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A Simple Spectral Index Using Reflectance of 735 nm to Assess Nitrogen Status of Rice Canopy
Author(s) -
Lee YuhJyuan,
Yang ChwenMing,
Chang KuoWei,
Shen Yuan
Publication year - 2008
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2007.0018
Subject(s) - canopy , normalized difference vegetation index , remote sensing , leaf area index , enhanced vegetation index , environmental science , panicle , mathematics , oryza sativa , vegetation (pathology) , agronomy , vegetation index , botany , geography , chemistry , medicine , biochemistry , pathology , gene , biology
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/dλ| 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/dλ| 735 relationship in mapping canopy N status within field. Results showed that values of dR/dλ| 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.