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多年平均物候能够反映植被生长发育节律的均衡状态，是植被物候模拟与预测的关键参数之一。遥感已广泛用于地表物候监测，是空间多年平均物候信息的重要来源。然而，基于遥感的多年平均物候存在不同计算方法，如先确定每年时序曲线的物候点再求平均值(平均法)，以及先求多年平均时序曲线再确定物候点(参考曲线法)。上述方法的结果可能存在差异，但目前尚缺乏对这一不确定性及其影响的认识。针对该问题，本研究利用2001-2016年遥感植被指数数据，分别在平均法和参考曲线法下提取我国森林生长季起始时间的多年平均值(SOS)，比较SOS的差异(△SOS)及其空间异质性；进一步选取物候研究中常用指标，即以SOS为基础的温度“季前时长(Preseason Duration，PD)”，分析SOS不同计算方法对物候-气候关系的潜在影响。结果表明，(1) 不同方法下的SOS差异显著，总体上平均法小于参考曲线法(-2.6 ± 2.2天，占88%)，其中存在8.7%的像元△SOS超过7天，主要分布在东南丘陵地区。(2) △SOS具有显著的空间异质性，主要表现为随年均温的升高而减小(Slope=0.07天/℃, P<0.01)，随年均降水的增加而增大(Slope=-0.0005天/mm, P<0.01)。(3) 不同方法下的PD存在差异，21%的有效像元PD差值超过15天，主要分布在东南丘陵和西南山区。研究结果将为遥感地表物候的模型空间参数化应用提供有益参考。
Multi-year mean phenology reflects the average state of vegetation growth and development rhythm, which is one of the key parameters predicting vegetation phenology. As an important source of spatial multi-year mean phenology, remote sensing is widely used for detection of phenology. There are different methods of calculation of multi-year mean phenology based on remote sensing. One is determining the phenological point of the annual time series curve first and then calculating the average (referred as the average method), and another is gaining the multi-year mean time series curve first and then determining the phenological point (referred as the reference curve method). The results of the above methods may be different. However, the uncertainty and its impacts are still less understood. Hence, this study used remote sensing vegetation index from 2001 to 2016 to extract the multi-year mean dates of the start of the growing season ((SOS) ?) with the two methods in forest in China, and detected the differences among (SOS) ? derived from the two methods (△(SOS) ?) and the spatial pattern. Furthermore, a commonly used indicator in phenological research, the temperature ‘Preseason Duration (PD)’ based on (SOS) ?, was used to explore the potential impact of (SOS) ? derived from different methods on the phenology-climate relationship. The results showed that (1) the (SOS) ? derived from different methods was significant different. The (SOS) ? of the average method was generally smaller than that of the reference curve method (-2.6 ± 2.2 days, accounting for 88%). About 8.7% of the effective pixels exhibited a difference with △(SOS) ? > 7 days, mainly distributed in southeastern hilly area. (2) There was a significant spatial heterogeneity in △(SOS) ?, which showed a decrease with the increase of the annual average temperature (Slope=0.07 days/℃, P<0.01) and the decrease of the average annual precipitation (Slope=-0.0005 days/mm, P<0.01). (3) PD derived from different methods was distinct. About 21% of the effective pixels showed a difference with PD > 15 days, mainly located in the southeast hills and southwest mountainous. Overall, the achievements of this study provide an beneficial reference of spatial parameterization of satellite-based phenology for modeling.