Scopus记录号: 2-s2.0-84904460985
论文题名: Detecting leaf nitrogen content in wheat with canopy hyperspectrum under different soil backgrounds
作者: Yao X ; , Ren H ; , Cao Z ; , Tian Y ; , Cao W ; , Zhu Y ; , Cheng T
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 32, 期: 1 起始页码: 114
结束页码: 124
语种: 英语
英文关键词: Detecting model
; Leaf nitrogen content
; Soil background
; Spectral index
; Vegetation coverage
; Wheat canopy
Scopus关键词: accuracy assessment
; detection method
; growth
; leaf area index
; NDVI
; nitrogen cycle
; nondestructive testing
; numerical model
; spectral analysis
; vegetation cover
; wheat
英文摘要: Hyperspectral sensing techniques can be effective for rapid, non-destructive detecting of the nitrogen (N) status in crop plants; however, their accuracy is often affected by the soil background. Under different fractions of soil background, the canopy spectra and leaf nitrogen content (LNC) in winter wheat (Triticum aestivum L.) were obtained from field experiments with different N rates and planting densities over 3 growing seasons. Five types of vegetation index (VIs: normalized difference vegetation index (NDVI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), optimize soil adjusted vegetation index (OSAVI), and perpendicular vegetation index (PVI)) were constructed based on three types of spectral information: (1) the original and the first derivative (FD) spectrum, (2) the spectrum adjusted with the vegetation coverage (FVcover), and (3) the pure spectrum extracted by a linear mixed model. Comprehensive relationships of above five types of VI with LNC were quantified for LNC detecting under different soil backgrounds. The results indicated that all five types of VI were significantly affected by the soil background, with R2 values of around 0.55 for LNC detecting, with the OSAVI (R514, R469)L=0.04 producing the best performance of all five indices. However, based on the FVcover, the coverage adjusted spectral index (CASI = NDVI(R513, R481)/(1 + FVcover)) produced the higher R2 value of 0.62 and the lower RRMSE of 13%, and was less sensitive to the leaf area index (LAI), leaf dry weight (LDW), FVcover, and leaf nitrogen accumulation (LNA). The results demonstrate that the newly developed CASI could improve the performance of LNC estimation under different soil backgrounds. © 2014 Elsevier BV. 资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79632
Appears in Collections: 气候变化事实与影响
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作者单位: National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, 1 Weigang Road, Nanjing, Jiangsu 210095, China
Recommended Citation:
Yao X,, Ren H,, Cao Z,et al. Detecting leaf nitrogen content in wheat with canopy hyperspectrum under different soil backgrounds[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,32(1)