Evaluation of data assimilation strategies on improving the performance of crop modeling based on a novel evapotranspiration assimilation framework

Yang, Cheng; Lei, Huimin

doi:10.1016/j.agrformet.2023.109882

Cited by 6 publications

(1 citation statement)

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“…The traditional extraction method of the vegetation index is based on the reflectance of a certain band in the pixel spectral curve, but in practical applications, a single band will make some specific spectral fea-tures (such as green, red, etc.) submerged or appear with a "false green" and "false red" phenomenon [34][35][36][37]. Therefore, it is of great significance to analyze and extract spectral features from different angles and levels.…”

Section: Calculation Of Vegetation Indexmentioning

confidence: 99%

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

Li,

Awais

et al. 2024

Sensors

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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 R2 of 0.855 and RMSE of 5.75. The tea yield was simulated by the AquaCrop-PSO model and the measured values of R2 and RMSE were 0.927 and 0.12, respectively. The canopy coverage R2 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.

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Section: Calculation Of Vegetation Indexmentioning

confidence: 99%