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<p>Accurate mapping of crop yields is essential for informed agricultural decision-making and optimal allocation of resources. Current crop yield datasets are deficient in large-scale, high-resolution information regarding the long-term spatial and temporal distribution of crop yields. To address this challenge, we developed a method of vegetation photosynthesis model combined with transition coefficient, producing a detailed dataset with 10 m resolution, covering major regions of maize, rice, and soybean in Northeast China from 2016 to 2021. The method introduces a dynamic observation index (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({{\rm{APAR}}}_{{\varepsilon }_{g}}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mrow> <mi mathvariant="normal">APAR</mi> </mrow> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>g</mi> </mrow> </msub> </mrow> </msub> </math></EquationSource> </InlineEquation>) and a composite yield-conversion coefficient (<i>a</i>), which presents an innovative method for estimating crop yields without field measurements. Validation results show that, for maize, rice, and soybean, the model achieves r values of 0.39, 0.51, and 0.52; MREs of 12.14%, 11.96%, and 14.06%; and rRMSEs of 16.97%, 16.12%, and 17.26%, respectively. The dataset offers valuable insights into crop yield distribution, supporting better agricultural decision-making and resource optimization.</p>

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A 10 m maize, rice and soybean yield dataset from 2016 to 2021 in Northeast China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月3日

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  • Fei Teng,
  • Minglei Wang,
  • Wenjiao Shi,
  • Li Pan,
  • Jinghan Guo,
  • Xiangming Xiao

摘要

Accurate mapping of crop yields is essential for informed agricultural decision-making and optimal allocation of resources. Current crop yield datasets are deficient in large-scale, high-resolution information regarding the long-term spatial and temporal distribution of crop yields. To address this challenge, we developed a method of vegetation photosynthesis model combined with transition coefficient, producing a detailed dataset with 10 m resolution, covering major regions of maize, rice, and soybean in Northeast China from 2016 to 2021. The method introduces a dynamic observation index ( \({{\rm{APAR}}}_{{\varepsilon }_{g}}\) APAR ε g ) and a composite yield-conversion coefficient (a), which presents an innovative method for estimating crop yields without field measurements. Validation results show that, for maize, rice, and soybean, the model achieves r values of 0.39, 0.51, and 0.52; MREs of 12.14%, 11.96%, and 14.06%; and rRMSEs of 16.97%, 16.12%, and 17.26%, respectively. The dataset offers valuable insights into crop yield distribution, supporting better agricultural decision-making and resource optimization.