# Monitoring Spatial and Temporal Variabilities of Gross Primary Production Using MAIAC MODIS Data
## 5 Conclusions  
Overall, our results indicate that RSVI are a suitable tool to track different features of GPP. Grasslands were the ecosystems best predicted by RSVI, while evergreen broad-leaved forests were the worst. On average across different ecosystems, EVI and NIR\({}_{v}\) indices performed slightly better than NDVI, while CCI performance was clearly lower. However, when removing seasonality from the time series of GPP and RSVI, the correlation decreased considerably, suggesting that the main source of correlation between both was related to the seasonality of the site, as confirmed by our statistical models. We found that spatial variability in annual GPP was well captured by NDVI and CCI while seasonality was well captured by all RSVIs. However, spatial variability in trends and in interannual variability of GPP was not well captured by RSVI according to our analyses.  
The following are available online at [[http://www.mdpi.com/2072-4292/11/7/874/s1](http://www.mdpi.com/2072-4292/11/7/874/s1)]([http://www.mdpi.com/2072-4292/11/7/874/s1](http://www.mdpi.com/2072-4292/11/7/874/s1)), Table S1: List of the sites with starting and ending dates (FLUXNET Tier 1). Vegetation types (IGBP); ENF, evergreen needleleaved forest; EBF, evergreen broadleaved forest; DBF, deciduous broadleaved forest; MF, mixed forest; OSH, open shrubland; GRA, grassland; CRO, cropland; WET, permanent wetland. Table S2: Amounts of variance explained (\(R^{2}\), mean \(\pm\) standard error of the mean) for the raw and desseasonalised weekly and monthly time series of GPP by NDVI, EVI, NIR\({}_{v}\), and CCI after removing winter weeks (1-12 and 46-52) and months (December, January, February, and March). Significant coefficients are highlighted in bold. ENF, evergreen needleleaved forest; EBF, evergreen broadleaved forest; DBF, deciduous broadleaved forest; MF, mixed forest; OSH, open shrubland; GRA, grassland; CRO, cropland; WET, permanent wetland. \"NA\" stands for not available.  
[PERSON], [PERSON], [PERSON], and [PERSON] conceived and design the paper. [PERSON] analysed the data. All authors contributed substantially to the writing of the manuscript.  
This research was funded by the Spanish Government project CGL2016-79835-P (FERTWARM), the European Research Council Synergy grant ERC-2013-726 SyG-610028 IMBALANCE-P, and the Catalan Government project SGR 2017-1005. [PERSON] is a postdoctoral fellow of the Research Foundation-Flanders (FWO). [PERSON] acknowledges the support provided by the EU Horizon 2020 Research and Innovation programme under the [PERSON] grant (INDRO, grant no. 702717). Support from the NASA ABoVE program (award #NNX15 AT78A) for JG, RY, and GH is also acknowledged. The APC was funded by [PERSON] FWO postdoctoral fellowship.  
This work used eddy-covariance data acquired and shared by the FLUXNET community, including the networks AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, Carboltaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The data from the ERA-Interim reanalysis were provided by ECMWF and processed by LSCE. The FLUXNET eddy-covariance data were processed and harmonised by the European Fluxes Database Cluster, AmeriFlux Management Project, and Fluxdata project of FLUXNET, with the support of CDIAC, the ICOS Ecosystem Thematic Center, and the offices of OzFlux, ChinaFlux, and AsiaFlux.  
The authors declare no conflict of interest.