Crop growth models were widely applied for simulating the dynamic growth of crops at multi-scales. The data assimilation by integrating the remote sensing data retrieved crop parameters and crop models have showed great potentials for describing the crop growth and assessing the agricultural yields. The purpose of this study was to integrates sequential observations of crop phenotyping traits from Unmanned Aerial Vehicles (UAV) remote sensing into World Food Studies (WOFOST) model to improve the simulation of crop growth processes. Two years of Leaf Area Index (LAI) of summer maize retrieved from Unmanned Aerial Vehicles (UAV)-RGB images was assimilated into the WOFOST using the Ensemble Kalman Filter (EnKF). The sensitive crop parameters of WOFOST were firstly identified using the Extended Fourier Amplitude Sensitivity Test (EFAST) global sensitivity analysis approach, and then the parameters were adjusted and confirmed using the SUBPLEX optimization algorithm. The LAI data was assimilated into WOFOST model using EnKF by minimizing the differences between the UAV-retrieved LAI and crop-simulated LAI. Results indicated assimilating LAI into WOFOST model significantly improved the accuracy of maize yield prediction. Compared with non-assimilation, data assimilation reduced the Root Mean Square Error (RMSE) from 413 to 132 kg/ha for 2020, and from 392 to 215 kg/ha for 2021, respectively. Through the effects of different ensemble size and different time-point for data assimilation, it was obtained that the accuracy of yield prediction achieved the highest when ensemble size was 100 at reproductive growth stage. Integrating UAV-based crop traits into WOFOST model using data assimilation (EnKF) could effectively improve the maize yield accuracy.

中文翻译：

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通过将基于无人机的 LAI 纳入 WOFOST 模型来改进玉米产量估算


作物生长模型广泛应用于模拟多尺度作物的动态生长。通过整合遥感数据获取的作物参数和作物模型进行数据同化，在描述作物生长和评估农业产量方面显示出巨大的潜力。本研究的目的是将无人机（UAV）遥感对作物表型性状的连续观察整合到世界粮食研究（WOFOST）模型中，以改进对作物生长过程的模拟。使用集成卡尔曼滤波器 (EnKF) 将从无人机 (UAV)-RGB 图像中检索到的夏玉米的两年叶面积指数 (LAI) 吸收到 WOFOST 中。首先利用扩展傅里叶振幅敏感性检验（EFAST）全局敏感性分析方法识别WOFOST的敏感作物参数，然后利用SUBPLEX优化算法对参数进行调整和确认。通过最小化无人机检索的 LAI 和作物模拟的 LAI 之间的差异，使用 EnKF 将 LAI 数据同化到 WOFOST 模型中。结果表明，将LAI同化到WOFOST模型中，显着提高了玉米产量预测的准确性。与未同化相比，数据同化将均方根误差（RMSE）分别从 2020 年的 413 公斤/公顷减少到 132 公斤/公顷，从 392 公斤/公顷减少到 2021 年的 215 公斤/公顷。通过不同集合规模和不同时间点数据同化的影响，得出生殖生长阶段集合规模为100时产量预测精度最高。利用数据同化（EnKF）将基于无人机的作物性状整合到WOFOST模型中可以有效提高玉米产量的准确性。