# Spatiotemporal Dynamics of Net Primary Productivity in China's Urban Lands during 1982-2015
## 2 Materials and Methods  
### Data Sources  
In this study, China's urban land covers in the 1980s, 1995, 2000, 2005, 2010, and 2015 were obtained from the Chinese land-use/cover dataset (CLUD) [18]. The CLUD is based on a variety of multispectral satellite data, including data from the Landsat multi-spectral scanner (MSS), thematic mapper (TM), enhanced thematic mapper plus (ETM+), operational land imager (OLI), and the Huanjing-1 (HJ-1) satellite. The land use/land cover (LULC) maps of CLUD were extracted through human-computer interaction and visual interpretation after a series of preprocessing methods [18; 19]. Through field surveys, [PERSON], et al. [18; 19] found that the average classification accuracy of the six primary land cover categories (i.e., cropland, forests, grassland, water bodies, built-up land, and unused land) of this dataset reached 94.3% [20; 21]. We extracted the urban land boundaries for the 1980s, 1995, 2000, 2005, 2010, and 2015 by converting raster urban land of CLUD to vector urban boundaries using ENVI software. In addition, we clipped NPP data based on urban boundaries in the 1980s and 2015 using ENVI software, and focused on analyzing changes in NPP in these ranges.  
In addition, [PERSON], et al. [21] produced annual 30-m land use/land cover maps of China for 1980-2015 using multisource remote sensing data based on the breaks for additive seasonal and trend (BFAST) algorithm. This dataset provides open access products with a 1 km resolution ([[http://doi.org/10.5281/zenodo.3923728](http://doi.org/10.5281/zenodo.3923728)]([http://doi.org/10.5281/zenodo.3923728](http://doi.org/10.5281/zenodo.3923728))), in which each pixel value represents the proportion of various LULC types. This dataset is more continuous than the CLUD and corresponds to the annual NPP. We obtained this dataset to represent the dynamic changes in the proportion of land cover types within cities and surrounding areas and explore the impact of changes in LULC on NPP.  
We collected monthly meteorological station data from the Chinese National Meteorological Information Center (data.cma.cn) from 1982 to 2015. To explore the importance and contribution of meteorological factors to NPP of urban lands and surrounding areas, the point-based data were spatially interpolated to produce continuous raster images. Firstly, we used stepwise regression in SPSS 22 to construct a three-dimensional quadratic trend surface of the monthly meteorological data and the elevation, longitude and latitude of meteorological stations [22; 23]. Secondly, we calculated the spatially gridded simulated meteorological dataset based on the above regression model using the Band math tool in ENVI 5.3. Thirdly, the residual values were calculated by subtracting the simulated values from the actual values. The residual values were spatially interpolated to raster data based on the inverse distance weighted (IDW) method [24] using ArcGIS. Finally, the simulated meteorological raster and residual raster data were added to obtain the monthly spatial meteorological data with a resolution of 1 km. This study aggregated the meteorological data at an annual scale and obtained the annual average air temperature, annual cumulative precipitation, and annual cumulative sunshine duration.