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AUTS: A Novel Approach to Mapping Winter Wheat by Automatically Updating Training Samples Based on NDVI Time Series

Author

Listed:
  • Chunyang Wang

    (College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China
    School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Huan Zhang

    (School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Xifang Wu

    (School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China)

  • Wei Yang

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

  • Yanjun Shen

    (Key Laboratory of Agricultural Water Resources, Hebei Laboratory of Water-Saving Agriculture, Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences (CAS), Shijiazhuang 050021, China)

  • Bibo Lu

    (College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China)

  • Jianlong Wang

    (College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China)

Abstract

Accurate and rapid access to crop distribution information is a significant requirement for the development of modern agriculture. Improving the efficiency of remote sensing monitoring of winter wheat planting area information, a new method of automatically updating training samples (AUTS), is proposed herein. Firstly, based on the Google Earth Engine (GEE) platform, a Sentinel-2 image with a spatial resolution of 10 m was selected to extract the distribution map of winter wheat in the city of Shijiazhuang in 2017. Secondly, combined with the NDVI time series, the weighted correlation coefficients from 2017, 2018, and 2019 were calculated. Then, the 2017 winter wheat distribution map and its most significant relevant areas were used to extract sample points from 2018 and 2019 automatically. Finally, the distribution map of winter wheat in Shijiazhuang in 2018 and 2019 was generated. In addition, to test the applicability of the automatically updating training sample at different scales and regions, the proposed method was applied to Landsat 8 image data with a spatial resolution of 30 m, as well as to Handan and Baoding. The results showed that the calculated winter wheat planting area is comparable with the officially published statistics, based on Sentinel-2, extracting three years of winter wheat, the R 2 values for all three years were above 0.95. The R 2 values for 2018 and 2019, based on Landsat 8 extractions, were 0.95 and 0.90, respectively. The R 2 values extracted from Handan and Baoding in 2018 were 0.94 and 0.86, respectively. These results indicate that the proposed method has high accuracy and can provide technical support and reference for winter wheat area monitoring and yield estimation.

Suggested Citation

  • Chunyang Wang & Huan Zhang & Xifang Wu & Wei Yang & Yanjun Shen & Bibo Lu & Jianlong Wang, 2022. "AUTS: A Novel Approach to Mapping Winter Wheat by Automatically Updating Training Samples Based on NDVI Time Series," Agriculture, MDPI, vol. 12(6), pages 1-16, June.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:6:p:817-:d:832849
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    References listed on IDEAS

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    1. Lin Zhang & Zhe Liu & Diyou Liu & Quan Xiong & Ning Yang & Tianwei Ren & Chao Zhang & Xiaodong Zhang & Shaoming Li, 2019. "Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China," Sustainability, MDPI, vol. 11(18), pages 1-17, September.
    2. Miina Porkka & Matti Kummu & Stefan Siebert & Olli Varis, 2013. "From Food Insufficiency towards Trade Dependency: A Historical Analysis of Global Food Availability," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
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