遥感技术与应用

地表温度的多源遥感数据反演算法对比分析

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  • 1. 中南大学地球科学与信息物理学院, 中南大学空间信息技术与可持续发展研究中心, 长沙 410083;
    2. 衡阳师范学院资源环境与旅游管理系, 衡阳 421008
郑文武(1978-),男,湖南常德人,博士研究生,从事遥感与地理信息系统应用研究.E-mail:zhwenwu@163.com

收稿日期: 2011-08-29

  修回日期: 2011-11-01

  网络出版日期: 2011-12-25

基金资助

国家自然科学基金项目(41171326,40771198); 湖南省自然科学基金项目(08JJ6023)资助.

Comparative Analysis of Two Land Surface Temperature Retrieval Algorithms Based on Multi-source Remote Sensing Data

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  • 1. Department of Surveying and Land Information Engineering, Central South University, Changsha 410083, China;
    2. Department of Resource Envioronment and Tourism Management, Hengyang 421008, China

Received date: 2011-08-29

  Revised date: 2011-11-01

  Online published: 2011-12-25

摘要

多源遥感数据的综合应用是提高地表温度反演精度的有效途径.MODIS数据和Landsat TM数据在我国同一地区获取的时间相差不大,可以获取近似同步的MODIS数据和TM数据.本文将基于MODIS数据反演的大气参数应用于TM影像的地表温度反演,分别对单窗口算法和普适性单通道算法进行了实验研究,应用气象站实测的地表温度数据对反演结果进行了检验,并对比分析了不同土地覆盖条件下两种算法的精度差异.结果表明:两种算法反演精度均较高,单窗口算法反演精度为0.76K,普适性单通道算法反演精度为1.23K;在不同的土地覆盖条件下,两种算法表现出明显的差异性,水体区反演结果差异最小,均值差异仅为0.02K,植被区差异最大,均值差异为0.62K.

关键词: 地表温度; MODIS; TM; 多源遥感

本文引用格式

郑文武, 曾永年 . 地表温度的多源遥感数据反演算法对比分析[J]. 地球信息科学学报, 2011 , 13(6) : 840 -847 . DOI: 10.3724/SP.J.1047.2011.00840

Abstract

Integrated application of multi-source remote sensing image is one of the best ways to improve the accuracy of land surface temperature (LST) retrieval. The difference between satellite overpass time of MODIS Terra and Landsat is small, so it is easy to get synchronized MODIS and Landsat TM images. The most challenging work of LST retrieval from Landsat TM images is acquisition of atmospheric profile parameters, and MODIS image has several near infrared bands that can be used to estimate atmospheric profile. In order to evaluate the effectiveness of atmospheric parameters estimated from MODIS image being used to LST retrieval algorithm of Landsat TM images, some experimental studies about Qins mono-window algorithm and Jimenez-Munozs generalized single-channel algorithm have been conducted in this paper, and the retrieval LST results of the two algorithm were validated using the observed data from weather stations. Finally, the differences in accuracy of the two algorithms from different land cover types were analyzed. The results show that (1)two LST retrieval algorithms can get high-precision result in support of atmospheric parameters from MODIS images, the average deviation of mono-window algorithm is 0.76K, and the deviation of generalized single-channel algorithm is 1.23k; (2) the differences in accuracy of the two algorithms from different land cover types are obvious, the difference of water area is smallest, the difference of mean is 0.02K, and the difference of vegetation area is largest, the difference of mean is 0.62K, and the LST results of mono-window algorithm are smaller than the LST results of generalized single-channel algorithm.

参考文献

[1] Dash P, Gottsche F-M, Olesen F-S, Fischer H. Land Surface Temperature and Emissivity Estimation from Passive Sensor Data: Theory and Practice—Current Trends[J]. International Journal of Remote Sensing, 2002, 23, 2563-2594.

[2] Raynolds M K, Comiso J C, Walker D A, et al. Relationship between Satellite-derived Land Surface Temperatures, Arctic Vegetation Types, and NDVI[J]. Remote Sensing of Environment, 2008,112,19:1884-1894.

[3] Pipunic R C, Walker J P, Western A. Assimilation of Remotely Sensed Data for Improved Latent and Sensible Heat Flux Prediction: A Comparative Synthetic Study[J]. Remote Sensing of Environment.2008,112:1295-1305.

[4] Qin Z, 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, 22:3719-3746.

[5] Jimenez-Munoz J C, Sobrino J A. A Generalized Single-channel Method for Retrieving Land Surface Temperature from Remote Sensing Data[J], Journal of Geophysics Research, 2003,108:4688-4697.

[6] Becker F, Li Z L. Toward a Local Split Window Method over Land Surface[J]. International Journal of Remote Sensing, 1990, 11(3):369-393.

[7] Li Z L, Becker F. Feasibility of Land Surface Temperature and Emissivity Determination from AVHRR Data[J]. Remote Sensing of Environment, 1993, 43: 67-85.

[8] 庄家礼,陈良富,徐希孺.用遗传算法反演连续植被的组分温度[J].遥感学报, 2001, 5(1): 1-6.

[9] 历华,曾永年,贠培东,等. 利用多源遥感数据反演城市地表温度[J].遥感学报,2007,11(6):891-897.

[10] 赵强,杨世植,乔延利,等. 利用MODIS红外资料反演大气参数以及表层温度的研究[J].武汉大学学报·信息科学版,2009,34(4):400-403.

[11] 覃志豪,Li W J, Zhang M H,等.单窗算法的基本大气参数估计方法[J].国土资源遥感, 2003,56(2): 37-73.

[12] 郑文武,曾永年,田亚平. 基于混合像元分解模型的TM6/ETM+热红外波段地表比辐射率估算[J]. 地理与地理信息科学,2010,26(3):25-28.

[13] Kaufman Y J, Gao B C. Remote Sensing of Water Vapor in the Near IR from EOS/MODIS[J]. IEEE Transaction on Geosciences and Remote Sensing,1992,30(5): 871-884.
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