基于遥感数据的多年平均物候不确定性研究

基于遥感数据的多年平均物候不确定性研究 Spatial uncertainty in multi-year mean phenology based on remote sensing data 1 2 first-author 金佳鑫，研究方向为生态遥感。E-mail： jiaxinking@hhu.edu.cn 金佳鑫，研究方向为生态遥感。E-mail： jiaxinking@hhu.edu.cn jiaxinking@hhu.edu.cn 1 3 1 1 河海大学 水文水资源学院, 南京 210024 河海大学 水文水资源学院, 南京 210024 College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China 国家科技资源共享服务平台—国家地球系统科学数据中心, 北京 100101 国家科技资源共享服务平台—国家地球系统科学数据中心, 北京 100101 National Earth System Science Data Center, National Science & Technology Infrastructure of China, Beijing 100101, China National Earth System Science Data Center, National Science & Technology Infrastructure of China, Beijing 100101, China 中国电建集团中南勘测设计研究院有限公司, 长沙 410014 中国电建集团中南勘测设计研究院有限公司, 长沙 410014 PowerChina Zhongnan Engineering Corporation Limited, Changsha 410014, China PowerChina Zhongnan Engineering Corporation Limited, Changsha 410014, China 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年—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中国森林生长季起始时间的多年平均值（ <math id="M1"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602595&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602587&type= 5.33400011 2.79399991 ），比较 <math id="M2"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602595&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602587&type= 5.33400011 2.79399991 的差异（ △ <math id="M3"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602595&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602587&type= 5.33400011 2.79399991 ）及其空间异质性；进一步选取物候研究中常用指标，即以 <math id="M4"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602595&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602587&type= 5.33400011 2.79399991 为基础的温度“季前时长PD（Preseason Duration）”，分析 <math id="M5"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602595&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602587&type= 5.33400011 2.79399991 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 <math id="M6"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602595&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602587&type= 5.33400011 2.79399991 差异显著，总体上平均法小于参考曲线法（-2.6±2.2 d，占88%），其中存在8.0%和6.0%的有效像元其动态平均法和固定平均法小于参考曲线法超过7 d，主要分布在东南丘陵地区。（2） △ <math id="M7"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602595&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602587&type= 5.33400011 2.79399991 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 d/℃， P < 0.01），随年均降水的增加而增大（Slope=-0.0005 d/mm， P < 0.01）。（3）不同方法下的PD存在差异，约40%有效像元的差异（ △ PD）超过5 d（其中近50%的像元 △ PD超过15 d），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 and is one of the key parameters for predicting vegetation phenology. As an important source of spatial multi-year mean phenology, remote sensing is widely used for phenology detection. Different methods of multi-year mean phenology calculation are 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 need further elucidation. Hence, this study used the remote sensing vegetation index from 2001 to 2016 to extract the multi-year mean dates of the start of the growing season ( <math id="M8"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602592&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602598&type= 5.92666674 2.70933342 ) using two methods in forests in China and detected the differences between the <math id="M9"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602606&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602604&type= 5.92666674 2.70933342 derived from the two methods ( △ <math