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Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China

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  • Lin Zhang

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China
    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Zhe Liu

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China
    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Diyou Liu

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China
    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Quan Xiong

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China
    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Ning Yang

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China
    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Tianwei Ren

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China
    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Chao Zhang

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China
    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Xiaodong Zhang

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China
    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Shaoming Li

    (College of Land Science and Technology, China Agricultural University, Beijing 100083, China
    Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

Abstract

Accurate, year-by-year crop distribution information is a key element in agricultural production regulation and global change governance. However, due to the high sampling costs and insufficient use of historical samples, a supervised classifying method for sampling every year is unsustainable for mapping crop types over time. Therefore, this paper proposes a method for the generation and screening of new samples for 2018 based on historical crop samples, and then it builds a crop mapping model for that current season. Pixels with the same crop type in the historical year (2013–2017) were extracted as potential samples, and their spectral features and spatial information in the current year (2018) were used to generate new samples based on clustering screening. The research result shows that when the clustering number is different, the number and structure of new generated sample also changes. The sample structure generated in Luobei County was not balanced, with the ‘other crop’ representing less than 3.97%, but the structure of southwest Hulin City was more balanced. Based on the newly generated samples and the ground reference data of classified year, the classification models were constructed. The average classification accuracies of Luobei County in 2018 based on new generated samples and field samples were 69.35% and 77.59%, respectively, while those of southwest Hulin City were 80.44% and 82.94%, respectively. Combined with historical samples and the spectral information of the current year, this study proposes a method to generate new samples. It can overcome the problem of crop samples only being collected in the field due to the difficulty of visual interpretation, effectively improve the use of historical data, and also provide a new idea for sustainable crop mapping in many regions lacking seasonal field samples.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:18:p:5052-:d:267657
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    References listed on IDEAS

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    1. Xuli Zan & Zuliang Zhao & Wei Liu & Xiaodong Zhang & Zhe Liu & Shaoming Li & Dehai Zhu, 2019. "The Layout of Maize Variety Test Sites Based on the Spatiotemporal Classification of the Planting Environment," Sustainability, MDPI, vol. 11(13), pages 1-15, July.
    2. Marcos Adami & Bernardo Friedrich Theodor Rudorff & Ramon Morais Freitas & Daniel Alves Aguiar & Luciana Miura Sugawara & Marcio Pupin Mello, 2012. "Remote Sensing Time Series to Evaluate Direct Land Use Change of Recent Expanded Sugarcane Crop in Brazil," Sustainability, MDPI, vol. 4(4), pages 1-12, April.
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    2. Xinlei Hu & Zuliang Zhao & Lin Zhang & Zhe Liu & Shaoming Li & Xiaodong Zhang, 2019. "A High-Temperature Risk Assessment Model for Maize Based on MODIS LST," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    3. Wenjie Zhang & Liang Zhu & Qifeng Zhuang & Dong Chen & Tao Sun, 2023. "Mapping Cropland Soil Nutrients Contents Based on Multi-Spectral Remote Sensing and Machine Learning," Agriculture, MDPI, vol. 13(8), pages 1-19, August.
    4. 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.

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