Research on Assimilation of Unmanned Aerial Vehicle Remote Sensing Data and AquaCrop Model.

Abstract

Taking the AquaCrop crop model as the research object, considering the complexity and uncertainty of the crop growth process, the crop model can only achieve more accurate simulation on a single point scale. In order to improve the application scale of the crop model, this study inverted the canopy coverage of a tea garden based on UAV multispectral technology, adopted the particle swarm optimization algorithm to assimilate the canopy coverage and crop model, constructed the AquaCrop-PSO assimilation model, and compared the canopy coverage and yield simulation results with the localized model simulation results. It is found that there is a significant regression relationship between all vegetation indices and canopy coverage. Among the single vegetation index regression models, the logarithmic model constructed by OSAVI has the highest inversion accuracy, with an R 2 of 0.855 and RMSE of 5.75. The tea yield was simulated by the AquaCrop-PSO model and the measured values of R 2 and RMSE were 0.927 and 0.12, respectively. The canopy coverage R 2 of each simulated growth period basically exceeded 0.9, and the accuracy of the simulation results was improved by about 19.8% compared with that of the localized model. The results show that the accuracy of crop model simulation can be improved effectively by retrieving crop parameters and assimilating crop models through UAV remote sensing.

Plant phenotyping relevance

UAVマルチスペクトル画像から茶園の樹冠被覆率を推定し、作物モデルへ同化する手法が研究の中心であり、植物形質の取得・検証を実施している。

Code and data availability

The paper's UAV multispectral tea-canopy dataset, field measurements, and AquaCrop-PSO analysis code are not publicly available. The Data Availability Statement explicitly withholds the study data, offering only the public China Meteorological Network as a generic external source, and no author code, model checkpoints,

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