# An Open Data Approach for Estimating Vegetation Gross Primary Production at Fine Spatial Resolution
## 2 Materials and Data
### Generating GPP on GEE  
We generated the 30-meter-spatial-resolution GPP at 16-day intervals on the GEE platform. The GEE is an open-access platform and provides access to Landsat 5, 7, and 8 data from 1984 to the present. Before we generated the GPP, we needed to prepare the time-series-reconstructed NDVI data, climatic data, and vegetation classification data. We summarized the climatic data at each 16-day interval. The PAR was transferred to the GEE platform from the short-wave radiation (SW) dataset from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS), calculated as:  
\[\text{PAR}=\frac{\sum\text{SW}_{\text{6h}}\times 0.45\times 6\times 3600}{100000 \times 16} \tag{6}\]  
where the original SW\({}_{\text{6h}}\) is in W m\({}^{-2}\). After transferring the PAR from Equation (3), it was changed to MJ m\({}^{-2}\) day\({}^{-1}\). Air temperature and VPD were derived from the ERA5 dataset ([[https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5)]([https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5)), accessed on 20 April 2022). The VPD was calculated from 2-m daily mean dewpoint temperature and 2-m air temperature at each 16-day interval [12]. We used a similar method to obtain a 16-day mean surface pressure. The CO\({}_{2}\) concentration at each 16-day interval was averaged from the nearest month's CO\({}_{2}\) data in the dataset of Trends in Atmospheric Carbon Dioxide ([[https://gml.noaa.gov/ccgg/trends/data.html](https://gml.noaa.gov/ccgg/trends/data.html)]([https://gml.noaa.gov/ccgg/trends/data.html](https://gml.noaa.gov/ccgg/trends/data.html)), accessed on 31 December 2021). We used the dynamic land-cover classification map (GLC-FCS30, [PERSON] et al., 2021 [17], [[https://data.casearth.cn/thematic/glc_fc330?lang=en_US](https://data.casearth.cn/thematic/glc_fc330?lang=en_US)]([https://data.casearth.cn/thematic/glc_fc330?lang=en_US](https://data.casearth.cn/thematic/glc_fc330?lang=en_US)), accessed on 15 May 2022), which changed every five years from 1980 to 2020, with a fine classification system at 30-meter spatial resolution to classify the vegetation. The data sources are shown in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Quality Definition**} & **Data Gap (x in days) between Two** & & \\  & **Nearest Observed NDVI in** & **QCtag** & **NDVI-Filled Method** \\  & **16 Days Intervals** & & \\ \hline Reconstructed NDVI based & 0 & 0 & Not applied \\ on observation & 0 \(<\) x \(\leq\) 48 & 1 & Linearly interpolated from the nearest two available NDVI \\ Reconstructed NDVI based on & & & Linearly interpolated from the nearest two available NDVI \\ long-term gap-filling & 48 \(<\) x & 2 & Linearly interpolated from the nearest two available NDVI \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data quality tag of gap-filled and reconstructed NDVI. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Data**} & **Specific Name** & **Transferred** & **Spatial** & **Data Source or** \\  & **Unit** & **Resolution** & **Reference** \\ \hline NDVI & Normalized-difference & Unitless & 30 m & This study \\ Shortwave radiation & Downward short-wave radiation & MJ m\({}^{-2}\) day\({}^{-1}\) & 0.5\({}^{\circ}\) & NCEP \\  & Flux surface 6-h average & & & \\ Surface pressure & Surface pressure & Pa & 0.5\({}^{\circ}\) & NCEP \\
2-m air temperature & Mean 2-m air temperature & \({}^{\circ}\)C & 0.25\({}^{\circ}\) & ERA5 \\
2-m dewpoint temperature & Dewpoint 2-m temperature & \({}^{\circ}\)C & 0.25\({}^{\circ}\) & ERA5 \\  & & & & https: \\  & & & & //gml.noaa.gov/ccgg/ \\ CO\({}_{2}\) & Global monthly mean CO\({}_{2}\) & ppm & / & trends/gl_trend.html, \\  & & & & accessed on 31 \\  & & & & December 2021 \\ Vegetation & GLC-FCS30 & & 30 m & [17] \\ classification map & & & & \\ \hline \hline \end{tabular}
\end{table}