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作物植被覆盖度的高光谱遥感估算模型
引用本文:朱蕾,徐俊锋,黄敬峰,王福民,刘占宇,王渊.作物植被覆盖度的高光谱遥感估算模型[J].光谱学与光谱分析,2008,28(8):1827-1831.
作者姓名:朱蕾  徐俊锋  黄敬峰  王福民  刘占宇  王渊
作者单位:1.浙江大学农业遥感与信息技术应用研究所, 浙江 杭州 310029
2.环境修复与生态健康教育部重点实验室,浙江大学环境与资源学院, 浙江 杭州 310029
3.浙江省农业遥感与信息技术重点研究实验室,浙江 杭州 310029
基金项目:国家自然科学基金,国家科技支撑计划,国家高技术研究发展计划(863计划)
摘    要:通过大田试验,使用ASD光谱仪测量了油菜、玉米、水稻三种作物不同覆盖度水平下的冠层光谱,同时拍照获取植被图片并用计算机求算了植被覆盖度。利用三种作物光谱求算“红边”变量,并对波段两两组合求算归一化植被指数(NDVI),建立这些光谱变量与覆盖度之间的估算模型,得到适用于三种作物的最优估算模型和最佳的NDVI波段组合。另外,利用响应函数模拟了TM归一化植被指数,同植被覆盖度进行了相关分析,回归方程的R2达到0.80,并通过了预留数据的检验,为TM数据植被覆盖度估算进行了探索性的研究。

关 键 词:作物  植被覆盖度  归一化植被指数  “红边”变量  高光谱估算模型  
收稿时间:2007-03-28

Study on Hyperspectral Estimation Model of Crop Vegetation Cover Percentage
ZHU Lei,XU Jun-feng,HUANG Jing-feng,WANG Fu-min,LIU Zhan-yu,WANG Yuan.Study on Hyperspectral Estimation Model of Crop Vegetation Cover Percentage[J].Spectroscopy and Spectral Analysis,2008,28(8):1827-1831.
Authors:ZHU Lei  XU Jun-feng  HUANG Jing-feng  WANG Fu-min  LIU Zhan-yu  WANG Yuan
Institution:1.Institute of Agriculture Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310029, China 2.Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health, College of Natural Resources and Environmental Science, Zhejiang University, Hangzhou 310029, China 3.Key Laboratory of Agricultural Remote Sensing & Information System, Zhejiang University, Hangzhou 310029, China
Abstract:In order to boost the study and application of hyperspectral remote sensing for the estimation of crop vegetation coverage percentage,an ASD FieldSpec Pro FRTM spectroradiometer was used for canopy spectral measurements of rape,corn and rice at different vegetation cover levels and photos of individual plants were taken simultaneously in order to calculate the vegetation cover percentage in computer.Firstly,data of three crops respectively and the mixed data of them were used to make correlation analysis between vegetation coverage percentage and reflectance spectra.There was a high correlation between them and no obvious difference in correlation coefficient among different types of crop in the region of blue,red and near-infrared band.This indicated that it was feasible to make correlation analysis and build estimation model using mixed data.Secondly,mixed data were used as unique analytical data to calculate red edge variables and pair combination of bands in the region of blue,red and near-infrared band was used to calculate normal difference vegetation index(NDVI).Hyperspectral estimation models with NDVI and red edge variable as independent variable were built individually.The correlation coefficient of the former was larger than the latter,which indicated that NDVI was most effective for the estimation of vegetation coverage percentage.Effective wavelength combinations of NDVI for vegetation cover percentage estimation were determined based on the principle of higher correlation coefficient.NDVI combined with bands in the regions from 350 to 590 nm and from 710 to 1 150 nm or bands in the regions from 590 to 710 nm and from 710 to 1 300 nm are most effective for vegetation coverage percentage estimation.The best estimation model is simple quadratic equation using NDVI696-921 as independent variable.The correlation coefficient matrix shows that most of the correlation coefficients of vegetation coverage percentage and NDVI combined with bands in the regions from 630 to 690 nm and from 760 to 900 nm are larger than 0.8.These two band regions correspond to TM3 and TM4 of landsat 4,5,7.It proves that NDVITM3-TM4 can be used to and has been used to simulate vegetation coverage percentage.In order to further the study,TM3 and TM4 of Landsat5 was modeled according to spectral response function to calculate NDVI.Correlation analysis was made with NDVI and corresponding vegetation coverage percentage.The correlation coefficient of them was 0.80 and the regression equation was verified by experimental data.This is exploratory research for the calculation of vegetation coverage percentage using TM data in large area.
Keywords:Crop  Vegetation cover percentage  Normalized difference vegetation index  Red edge variable  Hyperspectral estimation model
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