An Investigation of Winter Wheat Leaf Area Index Fitting Model Using Spectral and Canopy Height Model Data from Unmanned Aerial Vehicle Imagery

Author:

Zhang XueweiORCID,Zhang KefeiORCID,Wu Suqin,Shi Hongtao,Sun Yaqin,Zhao Yindi,Fu Erjiang,Chen ShuoORCID,Bian Chaofa,Ban Wei

Abstract

The leaf area index (LAI) is critical for the respiration, transpiration, and photosynthesis of crops. Color indices (CIs) and vegetation indices (VIs) extracted from unmanned aerial vehicle (UAV) imagery have been widely applied to the monitoring of the crop LAI. However, when the coverage of the crop canopy is large and only spectral data are used to monitor the LAI of the crop, the LAI tends to be underestimated. The canopy height model (CHM) data obtained from UAV-based point clouds can represent the height and canopy structure of the plant. However, few studies have been conducted on the use of the CHM data in the LAI modelling. Thus, in this study, the feasibility of combining the CHM data and CIs and VIs, respectively, to establish LAI fitting models for winter wheat in four growth stages was investigated, and the impact of image resolution on the extraction of remote sensing variables (the CHM data, CIs, and VIs) and on the accuracy of the LAI models was evaluated. Experiments for acquiring remote sensing images of wheat canopies during the four growth stages from the RGB and multispectral sensors carried by a UAV were carried out. The partial least squares regression (PLSR), random forest regression (RFR), and support vector machine regression (SVR) were used to develop the LAI fitting models. Results showed that the accuracy of the wheat LAI models can be improved in the entire growth stages by the use of the additional CHM data with the increment of 0.020–0.268 in R2 for three regression methods. In addition, the improvement from the Cis-based models was more noticeable than the Vis-based ones. Furthermore, the higher the spatial resolution of the CHM data, the better the improvement made by the use of the additional CHM data. This result provides valuable insights and references for UAV-based LAI monitoring.

Funder

National Natural Science Foundation of China

Jiangsu Provincial Science and Technology Initiative—Special Fund for International Science and Technology Cooperation

Key Laboratory of National Geographic Census and Monitoring, Ministry of Natural Resources

Natural Science Foundation of Jiangsu Province

Xuzhou Key R&D Program

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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