Share Email Print
cover

Proceedings Paper

Monitoring the ratio of leaf carbon to nitrogen in winter wheat with hyperspectral measurements
Author(s): Xin-gang Xu; Xiao-dong Yang; Xiao-he Gu; Hao Yang; Hai-kuan Feng; Gui-jun Yang; Xiao-yu Song
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In crop leaves the ratio of carbon to nitrogen (C/N), defined as ratio of LCC (leaf carbon concentration) to LNC (leaf nitrogen concentration), is a good indicator that can synthetically evaluate the balance of carbon and nitrogen, nutrient status in crop plants. Hence it is very important how to monitor changes of leaf C/N effectively and in real time for nutrient diagnosis and growing management of crops in fields. In consideration of the close relationships between chlorophyll, nitrogen (N) and C/N, some typical indices aimed at N estimation were tested to estimate leaf C/N in winter wheat as well as several indices aimed chlorophyll evaluation. The multi-temporal hyperspectral data from the flag-leaf, anthesis, filling, and milk-ripe stages were obtained to calculate these selected spectral indices for evaluating C/N in winter wheat. The results showed that some tested indices such as MCARI/OSAVI2, MTCI and Rep-Le had the better performance of estimating C/N. In addition, GRA (gray relational analysis) and Branch-and-Bound method were also used along with spectral indices sensitive to C/N for improving the accuracy of monitoring C/N in winter wheat, and obtained the better results with R2 of 0.74, RMSE of 0.991. It indicates that monitoring of leaf C/N in winter wheat with hyperspectral reflectance measurements appears very potential.

Paper Details

Date Published: 14 October 2015
PDF: 5 pages
Proc. SPIE 9637, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII, 96371N (14 October 2015); doi: 10.1117/12.2194937
Show Author Affiliations
Xin-gang Xu, Beijing Research Ctr. for Information Technology in Agriculture (China)
Xiao-dong Yang, Beijing Research Ctr. for Information Technology in Agriculture (China)
Xiao-he Gu, Beijing Research Ctr. for Information Technology in Agriculture (China)
Hao Yang, Beijing Research Ctr. for Information Technology in Agriculture (China)
Hai-kuan Feng, Beijing Research Ctr. for Information Technology in Agriculture (China)
Gui-jun Yang, Beijing Research Ctr. for Information Technology in Agriculture (China)
Xiao-yu Song, Beijing Research Ctr. for Information Technology in Agriculture (China)


Published in SPIE Proceedings Vol. 9637:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray