光谱学与光谱分析 |
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Change of LAI and Spectral Response for Rice under Flood and Waterlogging Stress |
XU Peng1, 2, 3, GU Xiao-he2, 3*, MENG Lu-min1, QIU He1, WANG Hui-fang2, 3 |
1. College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China 3. Key Laboratory of Information Technology in Agriculture Ministry of Agriculture, Beijing 100097, China |
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Abstract 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 680~760 nm where the red edge parameters existed, and the main peak is located in the 724~737 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 Dλ737/Dλ718 was recommended. Therefore the variation range of LAI for rice could response to the stress intensity directly, and the regression model LAI=3.138(Dλ737/Dλ718)-0.806 can precisely estimate the leaf area index under flooding and waterlogging stress.
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Received: 2013-03-23
Accepted: 2013-06-18
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Corresponding Authors:
GU Xiao-he
E-mail: guxh@nercita.org.cn
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