Assessing the ratio of leaf carbon to nitrogen in winter wheat and spring barley based on hyperspectral data

Xin-gang Xu; Xiao-he Gu; Xiao-yu Song; Bo Xu; Hai-yang Yu; Gui-jun Yang; Hai-kuan Feng

doi:10.1117/12.2241754

25 October 2016 Assessing the ratio of leaf carbon to nitrogen in winter wheat and spring barley based on hyperspectral data

Xin-gang Xu, Xiao-he Gu, Xiao-yu Song, Bo Xu, Hai-yang Yu, Gui-jun Yang, Hai-kuan Feng

Author Affiliations +

Proceedings Volume 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII; 999810 (2016) https://doi.org/10.1117/12.2241754
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

Abstract

The metabolic status of carbon (C) and nitrogen (N) as two essential elements of crop plants has significant influence on the ultimate formation of yield and quality in crop production. The ratio of carbon to nitrogen (C/N) from crop leaves, defined as ratio of LCC (leaf carbon concentration) to LNC (leaf nitrogen concentration), is an important index that can be used to diagnose the balance between carbon and nitrogen, nutrient status, growth vigor and disease resistance in crop plants. Thus, it is very significant for effectively evaluating crop growth in field to monitor changes of leaf C/N quickly and accurately. In this study, some typical indices aimed at N estimation and chlorophyll evaluation were tested to assess leaf C/N in winter wheat and spring barley. The multi-temporal hyperspectral measurements from the flag-leaf, anthesis, filling, and milk-ripe stages were used to extract these selected spectral indices to estimate leaf C/N in wheat and barley. The analyses showed that some tested indices such as MTCI, MCARI/OSAVI2, and R-M had the better performance of assessing C/N for both of crops. Besides, a mathematic algorithm, Branch-and-Bound (BB) method was coupled with the spectral indices to assess leaf C/N in wheat and barley, and yielded the R² values of 0.795 for winter wheat, R² of 0.727 for spring barley, 0.788 for both crops combined. It demonstrates that using hyperspectral data has a good potential for remote assessment of leaf C/N in crops.

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Xin-gang Xu, Xiao-he Gu, Xiao-yu Song, Bo Xu, Hai-yang Yu, Gui-jun Yang, and Hai-kuan Feng "Assessing the ratio of leaf carbon to nitrogen in winter wheat and spring barley based on hyperspectral data", Proc. SPIE 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, 999810 (25 October 2016); https://doi.org/10.1117/12.2241754

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