Predicting wheat productivity: Integrating time series of vegetation indices into crop modeling via sequential assimilation
2019
Guo, Caili | Lu, Bailie | Lü, Jingshan | Zhu, Yan | Cao, Weixing | Cheng, Tao | Zhang, Ling | Tian, Yongchao
Combining remote sensing (RS) information with crop modeling is an effective approach to accurately predict regional crop yields. Here, a sequential assimilation technique was developed by incorporating temporal remote-sensed vegetation indices (VIs) and the WheatGrow-PROSAIL model-simulated VIs. Experimental data from different sites and years were used to search for the optimal assimilating parameter and growth stage window. Our results showed the soil adjusted vegetation index (SAVI) was superior to other vegetation indices as the assimilating parameter. The most accurate prediction was achieved when SAVI from jointing to booting stage, and enhanced vegetation index (EVI) after booting stage were incorporated. Booting-heading stage was the optimal coupling window if only one phenological stage of RS data was available. For multiple stage data, the RS data from jointing to filling stage were the most informative. Model test results indicated that the new developed sequential assimilation technique improved the prediction accuracy of the WheatGrow model with root-mean-squared error (RMSE) values of 0.843, 1.202 g m−2, and 510.68 kg ha-1 for leaf area index, leaf nitrogen accumulation, and grain yield, respectively. These results provide significant technical support for regional crop growth monitoring and yield prediction.
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