id="M10"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602612&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602624&type= 5.92666674 2.70933342 ) and the spatial pattern. Furthermore, a commonly used indicator in phenological research, that is, the temperature (Preseason Duration (PD)) based on <math id="M11"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602628&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602614&type= 5.92666674 2.70933342 , was used to explore the potential impact of the <math id="M12"><mtext> </mtext><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602638&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602631&type= 6.60400009 2.79399991 derived from different methods on the phenology–climate relationship. Results show that (1) the <math id="M13"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602637&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602634&type= 5.92666674 2.70933342 derived from different methods was significant different. The <math id="M14"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602646&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602654&type= 5.92666674 2.70933342 of the average method was generally smaller than that of the reference curve method (-2.6±2.2 days, accounting for 88%). The pixels with △ <math id="M15"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602650&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602660&type= 5.92666674 2.70933342 > 7 between the dynamic average method and the reference method and that between the fixed average method and the reference method accounted for 8.0% and 6.0% of the effective pixels, respectively, which are mainly distributed in the southeastern hilly area. (2) A significant spatial heterogeneity of △ <math id="M16"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602671&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602669&type= 5.92666674 2.70933342 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) The PD derived from different methods was distinct. Approximately 40% of the effective pixels show a difference with PD > 5 days, and a half of them show a difference with PD > 15 days, which are mainly located in the southeast hills and the southwest mountains. Overall, the achievements of this study provide a beneficial reference for the spatial parameterization of satellite-based phenology for modeling. 多年平均物候 物候季前时长 遥感地表物候 时序数据 森林 multi-year average phenology phenological preseason duration remote sensing surface phenology time series forest 1　引言 1 植被物候是植物周期性的生长变化（ Zhu，1973 Zhu，1973 ； 张学霞 等，2004 张学霞 等，2004 ），一般指植物生长周期中某一事件发生的时间节点，如发芽、展叶、开花、结果和枯黄等；广义上，表现为植被不同状态之间的转折点，以及物质与能量的周期性变化。植被物候受环境调控，被认为是全球气候变化的最佳指示器之一，在陆地生态系统碳收支平衡中发挥着重要作用（ Menzel，2002 Menzel，2002 ； Richardson等，2010 Richardson等，2010 ），是陆面过程模型及全球植被模型的重要参数，已成为全球变化研究重点关注对象（ White等，1997 White等，1997 ； Schwartz，1998 Schwartz，1998 ； Chuine，2000 Chuine，2000 ； van Vliet等，2003 van Vliet等，2003 ； 刘浩龙 等，2021 刘浩龙 等，2021 ）。 随着遥感技术的发展，卫星遥感植被指数能较精确地反映植被绿度和光合作用强度，广泛用于定量化区域尺度乃至全球尺度的植被物候特征（ 王敏钰 等，2022 王敏钰 等，2022 ）。与传统物候描述单个植物的季节和年度变化不同，基于遥感的方法能够描述整个景观生态系统的物候变化，侧重表征植被生理生态功能过程的状态转换，称为遥感地表物候（ 武永峰 等，2008 武永峰 等，2008 ）。因具有快速、宏观、时序长、数据规则统一等特点，遥感在地表物候监测的应用中日益广泛，已成为物候模型空间参数化的重要来源（ 夏传福 等，2013 夏传福 等，2013 ）。 在物候的研究中，常关注生长季的开始期（SOS）、成熟期（POS）、结束期（EOS）和生长季时长（LOS）等指示植被生长状态的参量（ 耿丽英和马明国，2014 耿丽英和马明国，2014 ）。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，已成为物候研究中的重要指标。