水稻氮素营养高光谱遥感诊断模型

Hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status.

对水稻氮素含量与原始光谱反射率、一阶微分光谱以及高光谱特征参数间的相关性进行了分析,并构建和验证了以遥感参数为自变量的水稻氮素营养诊断模型.结果表明:氮素含量在水稻各器官中总的变化趋势为茎<鞘<穗<叶;各器官在可见光波段的光谱反射能力为叶<穗<鞘<茎,在近红外波段则与此相反.以波长796.7nm处的光谱反射率和738.4nm处的一阶微分光谱反射率为自变量的线性模型和指数模型的决定系数(R2)分别为0.7996和0.8606,二者均能较好地诊断水稻氮素营养,但最适合诊断水稻氮素含量的拟合模型是以植被指数的归一化变量(SDr-SDb)/(SDr+SDb)为自变量构建的水稻氮素营养高光谱遥感诊断模型[y=365.871+639.323(SDr-SDb)/(SDr+SDb),R2=0.8755,RMSE=0.2372,相对误差=11.36%],该模型可定量诊断水稻氮素营养.

The correlations of rice plant nitrogen content with raw hyperspectral reflectance, first derivative hyperspectral reflectance, and hyperspectral characteristic parameters were analyzed, and the hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status with these remote sensing parameters as independent variables were constructed and validated. The results indicated that the nitrogen content in rice plant organs had a variation trend of stem 〈 sheath 〈 spike 〈 leaf. The spectral reflectance at visible light bands was leaf 〈 spike 〈 sheath 〈 stem, but that at near-infrared bands was in adverse. The linear and exponential models with the raw hyperspectral reflectance at 796.7 nm and the first derivative hyperspectral reflectance at 738.4 nm as independ- ent variables could better diagnose rice plant nitrogen nutritional status, with the decisive coefficients （ R^2 ） being 0. 7996 and 0. 8606, respectively; while the model with vegetation index （ SDr - SDb）/（SDr ＋SDb） as independent variable, i. e. , y =365. 871 ＋639. 323（ （SDr -SDb）/（SDr ＋ SDb） ） , was most fit rice plant nitrogen content, with R^2 = 0. 8755, RMSE = 0. 2372 and relative error = 11.36% , being able to quantitatively diagnose the nitrogen nutritional status of rice.