Abstract
To improve the accuracy of winter wheat yield estimation, the Crop Environment Resource Synthesis for Wheat (CERES-Wheat) model with an assimilation strategy was performed by assimilating measured or remotely-sensed leaf area index (LAI) values. The performances of the crop model for two different assimilation methods were compared by employing particle filters (PF) and the proper orthogonal decomposition-based ensemble four-dimensional variational (POD4DVar) strategies. The uncertainties of wheat yield estimates due to different assimilation temporal scales (phenological stages and temporal frequencies) and spatial scale were also analyzed. The results showed that, compared with the crop model without assimilation and with PF-based assimilation, a better yield estimate performance resulted when the POD4DVar-based strategy was used at the field scale. When using this strategy, root mean square errors (RMSE) of 523 kg·ha−1, 543 kg·ha−1 and 172 kg·ha−1 and relative errors (RE) of 5.65%, 5.91% and 7.77% were obtained at the field plot scale, a pixel scale of 1 km and the county scale, respectively. Although the best yield estimates were obtained when all of the observed LAIs were assimilated into the crop model, an acceptable estimate of crop yield could also be achieved by assimilating fewer observations between jointing and anthesis periods of the crop growth season. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. Thus, it is important to consider reasonable spatio-temporal scales to obtain tradeoffs between accuracy and effectiveness in regional wheat estimates.
Highlights
And accurate regional crop growth monitoring and yield forecasting play a significant role in guiding agricultural production, ensuring national food security and maintaining sustainable agriculture development [1,2,3,4]
Compared with traditional assimilations of crop models, we mainly focus on the uncertainties of wheat yield estimates due to different assimilation strategies and multi-source errors, such as the remotely-sensed leaf area index (LAI), assimilation strategies, temporal scales and the spatial scale
Compared with the crop model without assimilation, the crop model with the particle filters (PF)-based and POD4DVar-based strategies provides significantly better regional crop yield estimates, especially for the POD4DVar-based strategy, which resulted in greater performance of the assimilation model
Summary
And accurate regional crop growth monitoring and yield forecasting play a significant role in guiding agricultural production, ensuring national food security and maintaining sustainable agriculture development [1,2,3,4]. 2017, 9, 190 crop growth models can offer powerful tools to simulate the crop growth and obtain the crop yield under various environmental and management conditions with the advantages of cost, timeliness, accuracy and suitability at the field scale [6,9,10,11,12,13,14], while they were dynamically accounting for several limiting factors (e.g., soil, weather, water, nitrogen and field management data) at the regional scale [3,15]. Data assimilation approaches that integrate crop growth models with remote sensing data have been proposed recently and recognized as important approaches for monitoring crop growth conditions and improving the accuracy of yield estimations at the regional scale [6,7,9,17,18,19,20,21,22,23,24,25].
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