سابِق|تال

Solar-induced chlorophyll fluorescence data-based study on the spatial and temporal patterns of vegetation phenology in the Northern Hemisphere during the period of 2007—2018

  • role： First author第一作者
  • Affiliation：

    School of Earth and Space Science, Peking University, Beijing 100871, China

  • Email：haoran.zhou@pku.edu.cn
  • Introduction：周浩然，研究方向为摄影测量与遥感。E-mail： haoran.zhou@pku.edu.cn
ZHOU Haoran1,  
  • Affiliation：

    PopSmart Technology Co., Ltd, Ningbo 315100, China

SUN Hua2,  
  • Affiliation：

    School of Earth and Space Science, Peking University, Beijing 100871, China

SHI Zhenwei1,  
  • Affiliation：

    School of Earth and Space Science, Peking University, Beijing 100871, China

PENG Fei1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Earth and Space Science, Peking University, Beijing 100871, China

  • Email：yi.lin@pku.edu.cn
  • Introduction：林沂，研究方向为激光遥感、陆地生态系统遥感和森林生态遥感等。E-mail： yi.lin@pku.edu.cn
LIN Yi1*

ملخص

Vegetation phenology refers to the specific timing of periodic events in plants and how these timings are adapted by periodic variations in climate and environmental factors such as air temperature and soil moisture content. Vegetation phenology change trends are closely related to global climate change; therefore, studies on vegetation phenology can help us better understand global climate change and how vegetation reacts to climate change. Remote sensing technology has been the main means for large-scale vegetation research; however, there have been problems when using remote sensing Vegetation Indexes (VIs) to monitor vegetation phenology due to the discrepancies between the vegetation greenness index and photosynthesis. Especially in evergreen forests, the periodic change in VI datasets is weak; thus, it is difficult to capture phenology metrics based on these VI datasets. Therefore, there is an urgent need to develop new technology to better monitor vegetation phenology. Recently, Sun-Induced Chlorophyll Fluorescence (SIF) has attracted increasing 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 in GPP over time and has great potential for monitoring vegetation phenology. 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 the following: (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 those derived from VI data, especially for SOS. (2) In the Northern Hemisphere, the multiannual average SOS was mainly (>90%) concentrated in 100—170 days, while the multiannual average EOS was mainly concentrated in 220—270 days. The SOS occurred later in high latitude areas and high-altitude areas, while the EOS showed the opposite trend. (3) From 2007 to 2018, the SOS derived from GOME-2 SIF data in the Northern Hemisphere showed a significant advancing trend (Senslope was -0.173), and the 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 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 first analyzed the vegetation phenology change trends in the past 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.

مفهوم

remote sensing;solar-induced chlorophyll fluorescence;vegetation phenology;spatial pattern;trend;climatic factors

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