## 1 Introduction
This introduces uncertainty when spatial or inter-annual variability in seasonal vegetation growth is large, e.g., in semi-arid regions, along altitude gradients, or between different vegetation communities. As an example, barren fields might be falsely classified as fallow if the information was acquired in an unfavorable phenological state [20], or seasonal variations in broadleaved canopies might erroneously be classified as deforestation [21]. As shown by [PERSON] et al. [22], the difference between static compositing and phenology-adaptive compositing can be substantial. This effect is aggravated when considering quantitative processes such as land degradation where the signal of monitored processes is often more subtle than that of the inter-annual variability [23]. Fully data-driven approaches are well established, however. They generally rely on some sort of temporal fitting method, e.g., harmonic models were used in the Continuous Change Detection and Classification (CCDC) [24; 25], as well as in the Breaks For Additive Seasonal and Trend (BFAST) [26; 27] algorithms. The CCDC algorithm is more focused on abrupt changes in long time series, whereas the BFAST model aims at capturing both changes and long-term trends. Harmonic models, however, are better suited for forecasting than for data reconstruction [28], and have problems in areas with large inter-annual variations in the timing of phenological processes, e.g., in drylands [25]. In these cases, [PERSON] et al. [29] recommend smoothing filters that preserve intra-seasonal variations, which are commonly used prior to deriving land surface phenology (LSP) metrics, e.g., [30; 31; 32; 33]. Annual LSP metrics are ecologically meaningful parameters that describe key aspects of the phenological cycle (e.g., date and value of peaking time, or the onset of vegetation growth). This feature space enables physical interpretability and the use of conceptually simpler models (as opposed to models that additionally model seasonal components) to capture long-term trends, e.g., [34; 35; 36], or may potentially be used as surrogate input feature space with algorithms that account for both abrupt and gradual change processes, e.g., [37; 38]. Nonetheless, quantitative knowledge about whether results of static approaches are comparable to data-driven approaches that effectively circumvent static selection strategies, hence making the analysis robust to these factors, are mostly missing (but see [39; 40] for a comparison of forest change algorithms in the United States). In addition, papers reporting on reproducing or replicating previously published remote sensing research are missing at large, although \"one of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it\" [41]. We thus deem it essential to replicate past case studies with all currently available data to assess whether their results still hold true and better understand limitations of static selection related to satellite data time series and long-term change analyses. This is especially important as such seminal landmark papers can still be heavily cited and referenced, or their workflows can be adopted in ongoing research. To this end, we replicate a representative case study in the light of a dramatically different data availability and processing reality. To aid the reader, we here reprint the definitions of the _National Academies of Sciences, Engineering, and Medicine_ for reproducibility and replicability [41]: _reproducibility_ means \"obtaining consistent results using the same input data, computational steps, methods, code, and conditions of analysis\"; _replicability_ means \"obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data\". Thus, a remote sensing example for reproducibility may be achieving a quasi-identical land cover classification when using the same data archive and method (e.g., classifier) as described in a previous paper.