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The Innovation Geoscience > 2024 Vol. 2 > No. 2 > 100070
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Challenges in remote sensing of vegetation phenology

  • 1.

    State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • 2.

    Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610299, China

  • 3.

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

  • 4.

    Center for Environmental Remote Sensing, Chiba University, Chiba 2638522, Japan

  • 5.

    Department of Land Surveying and GeoInformatics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China

  • 6.

    Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

  • 7.

    Geospatial Sciences Center of Excellence, Department of Geography Geospatial Sciences, South Dakota State University, Brookings 57007-1898, USA

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  • Corresponding author: chenjin@bnu.edu.cn 
  • Received Date:  November 22, 2023
    Accepted Date:  April 28, 2024
    Published Online:  May 15, 2024
The Innovation Geoscience  2(2),  https://doi.org/10.59717/j.xinn-geo.2024.100070  |  Cite this article
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    • REFERENCES
  • [1] Zeng, L., Wardlow, B.D., Xiang, D., et al. (2020). A review of vegetation phenological metrics extraction using time-series, multispectral satellite data. Remote Sensing of Environment 237: 111511. DOI: 10.1016/j.rse.2019.111511.

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    [2] Zhu, X., Zhan, W., Zhou, J., et al. (2022). A novel framework to assess all-round performances of spatiotemporal fusion models. Remote Sensing of Environment 274: 113002. DOI: 10.1016/j.rse.2022.113002.

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    [3] Liu, L., Cao, R., Shen, M., et al. (2019). How does scale effect influence spring vegetation phenology estimated from satellite-derived vegetation indexes. Remote sensing 11(18): 2137. DOI: 10.3390/rs11182137.

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    [4] Piao, S., Liu, Q., Chen, A., et al. (2019). Plant phenology and global climate change: Current progresses and challenges. Global Change Biology 25(6): 1922−1940. DOI: 10.1111/gcb.14619.

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    [5] Pan, Y., Peng, D., Chen, J.M., et al. (2023). Climate-driven land surface phenology advance is overestimated due to ignoring land cover changes. Environmental Research Letters 18(4): 044045. DOI: 10.1088/1748-9326/acca34.

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  • Cite this article:

    Shen M., Zhao W., Jiang N., et al., (2024). Challenges in remote sensing of vegetation phenology. The Innovation Geoscience 2(2): 100070. https://doi.org/10.59717/j.xinn-geo.2024.100070
    Shen M., Zhao W., Jiang N., et al., (2024). Challenges in remote sensing of vegetation phenology. The Innovation Geoscience 2(2): 100070. https://doi.org/10.59717/j.xinn-geo.2024.100070

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