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

文献类型： 外文期刊

第一作者： Wang, Huifang

作者： Wang, Huifang;Wang, Jihua;Wang, Qian;Miao, Naizhe;Huang, Wenjiang;Feng, Haikuang;Dong, Yingying;Wang, Huifang;Wang, Jihua

作者机构：

关键词： winter wheat;freeze injury;LWC;Hyperspectral Characteristics;sensitivity;inversion model

期刊名称：2012 FIRST INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS (AGRO-GEOINFORMATICS)

ISSN： 2334-3168

年卷期： 2012 年

页码：

收录情况： SCI

摘要： 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.

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