Lü et al. (2025) Spatiotemporal Variations and Seasonal Climatic Driving Factors of Stable Vegetation Phenology Across China over the Past Two Decades

Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-10-17
• Authors: Xingqiang Lü, Xiaobo Wu, Yue Gao, Yufei Cai, Yang Li, Yuquan Xiong, Qingchun Yang, Jiaxin Liu, Yijin Li, Zhiyong Deng, Qīng Wáng, Bing Li
• DOI: 10.3390/rs17203467

Research Groups

• College of Resources, Sichuan Agricultural University, Chengdu, China
• Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu, China

Short Summary

This study evaluated remote sensing methods for stable vegetation phenology (SVP) in China over two decades, finding solar-induced chlorophyll fluorescence (SIF) superior to traditional vegetation indices, and revealed distinct spatiotemporal patterns and seasonal climatic drivers, particularly the influence of spring temperature on the Start of Season (SOS) and summer/autumn vapor pressure deficit on the End of Season (EOS).

Objective

• To determine which of four remote sensing datasets (NDVI, EVI, LAI, and SIF) yields the highest accuracy in estimating phenological metrics (e.g., SOS).
• To analyze how stable vegetation phenology (SVP) in China has evolved spatially and temporally over the past two decades.
• To identify the relationship between changes in SVP and seasonal climate factors across different climate zones and vegetation types.

Study Configuration

• Spatial Scale: Continental China, spanning approximately 73°E to 135°E longitude and 3°N to 53°N latitude.
• Temporal Scale: Two decades, from 2003 to 2022.

Methodology and Data

• Models used:
  • Sen’s slope
  • Mann–Kendall (MK) test
  • Hurst index
  • Partial correlation analysis
  • Random forest regression model
  • Variance Inflation Factor (VIF)
  • Savitzky–Golay (SG) filter for SIF data reconstruction
  • Dynamic threshold method for phenological metric estimation (SOS threshold 0.2, EOS threshold 0.5)
• Data sources:
  • Satellite/Remote Sensing:
    • Global “OCO-2” SIF (GOSIF) (0.05° spatial resolution, 8-day temporal resolution)
    • MODIS products (NDVI, EVI from MOD13Q1 at 250 m, 16-day; LAI from MOD15A2H at 500 m, 8-day; MCD12C1 Land cover type at 0.05°, annual)
    • SRTM Digital Elevation Model (DEM) (30 m)
  • Reanalysis Data:
    • Terra Climate dataset (monthly climate data at 4 km resolution for soil moisture (sm), downward shortwave radiation (srad), precipitation (pr), air temperature (tem), vapor pressure deficit (vpd), and potential evapotranspiration (pet))
  • Observation:
    • Ground-based phenological observations from 22 stations (2003–2015) from the National Ecosystem Science Data Center.

Main Results

• Phenological metrics derived from SIF data showed the strongest correlation with ground-based observations (all correlation coefficients (R) exceeding 0.69, with an average of 0.75), outperforming NDVI (SOS R = 0.29), EVI, and LAI.
• The spatial distribution of SVP in China revealed three primary patterns: the Tibetan Plateau, and regions north and south of the Qinling–Huaihe Line. SOS advancement rate increases from arid, cold-to-warm, and humid regions; EOS transitions from earlier to nearly unchanged; and LOS delay rate increases. Latitudinal variation in SVP is more pronounced than longitudinal.
• Over the past two decades (2003–2022), vegetation SOS advanced in 74.96% of the area (18.79% significantly, p < 0.05), primarily in southern China. EOS showed comparable advanced (51.76%) and delayed (36.90%) trends, with delays mainly in the western Tibetan Plateau and eastern Inner Mongolia. The overall LOS extended across the study area.
• The spring climate primarily drives the advancement of SOS across China, contributing up to 70%, with spring temperatures generally having a significant negative effect on SOS (r = -0.53, p < 0.05).
• EOS is regulated by a complex interplay of climatic factors, exhibiting a strong positive partial correlation with summer vapor pressure deficit (vpd) (r = 0.77, p < 0.05) and a significant negative correlation with autumn vpd (r = -0.39, p < 0.05), indicating a dual 'limitation–promotion' effect.
• Grasslands are more sensitive to seasonal climatic variations than forests, with grassland SOS being influenced by both spring and winter temperatures, while forest SOS is primarily influenced by spring temperature.

Contributions

• Provided a systematic comparison of SIF data with traditional vegetation indices (NDVI, EVI, LAI) for stable vegetation phenology (SVP) estimation across China, demonstrating SIF's superior accuracy.
• Focused on "stable vegetation areas" by excluding short-term vegetation (e.g., crops) and urban areas, thereby isolating the fundamental response of natural vegetation to climate change from direct human management and urban heat island effects.
• Utilized higher spatial resolution (0.05°) SIF data to capture fine-scale regional phenology heterogeneity more precisely than previous large-scale studies.
• Identified the key seasonal climatic drivers of SVP across different climate zones and vegetation types, including the dual 'limitation–promotion' effect of vapor pressure deficit on EOS, which was previously underexplored.
• Contributed to a deeper scientific understanding of the interannual variability in stable vegetation phenology under seasonal climate change in China.

Funding

• Natural Science Foundation of Sichuan Province, China (grant numbers 2025ZNSFSC0329, 25NSFJQ0136)
• Natural Resources Research Project of Sichuan Province (grant numbers KJ-2025-61)
• Deployment project of the Overseas Science and Education Cooperation Center, Bureau of International Cooperation, Chinese Academy of Sciences (grant number 162GJHZ2023065MI)
• National Natural Science Foundation of China (grant number 42361144855)

Citation

@article{Lü2025Spatiotemporal,
  author = {Lü, Xingqiang and Wu, Xiaobo and Gao, Yue and Cai, Yufei and Li, Yang and Xiong, Yuquan and Yang, Qingchun and Liu, Jiaxin and Li, Yijin and Deng, Zhiyong and Wáng, Qīng and Li, Bing},
  title = {Spatiotemporal Variations and Seasonal Climatic Driving Factors of Stable Vegetation Phenology Across China over the Past Two Decades},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17203467},
  url = {https://doi.org/10.3390/rs17203467}
}