例如，在物候气候驱动因子判别的研究中，年平均物候作为计算季前时长PD（Preseason Duration）的起算点，影响季前平均温度、季前累积降水等气候参数，从而影响物候分析的准确性（ Matsumoto等，2003 Matsumoto等，2003 ； Menzel等，2006 Menzel等，2006 ； Fu等，2015 Fu等，2015 ）；此外，年平均物候对反映植被物候的时空变化特征（ Ding等，2013 Ding等，2013 ； 黄文琳 等，2019 黄文琳 等，2019 ），用作通用陆面模型（CLM）的基础数据（ White等，1997 White等，1997 ； Lawrence等，2011 Lawrence等，2011 ），以及生态系统仿真模型和耦合生物/大气模型具有重要意义（ White等，1997 White等，1997 ）。 然而，基于遥感数据的多年平均物候存在不同的计算方法，如先计算多年平均时序曲线，再确定物候点（参考曲线法）（ Piao等，2006 Piao等，2006 ； Ding等，2013 Ding等，2013 ），或先确定每年时序曲线的物候点再求平均值（平均法）（ Shen等，2015 Shen等，2015 ； Piao等，2015 Piao等，2015 ； Cao等，2018 Cao等，2018 ； Huang等，2020 Huang等，2020 ）。以上两种方法的结果可能存在差异，并进一步对物候研究造成一定的不确定性，但目前尚缺乏对这一不确定性及其影响的认识。 为了探讨这一问题，本研究利用2001年—2016年遥感植被指数数据，采用平均法（包括动态平均法和固定平均法）、参考曲线法分别提取生长季起始时间的多年平均值（ <math id="M17"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 ），并选取物候研究中常用指标，即以 <math id="M18"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 为基础的温度“季前时长”，探究不同方法下 <math id="M19"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602697&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602695&type= 6.18066692 3.21733332 的差异（ △ <math id="M20"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 ）及其对物候—气候关系的潜在影响。具体研究内容包括：（1）比较参考曲线法和平均法下 <math id="M21"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603081&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603080&type= 6.18066692 3.21733332 的差异及其空间异质性；（2）分析该差异随着温度和降水的梯度变化特征；（3）比较不同方法下 <math id="M22"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 对季前时长的影响。预期成果将有助于加深对遥感多年平均物候的认识，从而降低物候模型空间参数化的不确定性。 2　数据与方法 1 2.1　地表覆盖数据与有效像元 2 本研究使用的地表覆盖数据来源于ESA CCI LC（European Space Agency Climate Change Initiative Land Cover）空间分辨率为300 m的全球土地覆盖时间序列（ http：//maps.elie.ucl.ac.be/CCI/viewer/download.php http://maps.elie.ucl.ac.be/CCI/viewer/download.php [2021-01-29]）。该产品依照联合国土地覆盖分类系统（UN-LCCS）描述了37个原始土地覆盖类的地球陆地表面，它可以通过交叉布行表转换为植物功能类型（PFT），以定量划分土地覆盖类中各主要土地覆盖类型的比例（Li等，2018； Nowosad等，2019 Nowosad等，2019 ）。选取中国东北至西南的森林样带为研究区（ 图1 图1 ），提取0.1°范围内森林PFT类占比大于50%的区域作为研究有效像元，从而将空间分辨率升尺度为0.1°；经提取后，有效像元的总面积达77万km 2 。 审图号：GS（2022）734号 1,2;3,4;5,6; 2.2　遥感植被指数 2 增强型植被指数EVI（Enhanced Vegetation Index）是评估植被生长状况最有效的参数之一，能够反映植被覆盖、生物量等生态系统参数的变化，广泛应用于植被监测的研究中（ Liu和Huete，1995 Liu和Huete，1995 ； 王正兴 等，2003 王正兴 等，2003 ）。本文选用MODIS EVI数据（MOD13C1）提取植被物候参数中的生长季起始时间（SOS）用以研究。该数据从2001年1月—2016年12月，时间分辨率为16 d，空间分辨率为0.05°，参考土地覆盖数据，本研究将EVI数据的空间分辨率升尺度为0.1°。 2.3　气象数据 2 本文将结合温度和降水等气象数据，探究 △ <math id="M29"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 的影响因素。气象数据选自清华大学阳坤教授团队开发的中国区域高时空分辨率地面气象要素驱动数据集，相比于常用的国际气候再分析资料，该数据集具有较优精度（ He等，2020 He等，2020 ）。本研究选取2001年—2016年逐日、空间分辨率为0.1°的近地面日平均温度和日平均降水数据，与EVI数据进行匹配。 2.4　 <math id="M30"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603047&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603045&type= 6.18066692 3.21733332 的提取方法 2 为探究不同方法下遥感地表多年平均物候的差异，本研究采用平均法（包括动态平均法和固定平均法）、参考曲线法分别对2001年—2016年的EVI时序曲线进行 <math id="M31"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 的提取，具体方法如下： （1）平均法：首先将2001年—2016年的EVI时序曲线内插为1 d分辨率，并通过奇异谱分析（SSA）（ Keenan等，2014 Keenan等，2014 ）进行平滑滤波；其次设置阈值，确定每年EVI时序曲线的SOS；最后对16年的SOS求平均得到 <math id="M32"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 。其中动态平均法以当年EVI时序曲线的20%作为阈值，而固定平均法以2001年—2016年EVI参考曲线的20%作为阈值。 （2）参考曲线法：首先对2001年—2016年的EVI时序曲线求各参考点的平均值，得到参考曲线；其次将16 d分辨率的参考曲线内插为1 d分辨率，并进行SSA平滑滤波；最后通过20%的阈值确定参考曲线的SOS，即获得 <math id="M33"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 。 