光谱学与光谱分析 |
|
|
|
|
|
Using Hyperspectral Remote Sensing to Estimate Canopy Chlorophyll Density of Wheat under Yellow Rust Stress |
JIANG Jin-bao1, CHEN Yun-hao2, HUANG Wen-jiang3 |
1. College of Geoscince and Surveying Engineering, China Univeristy of Mine and Technology(Beijing), Beijing 100083, China 2. College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China 3. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China |
|
|
Abstract The canopy reflectance of winter wheat infected with different severity yellow rust was collected in the fields and canopy chlorophyll density (CCD) of the whole wheat was measured in the laboratory. The correlation was analyzed between hyperspectral indices and CCDs, the indices with relationship coefficients more than 0.7 were selected to build the inversion models, and comparing the predicted results and measured results to test the models, the results showed the first derivative index (D750-D550)/(D750+D550) has higher prediction precision than other indices, while the next is first derivative index (D725-D702)/(D725+D702). Saturation analysis was performed for the above indices, the result indicated that when CCD was more than 12 μg·cm-2, the first derivative index (D750-D550)/(D750+D550) was easiest to get to saturation level. Therefore, when CCD was less than 12 μg·cm-2, the first derivative index (D750-D550)/(D750+D550) could be used to estimate wheat CCD and had higher prediction precision than other indices; and when CCD was more than 12 μg·cm-2, the first derivative index (D725-D702)/(D725+D702) was not easiest to reach saturation level and could be used to estimate wheat CCD. There is a significant minus correlation between CCD and disease index (DI), moreover, accurate estimation of CCD by using hyperspectral remote sensing not only can monitor wheat growth, but also can provide assistant information for identification of wheat disease. Therefore, this study has important meaning for prevention and reduction of disaster in agricultural field.
|
Received: 2009-11-13
Accepted: 2010-02-28
|
|
Corresponding Authors:
JIANG Jin-bao
E-mail: jjb@ires.cn,ahdsjjb@126.com
|
|
[1] Minolta Co. Ltd. Radiometric Instruments Operations,1989. 17. [2] Pinar A. International Journal of Remote Sensing, 1996, 17(2): 351. [3] WU Chang-shan, XIANG Yue-qin, ZHENG Lan-fen, et al(吴长山, 项月琴, 郑兰芬, 等). Journal of Remote Sensing(遥感学报), 2000, 4(3): 228. [4] HUANG Chun-yan, WANG Deng-wei, YAN Jie, et al(黄春燕, 王登伟, 闫 洁,等). Acta Agronomica Sinica(作物学报), 2007, 33(6): 931. [5] WANG Deng-wei, HUANG Chun-yan, ZHANG Wei, et al(王登伟, 黄春燕, 张 伟, 等). Cotton Science(棉花学报), 2008, 20(5): 368. [6] Hansen P M, Schjoerring J K. Remote Sensing of Environment, 2003, 86(4): 542. [7] JIANG Jin-bao, CHEN Yun-hao, HUANG Wen-jiang(蒋金豹, 陈云浩, 黄文江). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(7): 1363. [8] WANG Ji-hua, ZHAO Chun-jiang, HUANG Wen-jiang(王纪华,赵春江,黄文江). Basis and Application of Quantitative Remote Sensing in Agriculture(农业定量遥感基础与应用). Beijing: Science Press(北京:科学出版社),2008. [9] Smith K L, Steven M D, Colls J J. Remote Sensing of Environment, 2004, 92: 207. [10] Rouse J W, Haas R H, Schell J A, et al. In: NASA/GSFC Final Report, NASA, Greenbelt, MD, USA, 1974. 1. [11] Penuelas J, Baret F, Filella I. Photosynthetica, 1995, 31:221. [12] Gamon J A,Penuelas J,Field C B. Remote Sensing of Environment, 1992, 41(1): 35. [13] Penuelas J, Filella I, Lloret P, et al. International Journal of Remote Sensing, 1995, 16, 2727. [14] Gitelson A A, Kaufman Y J, Merzlyak M N. Remote Sensing of Environment,1996, 58: 289. [15] Haboudane D, Miller J R, Tremblay N, et al. Remote Sensing of Environment, 2002, 81:416. [16] Rondeaux G, Steven M, Baret F. Remote Sensing of Environment, 1996, 55:95. [17] Zarco-Tejada P J, Miller J R, Mohammed G H, et al. Journal Environment Quality,2002, 31: 1433. [18] Zarco-Tejada P J, Miller J R, Morales A, et al. Remote Sensing of Environment,2004, 90: 463.
