Keywords: 
• botany /
• Phyllostachys violascens /
• chlorophyll content /
• hyperspectral vegetation indices /
• correlation analysis /
• model

Abstract: Reflectance data and relative chlorophyll content for Phyllostachys violascens at leaf scale were measured during the growth period from April, 5th to June, 18th using a portable Analytical Spectral Devices （ASD） field spectrometer and a hand-held Chlorophyll Content Meter （CCM）-200. Correlation analyses were conducted between hyper-spectral vegetation indices and chlorophyll content based on the data. Then individual univariate linear inversion models were developed for chlorophyll content and hyper-spectral vegetation indices, such as red edge indexes GM, Vog3, double difference index DD, modified normalized differential vegetation index mND705, modified simple ratio mSR705, and Red-edge positions （REP）. Also multivariate linear models for selected hyper-spectral vegetation indices and chlorophyll content were tested. Multivariate linear models are designed in two methods, strategy A is based on the 20 Phyllostachys violascens samples, and each data for the sample is the average for all the 14 times. On the contrary, strategy B is based on the data of 14 times, which average the 20 samples for each time. Results over the entire growth period showed （1） significant （P＜0.01） correlations between chlorophyll content and hyper-spectral vegetation indices, GM （r = 0.866 3）, Vog3 （r = 0.927 4 ）, DD （r = 0.880 6）, mND705 （r = 0.917 9）, mSR705 （r = 0.924 9）, and REP （r =0.895 4）. At the end of the growth period, all vegetation indices had a favorable relationship with chlorophyll content, showing as the high correlation coefficients, although some indexes perform bad in most other time periods；（2） Using the univariate linear model, correlation for hyper-spectral vegetation indices and chlorophyll content showed r ＞ 0.85. The multivariate linear models of the six hyper-spectral vegetation indices listed above and chlorophyll content using two strategies, both accurately predicted chlorophyll content of Phyllostachys violascens ［with correlation coefficients between predicted values and measured values that were all above r = 0.89］. The multivariate linear models can be used to predicte chlorophyll content in the leaf of Phyllostachys violascens. Considering the calculate method, strategy B is more fit for the dynamic change of chlorophyll content for Phyllostachys violascens at leaf scale. ［Ch, 4 fig. 2 tab. 45 ref.］