Leaf Area Index Estimation Based on UAV Hyperspectral Band Selection

文献类型： 外文期刊

第一作者： Kong Yu-ru

作者： Kong Yu-ru;Wang Li-juan;Xu Yi;Liang Liang;Xu Lu;Zhang Qing-qi;Kong Yu-ru;Feng Hai-kuan;Yang Xiao-dong

作者机构：

关键词： Unmanned aerial vehicle (UAV); Hyperspectral image; Band selection; Winter wheat; Leaf area index

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.609； 五年影响因子：0.516 ）

ISSN： 1000-0593

年卷期： 2022 年 42 卷 3 期

页码：

收录情况： SCI

摘要： Leaf area index (LAI) is an important parameter to evaluate crop condition and crop yield. In order to effectively utilize hyperspectral information and improve the estimation accuracy of LAI, the best band was selected, and the new two-band vegetation indexes were constructed. In this study, winter wheat was taken as the research object, the UAV hyperspectral data and ground LAI data were obtained at the booting stage. First, the successive projection algorithm (SPA), optimum index factor (OIF), and each band combination method (E) were used to screen the best band of UAV hyperspectral data, and then the selected best bands were constructed into the new two-band vegetation indexes (VI_OIF, VI_SPA, VI_ E). Then, the new two-band vegetation indexes and the conventional two-band vegetation indexes (VI_F) constructed were compared and analyzed for correlation with LAI. Finally, support vector regression (SVR) , partial least square (PLSR) and random forest for regression (RFR) were used to construct LAI estimation models. Meanwhile, comparing with the estimation accuracy of the conventional two-band vegetation indexes, the feasibility of LAI estimation was verified by the optimal regression model of the best new two-band vegetation indexes. The results were as follows : (1) The newly constructed two-band vegetation indexes VI_OIF, VI_SPA, VI _ E and VI_F correlated with LAI were all at the significant level of 0. 05, VI_SPA and VI_E correlated (r>0. 65) , among which RSI SPA and RSI E had the highest correlation coefficient with LAI (r >0. 71) ; (2) The accuracy of LAI estimation of winter wheat based on SVR model, PLSR model and RFR model constructed by VI_OIF, VI_SPA, VI_E and VI_F were compared and analyzed. It was found that the VI_SPA_PLSR model had the highest accuracy and the best predictive ability, whose coefficient of determination (R-2) and root mean square error (RMSE) were 0. 75 and 0. 90, respectively. The research results can provide technical support and theoretical reference for the band selection of UAV hyperspectral data and winter wheat LAI estimation.

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