Abstract: By using the airborne hyperspectral remote sensing data of Liangshui National Nature Reserve in Yichun of Heilongjiang Province, Northeast China, 15 spectral parameters including red edge area, triangular vegetation index, and normalized difference vegetation index, etc. were extracted， and in combining with 5 geographical parameters including slope, aspect, elevation, canopy density and total vegetation coverage, and by using SPAD-502, the vegetation canopy’s relative chlorophyll content in the reserve were measured, with the correlations of the leaf spectral reflectivity, its firstorder derivative and other deformations with the SPAD value analyzed. A prediction model for relative chlorophyll content was established by adopting the kernelbased partial least-squares regression, and a quantitative estimation of the vegetation canopy’s relative chlorophyll content in the study area was carried out with the established model. The results showed that the model performed best when the sections were three and the principle components were ten. The coefficient of determination of the model was R² = 0.855, the mean absolute percent error was 9.6%, and the prediction precision was 89.7%.

Key words: airborne hyperspectral remote sensing, estimation model for chlorophyll content, kernel partial least-squares.