# Monitoring Spatial and Temporal Variabilities of Gross Primary Production Using MAIAC MODIS Data
## 4 Discussion  
### Seasonality Induces Temporal Correlations between GPP and the RSVIs  
Our results indicated that all four RSVIs estimated GPP well when weekly and monthly data were used (Figure 3, Table 1). These findings are consistent with previous studies that have investigated the relationship between productivity and RSVIs [9; 10; 23; 24]. We also found, however, a sharp decrease in the performances of the RSVIs when the seasonal component was removed from the time series: the correlations between the temporal anomalies of the RSVIs and GPP were very weak (Table 1). These results remained unaltered even when winter values were removed from the analyses (Table S2), perhaps because GPP and the RSVIs may all share a very similar seasonal component that is also correlated mostly with the amount of photosynthesis at the site level. This hypothesis was further supported by our findings indicating that the larger the variance explained by the seasonalities of GPP and the RSVIs, the better the temporal correlation between the RSVIs and GPP when the time series included the seasonal component. The combined errors of the two sources (i.e., measurements from eddy-covariance towers, about 30% of the annual amount [37], and the calculations of the RSVIs) were larger than the variability of the anomalies in the RSVIs and GPP, masking a good relationship between both sets of data. This finding was also supported by the better correlations between the temporal anomalies of the RSVIs and GPP at sites with a higher interannual variability of GPP, especially using weekly deseasonalised time series. Further improvements in both fields (i.e., eddy covariance and  
Figure 7: Spatial relationships between the trends of GPP and the trends of the peak-season (**a**) and yearly average (**b**) remotely sensed vegetation indices (RSVI). Only sites with seven or more years of data for gross primary production (GPP) and RSVI are used. Shaded areas represent the standard error of the slopes. Nonsignificant relationships are indicated with “n.s.”. remote sensing) will help to improve the performance of RSVIs when monitoring GPP. The capacity to infer anomalies of GPP from RSVIs using the MAIAC data set and our methodology is currently low, accounting only for \(\leq\)15% of their variability in the best cases (Table 1). The correlation between RSVIs and GPP may also be hindered because spectral signatures may react with a certain delay to changes in rainfall and temperature compared to the resulting GPP. The temporal aggregation of the data, however, was not decisive for monitoring GPP. The weekly and monthly time series performed very similarly, albeit correlations were slightly stronger when monthly data were used. These results may be due to the smoothing of noise, artefacts, and outliers over larger sets of data when longer time periods are aggregated, decreasing the variance of the time series. Similarly, all four RSVIs provided very similar results, especially NDVI, EVI, and NIRV. CCI, however, always had the lowest correlations for all ecosystem types (Table 1), even for evergreen needle-leaved forests, for which CCI has very accurately monitored phenology on a daily scale [9]. Using daily data in our analyses would likely have improved the performance of CCI, although its performance improved slightly when monthly instead of weekly data were used. The performances of the RSVIs differed little amongst the ecosystem types. Evergreen broadleaved forests, open shrublands, and croplands had the lowest correlations with the RSVIs (Table 1). The RSVIs generally performed better for grasslands, even when deseasonalised time series were used. This result may indicate that these ecosystem types behaved differently than the other types. Grasslands often respond quicker to adverse environmental conditions (e.g., drought) than do evergreen broadleaved forests, which can sustain the same green biomass despite reductions in GPP due to adverse conditions.