DOI: 10.5281/zenodo.10416851
title: Reassessing the climate mitigation potential of Chinese ecological restoration: the undiscovered potential of urban areas

description: 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 xu.dong@u.nus.edu.






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.
description: We take Beijing as an example to introduce the production process of this dataset in detail. We combined multisource remote sensing data with ERA5-land reanalysis data and selected 8 variables, NTL, IBI, POP, TEMP, PRE, WIND, SR and PRES, to explore the sensitivity of natural factors and human activity factors to urban surface temperature (UST) from systematic time series observations. We analysed the multiple regression pixel-by-pixel based on the partial least squares regression of 8 variables and normalized the regression coefficients by the standard deviation of each variable. All variables were standardized, so they were unitless and comparable. Tests show that the dominant factor in Beijing’s surface temperature from 2001 to 2018 is urban impermeability (UI).

Conceptual diagram by taking Beijing, China as an example (please see uploaded file "Take Beijing as an example.png"). a, The linear statistical relationship between urban greening (UG) and urban impermeability (UI) (Pearson's r = -0.738, p < 0.001) in the observation dimension of system time series, and the change in UI under the no-UG scenario. b, The linear statistical relationship between UI and UST under no-UG scenario (Pearson's r = 0.566, p < 0.001) in the observation dimension of system time series. And the change in UST under the no-UG scenario. c, The calculation process of simulated Sen_UST. d, Sen_UST (light green), Sen_NDVI (magenta), and Sen_IBI (navy blue) values of pixels (3 by 3) in a window in Beijing. The UST and IBI in this region showed a significant upward trend from 2001 to 2018, while the NDVI showed a significant downward trend. e, Time series heat map of UST, IBI, and NDVI in this window from 2001 to 2018.

We define the climate mitigation rate as the UG-induced UST trend divided by the simulated UST trend.