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<p>Southeast Asia contributes 20% of the world’s rice production and 29% of global rice methane emissions, highlighting the need for accurate data on harvested areas to support food security and greenhouse gas accounting. However, existing paddy rice maps often lack information on cropping intensity, spatial resolution, and accuracy due to diverse cultivation practices. This study presents a 10-m resolution, open-access dataset of rice cropping intensity, enabling the precise estimation of growing and harvested areas across Southeast Asia. The Local Unsupervised Classification with Phenological Labelling (LUCK-PALM) was used to generate the map by combining Sentinel-1A and Sentinel-2A/B data (2020–2021). Validation at the pixel level (n = 58,885) shows an overall accuracy of 0.98, a kappa coefficient of 0.870, and an F1 score of 0.879 in identifying rice areas. This comprehensive dataset is available in a public repository and can be used to enhance food and water security strategies and refines estimates of methane emissions.</p>

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High-resolution maps of rice cropping intensity across Southeast Asia

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月12日

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  • Frisa Irawan Ginting,
  • Rudiyanto Rudiyanto,
  • Fatchurrachman,
  • Ramisah Mohd Shah,
  • Norhidayah Che Soh,
  • Sunny Goh Eng Giap,
  • Dian Fiantis,
  • Budi Indra Setiawan,
  • Sam Schiller,
  • Aaron Davitt,
  • Budiman Minasny

摘要

Southeast Asia contributes 20% of the world’s rice production and 29% of global rice methane emissions, highlighting the need for accurate data on harvested areas to support food security and greenhouse gas accounting. However, existing paddy rice maps often lack information on cropping intensity, spatial resolution, and accuracy due to diverse cultivation practices. This study presents a 10-m resolution, open-access dataset of rice cropping intensity, enabling the precise estimation of growing and harvested areas across Southeast Asia. The Local Unsupervised Classification with Phenological Labelling (LUCK-PALM) was used to generate the map by combining Sentinel-1A and Sentinel-2A/B data (2020–2021). Validation at the pixel level (n = 58,885) shows an overall accuracy of 0.98, a kappa coefficient of 0.870, and an F1 score of 0.879 in identifying rice areas. This comprehensive dataset is available in a public repository and can be used to enhance food and water security strategies and refines estimates of methane emissions.