## 5 Conclusions  
We reproduced a past case study on land-use change induced land degradation in a Mediterranean ecosystem (\"Vegetation Dynamics 1.0\" [42]), which was conducted before the opening of the Landsat archive, and well before the remigration of European data holdings into the main data archive, thus inevitably being limited by data scarcity. Now, for the first time, the complete cross-calibrated data record over Europe was available and allowed to replicate this study with a state-of-the-art, data-driven, phenology-adaptive methodology based on the peak of season metric (\"Vegetation Dynamics 2.0\"). Following our findings, we conclude that trends derived from a Vegetation Dynamics 1.0 methodology are systematically prone to uncertainty due to a volatile timing of statically selected images relative to the spatially and inter-annually variable phenological cycle--sometimes close to peak vegetation cover, while other times close to minimal vegetation cover. This is especially critical if:  
* a disturbance in the woody vegetation happened,
* a transition from/to woody/herbaceous vegetation took place, or
* inter-annual variability in seasonal herbaceous vegetation cover was high. Figure 11: Vegetation cover bookkeeping for all semi-natural areas in Crete. (**a**) Woody vegetation; (**b**) herbaceous vegetation. The gains/losses of the individual vegetation dynamics syndromes are subsequently booked on the initial vegetation cover (1984), thus resulting in the outgoing vegetation cover (2006). As expected, the majority of semi-natural areas in our study area were subject to at least one of these conditions. The employed Vegetation Dynamics 2.0 methodology yielded more robust results. Nonetheless, results also highlighted that:  
* linear regression is too simplistic a tool to assess long-term vegetation cover change when stand-replacing disturbances in the woody vegetation cannot be ruled out, and that
* peak vegetation cover is not the optimal parameter to analyze. A more nuanced and meaningful characterization could be achieved by further decomposing the total vegetation cover into woody and herbaceous components using the base level and seasonal amplitude metrics, respectively. The synoptic view on both change trajectories eventually allowed for:  
* a more reliable interpretation of vegetation changes with respect to their trend direction and ecological meaning,
* to disentangle certain land-use change processes with opposite trajectories in the vegetation components that were unobservable when analyzing total vegetation cover,
* generating a long-term budget of net cover change, which revealed that vegetation cover of both components has increased at large, mainly due to gradual processes. For the Earth observation-based assessment of long-term vegetation dynamics in semi-arid Mediterranean ecosystems, we conclude that static image selection study designs should be critically evaluated in the light of current data availability, analytical capabilities, and with regards to the ecosystem under investigation. This applies both to studies before the opening of the Landsat archive, and to contemporary papers implementing static selection. Although we only investigated one particular case study in the Mediterranean, we nevertheless postulate that similar findings would also apply to other cases and ecosystems. We recommend using all available data and take advantage of phenology-based approaches that remove the selection bias and hence reduce uncertainties in results. Conceptualization, D.F., P.H., P.R. and S.v.d.L. ; methodology, D.F. ; software, D.F. ; validation, S.E. ; formal analysis, D.F. ; investigation, D.F., P.R. and S.E. ; resources, P.H. ; data curation, D.F. ; writing--original draft preparation, D.F., P.H. and P.R. ; writing--review and editing, S.E., A.R. and S.v.d.L. ; visualization, D.F. ; supervision, P.H. ; project administration, D.F. and P.H. ; funding acquisition, D.F. and P.H. All authors have read and agreed to the published version of the manuscript.