基于混合像元分解理论的路域植被等量水厚度遥感反演

doi:10.13474/j.cnki.11-2246.2019.0391

测绘通报 ›› 2019, Vol. 0 ›› Issue (12): 83-86,95.doi: 10.13474/j.cnki.11-2246.2019.0391

基于混合像元分解理论的路域植被等量水厚度遥感反演

肖祥红¹, 刘海洋¹, 郭云开²

1. 湖南省第二测绘院, 湖南长沙 410119;
2. 长沙理工大学交通运输工程学院, 湖南长沙 410076

收稿日期:2019-08-28 发布日期:2020-01-03
作者简介:肖祥红(1971-),男,高级工程师,主要从事测绘地理信息技术和管理工作。E-mail:2278571975@qq.com
基金资助:
国家自然科学基金面上项目（41671498；41471421）

Remote sensing retrieval of vegetation equivalent water thickness based on mixed pixel decomposition theory

XIAO Xianghong¹, LIU Haiyang¹, GUO Yunkai²

1. The Second Survey and Mapping Institute of Hunan Province, Changsha 410119, China;
2. School of Transportation Engineering, Changsha University of Science&Technology, Changsha 410076, China

Received:2019-08-28 Published:2020-01-03

摘要/Abstract

摘要： 为削弱混合像元对植被参数反演的影响，提出了基于混合像元分解理论反演路域植被等量水厚度的方法。利用PRO4SAIL模型正演获得的高光谱窄波段数据，模拟Landsat 8遥感影像宽波段植被冠层光谱数据，并进行等量水厚度的敏感植被指数的筛选；对覆盖研究区域的Landsat 8遥感影像进行线性混合像元分解，获取更加精确的植被冠层光谱反射率；同时，利用支持向量机构建等量水厚度估测模型，实现对路域植被等量水厚度的遥感反演。研究结果表明，利用混合像元分解后得到的植被冠层光谱参与模型反演得到的路域植被等量水厚度更加符合实际情况，为遥感影像反演植被参数提供了有效数据。

关键词: 路域植被, 等量水厚度, 混合像元分解, 植被参数反演, Landsat 8

Abstract: In order to reduce the influence of mixed pixels on the inversion of vegetation parameters, an inversion of the equivalent water thickness of road vegetation is proposed based on the mixed pixel decomposition theory.In this paper, we use the hyperspectral narrow-band data obtained by PRO4SAIL model to simulate the broad-band vegetation canopy spectral data of Landsat 8 remote sensing image, and screen the sensitive vegetation index of equivalent water thickness.In addition, linear mixed pixel decomposition of Landsat 8 remote sensing images covering the study area is performed to obtain more accurate spectral reflectance of vegetation canopy.At the same time, the support vector machine is used to construct the equivalent water thickness estimation model to realize the remote sensing inversion of the equal water thickness of the vegetation in the road area.The results show that the equal water thickness of the vegetation in the vegetation inversion obtained by the decomposition of the vegetation canopy obtained by the mixed pixel is more in line with the actual situation, and provides effective data for the inversion of vegetation parameters by remote sensing images.

Key words: road vegetation, equivalent water thickness, mixed pixel decomposition, vegetation parameter inversion, Landsat 8

中图分类号:

P237

肖祥红, 刘海洋, 郭云开. 基于混合像元分解理论的路域植被等量水厚度遥感反演[J]. 测绘通报, 2019, 0(12): 83-86,95.

XIAO Xianghong, LIU Haiyang, GUO Yunkai. Remote sensing retrieval of vegetation equivalent water thickness based on mixed pixel decomposition theory[J]. Bulletin of Surveying and Mapping, 2019, 0(12): 83-86,95.

参考文献

[1] PEÑUELAS J, FILELLA I, BIEL C, et al. The reflectance at the 950-970 nm region as an indicator of plant water status[J]. International Journal of Remote Sensing,1993,14(10):1887-1905.
[2] MIRZAIE M, DARVISHZADEH R, SHAKIBA A, et al. Comparative analysis of different uni-and multi-variate methods for estimation of vegetation water content using hyper-spectral measurements[J]. International Journal of Applied Earth Observation and Geoinformation, 2014, 26(1):1-11.
[3] CHUVIECO E, RIANO D, AGUADO I, et al. Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data:applications in fire danger assessment[J]. International Journal of Remote Sensing, 2002, 23(11):2145-2162.
[4] 张苏. 冠层和叶片尺度植被参数的高光谱遥感反演研究[D].北京:中国科学院遥感与数字地球研究所,2017.
[5] 刘畅,孙鹏森,刘世荣.植物反射光谱对水分生理变化响应的研究进展[J].植物生态学报,2016,40(1):80-91.
[6] GAO B C. NDWI:a normalized difference water index for remote sensing of vegetation liquid water from space[J]. Remote Sensing of Environment,1996,58(3):257-266.
[7] HUNT E R, ROCK B N. Detection of changes in leaf water content using near-and middle-infrared reflectances[J]. Remote Sensing of Environment, 1989, 30(1):43-54.
[8] 张峰,周广胜.植被含水量高光谱遥感监测研究进展[J].植物生态学报, 2018,42(5):517-525.
[9] YI Q, WANG F, BAO A, et al. Leaf and canopy water content estimation in cotton using hyperspectral indices and radiative transfer models[J]. International Journal of Applied Earth Observation and Geoinformation, 2014, 33(1):67-75.
[10] 李丹娜,郭云开,朱善宽,等. 利用PRO4SAIL与支持向量机回归的组合模型反演植被等量水厚度[J]. 测绘通报, 2017(8):54-59.
[11] 李苗苗,吴炳方,颜长珍,等.密云水库上游植被覆盖度的遥感估算[J]. 资源科学, 2004,26(4):153-159.
[12] 沙莎,胡蝶,王丽娟,等. 基于Landsat 8 OLI数据的黄土高原植被含水量的估算模型研究[J]. 遥感技术与应用, 2016,31(3):558-563.
[13] 郭云开,冯超.路域植被覆盖度非密度模型方法探讨[J].测绘科学,2016,41(1):103-108.
[14] 赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003.

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基于混合像元分解理论的路域植被等量水厚度遥感反演

Remote sensing retrieval of vegetation equivalent water thickness based on mixed pixel decomposition theory

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