Improved maize leaf area index inversion combining plant height corrected resampling size and random forest model using UAV images at fine scale

Context

Accurate monitoring of leaf area index (LAI) is conducive to timely and targeted management measures. Unmanned aerial vehicle (UAV) remote sensing provides an important way for non-destructive monitoring of crop leaf area index.

Objective

In this study, visible light (RGB) and multispectral remote sensing data from the UAV and ground-measured LAI data from the Plant Canopy Analyzer LAI-2200 C were used to conduct inversion of maize LAI on a fine scale.

Methods

To address the problem of spatial scale mismatch between the spatial resolution of UAV images and the ground-measured LAI, the scale difference between UAV image data and ground-measured data was reduced by removing the outermost ring data measured by the LAI-2200 C instrument, calculating the spatial resolution of the UAV images after resampling based on the height of the plant, and the resampling method based on the circle. Finally, through the above method to resample the UAV images, we extract the vegetation index and canopy height features as the input variables of the random forest model to build the maize LAI inversion model in vegetative stages and reproductive stages respectively, which is referred to as the Vis_H+RF method.

Results and conclusions

The Vis_H+RF method of Tongliao experimental station has an R² of 0.96 in the vegetative stages and a R² of 0.61 in the reproductive stages, both of which perform well and have certain migration capabilities.

Significance

The LAI inversion model constructed based on the method in this study is basically consistent with the actual situation and can provide data support for maize growth monitoring.