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Spatial uncertainty in multi-year mean phenology based on remote sensing data

  • role： First author第一作者
  • Affiliation：

    College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China

    National Earth System Science Data Center, National Science & Technology Infrastructure of China, Beijing 100101, China

  • Email：jiaxinking@hhu.edu.cn
  • Introduction：金佳鑫，研究方向为生态遥感。E-mail： jiaxinking@hhu.edu.cn
JIN Jiaxin12,  
  • Affiliation：

    College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China

JI Yingying1,  
  • Affiliation：

    PowerChina Zhongnan Engineering Corporation Limited, Changsha 410014, China

GUO Fengsheng3,  
  • Affiliation：

    College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China

YU Han1,  
  • Affiliation：

    College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China

XIAO Yuanyuan1

résumé

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 (SOS¯) using two methods in forests in China and detected the differences between the SOS¯ derived from the two methods (△SOS¯) and the spatial pattern. Furthermore, a commonly used indicator in phenological research, that is, the temperature (Preseason Duration (PD)) based on SOS¯, was used to explore the potential impact of the SOS¯ derived from different methods on the phenology–climate relationship. Results show 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%). The pixels with △SOS¯>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 △SOS¯ 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.

mots-clés

multi-year average phenology;phenological preseason duration;remote sensing surface phenology;time series;forest

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