|
|
|
|
|
|
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.
|
Received: 2017-09-07
Accepted: 2018-01-19
|
|
Corresponding Authors:
LIU Jing-de
E-mail: 17371359@qq.com
|
|
[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. |
[1] |
XU Tian1, 2, LI Jing1, 2, LIU Zhen-hua1, 2*. Remote Sensing Inversion of Soil Manganese in Nanchuan District, Chongqing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 69-75. |
[2] |
LIANG Ye-heng1, DENG Ru-ru1, 2*, LIANG Yu-jie1, LIU Yong-ming3, WU Yi4, YUAN Yu-heng5, AI Xian-jun6. Spectral Characteristics of Sediment Reflectance Under the Background of Heavy Metal Polluted Water and Analysis of Its Contribution to
Water-Leaving Reflectance[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 111-117. |
[3] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[4] |
LI He1, WANG Yu2, FAN Kai2, MAO Yi-lin2, DING Shi-bo3, SONG Da-peng3, WANG Meng-qi3, DING Zhao-tang1*. Evaluation of Freezing Injury Degree of Tea Plant Based on Deep
Learning, Wavelet Transform and Visible Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 234-240. |
[5] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
[6] |
LIANG Shou-zhen1, SUI Xue-yan1, WANG Meng1, WANG Fei1, HAN Dong-rui1, WANG Guo-liang1, LI Hong-zhong2, MA Wan-dong3. The Influence of Anthocyanin on Plant Optical Properties and Remote Sensing Estimation at the Scale of Leaf[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 275-282. |
[7] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
[8] |
LIANG Ya-quan1, PENG Wu-di1, LIU Qi1, LIU Qiang2, CHEN Li1, CHEN Zhi-li1*. Analysis of Acetonitrile Pool Fire Combustion Field and Quantitative
Inversion Study of Its Characteristic Product Concentrations[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3690-3699. |
[9] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
[10] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
[11] |
HAO Zi-yuan1, YANG Wei1*, LI Hao1, YU Hao1, LI Min-zan1, 2. Study on Prediction Models for Leaf Area Index of Multiple Crops Based on Multi-Source Information and Deep Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3862-3870. |
[12] |
YANG Wen-feng1, LIN De-hui1, CAO Yu2, QIAN Zi-ran1, LI Shao-long1, ZHU De-hua2, LI Guo1, ZHANG Sai1. Study on LIBS Online Monitoring of Aircraft Skin Laser Layered Paint Removal Based on PCA-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3891-3898. |
[13] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
[14] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
[15] |
DANG Rui, GAO Zi-ang, ZHANG Tong, WANG Jia-xing. Lighting Damage Model of Silk Cultural Relics in Museum Collections Based on Infrared Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3930-3936. |
|
|
|
|