Extraction of Photosynthetic Parameters of Cotton Leaves under Disease Stress by Hyperspectral Remote Sensing
CHEN Bing1, WANG Gang4, LIU Jing-de1*, MA Zhan-hong2, WANG Jing3, LI Tian-nan1, 2
1. Cotton Institute, Xinjiang Academy Agricultural and Reclamation Science/Northwest Inland Region Key Laboratory of Cotton Biology and Genetic Breeding, Shihezi 832000, China
2. College of Plant Protection, China Agricultural University, Beijing 100083, China
3. Institute of Water Conservation and Architectural Engineering, Xinjiang Shihezi Vocational College, Shihezi 832003, China
4. Xinjiang Academy of Agricutural and Reclamation Science, Shihezi 832000, China
Abstract：Hyperspectra remote sensing technique was applied to detect photosynthetic parameters (PP) in cotton leaf defected Verticillium wilt. The reflectance data about 207 was acquired in 350～2 500 nm bands in different dates and severity level of cotton leaves, and PP were measured by photosynthetic instrument. Analysis of variance and relationship analysis were used to process the PP character, extra spectral sensitive bands and selected character parameters of spectra with PP linear and nor-linear models were applied to product PP of cotton leaf of disease and tested. The result showed: with the disease condition increase, the data was increased to leaf net photosynthetic rate (A), transpiration rate (E), stomatal conductance (GH2O), howeverintercellular CO2 (CI) firstly decreased and then up, and the difference was significant between severity levels and PP. The relationship became better between severity level and PP, which r were -0.97, -0.957, -0.886, 0.715, respectively. New spectral parameters，R704, R706, R699, R690, FD688, FD732, FD690, FD731, FD681 were built on the base of sensitive bands together with tradition spectral parameters to established revise models of A, E, GH2O, CI of cotton leaves with disease. Those models as PRI[FD732, FD688]), R706, RVI[890, 670]), R690 for the independent variable had the highest accuracy to estimate A, E, GH2O, CI, R2 of prediction, which were 0.827, 0.810, 0.658 and 0.573 respectively; RMSE were 5.466, 2.801, 109.500 and 63.500 respectively; RE were 0.041, 0.137, 0.158 and 0.021 respectively, which can realize the inversion of photosynthetic physiological parameters of cotton by remote sensing.
陈 兵，王 刚，刘景德，马占鸿，王 静，李天南. 高光谱的病害棉叶光合参数提取[J]. 光谱学与光谱分析, 2018, 38(06): 1834-1838.
CHEN Bing, WANG Gang, LIU Jing-de, MA Zhan-hong, WANG Jing, LI Tian-nan. Extraction of Photosynthetic Parameters of Cotton Leaves under Disease Stress by Hyperspectral Remote Sensing. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1834-1838.
[1] CHEN Bing, WANG Ke-ru, LI Shao-kun, et al(陈 兵, 王克如, 李少昆, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2011, 27(9): 86.
[2] Sales C R G，Marehiori P E R，Machado R S，et al. Photosynthetica，2015, 53(4)：1.
[3] Jay S, Maupas F, Bendoula R, et al. Field Crops Research, 2017, 210: 33.
[4] Hernández-Clemente R，Navarro-Cerrillo R M，Suárez L，et al. Remote Sensing of Environment，2011, 115(9)：2360.
[5] WU Nan, LIU Jun-ang, ZHOU Guo-ying, et al(伍 南, 刘君昂, 周国英, 等). Chinese Agricultural Science Bulletin(中国农学通报), 2012, 28(1): 73.
[6] HE Ru-yan, QIAO Xiao-jun, JIANG Jin-bao, et al(何汝艳, 乔小军, 蒋金豹, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(2): 141.
[7] CHENG Fan，ZHAO Yan-ru，YU Ke-qiang，et al(程 帆, 赵艳茹, 余克强, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2017，37(6)：1861.
[8] Ren H，Zhou G，Zhang X. Biosystems Engineering，2011, 109(4)：385.
[9] JIN Xiu-liang, LI Shao-kun, WANG Ke-ru, et al(金秀良, 李少昆, 王克如, 等), Cotton Science(棉花学报), 2011, 23(5): 447.
[10] QIU Ya-hong, YANG Feng, ZHAO Gang-cheng, et al(仇亚红, 杨 峰, 赵刚成, 等). Chinese Agricultural Science Bulletin(中国农学通报), 2015, 31(24): 71.
[11] Assal T, Anderson P, Sibold J. Forest Ecology and Management, 2016，46(365): 137.
