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<p>As the cornerstone of China’s food security, Northeastern China contributes nearly 20% of national rice production. However, we are still lacking of high-resolution rice maps with detailed and long time-series in this region, impeding crop management decisions for food security. Here we generated an annual 30 m resolution rice distribution dataset for Northeastern China since the 21st century (NECAR) using the Google Earth Engine platform and random forest classification. The workflow involved (1) hierarchical screening principle to select ground samples, (2) the linear interpolation and Whittaker smoothing Landsat5/7/8 time series data and (3) enhanced spectral-feature sets. The resultant annual maps have high overall accuracy (OA) ranging from 0.93 to 0.99, and the satellite estimates corresponded well with statistics for most cities (R<sup>2</sup> ≥ 0.7, p &lt; 0.01), with higher accuracy than that of similar crops mapping datasets. This is the first attempt in Northeastern China to reconstruct paddy rice patterns at a 30-m resolution over a detailed and extended time series, enabling in-depth analysis of potential environmental and economic impacts.</p>

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A 30-m annual paddy rice dataset in Northeastern China during period 2000–2023

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月5日

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  • Dawei Hou,
  • Jing Chen,
  • Jinwei Dong,
  • Chao Ji,
  • Jingxuan Feng,
  • Guoming Du,
  • Lixiao Yang

摘要

As the cornerstone of China’s food security, Northeastern China contributes nearly 20% of national rice production. However, we are still lacking of high-resolution rice maps with detailed and long time-series in this region, impeding crop management decisions for food security. Here we generated an annual 30 m resolution rice distribution dataset for Northeastern China since the 21st century (NECAR) using the Google Earth Engine platform and random forest classification. The workflow involved (1) hierarchical screening principle to select ground samples, (2) the linear interpolation and Whittaker smoothing Landsat5/7/8 time series data and (3) enhanced spectral-feature sets. The resultant annual maps have high overall accuracy (OA) ranging from 0.93 to 0.99, and the satellite estimates corresponded well with statistics for most cities (R2 ≥ 0.7, p < 0.01), with higher accuracy than that of similar crops mapping datasets. This is the first attempt in Northeastern China to reconstruct paddy rice patterns at a 30-m resolution over a detailed and extended time series, enabling in-depth analysis of potential environmental and economic impacts.