Leaf Area Index Estimation Based on UAV Hyperspectral Band Selection
KONG Yu-ru1, 2, WANG Li-juan1*, FENG Hai-kuan2, XU Yi1, LIANG Liang1, XU Lu1, YANG Xiao-dong2*, ZHANG Qing-qi1
1. School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
2. Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Abstract：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 (R2) 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.
Key words：Unmanned aerial vehicle (UAV); Hyperspectral image; Band selection; Winter wheat; Leaf area index
孔钰如，王李娟，冯海宽，徐 艺，梁 亮，徐 璐，杨小冬，张青琪. 无人机高光谱波段选择的叶面积指数反演[J]. 光谱学与光谱分析, 2022, 42(03): 933-939.
KONG Yu-ru, WANG Li-juan, FENG Hai-kuan, XU Yi, LIANG Liang, XU Lu, YANG Xiao-dong, ZHANG Qing-qi. Leaf Area Index Estimation Based on UAV Hyperspectral Band Selection. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 933-939.
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