其中阈值法的具体公式如下： <math display="block" id="M34"><mi mathvariant="normal">E</mi><mi mathvariant="normal">V</mi><mi mathvariant="normal">I</mi><mo>=</mo><mi mathvariant="normal">E</mi><mi mathvariant="normal">V</mi><msub><mrow><mi mathvariant="normal">I</mi></mrow><mrow><mi mathvariant="normal">m</mi><mi mathvariant="normal">i</mi><mi mathvariant="normal">n</mi></mrow></msub><mo>+</mo><mi mathvariant="normal">E</mi><mi mathvariant="normal">V</mi><msub><mrow><mi mathvariant="normal">I</mi></mrow><mrow><mi mathvariant="normal">r</mi><mi 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mathvariant="normal">V</mi><mi mathvariant="normal">I</mi></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602891&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602889&type= 17.10266685 3.55599999 为EVI时序曲线某一点的EVI数值， <math id="M36"><mi mathvariant="normal">E</mi><mi mathvariant="normal">V</mi><msub><mrow><mi mathvariant="normal">I</mi></mrow><mrow><mi mathvariant="normal">m</mi><mi mathvariant="normal">i</mi><mi mathvariant="normal">n</mi></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602899&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602911&type= 9.14400005 3.80999994 为EVI时序曲线的最小值， <math id="M37"><mi mathvariant="normal">E</mi><mi mathvariant="normal">V</mi><msub><mrow><mi mathvariant="normal">I</mi></mrow><mrow><mi mathvariant="normal">m</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">x</mi></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602916&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602901&type= 9.31333351 3.80999994 为EVI时序曲线的最大值， <math id="M38"><mi mathvariant="normal">E</mi><mi mathvariant="normal">V</mi><msub><mrow><mi mathvariant="normal">I</mi></mrow><mrow><mi mathvariant="normal">r</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">t</mi><mi mathvariant="normal">i</mi><mi mathvariant="normal">o</mi></mrow></msub></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602906&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602904&type= 9.65200043 3.80999994 为设定的阈值，SOS被定义为 <math id="M39"><mi mathvariant="normal">E</mi><mi mathvariant="normal">V</mi><mi mathvariant="normal">I</mi></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602925&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602923&type= 6.01133299 2.70933342 首次高于设定阈值EVI的日期（ Garonna等，2014 Garonna等，2014 ）。 2.5　 △ <math id="M40"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603047&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603045&type= 6.18066692 3.21733332 的影响因子分析 2 为探究不同方法下 △ <math id="M41"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 的影响因素，本研究选取非生物因子包括年均温、年均降水和生物因子植被变绿时长的标准差GD STD （Standard Deviation of Greenup Duration），分别与平均法和参考曲线法下的 △ <math id="M42"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 进行线性回归分析。其中植被变绿时长GD（Greenup Duration）是指植被生长季成熟期与起始期之间的时间间隔，它作为一种扩展的植被生长期，能够有效的反映植被的生长状况；其标准差能够反映植被各年间生长情况的差异，有助于探究 △ <math id="M43"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 的影响因素（ Jeong等，2011 Jeong等，2011 ； Deng等，2020 Deng等，2020 ）。 2.6　物候季前时长的确定 2 为进一步探究 △ <math id="M44"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 对物候研究造成的潜在不确定性，本文将比较平均法和参考曲线法下 <math id="M45"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 对季前时长识别的影响。季前时长是指物候事件发生时间点（本研究为SOS）前的一段累积时间，该段时间内的温度、降水等气象参数是确定年际SOS变化的重要决定因素，对物候—气候关系的研究具有重大意义（ Matsumoto等，2003 Matsumoto等，2003 ； Menzel等，2006 Menzel等，2006 ； Dai等，2013a Dai等，2013a ）。本研究采用偏相关分析方法，以季前累积降水为控制变量，定义2001年—2016年SOS与季前均温偏相关系数最大的候选时长为季前时长。其中，候选季前时长以 <math id="M46"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 为起算日期，每个候选季前时长往前增加1 d，直至当年的1月1日（ Menzel等，2006 Menzel等，2006 ； Piao等，2006 Piao等，2006 ； Dai等，2013b Dai等，2013b ； Fu等，2015 Fu等，2015 ）。 