NDLI: Hyperspectral characteristics of winter wheat under freezing injury stress and LWC inversion model

Content Provider	IEEE Xplore Digital Library
Author	Wang, H. Wang, J. Wang, Q. Miao, N. Huang, W. Feng, H. Dong, Y.
Copyright Year	2012
Description	Author affiliation: Beijing Research Center for Information Technology in Agriculture, Beijing, China (Wang, H.; Wang, J.; Wang, Q.; Miao, N.; Huang, W.; Feng, H.; Dong, Y.)
Abstract	In order to establish a ground support model for monitoring winter wheat freezing injury, different frost resistance winter wheat cultivars planted in the field were taken as the research object in this paper, through the extraction of hyperspectral characteristics and sensitivity analysis conducted on winter wheat in the winter, the sensitive waveband of the winter wheat under the low-temperature freezing injury stress was selected at the leaf scale, and the characterization indexes of freezing injury stress- leaf water content (LWC) and Inversion Model of hyperspectral characteristics parameters- were established. The results show that when the winter wheat is under low-temperature stress, the wavebands of 350–534 nm, 535–590 nm, 696–1380 nm, 1381–1553 nm and 1885–2500 nm will clearly characterize the freezing injury stress characteristics, that is, the characteristics of “green peak” and “red valley” are weakened, the reflectance of “red shoulder” is reduced, and the moisture absorption valley became shallow with the increasing severity of the freezing injury degree. On the basis of the above analysis, the correlation of LWC and hyperspectral characteristic parameters is analyzed, except that the reflectance of “red valley” and SDr/SDy fail to reach significant correlation, all the indexes have reached significant correlation or extreme correlation. Wherein, $R^{2}$ reaches 0.750 up when the first-order differential value of 777 nm, SDr/SDb, (SDr-SDb)/(SDr+SDb) are taken as the variables in the model, which are thus selected as the model indexes, and the model is verified. The results of the verification have reached extreme correlation since their R2 are 0.484, 0.799, 0.750 and 0.731 respectively. In summary, the estimation of LWC through high spectral characteristic variables is feasible. The results present a practical value to achieve the information of freezing injury crops by hyperspectral remote sensing, and it is of significant importance insuring food security and increase crop production.
Starting Page	1
Ending Page	6
File Size	409013
Page Count	6
File Format	PDF
ISBN	9781467324953
e-ISBN	9781467324960
e-ISBN	9781467324946
DOI	10.1109/Agro-Geoinformatics.2012.6311627
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-02
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Reflectivity Correlation Hyperspectral Characteristics Sensitivity Inversion model LWC Agriculture Freeze injury Injuries Stress Hyperspectral imaging Winter wheat
Content Type	Text
Resource Type	Article

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