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Updated: Jun 2, 2026

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
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Tea tree recognition based on multi-source satellite data across Southeast China.

Nan Cong1,2, Rongrong Zhao1,2, Yuxin Qiu2

  • 1Lhasa Plateau Ecosystem Research Station, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.

Frontiers in Plant Science
|June 1, 2026
PubMed
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This study uses Sentinel-1 radar and Sentinel-2 imagery to accurately map tea (Camellia Sinensis) plantations, overcoming cloud cover limitations. April was identified as the optimal month for distinguishing tea crops from other vegetation.

Area of Science:

  • Agricultural remote sensing
  • Vegetation mapping
  • Geospatial analysis

Background:

  • Accurate spatial data is crucial for tea (Camellia Sinensis) industry insights and sustainability.
  • Conventional remote sensing methods struggle to differentiate spectrally similar vegetation, including tea trees.
  • Yunnan's persistent cloud cover limits optical remote sensing, especially during non-growing seasons.

Purpose of the Study:

  • To develop an effective method for delineating tea plantations using integrated satellite data.
  • To overcome limitations of optical remote sensing in cloudy regions like Yunnan.
  • To identify the optimal phenological window for discriminating tea from similar vegetation types.

Main Methods:

  • Integration of Sentinel-1 radar data and Sentinel-2 imagery.
Keywords:
classification accuracycrop distributionmachine learningremote sensingtea trees

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  • Analysis of annual phenological dynamics to determine optimal observation periods.
  • Application of feature selection to optimize classification models.

    • Main Results:

    • April was identified as the optimal temporal window for discriminating tea trees from rubber and natural forests.
    • Classification accuracy was significantly enhanced from 87.1% to 89.1% after feature optimization.
    • The integrated approach effectively overcame optical observation limitations caused by cloud cover.

    Conclusions:

    • The combined use of Sentinel-1 and Sentinel-2 data provides a robust solution for mapping tea plantations.
    • Identifying optimal phenological windows is key to improving vegetation classification accuracy in challenging climates.
    • This methodology supports enhanced crop monitoring and timely agricultural management decisions.