## 1 Introduction  
Earth observation (EO) data have long been used to monitor land cover, land cover change, and long-term modifications of the land surface [1, 2, 3, 4]. Among all the satellite constellations that were launched into space [5], the Landsat program occupies the leading role for long-term environmental monitoring [6; 7]. It globally provides data at adequate spatial, temporal, and spectral resolutions. However, the pivotal aspect of the Landsat mission is the acquisition and curation of the most comprehensive and longest uninterrupted cross-calibrated [8] EO data record ever gathered [9], with ensured continuity [10], and with ongoing efforts to consistently reprocess the entire image archive to incorporate cutting-edge advances in data preprocessing, which are then distributed as 'collections' [11]. Historically, however, Landsat data were not free of charge and image costs were considerable. At peak charging times (1983-1998), a Landsat 5 image was sold for the price of 4000 USD [12]. Therefore, scientists, who were granted funds to acquire data had to carefully select satellite images. The available image metadata catalogs had to be meticulously screened concerning their foreseen application, e.g., considering cloud coverage, cloud distribution, solar angles, or seasonal suitability. This means that scientists aimed to buy as few as possible images with as high as possible quality. Accordingly, mono- or bi-temporal analyses prevailed in this period. Only in rare circumstances were analyses performed on longer time series. This was further cemented by the fact that computational power and storage capacities were limited, image processing software were often proprietary, expensive, and mostly distributed as \"point-and-click\" toolsets with non-automated workflows, and a comparably low level of analysis-readiness of the acquired data. Thus, a lot of manual labor and step-by-step processing were required to get analyses up and running. Although technological and scientific advances have drastically changed the way EO data are being used since then [13], only very recently, the full depth of the standardized USGS archive became accessible over Europe (i.e., this study's area of interest). This was enabled by the Landsat Global Archive Consolidation (LGAC) program [9], and by the successful restoration of missing payload correction data in 2018 [14]; before, a huge data gap existed in the 1990s. Thus, until very recently, the application of state-of-the-art algorithms that make use of dense time series information was not a feasible option for historic periods in Europe, also leaving us in the dark regarding whether past scientific case studies with limited access to data would still hold true when being replicated with all available data in the archive. Today, many long-term time-series analyses still rely on pre-selection of data. Recall that in the past, this was a necessity due to data-related and technical limitations. Today, however, improved variations of this approach are still common, potentially due to data analysis workflows outlined in highly cited, seminal papers of the past. A prominent example is pixel-based compositing for long-term change mapping, whereby the pixel selection is commonly optimized to match a specific point of the year, e.g., the peak of the growing season, which, in absence of further information is commonly defined as a static day of the year (DOY) without accounting for inter-annual or spatial variability, see e.g., [15]. While compositing itself does indeed consider the full archive depth, subsequent inter-annual analyses are still fueled by synthetic images that correspond to the predefined static target date, i.e., the same date for different years, see e.g., [16; 17; 18; 19] (note that this list is by definition incomplete and only includes papers with participation from this article's author team).