生物量和叶面积指数(LAI)是描述作物长势的重要参数, 叶干重和LAI的实时动态监测对小麦(Triticum aestivum)生长诊断和管理调控具有重要意义。为分析多种高光谱参数估算小麦叶干重和LAI的效果, 建立小麦叶干重和LAI的定量监测模型, 该研究连续3年采用不同小麦品种进行不同施氮水平的大田试验, 于小麦不同生育期采集田间冠层高光谱数据并测定叶片叶干重和LAI。试验结果显示, 小麦叶干重和LAI随施氮水平的提高而增加, 随生育进程呈单峰动态变化模式。小麦叶干重和LAI与光谱反射率间相关性较好的区域主要位于红光波段(590~710 nm, r<-0.60)和近红外波段(745~1 130 nm, r>0.69)。对于不同试验条件下的叶干重和LAI, 可以使用统一的光谱参数进行定量反演, 其中基于RVI (810, 560)、FD755、GM1、SARVI (MSS)和TC3等光谱参数的方程拟合效果较好。经不同年际独立试验数据的检验表明, 以参数RVI (810, 560)、GM1、SARVI (MSS)、PSSRb、(R_750-800/R_695-740) -1、VOG2和MSR705为变量建立的叶干重和LAI监测模型均给出较好的检验结果。因此, 利用关键特征光谱参数可以有效地评价小麦叶片生长状况, 尤其是光谱参数RVI (810, 560)、GM1和SARVI (MSS)可以对不同条件下小麦叶干重和LAI进行准确可靠的监测。

Aims Biomass and leaf area index (LAI) are important parameters for indicating crop growth potential and photosynthetic productivity in wheat. Non-destructive, quick assessment of leaf dry weight and LAI is necessary for growth diagnosis and cultural regulation in wheat production. The objectives of this study were to determine the relationships of leaf dry weight and LAI to ground-based canopy hyperspectral reflectance and spectral parameters and to derive regression equations for monitoring leaf dry weight and LAI in winter wheat (Triticum aestivum) with hyperspectral remote sensing.
Methods Three field experiments were conducted with different wheat varieties and nitrogen levels for three growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and leaf dry weight and LAI during the experiments. Experiment one was conducted in 2005-2006 to construct a monitoring model with four N rates of 0, 90, 180 and 270 kg·hm^-2 using cultivars ‘Ningmai9’ and ‘Yumai34’ (low and high protein types, respectively). Experiment two was undertaken in 2004-2005 to construct a monitoring model with four N rates of 0, 75, 150 and 225 kg·hm^-2 using cultivars ‘Ningmai9’, ‘Yangmai12’ and ‘Yumai34’ (low, medium, high protein types, respectively). Experiment three was conducted in 2003-2004 to test a monitoring model with four N rates of 0, 75, 150, 225 and 300 kg·hm^-2 using cultivars ‘Ningmai9’, ‘Huaimai20’ and ‘Xuzhou26’ (low, medium, high protein types, respectively).
Important findings Leaf dry weight and LAI in wheat increased with increasing nitrogen rates and with significant differences between stages of growth. The dynamics of leaf dry weight and LAI during growth exhibited single peak patterns. The sensitive spectral bands were located mostly within red light and near infrared regions, with correlation coefficients <-0.60 in 590~710 nm and >0.69 in 745~1 130 nm. The regression analyses between existing vegetation indices and leaf dry weight and LAI revealed that some key spectral parameters could accurately estimate changes in leaf dry weight and LAI across a broad range of stages of growth, nitrogen levels and growing seasons, with unified spectral parameters for each growth parameter. Among them, regression models based on RVI (810, 560), FD755, GMI, SARVI (MSS) and TC3 produced better estimation of leaf dry weight and LAI. Testing of the monitoring models with an independent dataset indicated that the spectral indices of RVI (810, 560), GMI, SARVI (MSS), PSSRb, (R_750-800/R_695-740)-1, VOG2 and mSR705 gave accurate growth estimation under the experimental conditions. Overall, leaf dry weight and LAI in wheat could be monitored by key vegetation indices, with more reliable estimation from RVI (810, 560), GMI and SARVI (MSS).