Analysis of Common Canopy Reflectance Spectra for Indicating Leaf Nitrogen Concentrations in Wheat and Rice
Analysis of Common Canopy Reflectance Spectra for Indicating Leaf Nitrogen Concentrations in Wheat and Rice
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DOI：
10.1626/pps.10.400
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发表时间：
2007-01
影响因子：
2.5
通讯作者：
Yan Zhu;Yongchao Tian;Xia Yao;Xiaojun Liu;W. Cao
中科院分区：
文献类型：
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作者：
Yan Zhu;Yongchao Tian;Xia Yao;Xiaojun Liu;W. Cao
摘要植物氮素浓度的非破坏性监测与诊断对大田作物氮素精确管理和生产力预测具有重要意义。本文研究了指示水稻叶片氮素浓度(LNC，mg Ng-1DW)的常见光谱波段和冠层反射光谱参数，并确定了LNC与冠层反射光谱之间的定量关系。和小麦(Triticum aestivum L.)通过7个不同小麦品种和5个不同水稻品种、不同施氮量的7个田间试验，测定了小麦3个生长季和水稻4个生长季的地面冠层光谱反射率和LNC。计算了所有可能的比值植被指数(RVI)、差异植被指数(DVI)和归一化差异植被指数(NDVI)。结果表明，小麦和水稻的LNC随施氮量的增加而增加。而在不同施氮量下，冠层反射率的关系更为复杂。在光谱的近红外部分(760−1220 nm)，冠层光谱反射率随着氮素供应的增加而增加，而在可见光区域(460−710 nm)，冠层反射率随着氮素供应的增加而减少。水稻和小麦的LNC最好估计在610、660和680 nm。在MSR16辐射计关键波段的所有可能的RVI、DVI和NDVI中，NDVI(1220,610)和RVI(1220,610)与小麦和水稻的LNC相关性最高。此外，小麦和水稻的NDVI(1220,610)和RVI(1220,610)与LNC的相关性均高于610、660和680 nm的单独波段。因此，小麦和水稻的LNC可以用共同的波段和植被指数来表示，但要准确地描述小麦和水稻的LNC的动态变化规律，则需要单独的回归方程。当独立数据拟合时，NDVI(1220,610)和RVI(1220,610)预测的LNC与观测值的均方根误差(RMSE)分别为10.50%和10.52%，水稻为13.04%和12.61%，具有较好的拟合效果。这些结果将提高对谷类作物叶片氮素状况的非破坏性监测的认识。
Abstract Non-destructive monitoring and diagnosis of plant nitrogen (N) concentration are of significant importance for precise N management and productivity forecasting in field crops. The present study was conducted to identify the common spectra wavebands and canopy reflectance spectral parameters for indicating leaf nitrogen concentration (LNC, mg N g-1 DW) and to determine quantitative relationships of LNC to canopy reflectance spectra in both rice (Oryza sativa L.) and wheat (Triticum aestivum L.). Ground-based canopy spectral reflectance and LNC were measured with seven field experiments consisting of seven different wheat cultivars and five different rice cultivars and varied N fertilization levels across three growing seasons for wheat and four growing seasons for rice. All possible ratio vegetation indices (RVI), difference vegetation indices (DVI), and normalized difference vegetation indices (NDVI) of key wavebands from the MSR16 radiometer were calculated. The results showed that LNC of wheat and rice increased with increasing N fertilization rates. Canopy reflectance, however, was a more complicated relationship under different N application rates. In the near infrared portion of the spectrum (760−1220 nm), canopy spectral reflectance increased with increasing N supply, whereas in the visible region (460−710 nm), canopy reflectance decreased with increasing N supply. For both rice and wheat, LNC was best estimated at 610, 660 and 680 nm. Among all possible RVI, DVI and NDVI of key bands from the MSR16 radiometer, NDVI(1220, 610) and RVI(1220, 610) were most highly correlated to LNC in both wheat and rice. In addition, the correlations of NDVI(1220, 610) and RVI(1220, 610) to LNC were found to be higher than those of individual wavebands at 610, 660 and 680 nm in both wheat and rice. Thus LNC in both wheat and rice could be indicated with common wavebands and vegetation indices, but separate regression equations are necessary for precisely describing the dynamic change patterns of LNC in wheat and rice. When independent data were fit to the derived equations, the root mean square error (RMSE) values for the predicted LNC with NDVI(1220, 610) and RVI(1220, 610) relative to the observed values were 10.50% and 10.52% in wheat, and 13.04% and 12.61% in rice, respectively, indicating a good fit. These results should improve the knowledge on non-destructive monitoring of leaf N status in cereal crops.