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<p>Accurate, continuous and high-resolution mapping of multiple crop types, including both grain and cash crops, is vital for supporting sustainable agricultural development. While substantial progress has been made in mapping major grain crops, China still lacks a long-term, high-resolution dataset that simultaneously captures winter wheat, maize, rice and sugarcane. In this study, we used machine learning method to produce the China Crop Dataset (CCD), a 30 m resolution multi-crop dataset spanning 2001–2024, by integrating Landsat imagery and fused product with high spatial resolution. Validation based on field surveys and visually interpreted Google Earth samples confirmed the high accuracy of the CCD, with producer’s accuracy, user’s accuracy and overall accuracy reaching 88.45%, 87.2% and 91.01%, respectively. Furthermore, the CCD exhibited strong spatial consistency with existing datasets. The classified areas of winter wheat, maize, single-season rice, double-season rice and sugarcane showed good agreement with statistical area, with correlation coefficients (R<sup>2</sup>) exceeding 0.6 in most provinces. This dataset provides a robust and long-term resource for supporting agricultural planning, and facilitating research on land use and food security in China.</p>

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A 30-m annual distribution dataset of major crops in China from 2001-2024

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月11日

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  • Yangyang Fu,
  • Jie Dong,
  • Ruoque Shen,
  • Qiongyan Peng,
  • Xiangqian Li,
  • Yi Zheng,
  • Baihong Pan,
  • Jinwei Dong,
  • Bingwen Qiu,
  • Jianxi Huang,
  • Xiaojuan Huang,
  • Wenping Yuan

摘要

Accurate, continuous and high-resolution mapping of multiple crop types, including both grain and cash crops, is vital for supporting sustainable agricultural development. While substantial progress has been made in mapping major grain crops, China still lacks a long-term, high-resolution dataset that simultaneously captures winter wheat, maize, rice and sugarcane. In this study, we used machine learning method to produce the China Crop Dataset (CCD), a 30 m resolution multi-crop dataset spanning 2001–2024, by integrating Landsat imagery and fused product with high spatial resolution. Validation based on field surveys and visually interpreted Google Earth samples confirmed the high accuracy of the CCD, with producer’s accuracy, user’s accuracy and overall accuracy reaching 88.45%, 87.2% and 91.01%, respectively. Furthermore, the CCD exhibited strong spatial consistency with existing datasets. The classified areas of winter wheat, maize, single-season rice, double-season rice and sugarcane showed good agreement with statistical area, with correlation coefficients (R2) exceeding 0.6 in most provinces. This dataset provides a robust and long-term resource for supporting agricultural planning, and facilitating research on land use and food security in China.