CO₂浓度和温度升高下不同类型水稻高光谱特征及叶绿素含量反演对比分析

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Abstract：To explore the effects of elevated atmospheric CO2 concentration and increased temperature on the hyperspectral characteristics and chlorophyll content of different types of rice， the Japonica variety‘Jinxiangyu No. 1’and the Indica variety‘Yangdao No. 6’were selected as test subjects. Field experiments were conducted using open-top chambers（OTC）with four treatments：control group（CK）， elevated CO2 concentration group（ C+ ）， elevated temperature group（T + ）， and combined elevated CO2 concentration and temperature group （ （C+T+ ）. The relative chlorophyll content（SPAD）and hyperspectral reflectance of rice leaves were measured under each treatment. The original spectra were subjected to first derivative transformation to compare the hyperspectral characteristics of Japonica and Indica rice， and the optimal estimation models for chlorophyll content were constructed using univariate and multivariate methods. The results showed that：The C+ treatment increased the SPAD value by 2.8%-8.8% ， while the T+ treatment decreased it by 4.4%-11.1% ， with the chlorophyll content of Japonica rice“Jinxiangyu No. 1”decreasing more significantly under T + treatment. The CO2 concentration and temperature treatments did not change the waveform of the raw spectral curves of the rice canopy but significantly affected the reflectance magnitude. The first-order differential spectral reflectance of rice showed "single-peak" or "double-peak" characteristics. The main peak position of Japonica rice in all treatments exhibited a blue shift throughout the growing season， while Indica rice showed a red shift followed by a blue shift under T + and C + T + treatments. The optimal chlorophyll inversion model for Japonica rice was Support Vector Regression （SVR）， with a coefficient of determination （R2） of 0.63 and a root mean square error（RMSE）of 2.94. The optimal chlorophyll inversion model for Indica rice was the BP neural network， with an R2 of 0.66 and an RMSE of 2.73. In summary， under elevated CO -2 concentration and temperature conditions， the hyperspectral characteristics of different rice varieties show significant differences， and the multivariate model demonstrats superior estimation performance in chlorophyll content inversion.

Keywords： CO2 concentration; temperature; rice; chlorophyll content; spectral paramete

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