scholarly journals Multivariate analysis of nitrogen content for rice at the heading stage using reflectance of airborne hyperspectral remote sensing

2011 ◽  
Vol 122 (3) ◽  
pp. 214-224 ◽  
Author(s):  
Chanseok Ryu ◽  
Masahiko Suguri ◽  
Mikio Umeda
Keyword(s):  
Remote Sensing ◽  
Multivariate Analysis ◽  
Nitrogen Content ◽  
Hyperspectral Remote Sensing ◽  
Heading Stage

scholarly journals Measurement of nitrogen content in rice by inversion of hyperspectral reflectance data from an unmanned aerial vehicle

2018 ◽  
Vol 48 (6) ◽  
Author(s):  
Du Wen ◽  
Xu Tongyu ◽  
Yu Fenghua ◽  
Chen Chunling
Keyword(s):  
Remote Sensing ◽  
Nitrogen Content ◽  
Unmanned Aerial Vehicle ◽  
Gaussian Process Regression ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Ground Truth ◽  
Hyperspectral Remote Sensing ◽  
Inversion Algorithm ◽  
Aerial Vehicle

ABSTRACT: The Nitrogen content of rice leaves has a significant effect on growth quality and crop yield. We proposed and demonstrated a non-invasive method for the quantitative inversion of rice nitrogen content based on hyperspectral remote sensing data collected by an unmanned aerial vehicle (UAV). Rice canopy albedo images were acquired by a hyperspectral imager onboard an M600-UAV platform. The radiation calibration method was then used to process these data and the reflectance of canopy leaves was acquired. Experimental validation was conducted using the rice field of Shenyang Agricultural University, which was classified into 4 fertilizer levels: zero nitrogen, low nitrogen, normal nitrogen, and high nitrogen. Gaussian process regression (GPR) was then used to train the inversion algorithm to identify specific spectral bands with the highest contribution. This led to a reduction in noise and a higher inversion accuracy. Principal component analysis (PCA) was also used for dimensionality reduction, thereby reducing redundant information and significantly increasing efficiency. A comparison with ground truth measurements demonstrated that the proposed technique was successful in establishing a nitrogen inversion model, the accuracy of which was quantified using a linear fit (R2=0.8525) and the root mean square error (RMSE=0.9507). These results support the use of GPR and provide a theoretical basis for the inversion of rice nitrogen by UAV hyperspectral remote sensing.

scholarly journals Hyperspectral remote sensing of foliar nitrogen content

2012 ◽  
Vol 110 (3) ◽  
pp. E185-E192 ◽  
Author(s):  
Y. Knyazikhin ◽  
M. A. Schull ◽  
P. Stenberg ◽  
M. Mottus ◽  
M. Rautiainen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Nitrogen Content ◽  
Hyperspectral Remote Sensing ◽  
Foliar Nitrogen
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