Reassessing the climate mitigation potential of Chinese ecological restoration: the undiscovered potential of urban areas

The dataset includes urban climate mitigation benefit data for all 371 cities and 1,721 clusters in China, as well as aggregate data of different ecological restoration projects and climate backgrounds. Combining multi-source high-resolution remote sensing data sets and the reanalysis dataset, the absolute value standard deviation method of multiple linear regression is used to extract the largest dominant factor (pixel-by-pixel calculation) of daytime surface temperature in each city except NDVI (as a greening indicator). Then, the time trends of surface temperature, NDVI, and dominant factors were calculated based on the Theil-Sen Median slope estimation method. Finally, based on the linear statistical relationship of the three indicators, the surface temperature trend under the no-greening scenario was constructed, and the difference between the simulated and observed surface temperature trends was used to characterize the urban climate mitigation benefits of the ecological restoration project. The data details are as follows: If you have any questions or comments, please feel free to contact Mr. Dong Xu via [e-mail]. Data name Spatial resolution Time resolution Source Unit Note MOD13A2 1000 m 16-Day USGS.a / UG index MOD11A1 1000 m Daily USGS.a Kelvin UST index MOD09A1 500 m 8-Day USGS.a / Calculate IBI index ERA5-Land reanalysis dataset 10000 m Day ECMWF.b / Sensitivity analysis Population density datasets 1000 m Annual WorldPop.c / Sensitivity analysis Global Urban Boundaries 30 m Annual Li et al. / Delineate LUBs Note: a, United States Geological Survey (https://earthexplorer.usgs.gov/). b, European Centre for Medium-Range Weather Forecasts (https://www.ecmwf.int/). c, WordPop (https://www.worldpop.org/). 1. Li X, Gong P, Zhou Y, et al. Mapping global urban boundaries from the global artificial impervious area (GAIA) data[J]. Environmental Research Letters, 2020, 15(9): 094044.