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Urban impervious surface area (UISA) and urban green space (UGS) are two core components of cities for characterizing urban environments. Although several global or national urban land use/cover products such as Globeland30 and FROM-GLC are available, they cannot effectively delineate the complex intra-urban land cover components. Here we proposed a new approach to map fractional UISA and UGS in China using Google Earth Engine (GEE) based on multiple data sources. The first step is to extract the vector boundaries of urban areas from China's Land Use/cover Dataset (CLUD). The UISA was retrieved using the logistic regression from the Landsat-derived annual maximum Normalized Difference Vegetation Index (NDVI). The UGS was developed through linear calibration between reference UGS from high spatial resolution image and the normalized NDVI. Thus, the China's UISA and UGS fraction datasets (CLUD-Urban) at 30-meter resolution are generated from 2000 to 2018. The overall accuracy of national urban areas is over 92 %. The root mean square errors of UISA and UGS fractions are 0.10 and 0.14, respectively. The datasets indicate that total urban area of China was 7.10 x 104 km² in 2018, with average fractions of 70.70 % for UISA and 26.54 % for UGS. The UISA and UGS increased with unprecedented annual rates of 1,492.63 km²/yr and 400.43 km²/yr during 2000-2018. CLUD-Urban can enhance our understanding of urbanization impacts on ecological and urban dwellers' environments, and can be used in such applications as urban planning, urban environmental studies and practices. The datasets can be downloaded from https://doi.org/10.5281/zenodo.3778424 (Kuang et al., 2020). [ABSTRACT FROM AUTHOR]

Accurate urban land-cover datasets are essential for mapping urban environments. However, a series of national urban land-cover data covering more than 15 years that characterizes urban environments is relatively rare. Here we propose a hierarchical principle on remotely sensed urban land-use/cover classification for mapping intra-urban structure/component dynamics. China's Land Use/cover Dataset (CLUD) is updated, delineating the imperviousness, green surface, waterbody and bare land conditions in cities. A new data subset called CLUD-Urban is created from 2000 to 2015 at five-year intervals with a medium spatial resolution (30 m). The first step is a prerequisite to extract the vector boundaries covered with urban areas from CLUD. A new method is then proposed using logistic regression between urban impervious surface area (ISA) and the annual maximum Normalized Difference Vegetation Index (NDVI) value retrieved from Landsat images based on a big-data platform with Google Earth Engine. National ISA and urban green space (UGS) fraction datasets for China are generated at 30-meter resolution with five-year intervals from 2000 to 2015. The overall classification accuracy of national urban areas is 92 %. The root mean square error values of ISA and UGS fractions are 0.10 and 0.14, respectively. The datasets indicate that the total urban area of China was 6.28 × 104 km² in 2015, with average fractions of 70.70 % and 26.54 % for ISA and UGS, respectively. The ISA and UGS increased between 2000 and 2015 with unprecedented annual rates of 1,311.13 km²/yr and 405.30 km²/yr, respectively. CLUD-Urban can be used to enhance our understanding of urbanization impacts on ecological and regional climatic conditions and urban dwellers' environments. CLUD-Urban can be applied in future researches on urban environmental research and practices in the future. The datasets can be downloaded from https://doi.org/10.5281/zenodo.2644932. [ABSTRACT FROM AUTHOR]