光谱学与光谱分析 |
|
|
|
|
|
Monitoring Canopy Nitrogen Status in Winter Wheat of Growth Anaphase with Hyperspectral Remote Sensing |
TANG Qiang1,2,LI Shao-kun1,2,WANG Ke-ru1,2,XIE Rui-zhi2,CHEN Bing1,2,WANG Fang-yong1,2,DIAO Wan-ying1,XIAO Chun-hua1,2* |
1. Shihezi University,Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops, Shihezi 832003, China 2. Chinese Academy of Agricultural Sciences,Key Laboratory of Crop Physiology and Production Ministry of Agriculture, Beijing 100081, China |
|
|
Abstract Biomass, leaf area index (LAI) and nitrogen status are important parameters for indicating crop growth potential and photosynthetic productivity in wheat. Nondestructive, quick assessment of leaf dry weight, LAI and nitrogen content is necessary for nitrogen nutrition diagnosis and cultural regulation in wheat production. In order to establish the monitoring model of nitrogen richness in winter wheat of growth anaphase, studying the relationship between the nitrogen richness (NR) containing nitrogen density, LAI and leaf dry weight and the difference of hyperspectral reflectance rates (ΔR), we conducted a comparable experiment with five winter wheat varieties under nitrogen application level of 0, 100, 200 and 400 kg·N·ha-1. The results indicated the NRs of the different varieties of winter wheat leaves increased with increasing growth stage while in the different nitrogen levels it was sequenced as: N0>N3>N1>N2. Twelve vegetation indices were compared with corresponding NR. The NR had significantly negative correlation to TCARI and VD672 in those vegetation indices, and their correlations (r) arrived at 0.870 and 0.855, respectively. The coefficients of determination (R2) of two models were 0.757 and 0.731 by erecting model with the two indexes and NR. Root mean square error (RMSE), relative error (RE) and determination coefficient between measured and estimated NR were employed to test the model reliability and predicting accuracy. Accuracy rates of the models based on TCARI and VD672 achieved 84.56% and 80.13%. The overall results suggested that leaf nitrogen status of growth anaphase in winter wheat has stable relationships with some vegetation indexes, especially index of TCARI and VD672.
|
Received: 2009-12-19
Accepted: 2010-03-22
|
|
Corresponding Authors:
XIAO Chun-hua
E-mail: xiaochunhuaxj@163.com
|
|
[1] Scheumann V, Schoch S, Rüdiger W. Planta, 1999, 209(3): 364. [2] Lu C, Lu Q, Zhang J, et al. Journal Experim. Bot., 2001, 52(362): 1805. [3] Pefiuelas J, Filella I. Trends in Plant Science, 1998, 3(4): 151. [4] HUANG Wen-jiang, ZHAO Chun-jiang,WANG Ji-hua, et al(黄文江, 赵春江, 王纪华, 等). Transaction of the Chinese Society of Agricultural Engineering(农业工程学报), 2004, 20(6): 1. [5] ZHANG Liang-pei, ZHENG Lan-fen, TONG Qing-xi(张良培, 郑兰芬, 童庆禧). Journal of Remote Sensing(遥感学报), 1997, 1(2): 111. [6] LIU Wei-dong, XIANG Yue-qin, ZHENG Lan-fen, et al(刘伟东, 项月琴, 郑兰芬, 等). Journal of Remote Sensing (遥感学报), 2000, 4(4): 279. [7] WANG Xiu-zhen, HUANG Jing-feng, LI Yun-mei, et al(王秀珍, 黄敬峰, 李云梅, 等), Acta Agronomica Sinica(作物学报), 2003, 29(6): 815. [8] Serrano L, Filella I, Penuelas J. Crop Science, 2000, 40(3): 723. [9] Vaesen K, Gilliams S, Nackaerts K, et al. Field Crops Research, 2001, 69(1): 13. [10] Jensen R R, Binford M W. International Journal of Remote Sensing, 2004, 25(20): 4251. [11] Lu D S. International Journal of Remote Sensing, 2006, 27(7): 297. [12] ZHAO Du-li, Raja Reddy K, Gopal Kakani V, et al. European Journal of Agronomy, 2005, 