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<p>Rice is a staple food for over half the global population and contributes to more than 10% of global anthropogenic methane emissions. Precise mapping of rice distribution in Asia, the primary region for rice cultivation responsible for over 60% of global production, is crucial for monitoring food security and greenhouse gas emissions. However, due to cloud cover impacts on optical remote sensing imagery, there is still a lack of long-term, high-resolution rice distribution datasets for the entire Asian region. This study develops the Global Crop Dataset-Rice (GCD-Rice) dataset to map rice cultivation across three seasons in 16 Asian countries from 1990 to 2023. Using Landsat and Sentinel-1 datasets, along with a phenological approach and a random forest model, the maps were validated with 258,547 field samples. Results show an average user accuracy of 89.88%, a producer accuracy of 88.52%, and an overall accuracy of 88.85%. Furthermore, comparing with statistical area reveals an overall average R² value of 0.768, a slope of 0.874, and an RMSE of 0.346.</p>

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A long-term paddy rice distribution dataset in Asia at a 30 m spatial resolution

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月21日

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  • Shaoping Li,
  • Ruoque Shen,
  • Jiale Jiang,
  • Qiongyan Peng,
  • Xuebing Chen,
  • Jie Dong,
  • Jinwei Dong,
  • Wenping Yuan

摘要

Rice is a staple food for over half the global population and contributes to more than 10% of global anthropogenic methane emissions. Precise mapping of rice distribution in Asia, the primary region for rice cultivation responsible for over 60% of global production, is crucial for monitoring food security and greenhouse gas emissions. However, due to cloud cover impacts on optical remote sensing imagery, there is still a lack of long-term, high-resolution rice distribution datasets for the entire Asian region. This study develops the Global Crop Dataset-Rice (GCD-Rice) dataset to map rice cultivation across three seasons in 16 Asian countries from 1990 to 2023. Using Landsat and Sentinel-1 datasets, along with a phenological approach and a random forest model, the maps were validated with 258,547 field samples. Results show an average user accuracy of 89.88%, a producer accuracy of 88.52%, and an overall accuracy of 88.85%. Furthermore, comparing with statistical area reveals an overall average R² value of 0.768, a slope of 0.874, and an RMSE of 0.346.