Please wait a minute...
img

官方微信

遥感技术与应用  2016, Vol. 31 Issue (1): 63-73    DOI: 10.11873/j.issn.1004-0323.2016.1.0063
山地遥感专栏     
基于Landsat8热红外遥感数据的山地地表温度地形效应研究
赵伟1,2,李爱农1,张正健1,边金虎1,3,靳华安1,尹高飞1,南希1,雷光斌1,3
(1.中国科学院水利部成都山地灾害与环境研究所,四川 成都610041;
2.中国科学院遥感与数字地球研究所遥感科学国家重点实验室,北京100101;
3.中国科学院大学,北京100049)
A Study on Land Surface Temperature Terrain Effect over Mountainous Area based on Landsat 8 Thermal Infrared Data
Zhao Wei1,2,Li Ainong1,Zhang Zhengjian1,Bian Jinhu1,3,Jin Huaan1,Yin Gaofei1,Nan Xi1,Lei Guangbin1,3
(1.Institute of Mountain Hazards and Environment,Chinese Academy of Sciences,Chengdu 610041,China;2.State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,
Chinese Academy of Sciences.Beijing 100101,China;
3.University of Chinese Academy of Sciences,Beijing 100049,China)
 全文: PDF(5169 KB)  
摘要:

地表温度是影响地表能量收支平衡的重要参量,能够综合反演地表的水热交换过程。虽然当前在基于地表温度开展全球或者区域尺度的地表能量平衡研究方面取得一系列的进展,但是面向山地区域尺度的类似研究仍然面临较大的挑战。为分析山地复杂地形对山地地表温度时空分布的影响规律,基于具有较高空间分辨率的Landsat 8热红外数据,以我国西南典型山地为研究对象,定量反演该区域的地表温度空间分布状况,结合SRTM90 DEM数据,选择从海拔、坡度和坡向3个关键地形因子角度分析山地地表温度的地形效应特征。结果发现:山地地表温度随地形因子均呈现出十分显著的变化特征。总体而言,地表温度均随着海拔和坡度的升高而降低,而在坡向方面,南坡的温度相比北坡的温度要高。在地形效应分析的基础上,通过开展1 km空间尺度地形和地表温度的空间统计分析发现,山地1 km尺度下地表温度存在较大的空间异质性,且其影响不可忽略。研究结果表明:开展山地地表水热过程遥感动态监测需高空间分辨率地表温度作为数据支持,以准确描述山地地形因素对地表能量交换过程的影响。

关键词: 地表温度(LST)Landsat 8地形效应山地    
Abstract:

Land surface temperature (LST) plays an important role in land surface energy budget.It is also a key parameter related to land surface water and heat transfer processes.With the fast development of LST retrieval algorithm by remote sensing methods,LST products are provided by coarse resolution satellite observations such as MODIS and AVHRR,which are commonly used for global or regional study.However,it is still a big challenge for their application over mountainous area due to the high resolution demanded and terrain effect.In order to investigate the influences of topographic factors such as elevation,slope,and aspect on LST,a systematic study was conducted based on LST estimations in the typical mountain environment with Landsat 8 thermal infrared data.The results indicated that there are significant effects from topographic factors on LST distribution.Generally,LST decreases with the increase of elevation and slope.LST in south face usually is higher than that in north face.To discuss the scale problem over mountainous area,the estimated LST was analyzed at 1 km scale,close to MODIS LST spatial resolution,at the high mountain area and hilly area respectively.It was found that the LST at 1 km scale shows higher spatial heterogeneity with elevation and land cover at the complex terrain area.Therefore,high spatial resolution LST data is necessary for land surface water and heat transfer studies over mountain area to consider the impacts from topographic changes.

Key words: Land surface temperature(LST)    Landsat 8    Terrain effect    Mountain
收稿日期: 2015-12-03 出版日期: 2016-04-05
:  TP 79  
基金资助:

国家自然科学基金(41271433\,41401425),中国科学院国际合作局对外合作重点项目(GJHZ201320),中国科学院西部之光西部博士项目(Y4R2110110),遥感科学国家重点实验室开放基金(OFSLRSS201503),中国科学院水利部成都山地灾害与环境研究所青年百人团队计划(SDSQB\|2015\|02)联合资助。〖ZK)〗

通讯作者: 李爱农(1974-),男,安徽庐江人,研究员,主要从事山地定量遥感及其应用研究。Email:ainongli@imde.ac.cn。    
作者简介: 赵伟(1984-),男,江西上高人,副研究员,主要从事山地地表水热通量遥感定量反演研究。Email:zhaow@imde.ac.cn。
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
赵伟
李爱农
张正健
边金虎
靳华安
尹高飞
南希
雷光斌

引用本文:

赵伟,李爱农,张正健,边金虎,靳华安,尹高飞,南希,雷光斌. 基于Landsat8热红外遥感数据的山地地表温度地形效应研究[J]. 遥感技术与应用, 2016, 31(1): 63-73.

