Abstract:

【Objective】 The aims of this study are to analyze hyperspectral characteristics of apple leaves, to build hyperspectral monitoring models for pigment content in apple leaves and to promote the application of hyperspectral techniques in apple growth monitoring. 【Method】 Hyperspectral characteristics of apple functional leaves of different leaf parts with different water contents and apple species were analyzed by using variance analysis method at apple spring and autumn branch stop-growing stages. Relationship between leaf hyperspectral parameters and pigment content was investigated by using relevance analysis method. Hyperspectral parameter based monitoring models for apple leaf pigment content estimation were established. 【Result】 The results showed that in 760-1 300 nm near-infrared band, spectral reflectance of the rear part of the leaf was the lowest, that of the front part was the highest and reflectance of the middle area was in the between. The reflectance gradually increased with the decrease of leaf water content, and it appeared different obviously for different apple varieties. Spectral parameters R800/R550, red edge area Sr and green peak reflectivity Rg showed a close correlation with leaf pigment content, and their monitoring models were established accordingly. Models established with Sr for Chl a, Chl (a+b) and Car content monitoring and models established with R800/R550 for Chl b monitoring showed the best results. These models were proved to be reliable for apple leaf pigment monitoring by the index validation of root mean square deviation (RMSE) and relative error (RE%). 【Conclusion】 The results indicated the effectiveness of red edge area Sr and band combination of R800/R550 for apple leaf pigment content monitoring and provided a theoretical basis for apple growth remote sensing monitoring.

Key words: apple leaf')">apple leaf, hyperspectral characteristic, leaf pigment content, red edge area