insight to explore the extent of regional and global savannain the future.The code and generated maps are publicly accessible. To make the method to reproduce and exploit and to promote its usage, we have provided users with the python code used to produce temperate savanna map. The generated maps of fractional woody vegetation cover, fractional herbaceous vegetation cover and temperate savanna in Northeastern China are stored in TIFF format, including the fractional woody and herbaceous vegetation cover map of 20 m with the file name of FWVC20m.tif and FHVC20m.tif, and the temperate savanna map at 70.7 m (70.7 m ×70.7 m equivalent to unit area scale of 5000 m2) with the file name of tsavanna71m.tif. In temperate savanna map, we recorded temperate savanna as “1”. All image data can be viewed by geographic information system software, such as ArcGIS.The UAV imagery dataset consists of 75 TIFF files that are publicly accessible through Figshare (https://doi.org/10.6084/m9.figshare.27651468.v1).

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