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

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 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub><mml:mrow><mml:mi>APAR</mml:mi></mml:mrow> <mml:mrow> <mml:msub><mml:mrow><mml:mi>ε</mml:mi></mml:mrow> <mml:mrow><mml:mi>g</mml:mi></mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> ) 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.