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植被物候是指植被长期适应生活环境的周期性变化，形成与此相适应的生产发育节律。研究植被物候有助于我们更好地理解气候变化。目前利用植被遥感指数进行物候监测依然存在许多问题，而日光诱导叶绿素荧光（SIF）与总初级生产力（GPP）具有强耦合关系，在植被物候研究中具有很大潜力。本文利用双逻辑斯蒂函数模型，基于三种SIF数据（GOME-2,GOSIF和CSIF）计算了北半球地区2007-2018年的物候特征，并与两种GPP数据和五种植被指数（VIs）数据进行对比验证。接下来利用GOME-2 SIF数据分析了北半球地区的物候分布特征，并利用Sen斜率因子检测北半球地区的物候变化趋势，最后计算了三个主要的气候因子对植被物候变化的影响。结果表明：①基于SIF数据计算的SOS与EOS比基于VIs数据的计算结果要更加接近于基于GPP数据的计算结果。②基于GOME-2 SIF数据计算的北半球地区植被2007-2018年平均生长季始期（SOS）主要（>90%）集中在100-170 天，平均生长季末期（EOS）则主要集中在220-270 天。高海拔地区和高纬度地区相对于其他地区，SOS较晚，而EOS则较早。③2007-2018年间北半球地区的SOS主要呈显著提前趋势（平均Sen斜率因子为-0.173），而EOS则为非显著提前趋势（平均Sen斜率因子为-0.002）。④高纬度寒冷地区的物候主要受温度影响，而中低纬度干旱地区的物候主要受降水影响。总的来讲，相对于传统的VIs数据，SIF更加适合表达基于植被GPP的物候特征。相对于过去几十年，最近十年的物候变化速度减慢，尤其是EOS，保持在较为稳定的状态。
Vegetation phenology refers to the specific timing of periodic events in plants and how these timings are adapted by periodic variations of climate and environmental factors such as air temperature and soil moisture content. Vegetation phenology change trends are closely related to global climate change, therefore, study on vegetation phenology can help us to better understand global climate change and how vegetation reacts to climate changes. Remote sensing technology has been the main means for large-scale vegetation research, however, there have been problems when using remote sensing vegetation indices (VIs) to monitor vegetation phenology, due to the discrepancies between vegetation greenness index and photosynthesis. Especially in evergreen forests, the periodic change of VIs data sets is weak, thus it’s hard to capture the phenology metrics based on these VIs data sets. Therefore, there is an urgent need of developing new technology to better monitor vegetation phenology. Recently, sun-induced chlorophyll fluorescence (SIF) has attracted more and more attention, since it is strongly coupled with photosynthesis and has good performance in estimating vegetation gross primary productivity (GPP). Due to its strong correlation with GPP, SIF is capable of capturing the rapid change of GPP in time and has great potential on vegetation phenology monitoring. Based on GOME-2 SIF, GOSIF, and CSIF data in the Northern Hemisphere during 2007-2018, this study mainly calculated the vegetation phenology metrics by using a double logistic model and analyzed the vegetation phenology change trends by using Sen’s slope trend test. The results showed: 1) The double logistic model used in this study could capture the start of the growing season (SOS) better than the end of the growing season (EOS), and vegetation phenology metrics derived from SIF data have stronger correlations with vegetation phenology metrics derived from GPP data than that derived from VIs data, especially for SOS. 2) In the Northern Hemisphere, the multi-annual average SOS was mainly (> 90%) concentrated in 100-170 days, while the multi-annual average EOS was mainly concentrated in 220-270 days. SOS was later in high latitude areas and high altitude areas, while EOS was just the opposite. 3) From 2007 to 2018, SOS derived from GOME-2 SIF data in the Northern Hemisphere showed a significant advancing trend (Senslope was -0.173), and EOS showed an insignificant advancing trend (Senslope was -0.002). 4) The vegetation phenology in high latitude cold areas was mainly affected by air temperature, while the vegetation phenology in middle and low latitude arid areas was mainly affected by precipitation. SIF has great potential to calculate the phenological characteristics based on vegetation photosynthesis, and vegetation phenology derived from GOME-2 SIF data showed a weaker change trend over the last 10 years compared with that of the period from 1980 to 2010. Overall, this study firstly analyzed the vegetation phenology change trends in the recent 10 years based on long-term GOME-2 SIF datasets, and the results in this study could promote our understanding of global climate change and the terrestrial carbon cycle.