3　结果与分析 1 3.1　 <math id="M47"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603047&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603045&type= 6.18066692 3.21733332 的空间分布 2 本研究采用不同方法获取多年平均生长季起始期（ <math id="M48"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 ），包括 图1（a） 图1（a） 固定平均法（ <math id="M49"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 FIX ）、 图1（d） 图1（d） 动态平均法（ <math id="M50"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 DYN ）、 图1（e） 图1（e） 参考曲线法（ <math id="M51"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 REF ）；进一步比较不同方法下 <math id="M52"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 的差值，包括 图1（b） 图1（b） 动态平均法与固定平均法的差值（ △ <math id="M53"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602950&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602965&type= 6.18066692 3.21733332 （DYN-FIX） ）、 图1（c） 图1（c） 固定平均法与参考曲线法的差值（ △ <math id="M54"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603168&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603167&type= 6.18066692 3.21733332 （FIX-REF） ）、 图1（f） 图1（f） 动态平均法与参考曲线法的差值（ △ <math id="M55"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 （DYN-REF） ）；其中误差线表示 △ <math id="M56"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 的均值与标准差。研究结果表明，不同方法下的 <math id="M57"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602981&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602978&type= 6.18066692 3.21733332 总体上具有相似的空间分异（图 1（a） 1（a） 、 1（d） 1（d） 、 1（e） 1（e） ），东南地区较小（ < 85 d），生长季开始的较早；西南、中部和东北地区较大（ > 85 d），生长季开始的较晚。 比较固定平均法（ 图1（a） 图1（a） ）和动态平均法 （ 图1（d） 图1（d） ）下的 <math id="M58"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603062&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603061&type= 6.18066692 3.21733332 ，两者具有相似的结果，均有4.2%有效像元的 <math id="M59"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 小于75 d，生长季开始的最早主要分布在海南省，分别有38.8%和35.3%有效像元的 <math id="M60"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 在75—85 d，占比最大；其次，31.5%和33.0%有效像元的 <math id="M61"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603152&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603177&type= 6.18066692 3.21733332 在85—95 d，均分布在东南地区；15.2%和16.2%有效像元的 <math id="M62"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 在95—105 d，主要分布在西南和中部地区；生长季开始最晚的地区主要分布在东北地区， <math id="M63"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603038&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603050&type= 6.18066692 3.21733332 大于105 d，占7.3%和11.2%。进一步比较动态平均法和固定平均法的 △ <math id="M64"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 （ 图1（b） 图1（b） ），结果表明两种平均法的差异较小（-0.6±1.7 d）；仅在南方地区存在1.7%的有效像元其动态平均法小于固定平均法超过7 d；零星存在0.6%的有效像元其动态平均法大于固定平均法超过7 d。 对于参考曲线法下的 <math id="M65"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 （ 图1（e） 图1（e） ），41.5%有效像元的 <math id="M66"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603152&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603177&type= 6.18066692 3.21733332 在85—95 d，占比最大，22.3%有效像元的 <math id="M67"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603017&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603029&type= 6.18066692 3.21733332 在75—85 d，均主要分布在东南地区；其他范围的 <math id="M68"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 与平均法差异不大。进一步比较平均法与参考曲线法的 △ <math id="M69"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603019&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603034&type= 6.18066692 3.21733332 （ 图1（c） 图1（c） 和 图1（f） 图1（f） ），结果表明，参考曲线法与平均法的 △ <math id="M70"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603152&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603177&type= 6.18066692 3.21733332 较大（-2.6±2.2 d），总体上平均法的 <math id="M71"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 小于参考曲线法（占88.0%）；其中北纬30°以北的地区， △ <math id="M72"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603044&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603027&type= 6.18066692 3.21733332 均小于0；存在8.0%和6.0%的有效像元其动态平均法和固定平均法小于参考曲线法超过7 d，主要分布在东南丘陵地区；存在1.6%和1.8%的有效像元其动态平均法和固定平均法大于参考曲线法超过7 d，主要分布在西南地区、广西壮族自治区和海南省。 