# Monitoring Spatial and Temporal Variabilities of Gross Primary Production Using MAIAC MODIS Data
## 3 Results
### Monitoring Spatial Variability, Seasonality, Interannual Variability, and Trends
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**MAT** & **VPD** & **IGEP** & **GTP** & **GFP PV** & **RSVI PV** & **GFP PV\%** & **RSVI** & **GFP IPV** & **RSVI IPV** & **R2** \\ \hline \multicolumn{1}{c}{**Weekly**} & & & & & & & & & \\ NDVI & & GRA & & 0.49 \(\pm\) 0.12 & & 0.45 \(\pm\) 0.11 & 0.24 \(\pm\) 0.08 & & 0.81 \\ EVI & & & & & 0.63 \(\pm\) 0.08 & 0.51 \(\pm\) 0.07 & 0.26 \(\pm\) 0.08 & & 0.76 \\ NR\({}_{\
u}\) & & & & 0.60 \(\pm\) 0.09 & 0.47 \(\pm\) 0.09 & 0.25 \(\pm\) 0.09 & & 0.72 \\ CCI & & & 0.36 \(\pm\) 0.12 & 0.44 \(\pm\) 0.12 & 0.47 \(\pm\) 0.10 & & & 0.48 \\ \hline \multicolumn{1}{c}{**Weekly anomalies**} & & & & & & & 0.00 \\ EVI & 0.24 \(\pm\) 0.12 & & & & & & 0.27 \(\pm\) 0.12 & 0.43 \(\pm\) 0.13 & 0.33 \\ NR\({}_{\
u}\) & & & & & & 0.41 \(\pm\) 0.12 & 0.17 \\ CCI & 0.50 \(\pm\) 0.12 & & & & & 0.25 \(\pm\) 0.12 & 0.29 \\ \hline \multicolumn{1}{c}{**Monthly**} & & & & & & & & \\ EVI & & & & & & 0.95 \(\pm\) 0.09 & & 0.56 \(\pm\) 0.09 & 0.68 \\ NR\({}_{\
u}\) & & & & & 0.63 \(\pm\) 0.09 & 0.52 \(\pm\) 0.09 & 0.19 \(\pm\) 0.09 & & 0.65 \\ CCI & & & & & 0.67 \(\pm\) 0.12 & 0.31 \(\pm\) 0.10 & 0.30 \(\pm\) 0.11 & & 0.59 \\ \hline \multicolumn{1}{c}{**Monthly anomalies**} & & & & & & & & \\ EVI & 0.34 \(\pm\) 0.15 & & & & & & 0.00 \\ NR\({}_{\
u}\) & & & & & & & 0.31 \(\pm\) 0.15 & 0.21 \\ CCI & & & & & & 0.30 \(\pm\) 0.13 & & 0.09 \\ \hline \multicolumn{1}{c}{**Romote Sens.**} & & & & & & & & \\ \end{tabular}
\end{table} 
Table 2: Summary of the statistical models indicating the controls of the performances of the RSVIs explaining weekly and monthly GPP and their standardised anomalies. Values indicate the standardised coefficients (\(\beta\) weights) \(\pm\) standard error of the mean of each predictor except for the vegetation type (IGBP), indicating the vegetation type with the statistically best performance. GRA, grassland; MAT, mean annual temperature; VPD, mean annual vapour-pressure deficit; GPP, average GPP; GPP PV, seasonality of GPP; RSVI PV, seasonality of the vegetation indices; GPP PV%, variance explained by the seasonality of the GPP; RSVI PV%, variance explained by the seasonality of the vegetation indices; GPP IPV, interannual variability of GPP; RSVI IPV, interannual variability of the vegetation indices. Figure 4: Spatial relationships between average GPP and the four remotely sensed vegetation indices (RSVI) using the average using peak-season (**a**) and yearly (**b**) averages. Shaded areas represent the standard error of the slopes. Nonsignificant relationships are indicated with “n.s.”. The average interannual variability of GPP for each site was not well monitored by the average interannual variability of the RSVIs: neither peak-season nor yearly average interannual variability of the RSVIs were significantly correlated with the interannual variability of GPP (Figure 6). Again, the range of variability was clearly smaller for CCI than the other RSVIs, indicating a lower interannual variability than for the other RSVIs. The trends of annual GPP were similarly not well monitored by any of the RSVIs, using either peak-season values or yearly averages (Figure 7). All indices had negative slopes with the trends in annual GPP when the trends of the yearly average RSVI values were used, being even significant for NDVI (Figure 7b). Figure 5: Spatial relationships between GPP seasonality and the seasonalities of the four remotely sensed vegetation indices (RSVI). Seasonality was calculated as the intra-annual PV of the monthly averages. Shaded areas represent the standard error of the slopes. Figure 6: Spatial relationships between the interannual variability of GPP and the interannual variabilities of peak-season (**a**) and yearly average (**b**) remotely sensed vegetation indices. Nonsignificant relationships are indicated with \"n.s.\".