Analysis of Common Canopy Reflectance Spectra for Indicating Leaf Nitrogen Concentrations in Wheat and Rice(Crop Physiology and Ecology)
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概要
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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 Ng^<-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 (760-1220nm), canopy spectral reflectance increased with increasing N supply, whereas in the visible region (460-710nm), canopy reflectance decreased with increasing N supply. For both rice and wheat, LNC was best estimated at 610, 660 and 680nm. 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 680nm 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.
- 日本作物学会の論文
著者
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Liu Xiaojun
Jiangsu Key Laboratory For Information Agriculture Nanjing Agricultural University
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Cao Weixing
Jiangsu Key Laboratory For Information Agriculture Nanjing Agricultural University
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Zhu Yan
Jiangsu Key Laboratory For Information Agriculture Nanjing Agricultural University
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Zhu Yan
Hi-Tech Key Lab of Information Agriculture of Jiangsu Province, Nanjing Agricultural University
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Cao Weixing
Hi-Tech Key Lab of Information Agriculture of Jiangsu Province, Nanjing Agricultural University
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Yao Xia
Hi-tech Key Lab Of Information Agriculture Of Jiangsu Province Nanjing Agricultural University
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Tian Yongchao
Hi-Tech Key Lab of Information Agriculture of Jiangsu Province, Nanjing Agricultural University
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Liu Xiaojun
Hi-Tech Key Lab of Information Agriculture of Jiangsu Province, Nanjing Agricultural University
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Tian Yongchao
Hi-tech Key Lab Of Information Agriculture Of Jiangsu Province Nanjing Agricultural University
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Cao Weixing
Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University
関連論文
- A Knowledge-Based Model for Nitrogen Management in Rice and Wheat(Agronomy)
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- A Quantitative Knowledge-based Model for Designing Suitable Growth Dynamics in Rice(Crop Physiology and Ecology)
- Analysis of Common Canopy Reflectance Spectra for Indicating Leaf Nitrogen Concentrations in Wheat and Rice(Crop Physiology and Ecology)
- Spatial Distribution of Leaf Area Index and Leaf N Content in Relation to Grain Yield and Nitrogen Uptake in Rice(Agronomy)
- A Knowledge-Based Model for Nitrogen Management in Rice and Wheat