## 1 Introduction  
Vegetation phenology refers to the periodical biological life cycles in plants' development [1, 2]. A vegetation growth cycle may consist of many stages, including bud, leaf and shoot development, inflorescence emergence, flowering, fruit development, fruit maturity, vegetation senescence, and dormancy [3]. Previous studies have shown the importance of vegetation phenology, as the shifts in vegetation phenology can indicate environmental and climate changes [4, 5, 6]. The changes in vegetation phenology were found to alter the water exchange, energy flow, and carbon cycle between soil, vegetation, and the atmosphere [7, 8, 9]. Mapping the spatial patterns in vegetation phenology and their dynamics over time helps to understand vegetation growth cycles, its response to climate changes, and feedback on climate changes. On-site field observation and remote sensing techniques are the two principal ways applied to extract the dates in the vegetation phenology. Flux-based ecological stations or long-term field observation networks are the most valuable infrastructure to accurately detect vegetation phenology [10]. However, field observations are limited in number and infeasible to be applied over a large area. Advances in remote sensing technology have enabled continuous vegetation mapping at large scales and remotely sensed imagery has been successfully compiled to extract vegetation phenology at regional and global scales [1; 2]. Phenology dates extracted from site-based observations and from remote sensing modeling showed comparable results [11; 12]. Long-term remote sensing imagery from satellite platforms makes it possible to map historical vegetation phenology [13; 14]. Obtaining vegetation phenology from both remote sensing products and field observations depends on the time-series analysis of vegetation greenness levels, e.g., normalized difference vegetation index (NDVI), which can be readily retrieved from remotely sensed imagery. The intensity of vegetation greenness or the vegetation growth can similarly be reflected by vegetation productivity such as gross primary productivity (GPP) or net primary productivity (NPP) [15]. Time-series analysis on the dynamics of NDVI, GPP, or NPP along the day of year (DOY) can reveal the changes in phenological events. However, [PERSON] mentioned in their study that vegetation-index-based phenology is not sufficient to represent the seasonal characteristics of photosynthesis, which is important for studying carbon sequestration in vegetation and terrestrial ecosystems [16]. Traditional vegetation-index-based phenology reflects changes in vegetation greenness, but vegetation phenology extracted from GPP time series (also known as vegetation photosynthetic phenology) can accurately capture changes in vegetation photosynthetic activity, which is more important for studying carbon sequestration from vegetation in terrestrial ecosystems. Thus, we chose GPP instead of NDVI to extract vegetation phenology. The current methods for remote-sensing-based vegetation phenology extraction can be divided into two categories, the threshold-based method and the curvature-point-based method [14; 17]. The threshold-based method can rely on a predefined static threshold or a dynamic threshold and is relatively simple to compute. On the other hand, the threshold-based methods are challenged by the difficulty of setting appropriate threshold values in different study areas or for different vegetation types. Thus, the threshold methods are primarily applicable to small areas with homogeneous environments [12]. The curvature-based methods look for critical points such as the highest or lowest first-order derivative, or curvature reversing points based on a time-series curve, and then identify the time corresponding to the critical points as the dates of the phenological events.