Estimation of chlorophyll content in rice canopy leaves based on main base analysis and dimensionality reduction method - YUAN Weinan; XU Tongyu; CAO Yingli; WANG Yang; YU Fenghua (浙江大学学报. 农业与生命科学版, 2018)
Due to high dimensional characteristics of unmanned aerial vehicle (UAV) hyperspectral remote sensing data, we proposed a dimension reduction method based on main base analysis. The 400-1 000 nm bands which are sensitive to chlorophyll, were selected to be subjected to Gram_Schmidt transform. After finding the projection space, the main base of concentrated band information was constructed, and the least square regression model was established to estimate the chlorophyll content in rice canopy leaves. The results showed that the modeling coefficient of determination (R2) was 0.689, with the root mean square error (RMSE) of 2.20, and the RMSE of validation model was 1.20. Compared with the prediction accuracy of the same model established by three vegetation indexes PRI, RD2 and MCARI, the modeling R2 was greatly improved, while the RMSE of verification model was greatly reduced. It is proved that the proposed method is effective, and it is significance for estimating chlorophyll content of plant leaves.