|
[1] |
ZHANG Jie1, 2, XU Bo1, FENG Hai-kuan1, JING Xia2, WANG Jiao-jiao1, MING Shi-kang1, FU You-qiang3, SONG Xiao-yu1*. Monitoring Nitrogen Nutrition and Grain Protein Content of Rice Based on Ensemble Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1956-1964. |
[2] |
JING Xia1, ZHANG Jie1, 2, WANG Jiao-jiao2, MING Shi-kang2, FU You-qiang3, FENG Hai-kuan2, SONG Xiao-yu2*. Comparison of Machine Learning Algorithms for Remote Sensing
Monitoring of Rice Yields[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1620-1627. |
[3] |
FU Yan-hua1, LIU Jing2*, MAO Ya-chun2, CAO Wang2, HUANG Jia-qi2, ZHAO Zhan-guo3. Experimental Study on Quantitative Inversion Model of Heavy Metals in Soda Saline-Alkali Soil Based on RBF Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1595-1600. |
[4] |
YANG En1, WANG Shi-bo2*. Study on Directional Near-Infrared Reflectance Spectra of Typical Types of Coal[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 847-858. |
[5] |
DUAN Wei-na1, 2, JING Xia1*, LIU Liang-yun2, ZHANG Teng1, ZHANG Li-hua3. Monitoring of Wheat Stripe Rust Based on Integration of SIF and Reflectance Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 859-865. |
[6] |
KONG Yu-ru1, 2, WANG Li-juan1*, FENG Hai-kuan2, XU Yi1, LIANG Liang1, XU Lu1, YANG Xiao-dong2*, ZHANG Qing-qi1. Leaf Area Index Estimation Based on UAV Hyperspectral Band Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 933-939. |
[7] |
ZHANG Xiao-yan, HOU Xue-hui, WANG Meng, WANG Li-li*, LIU Feng*. Study on Relationship Between Photosynthetic Rate and Hyperspectral Indexes of Wheat Under Stripe Rust Stress[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 940-946. |
[8] |
LU Qi-peng1, WANG Dong-min2*, SONG Yuan1*, DING Hai-quan3, GAO Hong-zhi3. Effect of Wavelength Drift on PLSR Calibration Model of Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 405-409. |
[9] |
JIANG Jie1, YU Quan-zhou1, 2, 3*, LIANG Tian-quan1, 2, TANG Qing-xin1, 2, 3, ZHANG Ying-hao1, 3, ZHANG Huai-zhen1, 2, 3. Analysis of Spectral Characteristics of Different Wetland Landscapes Based on EO-1 Hyperion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 524-529. |
[10] |
MAO Ya-chun1, WEN Jian1*, FU Yan-hua2, CAO Wang1, ZHAO Zhan-guo3, DING Rui-bo1. Quantitative Inversion Model Based on the Visible and Near-Infrared Spectrum for Skarn-Type Iron Ore[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 68-73. |
[11] |
DU Meng-meng1, Ali Roshanianfard2, LIU Ying-chao3. Inversion of Wheat Tiller Density Based on Visible-Band Images of Drone[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3828-3836. |
[12] |
LUO De-fang1, PENG Jie1*, FENG Chun-hui1, LIU Wei-yang1, JI Wen-jun2, WANG Nan3. Inversion of Soil Organic Matter Fraction in Southern Xinjiang by Visible-Near-Infrared and Mid-Infrared Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3069-3076. |
[13] |
XIA Tian1*, YANG Ke-ming2, FENG Fei-sheng3, GUO Hui4, ZHANG Chao2. A New Copper Stress Vegetation Index NCSVI Explores the Sensitive Range of Corn Leaves Spectral Under Copper Pollution[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2604-2610. |
[14] |
GONG Yue-hong1, YANG Tie-jun2*, LIANG Yi-tao1, 3, GE Hong-yi1, 3. Fast and Non-Destructive Determination on Fresh Degree of Wheat Kernels Based on Biophotons[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 2166-2170. |
[15] |
YU Xin-hua1, ZHAO Wei-qing2*, ZHU Zai-chun2, XU Bao-dong3, ZHAO Zhi-zhan4. Research in Crop Yield Estimation Models on Different Scales Based on Remote Sensing and Crop Growth Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 2205-2211. |
|
|
|
|