论文部分内容阅读
为无损和定量研究高光谱技术在冬油菜植株氮素积累量(PNA,plant nitrogen accumulation)时空变化监测的适宜性及准确性,该文以两年田间氮肥水平试验为基础,采用单变量线性和非线性回归方法,建立基于特征光谱参数的冬油菜P NA高光谱估算模型。结果表明,采用比值光谱的方法可显著提高冬油菜冠层光谱反射率与PNA间的相关性,其最佳的波段组合为1 259 nm与492 nm处光谱反射率比值(R1259/R492),决定系数R2为0.85。高光谱参数间,以比值植被指数(RVI-5)、归一化光谱指数(NDSI)、线性内插法红边位置(REIP)、三角植被指数(TVI)、742 nm处一阶微分光谱值(FD742)和红边面积(SDR)等光谱参数与PNA相关性较好(平均R2和标准误SE分别为0.69和42.70),且以FD742表现最优(R2=0.79,SE=35.66)。精度分析结果显示,以光谱参数R1259/R492和FD742为自变量的指数方程模型作为高光谱监测油菜PNA的最佳模型,各生育期Noise Equivalent(NE)均较低且表现稳定,同时模型估测精度较高,R2分别为0.98和0.98,相对均方根误差RRMSE分别为0.73和0.72,相对误差MRE分别为14.42%和10.31%。该方法为快捷和精确评估冬油菜PNA提供了新的研究思路。
In order to study the suitability and accuracy of hyperspectral monitoring of temporal and spatial variation of plant nitrogen accumulation (PNA) in winter rapeseed based on non-destructive and quantitative methods, based on the two-year field experiment of N fertilizer level, Nonlinear regression method was used to establish PNA hyperspectral estimation model of winter rape based on characteristic spectral parameters. The results showed that the ratio of spectral reflectance could significantly improve the correlation between canopy spectral reflectance and PNA. The best band combination was R1259 / R492 (R1259 / R492) The coefficient R2 is 0.85. (RVI-5), normalized spectral index (NDSI), linear interpolation red edge position (REIP), triangular vegetation index (TVI), first order differential spectral value at 742 nm (FD742) and red edge area (SDR) were correlated well with PNA (average R2 and standard error SE were 0.69 and 42.70, respectively) and FD742 (R2 = 0.79, SE = 35.66). The accuracy analysis showed that the exponential equation model with spectral parameters R1259 / R492 and FD742 as independent variables was the best model for hyperspectral monitoring of canola PNA, and the Noise Equivalent (NE) in all growth stages was low and stable, and the model estimation The accuracy was higher with R2 of 0.98 and 0.98, respectively. The relative root mean square error (RRMSE) were 0.73 and 0.72, respectively, and the relative errors MRE were 14.42% and 10.31% respectively. This method provides a new research idea for rapid and accurate assessment of winter rape PNA.