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Assimilating remote sensing information with crop model using Ensemble Kalman Filter for improving LAI monitoring and yield estimation

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  • Zhao, Yanxia
  • Chen, Sining
  • Shen, Shuanghe

Abstract

The crop model (PyWOFOST) which coupled remote sensing information and a crop model (WOFOST) with Ensemble Kalman Filter (EnKF) was used to simulate maize growth and yield in Northeastern China with MODIS LAI as the coupling point. The assimilation plan focused on analyzing the impact of uncertainties of remote sensing observations (MODIS LAI) and crop model parameters (thermal time from emergence to anthesis, TSUM1) on the modeling results. First, the PyWOFOST model is used to simulate the maize LAI, yield and growth duration at site's scale; then the impact of remote sensing and crop model uncertainties on crop growth simulation is analyzed; finally, the regional maize yield is estimated with the PyWOFOST model, and the results are verified using the maize statistical yield. Results show that the simulated maize yield with assimilation has significantly improved compared to the one without assimilation. Under a business-as-usual scenario, the modeling results without assimilation has an error of 14.04%. The assimilated results show errors of 12.71%, 11.91%, 10.44%, and 10.48% at different TSUM1 uncertainty levels at 0, 10, 20, and 30°C, respectively. The simulated LAI with assimilation agree better with the field observations than the one without assimilation. Without assimilation, the simulated growth duration has a mean deviation from the observed results at 3.4 days; with assimilation, the deviation would be 3.5, 4.3, 5.0, and 5.5 days respectively at different TSUM1 uncertainty levels. The results show that the errors for 58.82% areas are smaller than 15%. The simulated and statistical yields are highly correlated (R=0.875), and the determination coefficient is at 0.806. The study shows that it is applicable to simulate crop growth using a crop model assimilated with remote sensing data based on EnKF and it is significant to estimate the remote sensing and crop model uncertainties in crop yield estimation.

Suggested Citation

  • Zhao, Yanxia & Chen, Sining & Shen, Shuanghe, 2013. "Assimilating remote sensing information with crop model using Ensemble Kalman Filter for improving LAI monitoring and yield estimation," Ecological Modelling, Elsevier, vol. 270(C), pages 30-42.
    • Handle: RePEc:eee:ecomod:v:270:y:2013:i:c:p:30-42
    DOI: 10.1016/j.ecolmodel.2013.08.016
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    References listed on IDEAS

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    1. Naud, Cédric & Makowski, David & Jeuffroy, Marie-Hélène, 2007. "Application of an interacting particle filter to improve nitrogen nutrition index predictions for winter wheat," Ecological Modelling, Elsevier, vol. 207(2), pages 251-263.
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    Cited by:

    1. Linker, Raphael, 2021. "Stochastic model-based optimization of irrigation scheduling," Agricultural Water Management, Elsevier, vol. 243(C).
    2. Huang, Jiacong & Gao, Junfeng, 2017. "An improved Ensemble Kalman Filter for optimizing parameters in a coupled phosphorus model for lowland polders in Lake Taihu Basin, China," Ecological Modelling, Elsevier, vol. 357(C), pages 14-22.
    3. 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).
    4. 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).
    5. 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).
    6. Mohamed Sallah, Abdoul-Hamid & Tychon, Bernard & Piccard, Isabelle & Gobin, Anne & Van Hoolst, Roel & Djaby, Bakary & Wellens, Joost, 2019. "Batch-processing of AquaCrop plug-in for rainfed maize using satellite derived Fractional Vegetation Cover data," Agricultural Water Management, Elsevier, vol. 217(C), pages 346-355.
    7. Pagani, Valentina & Guarneri, Tommaso & Busetto, Lorenzo & Ranghetti, Luigi & Boschetti, Mirco & Movedi, Ermes & Campos-Taberner, Manuel & Garcia-Haro, Francisco Javier & Katsantonis, Dimitrios & Stav, 2019. "A high-resolution, integrated system for rice yield forecasting at district level," Agricultural Systems, Elsevier, vol. 168(C), pages 181-190.
    8. Zhang, Yuxi & Walker, Jeffrey P. & Pauwels, Valentijn R.N., 2022. "Assimilation of wheat and soil states for improved yield prediction: The APSIM-EnKF framework," Agricultural Systems, Elsevier, vol. 201(C).

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