Research on inversion model of wheat LAI using cross-validation
REN Zhe1,2, CHEN Huailiang3, WANG Lianxi1,2, LI Ying3, LI Qi1,2
1. Jiangsu Key Laboratory of Atmospheric Environmental Monitoring and Pollution Control, Nanjing University of Information Science and Technology, Nanjing 210044, China;
2. School of Environmental Science and Engineering of Nanjing University of Information Science and Technology, Nanjing 210044, China;
3. CMA/Henan Key Laboratory of Agrometeorological Support and Applied Technique, Zhengzhou 450003, China
Leaf area index (LAI) is the key parameter to signify the growth condition and canopy structure of vegetation. Inversion of LAI using remote sensing technology is always one of the hotspots and difficulties in the field of remote sensing. In this paper, the first and second order derivatives of hyperspectral data of wheat were calculated, and several vegetation indices (RVI, NDVI, EVI, DVI and MSAVI) and trilateral variable parameters were built for the analysis. The correlation analysis between the parameters and wheat LAI data was carried out, and the method of cross-validation was used for multiple regression analysis so as to determine the sensitive parameters for wheat inversion of LAI and choosing model type of inversion. At last, the inversion models of all the samples were built by using these sensitive parameters, and their imitative effects were comparatively studied. The results show that the majority of root mean square errors(RMSE)of the inverse models using cross-validation are larger than those of the models which do not use cross-validation. In addition, among all the models built by the sensitive parameters, the cubic regression model of RVI is the optimal model for inversion of wheat LAI with remote sensing data.
任哲, 陈怀亮, 王连喜, 李颖, 李琪. 利用交叉验证的小麦LAI反演模型研究[J]. 国土资源遥感, 2015, 27(4): 34-40.
REN Zhe, CHEN Huailiang, WANG Lianxi, LI Ying, LI Qi. Research on inversion model of wheat LAI using cross-validation. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(4): 34-40.
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2015, Vol. 27 
