Variation of vegetation autumn phenology and its climatic drivers in temperate grasslands of China

Spatiotemporal variation in vegetation phenology and its response to climate change in marshes of Sanjiang Plain, China

“…For the purpose of avoiding the influences of land use/cover variation on our results, we extracted unchanged marshes (pertaining to marsh in both 2000 and 2015) during the study period and chose them as study area. Consistent with previous researches (Piao et al, 2006, 2017; Shen et al, 2018; Shen, Liu, et al, 2019; Ma, Shen, et al, 2022; Su et al, 2022), we used the polyfit‐maximum approach to determine the phenology of marshes. First, we calculated the average of NDVI and analyzed the temporal variation in NDVI based on the formula ():$$$$ \mathrm{NDVIrate}(t)=\frac{\mathrm{NDVI}\left(t+1\right)-\mathrm{NDVI}(t)}{\mathrm{NDVI}(t)} $$$$Here, t refers the Julian date (DOY), and NDVI ( t ) represents NDVI variation.…”

“…However, this result was different from a previous study (Liu et al, 2016 ), in which precipitation was the main determinant of grassland EOS and that increasing precipitation can alleviate water stress and delay EOS. The marsh ecosystem has sufficient water compared with the arid grassland ecosystem (Liu et al, 2022 ; Ma, Shen, et al, 2022 ; Ma, Xia, et al, 2022 ; Shen, Liu, Zhang, et al, 2022 ). Therefore, precipitation in autumn may have no significant impact on the marsh EOS.…”

Critical role of water conditions in the responses of autumn phenology of marsh wetlands to climate change on the Tibetan Plateau

“…In general, with increasing Julian day, the NDVI value for a vegetation pixel gradually increases and then declines after reaching its maximum. Consistent with many previous studies (e.g., Ma et al, 2022;Piao et al, 2011;Shen et al, 2018Su et al, 2022;Wu & Liu, 2013;Zhang et al, 2013), this study used the polyfit-maximum method (Piao et al, 2006(Piao et al, , 2011 to represent the seasonal changes in NDVI as a function of Julian day and extract phenological information. Because of the impact of some non-vegetation effects of cloud, atmosphere, solar zenith angle, and other factors, some NDVI values are lower than their two adjacent ones.…”

“…Therefore, in the polyfit‐maximum approach, the EOS date is set to correspond to the time of the largest decrease in NDVI at the end of the growth period (Piao et al., 2006). The polyfit‐maximum method has been widely used to extract vegetation phenology owing to its excellent performance (e.g., Cong et al., 2013; Fu et al., 2014; Jeong et al., 2011; Kafaki et al., 2009; Li et al., 2023; Liu et al., 2016, 2023; Ma et al., 2022; Piao et al., 2015; Shen et al., 2018, 2019, 2023; Su et al., 2022; Wang et al., 2016, 2018; Wu & Liu, 2013; Yang et al., 2015, 2021; Zhang et al., 2013; Zhou et al., 2020) and consists in a number of steps.…”

“…Global climate change has significantly changed the autumn phenology worldwide, affecting the regional and global energy balance, water flux, and carbon budget (Che et al., 2014; Estiarte & Peñuelas, 2015; Kelsey et al., 2021; Richardson et al., 2013; Yang et al., 2021). Although the variations in the autumn phenology of vegetation and its response to regional and global climate changes have been extensively analyzed, most studies have focused on grassland or forest ecosystems, with few investigations of wetland ecosystems (Coleman et al., 2022; Ge et al., 2015; Ma et al., 2022; Rice et al., 2018; Yang et al., 2015). Due to the unique environmental conditions in wetlands, climate change may have different effects on the autumn phenology of vegetation compared to other ecosystems (Keppeler et al., 2021; Ma et al., 2022; Molino et al., 2022; Shen, Wang, et al., 2022).…”