22: 391. [13] Wright D L, Rasmussen V P, Ramsey R D. GIScience and Remote Sensing, 2004, 41(4): 287. [14] Peason R L, Miller D L. Proceedings of the Eighth International Symosium on Remote Sensing of Environment, 1972, 2: 1357. [15] Gamon J A, Penuelas J, Field C B. Remote Sens Environ., 1992, 41(1): 35. [16] Penuelas J, Baret F, Filella I. Photosynthetica, 1995, 31: 221. [17] Lyon J G, Yuan D, Lunetta R S. Photogrammetric Engineering and Remote Sensing, 1998, 64: 143. [18] Blackburn G A. International Journal of Remote Sensing, 1998, 19: 657. [19] Gitelson A A, Merzlyak M N . Journal of Plant Physiology, 1994, 143: 286. [20] Rondeaux G Steven,Baret M F. Remote Sensing of Environment, 1996, 55: 95. [21] Baret F, Guyot G, Major D J. Geoscience and Remote Sensing Society of Institute of Electrical and Electronics Enginess, 1989, 3: 1355. [22] Zarco-Tejada P J, Miller J R, Mohammed G H, et al. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39: 1491. [23] Rouse J W, Haas R H, Schell J A, et al. Third Earth Resources Technology Satellite Symposivm. Technical Presentations, Section A, 1973, (Ⅰ): 309. [24] ZHAO Quan-zhi, DING Yan-feng, WANG Qiang-sheng, et al(赵全志, 丁艳锋, 王强盛, 等). Scientia Agricultura Sinica(中国农业科学), 2006, 39(5): 916. [25] DUAN Jun, LIANG Cheng-ye, HUANG Yu-wen(段 俊, 梁承邺, 黄毓文). Acta Phytophysiologica Sinica(植物生理学报), 1997, 23(2): 139. [26] FENG Wei, ZHU Yan, YAO Xia, et al(冯 伟, 朱 艳, 姚 霞, 等). Chinese Journal of Plant Ecology(植物生态学报), 2009, 33(1): 34. [27] Rondeaux G, Steven M, Baret F. Remote Sensing of Environment, 1996, 55: 95. [28] Daughtry C S T, Walthall C L, Kin M S, et al. Remote Sensing of Environment, 2000, 74: 229. [29] LI Ying-xue, ZHU Yan, TIAN Yong-chao, et al(李映雪, 朱 艳, 田永超, 等). Scientia Agricultura Sinica(中国农业科学), 2005, 38(7): 1332. |
[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] |
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. |
[4] |
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. |
[5] |
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. |
[6] |
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. |
[7] |
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. |
[8] |
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. |
[9] |
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. |
[10] |
LI Si-yuan, JIAO Jian-nan, WANG Chi*. Specular Reflection Removal Method Based on Polarization Spectrum
Fusion and Its Application in Vegetation Health Monitoring[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3607-3614. |
[11] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[12] |
YAN Xing-guang, LI Jing*, YAN Xiao-xiao, MA Tian-yue, SU Yi-ting, SHAO Jia-hao, ZHANG Rui. A Rapid Method for Stripe Chromatic Aberration Correction in
Landsat Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3483-3491. |
[13] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
[14] |
SUN Lin1, BI Wei-hong1, LIU Tong1, WU Jia-qing1, ZHANG Bao-jun1, FU Guang-wei1, JIN Wa1, WANG Bing2, FU Xing-hu1*. Identification Algorithm of Green Algae Using Airborne Hyperspectral and Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3637-3643. |
[15] |
LIU Bo-yang1, GAO An-ping1*, YANG Jian1, GAO Yong-liang1, BAI Peng1, Teri-gele1, MA Li-jun1, ZHAO San-jun1, LI Xue-jing1, ZHANG Hui-ping1, KANG Jun-wei1, LI Hui1, WANG Hui1, YANG Si2, LI Chen-xi2, LIU Rong2. Research on Non-Targeted Abnormal Milk Identification Method Based on Mid-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3009-3014. |
|
|
|
|