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Crop yield estimation based on assimilation of crop models and remote sensing data: A systematic evaluation

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Listed:
  • Luo, Li
  • Sun, Shikun
  • Xue, Jing
  • Gao, Zihan
  • Zhao, Jinfeng
  • Yin, Yali
  • Gao, Fei
  • Luan, Xiaobo

Abstract

With the warming trend and the increasing frequency of extreme weather events, accurate crop yield estimation is becoming urgent. Crop yield estimation mainly consists of two methods: crop model simulation and remote sensing observations. Crop models can achieve accurate simulations of crop growth at field scales. However, in regional applications, they are limited by the spatial heterogeneity of certain input parameters. Remote sensing observations can obtain crop status over large areas quickly and conveniently, while lacking knowledge of crop growth processes. By combining the advantages of crop models and remote sensing, crop yield estimation with spatiotemporal continuity can be achieved using data assimilation methods.

Suggested Citation

  • Luo, Li & Sun, Shikun & Xue, Jing & Gao, Zihan & Zhao, Jinfeng & Yin, Yali & Gao, Fei & Luan, Xiaobo, 2023. "Crop yield estimation based on assimilation of crop models and remote sensing data: A systematic evaluation," Agricultural Systems, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:agisys:v:210:y:2023:i:c:s0308521x23001166
    DOI: 10.1016/j.agsy.2023.103711
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    References listed on IDEAS

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