## 4 Discussion
### Limitations and Opportunities for Future Improvements  
The global scale GC analysis of distinct VPs, acquired by multiple spaceborne sensors, is not without its inherent limitations, which must be acknowledged and addressed. The analysis of the two-year time frame already yielded consistent results, whilst still being relatively short, as compared to multi-decadal analysis [87; 88]. Using two years of input data showed significant improvements as compared to only using one year. However, when augmenting the temporal window to three years the results showed changes that were not significantly different compared to the two-year analysis. Increasing to multi-decadal temporal domains could possibly yield additional findings; however, the TROPOSIF dataset's availability limited the time frame of analysis. Another limitation induced by the TROPOSIF dataset is its product integrity. At the current state of the art, the valuable physiological information contained in the SIF data remains obscure. This can be attributed to SIF retrieval errors, the SIF signal's weakness compared to reflected radiation, and the high signal-to-noise ratio needed to capture telluric atmospheric absorption lines in the red and far-red bands [89; 90].  
To gain a deeper understanding of the global biophysical and climatological interconnections, a reversed-directional GC would be the next step, analyzing the vegetation's effect on its surrounding environment. Retrieving GC metrics in the reverse direction (VP\(\rightarrow\)ELV) could potentially unravel additional environmental connections. Expanding the investigation with such results may reveal interconnections in tropical deforestation to reduced evapotranspiration and, thus, precipitation [91] or the influences of root water uptakes on soil moisture [92]. Further advancements can be achieved when calculating the GC that involve various environmental factors. For situations with three or more time series, the extended GC is proposed [93] The case for the GC link between R\(\rightarrow\)VP revealed high causality, but further analyses could reveal the full chain of causal connections: R\(\rightarrow\)snowmelt\(\rightarrow\)SM\(\rightarrow\)VP.  
Extending GC analysis to monitor additional factors, such as anthropogenic influences, global forcing from El Nino Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and influences from regional circulations could enhance the understanding of global interconnections between environment and vegetation [94]. Analyzing additional ELVs, such as vapor pressure deficit (VPD), which is also a driving factor in stomatal opening, carbon uptake, and photosynthetic activity, could further reveal more complex causal links, although VPD is closely coupled to SM and T, and to what extent vegetation productivity in different climate zones is controlled by VPD remains in question [95].  
It is expected that future extensions of the GC model with machine learning autoregressive methods, such as neural networks [96] or random forests [97], will enable the modeling of the aforementioned multi-variate non-linear systems. Additionally, varying the lag value of previous observations, at which the VAR model develops the autoregressive model, may also lead to further pinpointing of distinct response characteristics governing the ELV\(\rightarrow\)VP links [34].  
Finally, with the upcoming ESA Earth Explorer 8 Fluorescence Explorer (FLEX) mission in mind, the expected full-SIF data streams provided at a 300 m resolution will provide a more accurate insight into terrestrial vegetation health, stress, and resilience [98; 99]. Hence, we anticipate that the presented causality analysis will greatly benefit from upcoming FLEX data streams.