基于冠层反射光谱的夏玉米叶片氮积累量估测

Monitoring Leaf Nitrogen Accumulation in Summer Maize with Canopy Reflectance Spectra

为实现夏玉米冠层氮素状况的实时无损监测,于2009—2010年连续2个生长季,通过不同玉米品种和施氮水平下的田间试验,研究夏玉米叶片氮积累量与冠层反射光谱的相关关系,提出叶片氮积累量的敏感光谱参数,并建立叶片氮积累量的定量估算模型。结果表明,夏玉米叶片氮积累量随施氮水平的提高而增加;可见光波段的460~670 nm和近红外区的780~1100 nm是监测玉米叶片氮积累量变化差异的敏感波段;归一化植被指数（NDVI）、优化的简单比值指数（MSR）、优化土壤调节植被指数（OSAVI）、修正土壤调整植被指数（MSAVI）和土壤调整植被指数（SAVI）与叶片氮积累量相关性较好。利用不同年际独立试验数据对监测模型进行检验,以OSAVI为自变量构建的叶片氮积累量监测模型效果最优,相关系数（r）为0.6745,均方根差（RMSE）为1.2368。利用本研究确立的玉米叶片氮积累量与冠层反射光谱的定量关系,可用来定量估测叶片氮积累量的变化状况。

To realize non-destructive and quick assessment of leaf nitrogen status in summer maize, two field experiments were conducted with different maize cultivars and nitrogen levels across two growing season, and time-course measurements were taken on canopy spectral reflectance and leaf nitrogen accumulation during the period of experiment. Base on the canopy reflectance and derived vegetation index, we analyzed the quantitative relationships between leaf nitrogen accumulation and canopy reflectance in different maize cultivars under varied nitrogen rates, and put forward the sensitive parameters and monitoring equation for leaf nitrogen accumulation. The results showed that, leaf canopy nitrogen accumulation in maize increased with increasing nitrogen rates. Wavelengths at 460-670 nm and 780-1100 nm were sensitive to leaf nitrogen accumulation for nitrogen diagnosis. Leaf nitrogen accumulation was highly correlated with normalized difference vegetation index（NDVI）, modified simple ratio（MSR）, optimal soil-adjusted vegetation index（OSAVI）, modified soil-adjusted vegetation index（MSAVI）, soil-adjusted vegetation index（SAVI）, respectively. Comparing the R2and standard error of regression model, the spectral index of OSAVI was the test parameter for predicting leaf nitrogen accumulation. The monitoring models were tested with an independent dataset, and the predictive precision（r） was 0.6745, and RMSE was 1.2368. It was concluded that leaf nitrogen accumulation in summer maize could be monitored by key vegetation indices, with more reliable estimation from OSAVI.