IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p6012-d1112035.html
   My bibliography  Save this article

Detecting the Spatiotemporal Variation of Vegetation Phenology in Northeastern China Based on MODIS NDVI and Solar-Induced Chlorophyll Fluorescence Dataset

Author

Listed:
  • Ruixin Zhang

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China)

  • Yuke Zhou

    (Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic and Nature Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Tianyang Hu

    (State Environmental Protection Key Laboratory of Quality Control in Environmental Monitoring, China National Environmental Monitoring Centre, Beijing 100012, China)

  • Wenbin Sun

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China)

  • Shuhui Zhang

    (State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China)

  • Jiapei Wu

    (Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic and Nature Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Han Wang

    (Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic and Nature Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

Abstract

Vegetation phenology is a crucial biological indicator for monitoring changes in terrestrial ecosystems and global climate. Currently, there are limitations in using traditional vegetation indices for phenology monitoring (e.g., greenness saturation in high-density vegetation areas). Solar-induced chlorophyll fluorescence (SIF), a novel remote sensing product, has great potential in depicting seasonal vegetation dynamics across various regions with different vegetation covers and latitudes. In this study, based on the GOSIF and MODIS NDVI data from 2001 to 2020, we extracted vegetation phenological parameters in Northeastern China by using Double Logistic (D-L) fitting function and the dynamic threshold method. Then, we analyzed the discrepancy in phenological period and temporal trend derived from SIF and NDVI data at multiple spatiotemporal scales. Furthermore, we explored the response of vegetation phenology to climate change and the persistence of phenological trends (Hurst exponent) in Northeastern China. Generally, there is a significant difference in trends between SIF and NDVI, but with similar spatial patterns of phenology. However, the dates of key phenological parameters are distinct based on SIF and MODIS NDVI data. Specifically, the start of season (SOS) of SIF started later (about 10 days), and the end of season (EOS) ended earlier (about 36 days on average). In contrast, the fall attenuation of SIF showed a lag process compared to NDVI. This implies that the actual period of photosynthesis, that is, length of season (LOS), was shorter (by 46 days on average) than the greenness index. The position of peak (POP) is almost the same between them. The great difference in results from SIF and NDVI products indicated that the vegetation indexes seem to overestimate the time of vegetation photosynthesis in Northeastern China. The Hurst exponent identified that the future trend of SOS, EOS, and POP is dominated by weak inverse sustainability, indicating that the future trend may be opposite to the past. The future trend of LOS SIF and LOS NDVI are opposite; the former is dominated by weak inverse sustainability, and the latter is mainly weak positive sustainability. In addition, we speculate that the difference between SIF and NDVI phenology is closely related to their different responses to climate. The vegetation phenology estimated by SIF is mainly controlled by temperature, while NDVI is mainly controlled by precipitation and relative humidity. Different phenological periods based on SIF and NDVI showed inconsistent responses to pre-season climate. This may be the cause of the difference in the phenology of SIF and NDVI extraction. Our results imply that canopy structure-based vegetation indices overestimate the photosynthetic cycle, and the SIF product can better track the phenological changes. We conclude that the two data products provide a reference for monitoring the phenology of photosynthesis and vegetation greenness, and the results also have a certain significance for the response of plants to climate change.

