Towards Optimising the Derivation of Phenological Phases of Different Crop Types over Germany Using Satellite Image Time Series  
[PERSON]  
1 Julius Kuhn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Crop and Soil Science, Bundesallee 58, 38116 Braunschweig, Germany 1  
[PERSON]  
1 Julius Kuhn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Crop and Soil Science, Bundesallee 58, 38116 Braunschweig, Germany 1  
[PERSON]  
2 Julius Kuhn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Strategies and Technology Assessment, Stahnsdorfer Damm 81, 14532 Kleinmachnow, Germany 2  
[PERSON]  
1 Julius Kuhn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Crop and Soil Science, Bundesallee 58, 38116 Braunschweig, Germany 1  
[PERSON]  
1 Julius Kuhn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Crop and Soil Science, Bundesallee 58, 38116 Braunschweig, Germany 1  
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research domains. This concerns crop-related applications in particular, where accurate estimates of crop physiological growth phases or phenophases are crucial for many agronomic practices, such as irrigation and fertilization scheduling, agricultural weather or biodiversity index derivation, soil erosion, crop condition monitoring, and yield estimation or integrated pest management [2; 3; 4; 5; 6; 7; 8; 9]. Crop phenophases were traditionally recorded by manual field observations and surveys, e.g., [10]. Although these types of data sets can accurately describe crop phenology, they are limited in their ability to provide continuous temporal and spatial phenological information on a large scale due to their time and resource-consuming nature [11]. Moreover, the large spatial variability of crop phenology, combined with the uneven spatial distribution of these limited manual ground observations, makes it challenging to fully characterise long-term crop phenology across a continuous spatial landscape [12]. On the other hand, the advent of satellite remote sensing has revolutionised the ability to obtain information about agricultural practices. Unlike traditional field surveys, remote sensing can considerably mitigate many of their limitations by providing wide spatial coverage, frequent temporal sampling, and improved operational configuration of modern satellites, thereby expanding the scope of crop phenological analysis well beyond what field surveys can achieve. Land surface phenology, or LSP, is the name given to phenology measurements obtained while using satellite-based observational data [13]. The detection of LSP itself basically relies on the observed variations of time series of remotely sensed vegetation indices (VIs), such as the normalized difference vegetation index (\(NDVI\)) [14], which has proven to be very effective in inferring the crop growth cycle and identifying the so-called LSP metrics, such as the start and end of growing season (SOS and EOS, respectively) [15]. Several studies have found that over croplands, LSP metrics can provide detailed information about crop calendar events (e.g., planting and harvesting dates) [11; 16]. Due to the requirement of dense and repeated observations for LSP investigations, most satellite-based crop phenology studies have been based on moderate to coarse resolution sensors (e.g., Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), Satellite Tour I'Observation de la Terre Vegetation (SPOT VGT)) as they provide frequent (almost daily) observations that are required during rapidly changing phenological stages [17; 18; 19; 20].