## 1 Introduction  
Terrestrial vegetation reflects the characteristics of climate change. Vegetation phenological parameters, such as the start of the growing season (SOS) and the end of the growing season (EOS) [1, 2, 3], can lead to significant changes in ecosystem function by affecting important processes, such as evapotranspiration and the surface energy cycle [4, 5]. Therefore, research on vegetation phenological parameters can help us better understand the corresponding shifts and alterations in land-air exchange and the energy cycle caused by climate change. Previous investigations into vegetation phenological parameters mainly involved monitoring remote sensing datasets [3, 6], so the accuracy of those parameters directly affected vegetation phenological research results. The extraction of parameters in vegetation phenology is thus a crucial link in the study of vegetation phenology. Since the 1980s, long time series remote sensing data have provided the possibility for large-scale monitoring of vegetation phenology [7]. These data are widely applied in the study of vegetation phenology. Datasets, such as SPOT [8; 9], MODIS and GIMMS [6; 10; 11], are used locally, regionally and globally to analyse vegetation phenology, while Landsat, Sentinel-2, and spatial and temporal fusion dataset are also used locally and regionally [12; 13; 14]. Analysing the normalised difference vegetation index (NDVI) from remote sensing data helps obtain the SOS and EOS, and the length of the growing season (LOS). However, most researchers extract large-scale vegetation phenological parameters using the GIMMS and MODIS datasets [15; 16; 17; 18; 19], and the phenological parameters are largely determined using the fitting models and extraction methods. The main fitting models used are the Asymmetric Gaussian (AG) filter, the harmonic analysis of time series (HANTS) method, the double logistic (DL) function, and the Savitzky-Golay (SG) filter [20; 21; 22; 23], while the main extraction techniques are the threshold, median, and derivative methods [24; 25; 26; 9]. For the study of vegetation phenology in the Northern Hemisphere, many researchers have employed the DL function to fit the NDVI from the GIMMS3g dataset (third-generation Global Inventory Modeling and Mapping Studies). The GIMMS3g dataset has the favourable characteristics of long time series, wide coverage, comparable time and space, and a strong ability to represent the dynamic changes of vegetation. However, the extraction methods differ [15; 28; 29; 30]. There are mainly the following five methods (Table 1). [PERSON] et al., used several different methods to extract the parameters of vegetation phenology and analysed the trend changes in spring and autumn phenology [29]. [PERSON] et al., used four different methods to extract the parameters of vegetation phenology and analysed the effect of preseason drought on vegetation phenology [28]. [PERSON] et al., used only one method to extract the parameters of vegetation phenology from the two datasets and compared the differences between them [15]. Other studies focused on the change in vegetation phenological parameters but ignored the inconsistency of vegetation phenological parameters under different extraction methods on vegetation phenological results. Accurate extraction of phenological parameters is critical for the study of vegetation phenology [31]. However, to our knowledge, the current understanding of how the various extraction methods lead to inconsistencies in vegetation phenological parameters is still limited. To evaluate the consistency of vegetation phenological parameters under different extraction methods above 30\({}^{\circ}\)N, we first used the DL function to fit the GIMMS3g NDVI time series from 1982 to 2015, and then applied five methods to extract the SOS and EOS (Figure 1). After that, we analysed the consistency of vegetation phenological parameters under different methods focusing on interannual variation, trend change, latitude change, different vegetation types and so on.