## 1 Introduction  
In the northern mid-high latitudes, warming-related shifts in vegetation phenology, particularly to an earlier spring and delayed autumn, and their influence on ecosystem productivity have been extensively reported [1; 2; 3; 4; 5]. Currently, remote sensing-based phenological studies focus primarily on changes in key endpoints of phenological events (e.g., dates for spring onset and autumn senescence), through fitting growth curves and extracting phenological parameters [6; 7; 8; 9; 10]. Seasonal growth curves will correspondingly change according to phenological shifts. However, our understanding of the detailed variation between vegetation growth activities during green up spring (increasing productivity) and senescence in autumn (decreasing productivity) remains limited over time and space. During this study, we developed a suite of simple asymmetrical indices capable of representing growth distinction between vegetation green onset and offset, as well as estimate their response to climatic change at regional scale in a temperate zone. Vegetation phenology, one of the most important land surface properties, is used as an essential indicator for depicting global changes [11; 12]. It is a key parameter that describe vegetation's growth patterns and is required for modeling land surface processes (e.g., carbon and energy cycles) [11; 13]. Currently, remote sensing techniques, from satellites to webcams, provide a continuous way to monitor vegetation phenological cycles at a large spatial scale [6; 14]. A considerable number of studies using long-term vegetation greenness indices have found that the earth is becoming more green, especially in the Northern Hemisphere [15]. It is well documented that shifts in vegetation phenological metrics play a key role in regulating greening trends through prolonging the growing season. This extended growing season is directly attributed to both advanced spring onset and postponed autumn ending, further compounding impacts resulting from warming air temperature [16]. Consequentially, these trends affect carbon, water, and energy fluxes between the terrestrial biosphere and the atmosphere [11]. Correlations between phenological metrics and vegetation indices (e.g., greenness, productivity) may overlook impacts from the pattern in the seasonal growth of vegetation, because vegetation greenness phenology metrics are taken at specific points in time and are zero-dimensional data. Furthermore, plant growth processes are affected by a variety of environmental factors that usually lead to changes in these patterns [17]. Thus, distinctive growth patterns for vegetation may result in different allocations of greenness or carbon across the growing season. Green up and senescence rates, peak growth value, and accumulated production are useful information to characterize the seasonal cycle of vegetation. [PERSON] et al. proposed a segmentation algorithm for characterizing asymmetries during green up and senescence, investigating its applicability in seasonal XCO2 data [18]. Long-term trends of global temperature also show a pronounced pattern of seasonal asymmetry [19]. The timing and length of vegetation growth events can influence ecosystem productivity and result in further effects on land surface processes. For instance, seasonal distributions of vegetation productivity can affect food availability for animals, thereby, altering animal migration and breeding patterns. As a result, it is crucial to examine asymmetrical features of vegetation phenology to comprehensively depict vegetation changes and their feedbacks to climate changes. With the development of methods for extracting phenological metrics based on remote sensing time series data, information can be obtained to characterize plant growth patterns in detail. There are a variety of methods to extract phenology dates from vegetation index time series data. Generally, these methods can be divided into two catalogs: the threshold method and the derivative method [1].