Abstract: [Objective] To establish a correlation model between cotton hyperspectral data and photosynthetic characteristic parameters in the hope of providing a scientific foundation for effective,rapid,non-destructive diagnose and surveillance of cotton growth process in large area.[Method] Using ASD hyperspectral radiometer and Li-6400 photosynthetic instrument,the hyperspectral data of the two varieties Xinluzao No.13 and No.33 in key growth stages and photosynthetic parameters were obtained by five water treatments:net photosynthetic rate (Pn) and stomatal conductance (Gs) were calculated using hyperspectral data to obtain the normalized difference vegetation index (NDVI),ratio vegetation index (RVI) and the second modified soil adjusted vegetation index (MSAVI2) of the two cotton cultivars,thus establishing the linear,logarithmic and power function correlation equations of Pn and Gs,respectively.[Result] The results showed that three type function models were of significant and extremely significant correlation,and the R value of the equation of the three models in the two varieties RV[and Pn and Gs was higher,among which,using Xinluzao 33 RVI and Pn,Gs power function equation to estimate the Pn and Gs and carry on correlation analysis of the predicted value of Pn and Gs with the measured value,it was found that R value reached a very significant level (R measured pn =0.827,RMSE =1.089,R measured gs-estimation gs =0.586,RMSE =0.138,n =20,P ＜ 0.01).The estimation accuracy of the model equation was greater than 80;.[Conclusion] There was a significant correlation between the spectral vegetation indexes and photosynthetic parameters of the two cotton varieties under different water treatments and the correlation model could be used to estimate the Pn and Gs,and real-time monitoring of the growth status of cotton.

摘要： [目的]建立棉花高光谱数据与光合特征参数的相关模型,有效、快速、非破坏的对棉花生长过程进行诊断与监测,为大面积应用高光谱遥感监测棉花的生长状况提供科学依据.[方法]利用ASD高光谱辐射仪和Li-6400光合仪分别获取5水分处理条件下,棉花新陆早13号、新陆早33号两品种关键生育期的高光谱数据和光合特征参数:净光合速率(Pn)和气孔导度(Gs),利用高光谱数据计算得到棉花两品种归一化植被指数(NDVI)、比值植被指数(RVI)和修改型二次土壤调节植被指数(MSAVI2),分别建立与两品种Pn和Gs的线性、对数和幂函数的相关方程.[结果]三种模型方程均达到显著和极显著的相关性,两品种RVI与Pn和Gs的三种相关模型方程的r值较高,其中,利用新陆早33号RVI与Pn,Gs幂函数方程分别对Pn和Gs估算,并将预测Pn、Gs与实测Pn,Gs进行相关分析,R值均达到极显著水平(r实测Pn-估测Pn=0.827**,RMSE=1.089,r实测Gs-估测Gs=0.586**,RMSE =0.138,n=20,P＜O.01),模型方程的估测精度均大于80;.[结论]不同水分处理下新陆早13号和新陆早33号的光谱植被指数与光合参数间存在着显著的相关性,可以利用相关模型对Pn和Gs进行遥感估测,实时监测棉花的生长状况.