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
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.
Key words：Rice;Flood and waterlogging stress;Spectral analysis;Red edge characteristic;Leaf area index
[1] LI Yong-he, SHI Ya-yue, CHEN Yao-yue(李永和, 石亚月, 陈耀岳). Journal of Narural Disasters(自然灾害学报), 2004, 3(6): 83. [2] Zahra G, Alireza S, Jamal M V. Nat Hazards, 2012, 64: 405. [3] Kar G, Sahoo N, Kumar A. Archives of Agronomy and Soil Science, 2012, 58(6): 573. [4] Khakwani A A, Shiraishi M, Zubair M, et al. J. Zhejiang Univ. Sci., 2005, 6B(5): 389. [5] XIE Yan, ZHANG Mao-wen, LIU Xu-sheng, et al(谢 彦, 张茂文, 刘许生, 等). Chinese Agricultural Science Bulletin(中国农学通报), 2011, 27(9): 281. [6] LI Yang-sheng, LI Shao-qing(李阳生, 李绍清). Journal of Wuhan Botanical Research(武汉植物学研究), 2000, 18(2): 117. [7] Stroppiana D, Boschetti M, Brivio A P, et al. Field Crops Research, 2009, 111(1-2): 119. [8] Wang Xiaohua, Huang Jingfeng, Wu Yaoping, et al. Science China Life Sciences, 2011, 54(3): 272. [9] Song Shalei, Gong Wei, Zhu Bo, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(5): 672. [10] XIE Xiao-jin, SHEN Shuang-he, LI Ying-xue, et al(谢晓金, 申双和, 李映雪, 等). Transactions of the Chinese Society of Agriculutral Engineering(农业工程学报), 2010, 26(3): 183. [11] XU Xin-gang, ZHAO Chun-jiang, WANG Ji-hua, et al(徐新刚, 赵春江, 王纪华, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2011, 31(1): 188. [12] Delegido J, Verrelst J, Alonso L, et al. Sensors, 2011, 11(7): 7063. [13] Darvishzadeha R, Atzbergerb C, Skidmorec A K, et al. International Journal of Remote Sensing, 2009, 30(23): 6199. [14] Filella I, Penuelas J. Remote Sensing, 1994, 15(7): 1459.
