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The leaf area index (LAI) of winter wheat is an important parameter for evaluating its growth and yield prediction, and UAV remote sensing technology enables rapid monitoring of LAI. In order to investigate the feasibility of adding other sensitive bands to the traditional vegetation index to improve the accuracy of LAI inversion, this paper improves the traditional vegetation index based on the UAV multispectral images of winter wheat at the time of jointing as the data source, using the more sensitive bands to LAI during the jointing stage. The improved results were also validated by constructing LAI inversion models through ridge regression and random forest regression algorithms. The results show that there is a higher correlation between the improved vegetation index and LAI than before, and that the inversion accuracy of the model based on the improved vegetation index has improved compared to that before the improved vegetation index. The correlation coefficients between each vegetation index and LAI were improved by 0-0.03 during the jointing stage. In terms of model inversion results, the inversion accuracy of the Ridge Regression and Random Forest models based on improved vegetation index modelling improved compared to the pre-improvement period, with the R2 of the Random Forest model improving from 0.85 to 0.86 and the R2 of the Ridge Regression model improving from 0.76 to 0.77. The study demonstrates the feasibility of improving the accuracy of LAI inversion of winter wheat based on UAV multispectral images of the jointing stage of winter wheat and introducing other sensitive wavebands to improve the traditional vegetation indices, and provides a methodological reference for improving the effectiveness of LAI inversion of winter wheat.
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