# Desertification Mitigation in Northern China Was Promoted by Climate Drivers after 2000
## 2 Dataset and Methodology
### Data Sources  
Net primary productivity (NPP) is the net amount of organic carbon sequestered via photosynthesis and serves as a sensitive indicator reflecting the impacts of climate change and human activities [46]. Considering a decrease in plant biomass as a partial indication of land degradation, changes in NPP were considered a proxy for desertification in this study, as NPP has been widely used to assess desertification (e.g., [47; 48; 49; 50]). In this study, NPP was calculated using the Carnegie-Ames-Stanford Approach (CASA) model, which relies on remote sensing (the normalized difference vegetation index (NDVI) and land cover data) and meteorological datasets. Specific information related to the data can be found in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Source** & **Spatial** & **Temporal** \\  & & **Resolution** & **Resolution** \\ \hline NDVI &
\begin{tabular}{c} Advanced Very High-Resolution \\ Radiometer (AVHRR) \\ \end{tabular} & \(1/12^{\circ}\) & 15 days \\ \hline \multirow{3}{*}{Land cover} & The multi-period land-use land & \multirow{3}{*}{30 m} & \multirow{3}{*}{-} \\  & cover remote sensing monitoring & & \\ \cline{1-1}  & dataset for China [51] & & \\ \hline \multirow{3}{*}{Solar radiation} & High spatial resolution (10 km) & \multirow{3}{*}{10 km} & \multirow{3}{*}{Monthly} \\ \cline{1-1}  & surface solar radiation dataset & & \\ \cline{1-1}  & with by merging sunshine hours & & \\ \cline{1-1}  & over China [52] & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data information. Figure 1: Location and subregions of the desertification-prone region (DPR) in NC. The NDVI data were derived from NOAA AVHRR satellite data, with outputs generated twice a month. The AVHRR NDVI has undergone intercalibration and corrections for atmospheric effects, cloud cover, and biases from the drift of satellite orbital [55]. It is regarded as the most reliable dataset for long-term NDVI trend analysis [56] and effectively represents vegetation conditions in China [57]. Monthly NDVI data were generated by applying a maximum-value compositing procedure to merge the 15-day data. The land-use data were obtained from a 1:10-scale multi-period thematic land-use database with manual visual interpretation, using Landsat remote sensing imagery data from the United States Landsat satellite as the main source of information. The solar radiation data were the result of fusing meteorological station datasets and the ISCCP-HXG cloud product through geo-weighted regression, and they had higher accuracy in long-term trend modeling than the ISCCP-HXG, GEWEX-SRB, and CMSAF-CLARA-A2 satellite radiation products [52]. The GPCC precipitation was based on quality-controlled data from 67,200 stations around the world with records lasting 10 years or more. The evapotranspiration data from the GLEAM maximized the recovery of evaporation information contained in current satellite observations of climate and environmental variables. ERA5 is currently commonly used for meteorological reanalysis. The GLASS NPP is based on the GLASS GPP algorithm, which is uniquely suited to reproducing inter-annual variability [58], and it is also mostly applied to the validation of NPP calculated by other models (i.e., CASA model) [59; 60]. To ensure temporal consistency and spatial comparability, the study period was set from 1984 to 2014, and all above data were interpolated onto a 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) grid by bilinear interpolation. The Pacific Decadal Oscillation (PDO) was defined by the empirical orthogonal function leading pattern of sea surface temperature anomalies in the North Pacific basin (typically, polewards from 20\({}^{\circ}\)N), and the Atlantic Multi-Decadal Oscillation (AMO) was identified based on the average anomalies of sea surface temperatures in the North Atlantic basin, typically over 0-80\({}^{\circ}\)N. As important factors affecting global climate change, the PDO and AMO have greatly impacted climate change in NC [61].