## 2 Materials and Methods
### Data Sources  
At the annual scale, the spatial and temporal changes of ecosystem carbon storage are relatively weak. Therefore, this paper takes five years as the time interval to select the data of the study area and selects the land use image data of the four time points of 2005, 2010, 2015 and 2019. The land use data of the areas in the Taihang Mountains in 2005, 2010, 2015, and 2019 were obtained from the Resource and Environment Science and Data Center ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)) (accessed on 28 January 2022)). According to the research needs, this paper took satellite remote sensing images as the data source. It classified the land use types into six types: arable land, grassland, forest, water, construction land, and unused land through visual interpretation. The carbon density data of this study came from relevant data of existing references [22; 25], and the data were revised according to the research needs to obtain the carbon density of various land use in the Taiwan Mountains (Table 1). Considering the natural conditions of the Taihang Mountains and referring to relevant literature [28; 29; 30; 31; 32; 33; 34], this paper selects elevation, slope, Normalized Difference Vegetation Index (NDVI), temperature, precipitation, soil texture, population density, and GDP as driving factors for spatial differentiation of regional carbon storage. Elevation data were obtained from the geospatial data cloud platform ([[https://www.gscloud.cn/](https://www.gscloud.cn/)]([https://www.gscloud.cn/](https://www.gscloud.cn/)) (accessed on 18 March 2022)) with a resolution of 90 m. Slope data were calculated and extracted by ArcGIS software. The NDVI of each period was obtained from the Resource and Environment Science and Data Center ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)) (accessed on 20 March 2022)), which was transformed by Mosaic, Projection, with a resolution of 1 km. The annual average temperature and precipitation data of the Taihang Mountains were collected from 390 meteorological stations in various periods, all of which were obtained from the National Meteorological Science Data Center ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)) (accessed on 18 March 2022)). These data were interpolated separately using ANUSPLIN software to obtain the spatially interpolated data of annual mean temperature and annual precipitation for \begin{table}
\begin{tabular}{c c c c c} \hline
**Type** & **C\({}_{\text{above}}\)** & **C\({}_{\text{below}}\)** & **C\({}_{\text{soil}}\)** & **C\({}_{\text{dead}}\)** \\ \hline farmland & 2.19 & 0.42 & 90.16 & 0.00 \\ grassland & 38.99 & 7.80 & 103.98 & 1.90 \\ forest & 0.65 & 3.38 & 83.69 & 0.10 \\ water & 6.38 & 0.00 & 170.13 & 0.00 \\ construction & 5.63 & 0.00 & 68.99 & 0.00 \\ land & 0.00 & 0.00 & 0.00 & 0.00 \\ \hline \end{tabular} Note: The data were obtained with corrections based on literature 22 and 25. C\({}_{\text{above}}\): all living organisms on the surface; C\({}_{\text{below}}\): all living organisms below the surface; C\({}_{\text{soil}}\): in mineral and organic soils; C\({}_{\text{dead}}\): all dead organic matter.  
\end{table} 
Table 1: Carbon density of various land use types in the Taihang Mountains (t-km\({}^{-2}\)). Figure 1: The geographical location of Taihang Mountains: (**a**) location of Taihang Mountains in China; (**b**) location of Taihang Mountains. (China Map from the website of China Standard Map Service ([[http://bzdt.ch.mnr.gov.cn/](http://bzdt.ch.mnr.gov.cn/)]([http://bzdt.ch.mnr.gov.cn/](http://bzdt.ch.mnr.gov.cn/)) (accessed on 28 January 2022)), the figure number is GS (2019) 1822). each period. The soil data were compiled according to the 1:1 million soil types map and the second soil census data. The data included three types: sand, silt, and clay, and the value was the percentage.