## 5 Results and Discussion
### Discussion  
Comparing FAPAR values retrieved from an analysis of remote sensing data to GPP values derived from field measurements acquired at a flux tower is a complex endeavor.  
One difficulty arises from the fact that remote sensing measurements correspond to instantaneous snapshots of the radiative state of the surface at the time of the satellite overpass (around 10:36 AM in the case of the MISR instrument), while in situ observations are taking place over the entire day and generate daily-averaged GPP values.  
Another drawback is the irregular availability of FAPAR data, as prevailing cloudiness prevents the observation of land surfaces during the rainy season, which is the period of intense growth for vegetation. This issue could be alleviated in at least four different ways:  
* Spatial analysis: Terrestrial ecosystems exhibit a high degree of spatial variability, due to the large number of plant species (especially in protected environments, such as the Kruger National Park), combined with a potentially similar variability in soil properties. These spatial fluctuations may be further enhanced by patchy precipitations, in particular in convective systems. However, spatial patterns may subsist over periods of weeks or more, because plants remain in place. Hence, if relevant spatial correlations can be established (e.g., by kriging), it may be possible to infer the likely value of FAPAR at one location where it is missing on the basis of its value at another location, provided both places share a common evolution.
* Temporal analysis: When excessively long gaps arise in the remote sensing data, it may be sufficient to interpolate the missing values on the basis of the last value available before and the first value after the gap. This approach tends to underestimate the actual variability of the variable (FAPAR in this case), as it will never generate higher (or lower) values than those effectively observed.
* Other sources: It may also be possible to ingest other data products. Indeed, FAPAR products are available from other instruments, such as NASA's MODIS instrument (over the period of interest here). However, that approach carries its own set of complexities, as the algorithms used to generate these products (including to implement atmospheric corrections) are different so that the products may not match, or may introduce systematic biases or shifts.
* As far as the future is concerned, a particularly promising approach consists in operating constellations of identical instruments, in order to expand the number of opportunities to observe the surface despite the cloud coverage.  
Another lesson learned from this study is that it is difficult to fit a parametric double S-shaped model to a seasonal phenology where one or a few data points with an anomalously low value occurs right in the middle of the peak period of photosynthetic activity. These events can occur as a result of multiple causes, including instrumental or environmental noise (e.g., a lingering cloud or aerosol contamination effect), or a legitimate process, such as biomass burning, for example. Additional external data sources are needed to reliably attribute definite causes to these events. If those are unavailable, it may be necessary to sift those points from the data set, so that the analysis can proceed.