## 2 Materials and Methods
### Data Sources and Processing  
We used the Gap Filling and Savitzky-Golay filtering (GF-SG) method [34; 35], synthesizing NDVI data from Landsat and MODIS products (Table 2) with a spatial resolution of 30 m. Meteorological data, including monthly average temperature, monthly total precipitation [36; 37], and solar radiation data [38], were sourced from the Third Pole Environment Data Center ([[http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)]([http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)), accessed on 7 January 2024) (Table 2). Due to the lack of solar radiation data for 2022, the 2021 data are taken as the solar radiation data for 2022. We interpolated these datasets to a 30 m spatial resolution using Kriging and Inverse Distance Weighting (IDW) methods (Figure 2). We selected the first-order trend removal method to remove linear trends and used the Gaussian kernel function to implement Kriging interpolation. Meanwhile, we used the IDW of two power functions for interpolation, whose searching radius is 12 neighboring points. The land use/cover data were obtained from the annual China Land Cover Dataset (CLCD) (Table 2), which has a spatial resolution of 30 m [39]. The accuracy verification results based on 5463 independent reference samples show that the overall accuracy of the CLCD reaches 79.31% [39]. In Nanjing and Shaoguan, the main seven land types are farmland, forest, grassland, shrub, water, barren land, and impervious surface. We imported the processed data into the CASA model to obtain the study area's net primary productivity (NPP) from 2000 to 2022.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Type** & **Selected Band** & **Resolution** & **Period** \\ \hline CLCD & Land Cover & & 30 m, Yearly [39] & 2000–2022 \\ Landsat 5 TM & NDVI & Band 3 and Band 4 & 30 m, 16-day & 2000–2011 \\ Landsat 7 ETM & NDVI & Band 3 and Band 4 & 30 m, 16-day & 2012–2013 \\ Landsat 8 OLI & NDVI & Band 5 and Band 4 & 30 m, 16-day & 2014–2022 \\ MOD09Q1 & NDVI & Band 1 and Band 2 & 250 m, 8-day & 2000–2022 \\ Average Temperature & Temperature & & 0.833”, Monthly [36] & 1901–2022 \\ Total Precipitation & Precipitation & & 0.833”, Monthly [37] & 1901–2022 \\ Total Solar Radiation & Solar Radiation & & 2473 Stations [38] & 1960–2021 \\ Soil Type Distribution Data & Soil & & 1:1,000,000 & 1995 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of datasets used in this study.  
Figure 1: Locations of the study area. The background image shows the elevation in two regions based on the digital elevation model (DEM).  
Carbon density data were derived from land use/cover data, soil type distribution data, the literature, and field measurements. Soil type distribution data came from China's Second National Soil Survey (1:1,000,000), which we converted to a 30 m spatial resolution through vector-to-raster conversion. Vegetation average carbon density (Table S1) and soil average carbon density data (0-20 cm) (Table S2) were estimated from various literature sources [40; 41; 42]. To verify the accuracy of the CASA model in calculating carbon density, this research used forest sub-compartment survey data from the Guangdong Provincial Center for Forest Resources Monitoring, China, as validation data in the Shaoguan region in 2020 (Figure 2).