Suggested Citation

  • Ruixin Zhang & Yuke Zhou & Tianyang Hu & Wenbin Sun & Shuhui Zhang & Jiapei Wu & Han Wang, 2023. "Detecting the Spatiotemporal Variation of Vegetation Phenology in Northeastern China Based on MODIS NDVI and Solar-Induced Chlorophyll Fluorescence Dataset," Sustainability, MDPI, vol. 15(7), pages 1-21, March.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6012-:d:1112035
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/6012/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/7/6012/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yongxia Ding & Shouzhang Peng, 2020. "Spatiotemporal Trends and Attribution of Drought across China from 1901–2100," Sustainability, MDPI, vol. 12(2), pages 1-17, January.
    2. Alistair W. R. Seddon & Marc Macias-Fauria & Peter R. Long & David Benz & Kathy J. Willis, 2016. "Sensitivity of global terrestrial ecosystems to climate variability," Nature, Nature, vol. 531(7593), pages 229-232, March.
    3. Yao Zhang & Róisín Commane & Sha Zhou & A. Park Williams & Pierre Gentine, 2020. "Light limitation regulates the response of autumn terrestrial carbon uptake to warming," Nature Climate Change, Nature, vol. 10(8), pages 739-743, August.
    4. Kunshan Gao & Juntian Xu & Guang Gao & Yahe Li & David A. Hutchins & Bangqin Huang & Lei Wang & Ying Zheng & Peng Jin & Xiaoni Cai & Donat-Peter Häder & Wei Li & Kai Xu & Nana Liu & Ulf Riebesell, 2012. "Rising CO2 and increased light exposure synergistically reduce marine primary productivity," Nature Climate Change, Nature, vol. 2(7), pages 519-523, July.
    5. Gian-Reto Walther & Eric Post & Peter Convey & Annette Menzel & Camille Parmesan & Trevor J. C. Beebee & Jean-Marc Fromentin & Ove Hoegh-Guldberg & Franz Bairlein, 2002. "Ecological responses to recent climate change," Nature, Nature, vol. 416(6879), pages 389-395, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mayeul Dalleau & Stéphane Ciccione & Jeanne A Mortimer & Julie Garnier & Simon Benhamou & Jérôme Bourjea, 2012. "Nesting Phenology of Marine Turtles: Insights from a Regional Comparative Analysis on Green Turtle (Chelonia mydas)," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-13, October.
    2. Bu, Lingduo & Chen, Xinping & Li, Shiqing & Liu, Jianliang & Zhu, Lin & Luo, Shasha & Lee Hill, Robert & Zhao, Ying, 2015. "The effect of adapting cultivars on the water use efficiency of dryland maize (Zea mays L.) in northwestern China," Agricultural Water Management, Elsevier, vol. 148(C), pages 1-9.
    3. Anne Goodenough & Adam Hart, 2013. "Correlates of vulnerability to climate-induced distribution changes in European avifauna: habitat, migration and endemism," Climatic Change, Springer, vol. 118(3), pages 659-669, June.
    4. Monika Punia & Suman Nain & Amit Kumar & Bhupendra Singh & Amit Prakash & Krishan Kumar & V. Jain, 2015. "Analysis of temperature variability over north-west part of India for the period 1970–2000," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 935-952, January.
    5. Wesley R. Brooks & Stephen C. Newbold, 2013. "Ecosystem damages in integrated assessment models of climate change," NCEE Working Paper Series 201302, National Center for Environmental Economics, U.S. Environmental Protection Agency, revised Mar 2013.
    6. Nicoletta Cannone & M. Guglielmin & P. Convey & M. R. Worland & S. E. Favero Longo, 2016. "Vascular plant changes in extreme environments: effects of multiple drivers," Climatic Change, Springer, vol. 134(4), pages 651-665, February.
    7. Groeneveld, Jürgen & Johst, Karin & Kawaguchi, So & Meyer, Bettina & Teschke, Mathias & Grimm, Volker, 2015. "How biological clocks and changing environmental conditions determine local population growth and species distribution in Antarctic krill (Euphausia superba): a conceptual model," Ecological Modelling, Elsevier, vol. 303(C), pages 78-86.
    8. Norman Myers, 2003. "Conservation of Biodiversity: How Are We Doing?," Environment Systems and Decisions, Springer, vol. 23(1), pages 9-15, March.
    9. Donohue, John G. & Piiroinen, Petri T., 2015. "Mathematical modelling of seasonal migration with applications to climate change," Ecological Modelling, Elsevier, vol. 299(C), pages 79-94.
    10. John H Matthews & Bart AJ Wickel & Sarah Freeman, 2011. "Converging Currents in Climate-Relevant Conservation: Water, Infrastructure, and Institutions," PLOS Biology, Public Library of Science, vol. 9(9), pages 1-4, September.
    11. Meng Luo & Shengwei Zhang & Lei Huang & Zhiqiang Liu & Lin Yang & Ruishen Li & Xi Lin, 2022. "Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
    12. Sharaniya Vijitharan & Nophea Sasaki & Manjunatha Venkatappa & Nitin Kumar Tripathi & Issei Abe & Takuji W. Tsusaka, 2022. "Assessment of Forest Cover Changes in Vavuniya District, Sri Lanka: Implications for the Establishment of Subnational Forest Reference Emission Level," Land, MDPI, vol. 11(7), pages 1-25, July.
    13. Feng, Zhiying & Tang, Wenhu & Niu, Zhewen & Wu, Qinghua, 2018. "Bi-level allocation of carbon emission permits based on clustering analysis and weighted voting: A case study in China," Applied Energy, Elsevier, vol. 228(C), pages 1122-1135.
    14. Feng Dong & Chih-Ming Hung & Shou-Hsien Li & Xiao-Jun Yang, 2021. "Potential Himalayan community turnover through the Late Pleistocene," Climatic Change, Springer, vol. 164(1), pages 1-10, January.
    15. Ding, Helen & Nunes, Paulo A.L.D., 2014. "Modeling the links between biodiversity, ecosystem services and human wellbeing in the context of climate change: Results from an econometric analysis of the European forest ecosystems," Ecological Economics, Elsevier, vol. 97(C), pages 60-73.
    16. Chan, Nathan & Wichman, Casey, 2017. "The Effects of Climate on Leisure Demand: Evidence from North America," RFF Working Paper Series 17-20, Resources for the Future.
    17. Li Yang & Yue Xu & Junqi Zhu & Keyu Sun, 2024. "Research on Water Ecological Resilience Measurement and Influencing Factors: A Case Study of the Yangtze River Economic Belt, China," Sustainability, MDPI, vol. 16(16), pages 1-23, August.
    18. Zhang, Jiarui & Jørgensen, Sven E. & Lu, Jianjian & Nielsen, Søren N. & Wang, Qiang, 2014. "A model for the contribution of macrophyte-derived organic carbon in harvested tidal freshwater marshes to surrounding estuarine and oceanic ecosystems and its response to global warming," Ecological Modelling, Elsevier, vol. 294(C), pages 105-116.
    19. Richter, Andries & Grasman, Johan, 2013. "The transmission of sustainable harvesting norms when agents are conditionally cooperative," Ecological Economics, Elsevier, vol. 93(C), pages 202-209.
    20. Liqiang Yang & Xiaotong He & Shaoguo Ru & Yongyu Zhang, 2024. "Herbicide leakage into seawater impacts primary productivity and zooplankton globally," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6012-:d:1112035. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.