## 4 Discussion  
The concurrent availability of the MODIS record and the FLUXNET2015 database up to 15 years (2000-2014) for a large number of sites across the globe allowed us to examine the relationships between the MODIS-derived VIs and tower GPP at both monthly and annual scales and also to compare these relationships across sites and biomes globally. Our results showed that at the monthly scale there was a strong relationship between each VI and tower GPP for ten biomes except evergreen broadleaf forests. Previous studies also showed close relationships between VIs and GPP. For example, there was a moderate to strong relationship between the daily NDVI and daily GPP for cropland [10], deciduous forest [11], and evergreen forest [12]. Moderate to strong relationships between EVI and GPP at the daily scale were found for grassland [39], temperate evergreen broadleaf forest [40], and wetlands [21]. Previous studies also showed strong correlation between EVI2 and GPP for cropland [10] and evergreen  
Figure 12: The seasonal cycles and relationship of tower GPP, two environmental scalars (\(\mathrm{f_{Topt}}\) and \(\mathrm{f_{VPD}}\)), and PAR at the Daly River Savanna site (AU-Das, Australia) from 2010 to 2014: (**a**) environmental scalars (\(\mathrm{f_{Topt}}\) and \(\mathrm{f_{VPD}}\)) and PAR; (**b**) \(\mathrm{NIR_{V,BRDF}}\times\mathrm{f_{Topt}}\times\mathrm{F_{VPD}}\times\mathrm{PAR}\) and tower GPP; (**c**) relationships of tower GPP with \(\mathrm{NIR_{V,BRDF}}\), \(\mathrm{f_{VPD}}\), and PAR as well as \(\mathrm{NIR_{V,BRDF}}\) combined with environmental factors. forest [24]. A more recent study showed that the MODIS-derived VIs had strong correlation with tower GPP (R\({}^{2}\) = 0.55-0.65, p \(<\) 0.0001) at a number of flux sites across eight biomes [2]. The weak relationship for evergreen broadleaf forests found in our study may be caused by the following reasons. First, evergreen broadleaf forests, mainly distributed in the tropics, are more often covered with clouds. Satellite-based indicators are sensitive to cloud contamination or sun-sensor geometry, and the resulting changes in VIs can confound the real seasonality of forests [2]. Second, optical sensors still lack the capability to detect the full canopy activity of the tropical forests. Third, VIs reflect vegetation greenness and are not able to capture seasonal photosynthetic activity of evergreen trees because of the common decoupling of greenness and photosynthesis [41]. Finally, the EC technique can lead to large uncertainty in GPP estimates for these dense, multi-layered forests [42]. Monthly EVI performed slightly better in approximating GPP for seven biomes except ENF, COSH, and SAV than monthly NDVI. Previous studies also found that daily EVI was more useful for estimating GPP in both forest and crop ecosystems than NDVI [43; 44]. EVI was more strongly related to GPP than was NDVI likely because EVI does not saturate as rapidly as NDVI when vegetation greenness increases in dense vegetation. Our results showed that EVI was a more robust proxy for GPP than was EVI, which supported and substantially expanded the results of the previous studies validating such EVI use for GPP estimation at the daily scale for forest, cropland, and grassland ecosystems [19; 44; 45]. At short time scales, when the atmospheric effects are insignificant, the blue band does not contribute much additional signal about the land surface than the red band at the canopy level [22]. The two-band EVI2 has no significant loss of information on the land surface, and EVI2 could be even slightly better than the traditional EVI with three bands. Vegetation indices took advantage of NIR band with high reflective properties and red band with absorbed properties of foliage to gain proxies of surface greenness, which were then related to canopy physiology or GPP [23].