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.
Key words：Hyperspectral remote sensing;Wheat;Yellow rust stress;Canopy chlorophyll density;Saturation analysis;Inversion model
[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.
