## 1 Introduction
### Case Study: Understanding Grazing Pressure in the European Mediterranean from Landsat Time Series  
One of the first studies using the multi-decadal and multi-sensor time series of Landsat targeted the analysis of grazing pressure for the island of Crete, Greece [42; 43]. Crete's elevation gradients extend from sea level to 2500 m a.s.l. and cover a vast range of Mediterranean ecosystems. At the same time, remote areas at high altitudes are embedded in intensively and extensively managed agricultural systems, also allowing the study of diverse land use [44]. One of the major concerns on humans impacting Mediterranean lands is related to grazing [45; 46]. In many respects, research still struggles in answering the question of if Mediterranean grazing lands, such as the rangelands of Crete, are notoriously over-grazed and what implications such over-grazing may have for the diverse ecosystem services at stake [47; 48].  
[PERSON] et al. [42] employed for the first time a rigorously inter-calibrated 20-year time series of Landsat MSS and TM imagery from 1977 to 1996 to quantify how rangeland vegetation in the mountainous grazing lands of Crete changed over time, which was later extended to 2006 by [PERSON] et al. [49]; please note that although published in 2011,this study still followed a pre-open-data-era study design. At the time of their study, data were provided as pixel-wise radiance data without ortho-correction. Standardized pre-processing schemes were not operationalized yet, and each image was independently and iteratively corrected with considerable user interaction. A rigorous pre-processing protocol enabling level-2 time-series was therefore developed from scratch, including steps for ortho-correction [50], atmospheric and topographic correction [51; 52], and interactive cloud screening [42].  
However, even when following such a well-defined protocol, human productivity, data availability, and/or cost was still the limiting factor, as the free and open data access to Landsat imagery was not implemented until 2008 [13; 53; 54] and a majority of historic scenes over Europe were not ingested into the consolidated and cross-calibrated USGS archive until 2018 due to missing payload correction data [14]. Data availability was restricted to a few scenes, largely limited by the financial resources needed to acquire a time series [12], and the lack of operational cloud screening and compositing algorithms that would have allowed to include partially cloud-covered datasets to the analyses [15; 55; 56; 57].  
We here ask if past time-series analyses from Landsat data would yield the same or at least similar results when being replicated with today's state-of-the-art approaches based on the wealth of freely available Landsat data. To answer this question, we reproduce the original study, yet with the constraint of not including the Landsat MSS data for the period before 1984. The 80 m resolution, 4-band, 6-bit MSS data would introduce a bias in our comparisons; the integration of MSS into fully operationalized workflows is only advancing as of late [58], yet still precluding the use of the state-of-the-art workflows and algorithms we need to employ here.  
We also enlarge the study area to all semi-natural areas of Crete (as compared to the restricted original study, only covering the mountains of central Crete). Against this background, we create a comparative study design that allows contrasting the methodology of Landsat studies from limited historic time series and methodologies with today's data wealth and contemporary methodologies.