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Change of LAI and Spectral Response for Rice under Flood and Waterlogging Stress

文献类型： 外文期刊

作者： Xu Peng 1 ; Gu Xiao-he 2 ; Meng Lu-min 1 ; Qiu He 1 ; Wang Hui-fang 2 ;

作者机构： 1.Xian Univ Sci & Technol, Coll Geomat, Xian 710054, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Minist Agr, Key Lab Informat Technol Agr, Beijing 100097, Peoples R China

关键词： Rice; Flood and waterlogging stress; Spectral analysis; Red edge characteristic; Leaf area index

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2013 年 33 卷 12 期

页码：

收录情况： SCI

摘要： In order to provide the foundational theoretical support for flood loss estimation of rice with RS, the change of leaf area index (LAI) and canopy spectral response during four developmental stages and three waterlogging depths were studied, and the LAI estimation model was established with spectra characteristics parameter using regression analysis method. The results show that LAI value decreases as water depth increases in tillering, jointing and heading stages, and LAI value under complete submergence decreased by 36.36% than CK in jointing stages. "Double-Peak" presented in the canopy first derivative spectra of 680 similar to 760 nm where the red edge parameters existed, and the main peak is located in the 724 similar to 737 nm with 701 and 718 nm exhibiting secondary peak. With water depth increasing, "Triple-Peak" emerges especially. The red edge position moves to long-wavelength direction in each developmental stage. Blue shift of red edge amplitude and red edge area was detected in tillering, jointing and filling stages, while red shift appeared in heading stage. The relationship between spectra characteristics parameters and LAI were investigated during 4 growth stages, results were not consistently significant at any wavelengths, and the leaf area indices were significantly correlative to the spectra parameters before heading stage, so the spectra parameters before heading stage can be used to estimate the leaf area indices, and a regression model based on parameter D-lambda 737/D-lambda 718 was recommended. Therefore the variation range of LAI for rice could response to the stress intensity directly, and the regression model LAI=3.138(D-lambda 737/D-lambda 718)-0.806 can precisely estimate the leaf area index under flooding and waterlogging stress.

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