3.2　 △ <math id="M73"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603047&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603045&type= 6.18066692 3.21733332 的影响因子分析 2 图1 图1 结果显示，动态平均法与固定平均法的 △ <math id="M74"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 较小（-0.6±1.7 d），因此仅选用动态平均法进行后续研究。本研究分析了动态平均法和参考曲线法的 △ <math id="M75"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603038&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603050&type= 6.18066692 3.21733332 与年均温和年均降水的梯度变化关系（ 图2 图2 ）；进一步将 △ <math id="M76"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603062&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603061&type= 6.18066692 3.21733332 与非生物因子年均温（ 图3 图3 （a））、年均降水（ 图3 图3 （b））和生物因子变绿时长的标准差（GD STD ）（ 图3 图3 （c））以及年均温与GD STD （ 图3 图3 （d））进行线性回归分析，以探究不同方法 △ <math id="M77"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 的影响因素。结果表明，平均法和参考曲线法的 △ <math id="M78"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 不仅存在空间异质性，而且与气候梯度相关（ 图2 图2 ）。平均法和参考曲线法的 △ <math id="M79"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603062&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603061&type= 6.18066692 3.21733332 主要为负值（ 图2 图2 ），且与年均温呈现显著正相关，即随着温度的升高， △ <math id="M80"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603066&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603064&type= 6.18066692 3.21733332 减小（Slope=0.07 d/℃， P < 0.01）（ 图3 图3 （a））；而 △ <math id="M81"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603069&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603067&type= 6.18066692 3.21733332 与年均降水呈现显著负相关，即 △ <math id="M82"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603073&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603071&type= 6.18066692 3.21733332 随着降水增加而增大（Slope=-0.0005 d/mm， P < 0.01）（ 图3 图3 （b））。此外，如 图2 图2 所示，在低温高降水到高温低降水的梯度变化上， △ <math id="M83"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 总体上呈现递增的趋势，即随着温度的升高和降水的减小， <math id="M84"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603152&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603177&type= 6.18066692 3.21733332 由平均法小于参考曲线法变化到平均法大于参考曲线法（由-12 d增至12 d）。而在低温低降水到高温高降水的梯度上， △ <math id="M85"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603081&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603080&type= 6.18066692 3.21733332 没有显著的变化趋势，主要表现为平均法 <math id="M86"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603152&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603177&type= 6.18066692 3.21733332 小于参考曲线法 <math id="M87"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603152&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603177&type= 6.18066692 3.21733332 。 1,2;3,4; 进一步分析表明，平均法和参考曲线法的 △ <math id="M92"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603149&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603147&type= 6.18066692 3.21733332 可能与植被变绿时长（GD）相关。图 3 3 （c）和 3 3 （d）表明，年均温与GD STD 呈现显著正相关（Slope=0.44 d/℃， P < 0.01），GD STD 与 △ <math id="M93"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 呈现显著正相关（Slope=0.03， P < 0.01），即年均温越高，GD STD 越大， <math id="M94"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603152&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603177&type= 6.18066692 3.21733332 的差值越小。