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<p>Northeast China, a crucial agricultural region contributing one-third of China’s commodity grain production, lacks detailed, high-resolution crop maps pre-2013 due to limited satellite observations. To bridge this gap, this study developed annual 30 m crop type maps for Northeast China (2001–2022) using all available Landsat and MODIS imagery and the Highly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm. The accuracy of crop-type maps was assessed using three complementary approaches. First, validation against ground-truth data (2017–2022) yielded overall accuracies of 80.7%–91%. Second, validation using ‘trusted pixels’ from existing crop-mapping products (2001–2020) produced overall accuracies of 85%–95%. Third, comparison with government statistics (2001–2022) showed average <i>R</i><sup>2</sup> of 0.98 (paddy rice), 0.83 (maize), and 0.90 (soybean). The generated maps were found to be the most consistent with government statistics compared to pre-existing crop maps, while providing comprehensive spatial and temporal details. This dataset is an important contribution to long-term fine-resolution crop mapping at the regional scale in China, which provide valuable guidance for sustainable agriculture practices in China’s primary grain-producing region.</p>

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30 m-resolution annual crop type maps in Northeast China from 2001 to 2022

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月5日

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  • Yuanyuan Di,
  • Jinwei Dong,
  • Nanshan You,
  • Zhichao Li,
  • Álvaro Moreno-Martínez,
  • Emma Izquierdo-Verdiguier,
  • Jing Sun,
  • Ping Fu

摘要

Northeast China, a crucial agricultural region contributing one-third of China’s commodity grain production, lacks detailed, high-resolution crop maps pre-2013 due to limited satellite observations. To bridge this gap, this study developed annual 30 m crop type maps for Northeast China (2001–2022) using all available Landsat and MODIS imagery and the Highly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm. The accuracy of crop-type maps was assessed using three complementary approaches. First, validation against ground-truth data (2017–2022) yielded overall accuracies of 80.7%–91%. Second, validation using ‘trusted pixels’ from existing crop-mapping products (2001–2020) produced overall accuracies of 85%–95%. Third, comparison with government statistics (2001–2022) showed average R2 of 0.98 (paddy rice), 0.83 (maize), and 0.90 (soybean). The generated maps were found to be the most consistent with government statistics compared to pre-existing crop maps, while providing comprehensive spatial and temporal details. This dataset is an important contribution to long-term fine-resolution crop mapping at the regional scale in China, which provide valuable guidance for sustainable agriculture practices in China’s primary grain-producing region.