# An Open Data Approach for Estimating Vegetation Gross Primary Production at Fine Spatial Resolution
## 3 Results  
### Evaluation of Fine Resolution GPP  
The fine-spatial-resolution GPP (GPP\({}_{\text{L}}\)) correlated well with GPP measured from 193 eddy covariance towers (GPP\({}_{\text{FLUX}}\)) (Figure 3A). Overall, the GPP\({}_{\text{L}}\) had a coefficient of determination (R\({}^{2}\)) of around 0.64 and its RMSE was 2.34 g C m\({}^{-2}\) d\({}^{-1}\). For the single vegetation type, the GPP\({}_{\text{L}}\) had a high level of agreement (R\({}^{2}\) > 0.5) with the GPP\({}_{\text{FLUX}}\) for eight vegetation types, excluding the EBF (Table 4). Moreover, the GPP\({}_{\text{L}}\) had a low bias (<1 g C m\({}^{-2}\) d\({}^{-1}\)) with the GPP\({}_{\text{FLUX}}\) at all the selected sites among vegetation types. The GPP\({}_{\text{L}}\) captured the seasonal profiles of the GPP\({}_{\text{FLUX}}\) at the typical sites of each vegetation type (Figure 4). For the forest stands, the GPP\({}_{\text{L}}\) tracked the seasonal change in GPP well in the selected sites. In particular, the GPP\({}_{\text{L}}\) showed similar seasonal profiles to the EBF stand. At the non-forest stands, the GPP\({}_{\text{L}}\) also captured the magnitude of the GPP\({}_{\text{FLUX}}\). The GPP\({}_{\text{L}}\) tracked the GPP\({}_{\text{FLUX}}\) well at the savanna site, except at the peak of the growing season (Figure 4H).  
Figure 3: Three-spatial-resolution GPP data compared to GPP observed at flux sites. The three series of GPP data: Landsat (**A**), MODIS (**B**), and AVHRR (**C**) were consistent with the date at 16-day intervals. The dashed line is the 1:1 line.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Landau** \\ **R\({}^{2}\)** \\ \end{tabular} & \begin{tabular}{c} **MODIS** \\ **R\({}^{2}\)** \\ \end{tabular} & \begin{tabular}{c} **AVHRR** \\ **R\({}^{2}\)** \\ \end{tabular} & \begin{tabular}{c} **Landsat** \\ **RMSE** \\ \end{tabular} & \begin{tabular}{c} **MODIS** \\ **RMSE** \\ \end{tabular} & \begin{tabular}{c} **AVHRR** \\ **RMSE** \\ \end{tabular} & \begin{tabular}{c} **Landsat** \\ **bias** \\ \end{tabular} & \
\begin{tabular}{c} **MODIS** \\ **bias** \\ \end{tabular} &
\begin{tabular}{c} **AVHRR** \\ **bias** \\ \end{tabular} \\ \hline CRO & **0.53** & 0.37 & 0.40 & **4.84** & 5.59 & 5.47 & \(\mathbf{-0.81}\) & 3.08 & 3.28 \\ DBF & **0.73** & 0.72 & 0.61 & **2.38** & 2.39 & 2.85 & \(\mathbf{-0.31}\) & \(\mathbf{-0.03}\) & 0.74 \\ EBF & 0.31 & 0.35 & **0.40** & 2.39 & 2.33 & **2.24** & 0.52 & \(\mathbf{-0.07}\) & 0.32 \\ ENF & 0.66 & **0.71** & 0.41 & 1.80 & **1.67** & 2.38 & \(\mathbf{-0.14}\) & \(\mathbf{0.11}\) & 0.59 \\ GRA & 0.62 & **0.63** & 0.52 & 2.31 & **2.28** & 2.61 & \(\mathbf{-0.08}\) & 0.41 & 0.80 \\ MF & 0.65 & **0.65** & 0.64 & 1.98 & **1.97** & 2.01 & \(\mathbf{0.16}\) & \(\mathbf{-0.53}\) & \(\mathbf{-0.44}\) \\ SAV & **0.72** & 0.66 & 0.50 & **1.33** & 1.46 & 1.78 & \(\mathbf{-0.02}\) & 0.69 & 0.78 \\ SHR & **0.70** & 0.68 & 0.51 & **0.97** & 1.01 & 1.24 & \(\mathbf{0.11}\) & \(\mathbf{-1.35}\) & \(\mathbf{-1.62}\) \\ WET & **0.60** & 0.44 & 0.50 & **2.18** & 2.59 & 2.46 & \(\mathbf{-0.01}\) & \(\mathbf{-0.06}\) & \(\mathbf{-0.17}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: GPP validation for different vegetation types. Bold indicates the best statistic. The unit of RMSE and bias is g C m\({}^{-2}\) d\({}^{-1}\).  
Figure 4: _Cont._