предыдущий|следующий

Key issues of remote sensing-based vegetation phenology monitoring

  • role： First author第一作者
  • Affiliation：

    College of Water Sciences, Beijing Normal University, Beijing 100875, China

  • Email：xiezy@bnu.edu.cn
  • Introduction：谢志英，研究方向为植被与生态遥感。E-mail： xiezy@bnu.edu.cn
XIE Zhiying1,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：zhuwq75@bnu.edu.cn
  • Introduction：朱文泉，研究方向为植被与生态遥感。E-mail： zhuwq75@bnu.edu.cn
ZHU Wenquan2*,  
  • Affiliation：

    College of Water Sciences, Beijing Normal University, Beijing 100875, China

FU Yongshuo1

реферат

Vegetation phenology is one of the most sensitive biological indicators of terrestrial ecosystem responses to global climate change and plays a crucial role in terrestrial ecological processes and functions. Changes in vegetation phenology have been strongly linked to climate change patterns and various ecological processes within terrestrial ecosystems, and may significantly impact land-atmosphere exchanges of carbon, water, and energy fluxes, and interactions between different species. Therefore, accurate monitoring of vegetation phenology is essential for simulating terrestrial ecological processes and understanding how terrestrial ecosystems respond to climate change. To date, various observation and monitoring methods for vegetation phenology have been developed, including ground-based observations (such as manual observation, phenocam observation / monitoring, and carbon flux-based monitoring) and remote sensing-based monitoring. Benefiting from the reliability of ground-based phenology observations and the spatial coverage and rapid repeatability of remotely sensed monitoring, a regional and global-scale vegetation phenology monitoring framework has been established, with satellite remote sensing as the primary method and ground observations for validation. A general technical workflow for remote sensing-based vegetation phenology monitoring has been formed, including remote sensing data acquisition, time series data construction (e.g., calculation of vegetation parameters such as various vegetation indices, leaf area index, fraction of absorbed photosynthetically active radiation, and gross primary production, etc.), time series data reconstruction (e.g., filtering, smoothing, and fitting), phenological metrics (phenometrics, e.g., start, peak, end, and length of the growing season) extraction, and phenometrics validation. However, each processing step in this workflow introduces uncertainty into the monitored phenometrics. This study focuses on three key aspects of remote sensing-based phenology monitoring: (1) remote sensing time series data (especially vegetation indices), (2) phenometrics extraction, and (3) phenometrics validation. Additionally, it discusses the effects of complex land surface backgrounds (e.g., snow, soil, and dry vegetation) on remote sensing time series data, the differences between various phenometrics extraction methods (i.e., threshold-based vs. derivative-based methods), and the matching issues between remote sensing phenometrics and reference phenometrics during validation (e.g., scale matching and phenometrics matching). Finally, two essential directions are proposed to address these key issues: (1) developing new remote sensing monitoring methods for vegetation phenology to counter background interference, such as constructing new remote sensing indices resistant to complex land surface backgrounds from the perspective of remote sensing mechanisms, and (2) establishing a comprehensive observation network that integrates ground-based multi-sensor coordinated observations and “space-air-ground” multi-scale integrated observations. Addressing these key issues will enhance the reliability of remote sensing phenology data, expand their applications, and deepen the understanding of land-atmosphere interactions.

ключеви́че слова́

remote sensing;vegetation phenology;remote sensing time-series data;vegetation index;validation;scale effect

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