# Monitoring Spatial and Temporal Variabilities of Gross Primary Production Using MAIAC MODIS Data
## 3 Results  
### Monitoring Weekly and Monthly Temporal Variability of GPP Using the RSVIs  
All RSVIs correlated well with the weekly and monthly time series of GPP, but the correlations were slightly higher using the monthly than the weekly data (Figure 3, Table 1). The performances between the ecosystem types did not differ greatly, except the correlations for both weekly and monthly data were consistently highest and lowest for grasslands and evergreen broad-leaved forests, respectively. EVI and NIR\({}_{v}\) monitored the variability of GPP the best, accounting for \(\geq\)48% of the variance. CCI was the index, explaining the lowest variance in GPP for all ecosystem types using either weekly or monthly data. CCI, nonetheless, still accounted for averages of 19 and 28% of the GPP variability for the weekly and monthly time series, respectively. Only \(\leq\)15% of the variability of GPP, however, could be explained using the RSVIs when the same analysis was repeated after removing the seasonal component of the GPP and RSVI time series across all ecosystem types. Most of the ecosystem types, though, had no correlations. All RSVIs performed very similarly: the correlations between GPP and the RSVIs nearly disappeared when weekly or monthly anomalies were used. These results remained consistent after winter values from the analyses were removed to avoid biases because of snow cover (Table S2).  
The seasonal component of the time series, however, explained a large amount of the variance of monthly GPP (mean \(\pm\) standard error, 79.0 \(\pm\) 1.7%) and of the RSVIs (NDVI: 74.6 \(\pm\) 1.7%, EVI: 82.2 \(\pm\) 1.6%, NIR\({}_{v}\): 84.3 \(\pm\) 1.2%, CCI: 70.0 \(\pm\) 1.7%). Again, the results did not differ substantially for the weekly or monthly data. The RSVIs, therefore, monitored the seasonal cycle of GPP very well, at both weekly and monthly scales, but largely failed to monitor the anomalies of the GPP time series. Our analyses found that the performance of the RSVIs in explaining the variability of GPP, including its seasonal component, was better at sites where more of the variances of GPP and the RSVIs were explained by their seasonal component (Table 2). The interannual variability of GPP was also positively correlated with the goodness of fit of its relationships with the RSVIs using both monthly and weekly time series. A higher seasonality of GPP was also positively correlated with a better performance of NDVI and CCI in the weekly time series. CCI performed better at sites with larger GPP fluxes, and NDVI performed the best for grasslands. The interannual variability of GPP was the main factor explaining the performances of EVI, NIR\({}_{v}\), and CCI when the deseasonalised weekly time series was used. Only NIR\({}_{v}\) retained the same relationship when the monthly deseasonalised time series was used, and the interannual variability of EVI was positively correlated with the EVI performance when the weekly and monthly data were used. Climate had a very low influence on the relationship between RSVIs and GPP. Vapour-pressure deficit was positively linked to better performance of EVI and CCI using deseasonalised weekly time series. The same result was found for mean annual temperature when using deseasonalised monthly EVI time series.  
Figure 3: Boxplots of the variance explained (\(R^{2}\)) by the vegetation indices in the weekly (**a**) and monthly (**b**) raw (dark grey) and deseasonalised (light grey) time series of gross primary production (GPP).