如寒冷地区，考虑到春季冻害风险对植被生长季初期的严重影响，植被通常会以冷激天数（Chilling Days）和光周期调控春季物候，因此相对而言SOS年际波动较小（ Richardson等，2018 Richardson等，2018 ）， △ <math id="M95"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 较大；而在温暖地区，植被受到春季冻害的风险较低，因此植被生长季开始时间会随春季气候条件变化而变化，表现出较强的年际波动，导致各年间的生长状况差异较大，表现出较大的GD STD ；而GD STD 越大，该地区植被的年间SOS变化幅度越大， △ <math id="M96"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603168&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603167&type= 6.18066692 3.21733332 越小，从而影响了平均法和参考曲线法下 <math id="M97"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603152&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603177&type= 6.18066692 3.21733332 的结果，造成其差异越大。 3.3　 <math id="M98"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603175&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603173&type= 6.18066692 3.21733332 的不确定性对季前时长的影响 2 为探究不同方法下的 <math id="M99"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603152&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603177&type= 6.18066692 3.21733332 对物候研究的不确定性，本文利用动态平均法（ 图4（a） 图4（a） ）和参考曲线法（ 图4（b） 图4（b） ）下的 <math id="M100"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603180&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603179&type= 6.18066692 3.21733332 分别计算季前时长（PD）及其差值（ 图4（c） 图4（c） ），并展示以上两种方法计算季前时长时偏相关分析的显著度（ 图4（d） 图4（d） ）。结果表明，平均法（ 图4（a） 图4（a） ）和参考曲线法（ 图4（b） 图4（b） ）下的PD在数值分布上呈现较为接近的结果，PD主要集中在15 d（32.4%，32.0%）、30 d（24.3%，24.6%）和60 d（30.2%，31.1%），大于60 d的仅占13.1%和12.3%；空间分布上，在贵州、湖北、广西交界处，四川盆地周围山区和东南丘陵地区，其PD较长（大于30 d），且两种方法下的偏相关分析均表现为显著（ P < 0.05）。 审图号：GS（2022）734号 1,2;3,4; 进一步比较平均法和参考曲线法下季前时长的差值（ △ PD）（ 图4（c） 图4（c） ）。总体上，41.8%有效像元的 △ PD存在5 d以上的差异（-1.4±11 d）；其中，11.8%的有效像元平均法下的PD超过参考曲线法下的PD 15 d，在中西部地区（如陕西省）分布较为集中，其余地区分布相对均匀；同时，9.2%的有效像元平均法下的PD比参考曲线法下的PD小15 d以上，主要分布在东南丘陵和四川盆地周围山区。关于季前时长偏相关分析的显著性，42.1%的有效像元在平均法和参考曲线法下均表现为显著（ P < 0.05），集中分布在南部丘陵和中部地区；5.2%的有效像元仅在动态平均法下表现为显著（ P < 0.05），在空间上零星分布；9.2%的有效像元仅在参考曲线法下表现为显著（ P < 0.05），主要分布在东南丘陵地区。 4　讨论 1 遥感技术的发展为区域尺度的植被物候研究提供了新手段，成为多年平均物候数据的重要来源（ 王敏钰 等，2022 王敏钰 等，2022 ）。然而，由于计算方法的不同，多年平均物候参数空间化存在不确定性。本文结果表明，平均法和参考曲线法的 △ <math id="M102"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603293&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603292&type= 6.18066692 3.21733332 具有较大的空间异质性，不同的地区差异不同；其中 △ <math id="M103"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603281&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603284&type= 6.18066692 3.21733332 较大的地区主要分布在东南丘陵，其他地区差异较小；基于 <math id="M104"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603230&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603228&type= 6.18066692 3.21733332 的季前时长的差值在空间上的分布也有所不同， △ PD较大的地区主要分布在东南丘陵和西南山区。此外，由于不同地区具有不同的气候环境条件，发育着不同植被功能类型的森林，可能造成不同植被类型间平均法和参考曲线法的 △ <math id="M105"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603281&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603284&type= 6.18066692 3.21733332 存在差异。中国东北至西南的森林样带跨越了温带—亚热带—热带不同的气候区，森林植被类型丰富且变化显著，有落叶阔叶林、常绿针叶林、常绿阔叶林等（ Jin等，2014 Jin等，2014 ）；从结果来看，不同气候区以及不同森林植被类型间的 △ <math id="M106"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603281&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603284&type= 6.18066692 3.21733332 也存在差异。因此，建议在涉及遥感地表物候多年均值（特别是 <math id="M107"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603293&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603292&type= 6.18066692 3.21733332 ）的研究中，应当考虑不同方法对结果造成的影响，并结合局地的气候条件和不同的植被类型进行分析，特别是在某些差异较大的地区，如东南丘陵地区。 为验证多年遥感地表物候的指示意义，本文选取了中国物候观测网木本植物物候观测数据集进行分析（ Dai等，2013a Dai等，2013a ， 2013b 2013b ； Wang等，2015 Wang等，2015 ； Ge等，2015 Ge等，2015 ； Dai等，2019 Dai等，2019 ）。