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REMOTE SENSING FOR LAND & RESOURCES    2012, Vol. 24 Issue (3) : 54-59     DOI: 10.6046/gtzyyg.2012.03.11
Technology and Methodology |
A Method of TM6 Band Pixel Decomposition Based on the Earth Surface Types
WANG Fei1, QIN Zhi-hao1,2, WANG Qian-qian1
1. International Institute for Earth System Science, Nanjing University, Nanjing 210093, China;
2. Institute of Agro-Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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Abstract  The pixel spatial resolution of TM6 is 120 m. With the information of the other TM bands,the authors can identify the earth surface types for each 30 m sub-pixel. And taking advantage of the fact that different types of earth surface have different characteristics of thermal inertia, the authors can determine the weight of each sub-pixel in the original pixel as well as the radiance value of the sub-pixel. Then the mono-window algorithm is used to calculate the temperature of the earth surface. Compared with the spatial distribution variation of surface temperature calculated by cubic convolution interpolation resampling method,the method proposed in this paper can get a better spatial distribution of the earth surface temperature.
Keywords remote sensing      GIS      Laoha river basin      land use change      classification     
:  TP751.1  
Issue Date: 20 August 2012
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FANG Xiu-qin,REN Li-liang,LI Qiong-fang. A Method of TM6 Band Pixel Decomposition Based on the Earth Surface Types[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(3): 54-59.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2012.03.11     OR     https://www.gtzyyg.com/EN/Y2012/V24/I3/54
[1] Qin Z H,Karnieli A,Berliner P.A Mono-window Algorithm for Retrieving Land Surface Temperature from Landsat TM Data and Its Application to the Israel—egypt Border Region[J].International Journal of Remote Sensing,2001,22(18):3719-3746.
[2] 吴骅,李彤.TM热红外波段等效比辐射率估算[J].遥感信息,2006(3):26-28. Wu H,Li T.The Estimation of Land Surface Emissivity for Landsat TM Thermal Infrared Band[J].Remote Sensing Information,2006(3):26-28(in Chinese with English Abstract).
[3] 毛克彪,覃志豪.大气辐射传输模型及MODTRAN中透过率计算[J].测绘与空间地理信息,2004,27(4):1-3. Mao K B,Qin Z H.The Transmission Model of Atmospheric Radiation and the Computation of Transmittance of MODTRAN[J].Geomatics and Spatial Information Technology,2004,27(4):1-3(in Chinese with English Abstract).
[4] 徐希孺,王平荣.用蒙特―卡罗方法计算大气点扩散函数[J].遥感学报,1999,3(4):268-278. Xu X R,Wang P R.Computing Atmospheric Point Spread Function by Monte-carlo Method[J].Journal of Remote Sensing,1999,3(4):268-278(in Chinese with English Abstract).
[5] 杨贵军,柳钦火,刘强,等.中红外大气辐射传输解析模型及遥感成像模拟[J].光谱学与光谱分析,2009,29(3):629-634. Yang G J,Liu Q H,Liu Q,et al.Mid-infrared Atmosphere Radiation Transfer Analytic Model and Remote Sensing Images Simulation[J].Spectroscopy and Spectral Analysis,2009,29(3):629-634(in Chinese with English Abstract).
[6] 陈云浩,冯通,史培军,等.基于面向对象和规则的遥感影像分类研究[J].武汉大学学报:信息科学版,2006(4):316-320. Chen Y H,Feng T,Shi P J,et al.Classification of Remote Sensing Image Based on Object Oriented and Class Rules[J].Geomatics and Information Science of Wuhan University,2006(4):316-320(in Chinese with English Abstract).
[7] 梅安新,彭望琭,秦其明,等.遥感导论[M].北京:高等教育出版社,2001:109-111. Mei A X,Peng W L,Qin Q M,et al.Introduction to Remote Sensing[M].Beijing:Higher Education Press,2001:109-111(in Chinese).
[8] 彭正林,毛先成,刘文毅,等.基于多分类器组合的遥感影像分类方法研究[J].国土资源遥感,2011(2):19-25. Peng Z L,Mao X C,Liu W Y,et al.Method for Classification of Remote Sensing Images Based on Multiple Classifiers Combination[J].Remote Sensing for Land and Resources,2011(2):19-25(in Chinese with English Abstract).
[9] 刘振华,赵英时.一种改进的遥感热惯量模型初探[J].中国科学院研究生院学报,2005,22(3):380-385. Liu Z H,Zhao Y S.An Improved Thermal Inertia Model[J].Journal of the Graduate School of the Chinese Academy of Sciences,2005,22(3):380-385(in Chinese with English Abstract).
[10] 陈玉荣.城市下垫面热特性与城市热岛关系研究[D].北京:北京建筑工程学院,2008:42. Chen Y R.Research on the Relationship Between the Thermal Characteristic of Underlying Surface and Urban Heat Island[D].Beijing:Beijing University of Civil Engineering and Architecture,2008:42(in Chinese with English Abstract).
[11] Sospedra F,Caselles V,Valor E.Effective Wavenumber for Thermal Infrared Bands-application to Landsat-TM[J].International Journal of Remote Sensing,1998,19(11):2105-2117.
[12] 覃志豪,Zhang M H,Karnieli A,等.用陆地卫星TM6数据演算地表温度的单窗算法[J].地理学报,2001,56(4):456-466. Qin Z H,Zhang M H,Karnieli A,et al.Mono-window Algorithm for Retribing Land Surface Temperature from Landsat TM6 data[J].Acta Geographica Sinica,2001,56(4):456-466(in Chinese with English Abstract).
[13] 覃志豪,Li W J,Zhang M H,等.单窗算法的大气参数估计方法[J].国土资源遥感,2003(2):37-43. Qin Z H,Li W J,Zhang M H,et al.Estimating of the Essential Atmospheric Parameters of Mono-window Algorithm for Land Surface Temperature Retrieval from Landsat TM6[J].Remote Sensing for Land and Resources,2003(2):37-43(in Chinese with English Abstract).
[14] 覃志豪,李文娟,徐斌,等.陆地卫星TM6波段范围内地表比辐射率的估计[J].国土资源遥感,2004(3):28-32. Qin Z H,Li W J,Xu B,et al.The Estimation of Land Surface Emissivity for Landsat TM6[J].Remote Sensing for Land and Resources,2004(3):28-32(in Chinese with English Abstract).
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