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

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  • 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.

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  • 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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    1. Emily Burchfield & Neil Matthews-Pennanen & Justin Schoof & Christopher Lant, 2020. "Changing yields in the Central United States under climate and technological change," Climatic Change, Springer, vol. 159(3), pages 329-346, April.
    2. Jin, Ning & Tao, Bo & Ren, Wei & He, Liang & Zhang, Dongyan & Wang, Dacheng & Yu, Qiang, 2022. "Assimilating remote sensing data into a crop model improves winter wheat yield estimation based on regional irrigation data," Agricultural Water Management, Elsevier, vol. 266(C).
    3. Lu, Yang & Wei, Chunzhu & McCabe, Matthew F. & Sheffield, Justin, 2022. "Multi-variable assimilation into a modified AquaCrop model for improved maize simulation without management or crop phenology information," Agricultural Water Management, Elsevier, vol. 266(C).
    4. Alaa Jamal & Raphael Linker, 2020. "Genetic Operator-Based Particle Filter Combined with Markov Chain Monte Carlo for Data Assimilation in a Crop Growth Model," Agriculture, MDPI, vol. 10(12), pages 1-22, December.
    5. Li, Ziyue & Zhang, Zhao & Zhang, Lingyan, 2021. "Improving regional wheat drought risk assessment for insurance application by integrating scenario-driven crop model, machine learning, and satellite data," Agricultural Systems, Elsevier, vol. 191(C).
    6. Zhang, Chao & Liu, Jiangui & Shang, Jiali & Dong, Taifeng & Tang, Min & Feng, Shaoyuan & Cai, Huanjie, 2021. "Improving winter wheat biomass and evapotranspiration simulation by assimilating leaf area index from spectral information into a crop growth model," Agricultural Water Management, Elsevier, vol. 255(C).
    7. Dhakar, Rajkumar & Sehgal, Vinay Kumar & Chakraborty, Debasish & Sahoo, Rabi Narayan & Mukherjee, Joydeep & Ines, Amor V.M. & Kumar, Soora Naresh & Shirsath, Paresh B. & Roy, Somnath Baidya, 2022. "Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing," Agricultural Systems, Elsevier, vol. 195(C).
    8. Jin, Xiuliang & Li, Zhenhai & Feng, Haikuan & Ren, Zhibin & Li, Shaokun, 2020. "Estimation of maize yield by assimilating biomass and canopy cover derived from hyperspectral data into the AquaCrop model," Agricultural Water Management, Elsevier, vol. 227(C).
    9. Egerer, Sabine & Puente, Andrea Fajardo & Peichl, Michael & Rakovec, Oldrich & Samaniego, Luis & Schneider, Uwe A., 2023. "Limited potential of irrigation to prevent potato yield losses in Germany under climate change," Agricultural Systems, Elsevier, vol. 207(C).
    10. Guo, Shibo & Zhang, Zhentao & Guo, Erjing & Fu, Zhenzhen & Gong, Jingjin & Yang, Xiaoguang, 2022. "Historical and projected impacts of climate change and technology on soybean yield in China," Agricultural Systems, Elsevier, vol. 203(C).
    11. Lu, Yang & Chibarabada, Tendai P. & Ziliani, Matteo G. & Onema, Jean-Marie Kileshye & McCabe, Matthew F. & Sheffield, Justin, 2021. "Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model," Agricultural Water Management, Elsevier, vol. 252(C).
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    2. Asadollah, Seyed Babak Haji Seyed & Jodar-Abellan, Antonio & Pardo, Miguel Ángel, 2024. "Optimizing machine learning for agricultural productivity: A novel approach with RScv and remote sensing data over Europe," Agricultural Systems, Elsevier, vol. 218(C).

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