数据来源于国家科技基础条件平台—国家地球系统科学数据中心（ http://www.geodata.cn http://www.geodata.cn [2021-01-29]），将中国物候观测网全国站点与有效栅格进行匹配，并考虑实测数据与遥感数据时间同步性（2001年—2016年），最终选取了北京（40.0°N，116.3°E）、福州（26.1°N，119.3°E）、桂林（25.1°N，110.3°E）、贵阳（26.0°N，107.0°E）、西安（34.2°N，109.0°E）5个站点的木本植物“开始展叶期”数据集。因遥感地表物候表现为地表植物平均状态，且站点观测的植物种类较多且为区域优势物种，因此本文将站点中历年所有木本植物的开始展叶期均值作为该站点的物候多年均值（ <math id="M108"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603262&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603260&type= 6.18066692 3.21733332 OBS ）。结果表明（ 图5 图5 ），动态平均法和参考曲线法对实测开始展叶期的多年均值均有较好的估算效果，但动态平均法估算精度略高，5个站点的平均误差（ △ <math id="M109"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603262&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603260&type= 6.18066692 3.21733332 （DYN-OBS） ）为2.3±2.6 d，而参考曲线法平均估算误差（ △ <math id="M110"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603262&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603260&type= 6.18066692 3.21733332 （REF-OBS） ）为3.4±4.1 d，表明不同方法确实将造成物候多年均值估算差异。 本研究仍可以从以下方面进行改进，在数据方面，由于遥感数据的获取受到云、雨、气溶胶等干扰，存在一定误差，对物候的提取产生影响（ Xiao等，2002 Xiao等，2002 ）；本文采用了SSA平滑滤波的方法，去除噪声以进行质量控制。在时间分辨率上，植被指数数据存在一定的数据缺失，对结果造成一定的不确定性；本文选用16 d合成的EVI数据产品，在产品生成的过程中进行了时空数据的插补，对缺失的数据进行补充。在空间分辨率上，由于遥感数据存在混合像元的问题，如森林与草地的混合、森林与灌木的混合等，不同组合的混合像元对物候提取产生的影响也是不同的，对物候提取及年际变化分析造成了一定的不确定性；本文利用植被功能类型，提取森林PFT含量大于50%的像元，保证所选有效像元中有足够的森林，较好的表征森林生长状况。在物候提取方法上，本研究采用了主流的阈值法进行物候特征的提取，该方法认为当表征植被属性的数据（EVI）达到某一阈值时，对应某植被物候期的发生，该方法提取的物候指标在一些有先验信息的区域较为精确，然而对于不同森林类型其阈值的确定存在一定差异；本文综合对比前人的研究以及本文研究区的特点，选择20%作为阈值，在一定程度上可提高物候提取的稳定性（ 曹沛雨 等，2016 曹沛雨 等，2016 ）。此外，通过对比发现，多年平均的遥感地表物候与实测物候也具有较好的一致性。综上所述，植被物候及其多年平均值是陆面过程模型及全球植被模型的重要输入，评估长时序遥感地表物候数据的不确定性具有重要意义。尽管物候研究的数据与方法存在不足，本文通过滤波、插补、植被功能类型、阈值选取等手段实行质量控制，在一定程度上降低误差与不确定性，取得了较好结果。 5　结论 1 本研究针对生长季起始时间的多年平均值（ <math id="M111"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603293&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603292&type= 6.18066692 3.21733332 ），利用平均法（包括动态平均法和固定平均法）、参考曲线法，探究不同方法下遥感地表物候多年均值的差异及其空间异质性，并借助季前时长探究该差异对物候—气候关系的潜在影响。研究表明，（1）不同方法下的 <math id="M112"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603293&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603292&type= 6.18066692 3.21733332 存在显著空间差异，总体上平均法 <math id="M113"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603281&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603284&type= 6.18066692 3.21733332 小于参考曲线法 <math id="M114"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603293&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603292&type= 6.18066692 3.21733332 ，其中8.0%和6.0%的有效像元其动态平均法和固定平均法小于参考曲线法超过7 d，主要分布在东南丘陵地区。（2）平均法和参考曲线法的 △ <math id="M115"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math> http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603293&type= http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42603292&type= 6.18066692 3.21733332 随年像元所在地区的多年平均气温的升高而减小，随年均降水的增加而增大。（3）平均法和参考曲线法计算下季前时长具有明显差异，约40%有效像元的差异（ △ PD）超过5 d（其中近50%的像元 △ PD超过15 d），主要分布在东南丘陵和西南山区。本研究的结果有助于认识不同方法下遥感地表物候多年均值的差异，为遥感地表物候的模型空间参数化应用提供有益参考。 参考文献（References） Cao P Y , Zhang L M , Li S G and Zhang J H . 2016 . 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Spatial uncertainty in multi-year mean phenology based on remote sensing data. National Remote Sensing Bulletin， 26（9）：1814-1823 国家自然科学基金（编号：41971374）；国家科技基础条件平台建设项目（编号：2005DKA32300） Supported by National Natural Science Foundation of China (No. 41971374)；National Science & Technology Infrastructure of China (No. 2005DKA32300) 2021-01-29 2022-09-07 2023-04-21 2022-02-15 遥感学报 9 26