Estimation of Chlorophyll Content in Spartina Alterniflora Leaves Based on Continous Wavelet Transformation and Random Forest Algorithm

Chlorophyll content is a key indicator of the physiological status of plants, and accurate estimation of chlorophyll gontent is important for characterizing its component content traits and quantifying its physiological status, In this paper, the Ayperspectral reflectance and chlorophyll content (SPAD) of Spartina alterniflora in the Duliu river wetland were used as the Anta 1 source. the original spectrum was mathematically transformed and processed with continuous wavelet transformation CWT). The spectral features were extracted using Sequential Projection Algorithm (SPA). And the hyperspectral estimation podel of leaf chlorophyll content of Spartina alterniflora was developed based on random forest regression (RFR) algorithm. the results showed that (1) CWT had more accurate time resolution and higher frequency in the low scale spectra, corresponding tau omicron narrow waveret tunetion, wien cosa netter distinguish the amerences between the spectra and nignugnt me characteristic spectral information, (2) Except for reciprocal and logarithmic first derivative spectrals, the spectral mathematical transform and CWT methods could effectively respond to the spectral detail features, CWT was generally better than the spectral Buathematical transform, and the correlation between L10 scale and first derivative spectral reached 0.78 and 0. 77. (3) First Derivative spectral. reciprocal first derivative spectral, logarithmic derivative spectral and CWT could enhance the ability of pectral estimation of Spartina alterniflora chlorophyll content. The RF models based on first derivative spectral (R-2 0.776. RMSE 0.510. RPD 1.893) and CWT with the multiscale of L2, L3 and LA (R-2 0.871. RMSE 0.305. RPD 3.846) were the optimal models. This study shows that hyperspectral techniques could be used as non destructive means of detecting chlorophyll content in leaves of Spartina alterni flora and that the hyperspectral estimation model built by combining multiple cales after continuous wavelet decomposition could more estimate chlorophyll content in leaves of Spartina alterni flora.