%0 Dataset
%T 30 meter resolution dataset of impervious surface area and green space ratio in Chinese cities (2000-2018)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/fadbbaf7-6d8c-461a-9db6-20136d2ec3b6
%W NCDC
%R 10.5281/zenodo.4034161
%A None
%K Impermeable surface;land use;random forest algorithm;GEE platform
%X Urban Impermeable Surface (UIS) and Urban Green Space (UGS) are the two core components that describe the characteristics of the urban underlying environment. However, urban impervious surfaces (UIS) and urban green spaces (UGS) are often embedded together in urban landscapes, with complex structures and composites. Hard classification or binary single type cannot effectively delineate spatially clear urban surface attributes. Although six mainstream datasets of global or national urban land use and land cover products with a spatial resolution of 30 meters have been developed, they only provide binary patterns or dynamics of a single urban land type and cannot effectively divide the quantitative components or structures of urban land cover. Here, we propose a new surveying and mapping strategy that utilizes the advantages of collaborative big data processing and manual interpretation, leveraging geographic knowledge to obtain multi temporal and segmented information on basic types of urban land cover nationwide