论文部分内容阅读
通过测试大豆4个生育阶段350～2500nm波段的冠层高光谱数据,用近红外波段760nm～850nm及红光波段650nm～670nm的2个范围内的波段反射率,组成了高光谱比值植被指数(RVI)和800nm和670nm2个波段反射率组成修改型二次土壤调节植被指数(MSAVI2);基于RVI和MSAVI2植被指数,建立了大豆叶面积指数(LAI)的6种单变量线性与非线性函数模型,经检验均达到1%极显著水平。其中,以RVI所构建LAI的幂函数、MSAVI2所构建LAI的指数函数、对数函数估测模型的相关系数相对较高;用MSAVI2所构建的LAI精度较高的对数函数模型反演大豆叶面积指数,实测LAI与估测LAI呈极显著线性相关(R=0.9098**,n=46),模型方程的估算精度达84.9%,实测值与估算值的RMSE=0.2420,平均相对误差为0.1510。表明采用高光谱植被指数,能够实时、无损、动态、定量提取大豆叶面积指数,为设计理想的大豆群体和大豆遥感估产提供了科学的依据。
Canopy hyperspectral vegetation index (HGI) was calculated by testing the canopy hyperspectral data of 350-2500 nm band in four growing stages of soybean with the band reflectivity in the range of 760 nm-850 nm in the near-infrared band and 650 nm-670 nm in the red band RVI) and two bands reflectance at 800nm and 670nm, respectively. Based on the vegetation index of RVI and MSAVI2, six kinds of univariate linear and nonlinear function models of soybean leaf area index (LAI) , After testing all reached 1% extremely significant level. Among them, the correlation coefficient of exponential function and logarithm function estimation model of LAI constructed by RVI and LAI constructed by MSAVI2 is relatively high. The inversion of logarithm function model with LAI with MSAVI2 accuracy The area index, the measured LAI and the estimated LAI showed a significant linear correlation (R = 0.9098 **, n = 46), the estimated accuracy of the model equation was 84.9%, the RMSE of the measured value and the estimated value was 0.2420 and the average relative error was 0.1510 . The results showed that using hyperspectral vegetation index can extract soybean leaf area index in real time, nondestructively, dynamically and quantitatively, which provided a scientific basis for the design of ideal soybean population and soybean remote sensing yield estimation.