The phenological parameters estimated from different data may vary, especially in response to climatic factors. Therefore, we estimated the start of the growing season (SOS) and the end of the growing season (EOS) based on sunlight-induced chlorophyll fluorescence (SIF), the normalized difference vegetation index (NDVI) and the near-infrared reflectance of vegetation (NIRv). The SIF, NDVI and NIRv breakpoints were detected, and the trends and change-points of phenological parameters based on these data were analyzed. The correlations between the phenological parameters and snow-related factors, precipitation, temperature, soil moisture and population density were also analyzed. The results showed that SIF and NIRv could identify breakpoints early. SIF could estimate the latest SOS and the earliest EOS. NDVI could estimate the earliest SOS and the latest EOS. The change-points of SOS_SIF were mostly concentrated from 2001 to 2003, and those of SOS_NDVI and SOS_NIRv occurred later. The change-points of EOS_SIF and EOS_NIRv were mostly concentrated from 2001 to 2007, and those of EOS_SIF occurred later. Differently from the weak correlation with SOS_SIF, SOS_NDVI and SOS_NIRv were significantly correlated with snow-related factors. The correlation between the meteorological factors in the summer and autumn and EOS_SIF was the most significant. The population density showed the highest degree of interpretation for SOS_NIRv and EOS_NDVI. The results reveal the differences and potentials of different remote-sensing parameters in estimating phenological indicators, which is helpful for better understanding the dynamic changes in phenology and the response to changes in various influencing factors.

As a measure of the accumulated heat deficit during the growing season transition, cooling degree days (CDDs) play a crucial role in regulating vegetation phenology and ecosystem dynamics. However, systematic analyses of CDD trends and their driving mechanisms remain limited, particularly in high-altitude regions where climate variability is pronounced. This study investigated the spatiotemporal variability in CDDs from 1982 to 2022 in alpine grasslands on the Qinghai–Tibetan Plateau (TP) and quantified the contributions of key climatic factors. The results indicate that lower CDD values (600 °C-days) were concentrated in colder, wetter areas. Temporally, area-averaged CDDs exhibited a significant decline, decreasing from 490.9 °C-days in 1982 to 495.8 °C-days in 2022 at a rate of 3.8 °C-days per year. Elevation plays a critical role in shaping CDD patterns, displaying a nonlinear relationship: CDDs decrease as elevation increases up to 4300 m, beyond which they increase, suggesting a transition from global climate-driven warming at lower elevations to local environmental controls at higher elevations, where snow–albedo feedback, topographic effects, and atmospheric circulation patterns regulate temperature dynamics. Tmax was identified as the dominant climatic driver of CDD variation, particularly above 4300 m, while radiation showed a consistent positive influence across elevations. In contrast, precipitation had a limited and spatially inconsistent effect. These findings emphasize the complex interactions between elevation, temperature, radiation, and precipitation in regulating CDD trends. By providing a long-term perspective on CDD variations and their climatic drivers, this study enhances our understanding of vegetation–climate interactions in alpine ecosystems. The results offer a scientific basis for modeling late-season phenological changes, ecosystem resilience, and land-use planning under ongoing climate change.

Study RegionThis study examines alpine and temperate grasslands across the Qinghai–Tibetan Plateau (TP) and the Mongolian Plateau (MP) during 2001–2023, representing two contrasting hydroclimatic settings in Asia.Study FocusWe quantified seasonal-scale meteorological drought using the Standardized Precipitation Evapotranspiration Index at a 3-month timescale (SPEI-3) and characterized ecosystem stability from monthly satellite gross primary productivity (GPP) anomalies. Stability was described by four complementary metrics: resistance (Ω), resilience (Δ), constraint time (Ct), and recovery time (Rt). To diagnose controls, we combined random-forest (RF) attribution with residual-based partial correlations to assess both driver importance and directionality.New Hydrological Insights for the Region Under StudyTemperate grasslands on the MP experienced stronger and more persistent drying, whereas alpine grasslands on the TP remained near-neutral to wet with weaker trends. Consistent with these drought regimes, TP grasslands generally showed higher resistance and shorter Ct/Rt, while MP grasslands exhibited lower resistance but higher resilience and longer drought impacts. RF and partial-correlation analyses revealed a hierarchical contrast in controls: TP stability was primarily regulated by atmospheric demand and moisture–productivity coupling (vapor pressure deficit and soil water conditions, together with radiation and GPP), whereas MP stability was dominated by energy-related conditions (temperature and radiation), with atmospheric demand additionally shaping resilience. These region-specific patterns highlight distinct drought impact and recovery pathways across the two plateau systems.