Simple Spectral Index Using Reflectance of 735 nm to Assess Nitrogen Status of Rice Canopy

Spatial distribution of canopy N status is the primary information needed for precision management of N fertilizer. This study demonstrated the feasibility of a simple spectral index (SI) using the first derivative of canopy reflectance spectrum at 735 nm (dR/d|735) to assess N concentration of rice...

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Bibliographic Details
Published in:Agronomy journal 2008-01, Vol.100 (1), p.205-212
Main Authors: Lee, Y.J, Yang, C.M, Chang, K.W, Shen, Y
Format: Article
Language:English
Subjects:
Agronomy. Soil science and plant productions
Biological and medical sciences
Fundamental and applied biological sciences. Psychology
image analysis
nitrogen
normalized difference vegetation index
Oryza sativa
plant nutrition
precision agriculture
reflectance
remote sensing
rice
simple ratio vegetation index
spatial variation
spectral analysis
vegetation cover
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Summary:Spatial distribution of canopy N status is the primary information needed for precision management of N fertilizer. This study demonstrated the feasibility of a simple spectral index (SI) using the first derivative of canopy reflectance spectrum at 735 nm (dR/d|735) to assess N concentration of rice (Oryza sativa L.) plants, and then validated the applicability of a simplified imaging system based on the derived spectral model from the dR/d|735 relationship in mapping canopy N status within field. Results showed that values of dR/d|735 were linearly related to plant N concentrations measured at the panicle formation stage. The leaf N accumulation per unit ground area was better fitted than other ratio-based SIs, such as simple ratio vegetation index (SRVI), normalized difference vegetation index (NDVI), R810/R560, and (R1100 - R660)/(R1100 + R660), and remained valid when pooling more data from different cropping seasons in varied years and locations. A simplified imaging system was assembled and mounted on a mobile lifter and a helicopter to take spectral imageries for mapping canopy N status within fields. Results indicated that the imaging system was able to provide field maps of canopy N status with reasonable accuracy (r = 0.465-0.912, root mean standard error [RMSE] = 0.100-0.550) from both remote sensing platforms.
ISSN:0002-1962
1435-0645
1435-0645
DOI:10.2134/agrojnl2007.0018