## 4 Discussion
### How Does Circumvent the Problem of Soil Drought Limitation? A central question still remains unsolved: How to include soil drought limitations in the MODIS GPP algorithm. As seen previously, FPAR is relatively easy to evaluate from remote sensing. On the other hand, LUE has been proved to be more difficult to estimate because: (1) it is based on large spatial-scale meteorological data available from the NCEP-DOE Reanalysis II dataset [21]; (2) \(\mathrm{LUE_{max}}\) and parameters of the climate scalars are obtained from lookup tables on the basis of a limited number of vegetation types and (3) soil drought is not included in the calculation. Three axes have been followed to tackle the deficiency of MODIS GPP in soil water-limited ecosystems. The first one is to find remotely-sensed proxies of GPP, the second one is to find a remotely-sensed surrogates of LUE and finally the third one is to develop parallel algorithms that estimate evaporation fluxes and derive a soil water limitation scalar or directly measure the soil water storage. It would be easier and more direct if we could base GPP of water-limited ecosystems only from remotely-sensed data and thus have continuous estimates at the spatial resolution of the satellite data. A lot of works concerning a large range of vegetation types have been interested in the extent to which GPP could be estimated directly from greenness indices such as the enhanced vegetation index (EVI) without direct estimation of LUE [79, 80]. This approach has been shown to be very useful in up scaling EC fluxes at continental scale [81]. Unfortunately, poor correlations between EVI and EC GPP have been identified for water-limited sites [11, 82]. Moisture vegetation indices (MVI) combining NIR and SWIR in the middle infrared interval wavelengths (1200 to 2100 nm) have also been widely investigated to assess drought conditions as inter-annual precipitation surplus/deficit [83], or more specifically its impact on vegetation moisture content and water stress [84, 85] and in turn fire risk [86], but with decreasing efficiency when tree cover increases [87]. Differential plant responses to soil water deficit and desiccation rates however make it difficult to directly relate leaf moisture content to stomatal closure and subsequent controls on GPP. Some studies suggested that inclusion of MODIS land surface temperature (LST) or the land surface water index LSWI [11, 82] can partly address this limitation. LST could be used to restrict the active vegetation period and provide a measure of water limitation through its correlation with vapor pressure deficit in climates where VPD pattern captures the seasonality of soil drought. In EBF ecosystems including our site, the photochemical reflectance index (PRI) has been successfully applied [24, 88] and constitutes a promising basis for further developments. PRI has been shown to correlate well with LUE. A strong limitation of PRI is that the originally proposed reference band for PRI is not available on MODIS. We tested the reference bands 1 (620-670 nm), 4 (545-565 nm), 12 (546-556 nm), 13 (662-672 nm), and 14 (673-683 nm) [24]. MODIS spectral band 1 turned out to be the most suitable reference band, followed by the narrow red bands 13 and 14. The strongest correlation between LUE and PRI was also found when considering only a narrow range of viewing zenith angles at a time reducing drastically the potential availability of useful data. However, this indicates that, at site level, MODIS-based PRI is very competitive as a proxy for LUE. Despite the potential advantages of using PRI to estimate _LUE_ at site-level, we could not establish a universally applicable LUE model based on MODIS PRI. Models that were optimized from a pool of data from several contrasted sites did not perform well [89]. Rather than researching remotely-sensed surrogates of LUE, the last option is to evaluate evaporation flux in parallel and derive a water limitation index of the vegetation cover or measure directly the soil water storage.