## 2 Study Area, Data Sources, and Methodology
### Methodology  
For the case study, forests, croplands, rangelands, and urbanized areas were mapped based on the land cover classification schema (Table 1). The urbanized areas are mainly covered by imperious material but mixed with green spaces from road-side trees, gardens, or other recreation spots, and thus potentially contribute to carbon sequestration. The carbon uptake potential was assessed based on historical carbon capture capacity for  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Class \#** & **Area (\%)** & **Classification** & **Examples** \\ \hline
1 & 12.7 & Waters & Rivers, ponds, lakes, oceans, flooded salt plains. \\
2 & 45.4 & Trees/forests & Wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamps or mangroves. \\
5 & 7.9 & Crops & Corn, wheat, soy, fallow plots of structured land. \\
7 & 32.2 & Urbanized areas & Houses, dense villages/towns/cities, paved roads, asphalt. \\
11 & 1.3 & Rangelands & Natural meadows and fields with sparse to no tree cover; open savanna with few to no trees; parks/golf courses/lawns; pastures; moderate to sparse cover of bushes, shrubs, and tufts of grass; savannas with very sparse grasses, trees, or other plants. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Land (vegetation) cover classification schema.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Data Type** & **Data Description and Variables** & **Data Sources** \\ \hline Climate data & A number of variables are included: monthly minimum, mean and maximum temperature, monthly precipitation, monthly sun shortwave radiation, average dewpoint temperature, surface pressure, soil moisture, vapor pressure deficit (VPD), and reference evapotranspiration & ERA5-L and TerraClimate, both at monthly scale, are combined; it provides a consistent view of the evolution of land variables over several decades at a spatial resolution 5 km [43]. TerraClimate is a dataset of monthly climate and climatic water balance for global terrestrial surfaces, with coarser spatial resolution (\(\sim\)10 km) [44]. Sentinel-2 multispectral instrument (MSI), combined revisit time of 5 days. The monthly NDVI can be obtained by maximum composite and cloud gap filling [46]. \\ Soil cover & Dynamics of vegetation greenness proxied by monthly maximum NDVI which indicates the part, or the effective absorption, of solar radiation and thus vegetation productivity. Soil properties are largely reflected in soil classes. The same soil type usually shows similar contribution/constraint to vegetation growth. Soil cover data can map the difference in soil type distribution. Landforms represent the combined effect of slope, surface roughness, and local convexity for a given location, which is important for vegetation growth. Vegetation (land) cover determines the base level for vegetation productivity. For example, forests usually outperform grassland in vegetation productivity. Carbon uptake potential can be evaluated for the same land cover type. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data description and data sources.  
different ecosystems. We applied two phases to assess the potential increase of carbon capture in GGA. We first mapped the distribution of carbon uptake proxied by NPP. We then applied the segmentation of environmental factors and neighborhood analysis to compute the carbon uptake potential.