# Monitoring Spatial and Temporal Variabilities of Gross Primary Production Using MAIAC MODIS Data
## 4 Discussion
### Seasonality Induces Temporal Correlations between GPP and the RSVIs
The higher spatial homogeneity of grassland ecosystems may also improve estimates of greenness. The two open shrublands in this study (ES-LgS and ES-Ln2, Table S1) are Mediterranean shrublands with very low vegetation densities and subjected to very high water deficits that reduce photosynthesis [38], even though the vegetation may remain green [12]. Bare soil can also potentially bias RSVI values [39]. The evergreen broadleaved forests were also Mediterranean (FR-Pue, IT-Cp2, IT-Cp2) with typically low RSVI seasonalities and water deficits during summer [40; 41]. These typically Mediterranean characteristics may substantially reduce the performance of RSVIs in monitoring GPP in these types of ecosystems, in contrast to previous studies that found good correlations between productivity and RSVIs monitoring tree growth [12] and fruit production [2] in similar Mediterranean forests. Performance of the RSVIs in Explaining the Spatial Variabilities of Seasonality, Interannual Variability, and Trends of GPP  
Site-average NDVI and CCI monitored the spatial variability of site-average GPP moderately well, but EVI and NIRV did not (Figure 4). None of the RSVIs using average peak-season values for each site estimated productivity well. The difference in the performance when the average peak-season and annual average for each site were used may have been due to the larger effect of some circumstances on peak-season values over a short period of time, and annual averages may better integrate the overall annual state of the ecosystem. Based on our results, we would recommend using annual averages of NDVI and CCI to infer the productivities of different sites. The spatial variability of seasonality was monitored much better than average GPP (Figure 5). All indices performed well, but EVI and NIRV performed better than CCI and NDVI. These differences may be due to the higher sensitivity of EVI than NDVI to high levels of greenness, which would improve monitoring during the greenest periods of the growing season when NDVI can saturate. NIRV may then behave similarly to EVI [24]. In contrast, CCI may not perform as well as EVI and NIRV due to its small range of variability of seasonality amongst the sites. Our results indicated that sites where the interannual variabilities of the RSVIs were higher did not have higher interannual variabilities of GPP (Figure 6). The range of interannual variabilities was also clearly lower for the RSVIs than GPP. The RSVI estimates were less variable amongst years than the estimates of GPP, which may account for the lack of a significant correlation. The trends of GPPwere also not well monitored by the RSVIs (Figure 7). Even the trends of NDVI were significantly negatively correlated with the trends of GPP (Figure 7b). Several studies have previously found that RSVIs were useful for monitoring the trends of the growing season, browning, or greening [42; 43]. Our results, however, suggest that the trends of GPP may not follow the same trends of growing seasons or greening. This discrepancy may arise because: (i) equal green standing biomasses may photosynthesis differently depending on the environmental conditions, as discussed above (e.g., in Mediterranean sites experiencing droughts) or (ii) the combination of the two sources of error (GPP and RSVIs) were too large to correctly assess interannual trends, especially for single pixels. Using larger areas of land may have provided more positive results, similar to previous reports. Our results nonetheless contradict previous reports suggesting that RSVIs calculated using data from the MAIAC database improve their performance for the standard MODIS products [44]. Research comparing both sources of data is therefore warranted.