一种新的估算水稻上部叶片蛋白氮含量的植被指数

 【目的】阐明水稻顶部4张叶片蛋白氮含量和反射光谱特征的变化规律及其相互关系,建立快速、准确诊断水稻功能叶片蛋白氮含量的方法。【方法】通过3年不同施氮水平和不同品种类型的大田试验,分生育期同步测定顶部4张叶片的光谱反射率及蛋白氮含量,系统分析叶片蛋白氮含量与多种高光谱参数的定量关系。【结果】水稻叶片蛋白氮含量和光谱反射率在不同施氮水平、不同生育期及不同叶位间均存在明显差异,叶片蛋白氮含量的敏感波段主要存在于可见光绿光区530～580 nm及红边区域695～715 nm,其中红边区域表现最为显著。红边区域700 nm附近波段与近红外短波段的比值组合(SRs)可以有效地估算水稻上部功能叶片的蛋白氮含量,其次是绿光区587 nm左右的波段与近红外短波段的比值组合。基于新提出的SR（770,700）及已报道的GM-2、SR705、RI-half光谱指数,线性回归模型的拟合精度（R2）分别达到 0.874,0.873,0.871和0.867。经独立资料的检验表明,这些回归模型可以实时监测叶片蛋白氮含量变化,预测精度R2分别为0.810、0.806、0.804和0.800,相对误差RE 分别为12.1%、12.4%、12.6%和12.9%。【结论】可以利用关键特征光谱指数来诊断水稻上部叶片的蛋白氮含量状况,尤以SR（770,700）、GM-2、SR705和RI-half表现为较强的估测能力。

A New Spectral Index for Estimating Protein Nitrogen Concentrations in Top Leaves of Rice

【Objective】 The objectives of this study were to analyze the relationships between leaf protein nitrogen concentrations (LPNC) and spectral reflectance characteristics, and to establish useful hyperspectral bands and hyperspectral indices for nondestructive and quick assessment of LPNC in top leaves of rice (Oryza sativa L.). 【Method】 Three field experiments were conducted with different N rates and rice cultivars. Time-course measurements were taken on hyperspectral reflectance of 350-2 500 nm and LPNC in four top leaves. Statistical analyses were made on the relationships between LPNC and reflectance indicators such as simple ratio indices (SRλ1, λ2]) and normalized difference spectral index (NDλ1,λ2]) using all combinations of two wavelengths (λ1 and λ2 nm) and other existing indices. 【Result】 The results indicated that the LPNC in rice and spectral reflectance varied distinctly with nitrogen rates, growth stages and leaf positions. The sensitivity bands mostly occurred 530-580 nm within green light region and 695-715 nm within red edge region, and a close correlation existed between red-edge district and LPNC. The SR indices composed of reflectance around 700 nm and near infrared short wavelengths were significantly correlated with LPNC, next came the 587 nm. A new spectral index SR (770,700) and existing indices GM-2, SR705, RI-half were found to be good indicators for LPNC, and linear regression models were established with determination of coefficients (R2) as 0.874, 0.873, 0.871 and 0.867, respectively. Tests with other independent datasets showed that the models based on those key spectral indices could be used to predict LPNC reliably. 【Conclusion】 It can be concluded that the LPNC in rice could be monitored directly by key spectral indices, such as SR (770, 700), GM-2, SR705 and RI-half.