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Assimilating remote sensing information into a coupled hydrology-crop growth model to estimate regional maize yield in arid regions

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  • Li, Yan
  • Zhou, Qingguo
  • Zhou, Jian
  • Zhang, Gaofeng
  • Chen, Chong
  • Wang, Jing

Abstract

Regional crop yield prediction is a significant component of national food policy making and security assessments. A data assimilation method that combines crop growth models with remotely sensed data has been proven to be the most effective method for regional yield estimates. This paper describes an assimilation method that integrates a time series of leaf area index (LAI) retrieved from ETM+ data and a coupled hydrology-crop growth model which links a crop growth model World Food Study (WOFOST) and a hydrology model HYDRUS-1D for regional maize yield estimates using the ensemble Kalman filter (EnKF). The coupled hydrology-crop growth model was calibrated and validated using field data to ensure that the model accurately simulated associated state variables and maize growing processes. To identify the parameters that most affected model output, an extended Fourier amplitude sensitivity test (EFAST) was applied to the model before calibration. The calibration results indicated that the coupled hydrology-crop growth model accurately simulated maize growth processes for the local cultivation variety tested. The coefficient of variations (CVs) for LAI, total above-ground production (TAGP), dry weight of storage organs (WSO), and evapotranspiration (ET) were 13%, 6.9%, 11% and 20%, respectively. The calibrated growth model was then combined with the regional ETM+ LAI data using a sequential data assimilation algorithm (EnKF) to incorporate spatial heterogeneity in maize growth into the coupled hydrology-crop growth model. The theoretical LAI profile for the near future and the final yield were obtained through the EnKF algorithm for 50 sample plots. The CV of the regional yield estimates for these sample plots was 8.7%. Finally, the maize yield distribution for the Zhangye Oasis was obtained as a case study. In general, this research and associated model could be used to evaluate the impacts of irrigation, fertilizer and field management on crop yield at a regional scale.

Suggested Citation

  • Li, Yan & Zhou, Qingguo & Zhou, Jian & Zhang, Gaofeng & Chen, Chong & Wang, Jing, 2014. "Assimilating remote sensing information into a coupled hydrology-crop growth model to estimate regional maize yield in arid regions," Ecological Modelling, Elsevier, vol. 291(C), pages 15-27.
    • Handle: RePEc:eee:ecomod:v:291:y:2014:i:c:p:15-27
    DOI: 10.1016/j.ecolmodel.2014.07.013
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    1. Bouman, B.A.M. & Kropff, M.J. & Wopereis, M.C.S. & ten Berge, H.F.M. & van Laar, H.H., 2001. "ORYZA2000: modeling lowland rice," IRRI Books, International Rice Research Institute (IRRI), number 281825.
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    1. 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).
    2. Zhang, Yuliang & Wu, Zhiyong & Singh, Vijay P. & He, Hai & He, Jian & Yin, Hao & Zhang, Yaxin, 2021. "Coupled hydrology-crop growth model incorporating an improved evapotranspiration module," Agricultural Water Management, Elsevier, vol. 246(C).
    3. Corbari, C. & Ben Charfi, I. & Al Bitar, A. & Skokovic, D. & Sobrino, J.A. & Perelli, C. & Branca, G. & Mancini, M., 2022. "A fully coupled crop-water-energy balance model based on satellite data for maize and tomato crops yield estimates: The FEST-EWB-SAFY model," Agricultural Water Management, Elsevier, vol. 272(C).
    4. Zhang, Jing & Chen, Yi & Zhang, Zhao, 2020. "A remote sensing-based scheme to improve regional crop model calibration at sub-model component level," Agricultural Systems, Elsevier, vol. 181(C).
    5. Ji, Zhonglin & Pan, Yaozhong & Li, Nan, 2021. "Integrating the temperature vegetation dryness index and meteorology parameters to dynamically predict crop yield with fixed date intervals using an integral regression model," Ecological Modelling, Elsevier, vol. 455(C).
    6. Lee, Sangchul & Qi, Junyu & McCarty, Gregory W. & Anderson, Martha & Yang, Yun & Zhang, Xuesong & Moglen, Glenn E. & Kwak, Dooahn & Kim, Hyunglok & Lakshmi, Venkataraman & Kim, Seongyun, 2022. "Combined use of crop yield statistics and remotely sensed products for enhanced simulations of evapotranspiration within an agricultural watershed," Agricultural Water Management, Elsevier, vol. 264(C).
    7. 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).
    8. Shi, Yinfang & Wang, Zhaoyang & Hou, Cheng & Zhang, Puhan, 2022. "Yield estimation of Lycium barbarum L. based on the WOFOST model," Ecological Modelling, Elsevier, vol. 473(C).

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