## 1 Introduction  
Monitoring the spatio-temporal development of vegetation is a challenging task in heterogeneous and cloud-prone landscapes [1]. Consistent time series of high temporal and spatial resolution satellite imagery could substantially improve various regional analyses of land degradation, changes in land surface phenology (LSP), or changes in extent of agricultural area. However, no single satellite sensor has yet been able to provide time series to sufficiently monitor the vegetation development in these challenging areas. Moderate spatial resolution sensors such as MODIS (Moderate Resolution Imaging Spectroradiometer), MERIS (Medium Resolution Imaging Spectrometer) and SPOT-VEGFATION(Satellite Pour I' Observation de la Terre) provide information on the temporal development of LSP while they cannot sufficiently capture small-scale mosaics of different land cover types. On the other hand, current medium spatial resolution sensors such as Landsat provide the spatial detail necessary for various approaches in heterogeneous landscapes. Yet, in cloud-prone areas and generally for the first Landsat missions which did not provide frequent images globally, data availability is insufficient to assess the various growth stages and temporal changes of vegetation over the growing cycle. To overcome this classic discrepancy in remote sensing, fusing data from different sensors has gained increasing popularity in recent years for combining high temporal resolution sensors with medium to high spatial resolution sensors. Especially the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) [2] and its enhanced version ESTARFM [3] have been frequently applied as described in the literature (e.g., [4; 5; 6; 7; 8]). Recent comparison studies highlighted the respective advantages of the two algorithms compared to other simple fusion approaches, but the overall accuracies are found to be highest for ESTARFM [9; 10]. Furthermore, in heterogeneous landscapes, ESTARFM has been documented to reproduce the small-scale objects of the land surface more accurately than the original STARFM [10]. However, thus far, data fusion with these two algorithms has only been applied to relatively small study areas corresponding to a single Landsat scene (\(\sim\)33,300 km\({}^{2}\)) or less, and the vast majority of studies focused on regions in North America (USA, Canada), Australia and China. [PERSON] _et al._[11] investigated the use of STARFM time series for the monitoring of crop condition in a study area of 225 km\({}^{2}\) in central Iowa, USA. They found the synthetic time series derived from MODIS and Landsat to be well suited to the task and could even distinguish different crop types (soybean and corn) and forest areas. Focusing on forest disturbance and regrowth, [PERSON] _et al._[12] generated a 12-year time series with STARFM for a 2960 km\({}^{2}\) study area in Queensland, Australia. By analyzing the time series with the Breaks for Additive Seasonal and Trend (BFAST) algorithm [13], they identified forest-clearing events and monitored the regrowing vegetation. In their conclusion, the value of such data fusion for land management and forest conversion was highlighted [12]. [PERSON] _et al._[5] fused 250 m MODIS and 5 m RapidEye data using ESTARFM to monitor vegetation dynamics in semi-arid rangelands of South Africa (study area: 1434 km\({}^{2}\)). Their results indicate the suitability of ESTARFM to characterize phenological development of different vegetation types in such heterogeneous areas. Except for the latter study, ESTARFM in particular has only been used in performance and feasibility studies that show the potential of the algorithm for different purposes like urban flood mapping or crop monitoring, sometimes in comparison to other data fusion algorithms [8; 9; 10; 14]. However, to the best of the authors' knowledge, there have not yet been any studies applying ESTARFM to investigating specific geoscientific questions and for larger study areas.