Zhao Wei,Li Ainong,Zhang Zhengjian,Bian Jinhu,Jin Huaan,Yin Gaofei,Nan Xi,Lei Guangbin. A Study on Land Surface Temperature Terrain Effect over Mountainous Area based on Landsat 8 Thermal Infrared Data. Remote Sensing Technology and Application, 2016, 31(1): 63-73.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2016.1.0063        http://www.rsta.ac.cn/CN/Y2016/V31/I1/63

[1]Li Z L,Tang B H,Wu H,et al.Satellite-derived Land Surface Temperature:Current Status and Perspectives[J].Remote Sensing of Environment,2013,131:14-37.

[2]Carlson T N.An Overview of the “Triangle Method” for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery[J].Sensors,2007,7:1612-1629.

[3]Jiménez-Mu〖AKn~D〗oz J C,Sobrino J A.A Generalized Single-channel Method for Retrieving Land Surface Temperature from Remote Sensing Data[J].Journal of Geophysical Research:Atmospheres (1984~2012),2003,108.doi:10.1029/2003JD003480.

[4]Qin Z,Karnieli A,Berliner P.A Mono-window Algorithm for Retrieving Land Surface Temperature from Landsat TM Data〖HJ2mm〗 and Its Application to the Israel-Egypt Border Region[J].International Journal of Remote Sensing,2001,22:3719-3746.

[5]Wan Z,Dozier J A.Generalized Split-window Algorithm for Retrieving Land-surface Temperature from Space[J].IEEE Transactions on Geoscience and Remote Sensing,1996,34:892-905.

[6]Becker F,Li Z L.Temperature-independent Spectral Indices in Thermal Infrared Bands[J].Remote Sensing of Environment,1990,32:17-33.

[7]Wan Z,Li Z L.A Physics-based Algorithm for Retrieving Land-surface Emissivity and Temperature from EOS/MODIS Data[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35:980-996.

[8]Tang B,Bi Y,Li Z L,et al.Generalized Split-window Algorithm for Estimate of Land Surface Temperature from Chinese Geostationary Fengyun Meteorological Satellite (FY-2C) Data[J].Sensors,2008,8(2),933-951.

[9]Tang B,Shao K,Li Z L,et al.Estimation and Validation of Land Surface Temperature from Chinese Second Generation Polar-orbiting FY-3A VIRR Data[J].Remote Sensing,2015,7,3250-3273.

[10]Li Y Y,Zhang H,Kainz W.Monitoring Patterns of Urban Heat Islands of the Fast-growing Shanghai Metropolis,China:Using Time-series of Landsat TM/ETM+ Data[J].International Journal of Applied Earth Observation and Geoinformation,2012,19,127-138.

[11]Sheng Hui,Wan Hong,Cui Jianyong,et al.Urban Heat Island Effect Study and Pridiction Analysis based on Landsat TM Data[J].Remote Sensing Technology and Application,2010,25(1):8-14.[盛辉,万红,崔建勇,等.基于TM影像的城市热岛效应监测与预测分析[J].遥感技术与应用,2010,25(1):8-14.]

[12]Wang Ping.Spatial Analysis of Land Surface Temperature and Fluxes in Urban Heat Island over Xi’an City[J].Remote Sensing Technology and Application,2009,24(6):757-765.[王萍.城市热岛效应地表通量空间分布研究[J].遥感技术与应用,2009,24(6):757-765.]

[13]Li Z L,Tang R,Wan Z,et al.A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data[J]. Sensors,2009,9,3801-3853.

[14]Zhao W,Li A,Deng W.Surface Energy Fluxes Estimation Over the South Asia Subcontinent Through Assimilating MODIS/TERRA Satellite Data with In-situ Observations and GLDAS Product by SEBS Model[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2014,7:3704-3712

[15]Li A,Zhao W,Deng W.A Quantitative Inspection on Spatio-Temporal Variation of Remote Sensing-based Estimates of Land Surface Evapotranspiration in South Asia[J]. Remote Sensing,2015,7,4726-4752.

[16]Li Qin,Chen Xi,Bao Anming,et al.Evapotranspiration Estimation in Arid Areas based on SEBS Model[J].Remote Sensing Technology and Application,2014,29(2):195-201.[李琴,陈曦,包安明,等.基于SEBS模型干旱区蒸散发量研究[J].遥感技术与应用,2014,29(2):195-201.]

[17]Zhao W,Li A N.A Review on Land Surface Processes Modelling over Complex Terrain[J].Advances in Meteorology,2015,1-17.DOI:10.1155/2015/607181.

[18]Jiang Dalin,Kuang Honghai,Cao Xiaofeng,et al.Study of Land Surface Temperature Retrieval based on Landsat 8——With the Sample of Dianchi Lake Basin[J].Remote Sensing Technology and Application,2015,30(3):448-454.[蒋大林,匡鸿海,曹晓峰,等.基于 Landsat 8 的地表温度反演算法研究——以滇池流域为例[J].遥感技术与应用,2015,30(3):448-454.]

[19]Li Yao,Pan Jinghu.Spatial Pattern on Urban Heat Environment Using Split Window Algorithm and Spectral MixtureAnalysis based on Landsat 8 Images:A Case of Lanzhou City[J].Arid Land Geography,2015,38(1):111-119.[李瑶,潘竟虎.基于Landsat 8劈窗算法与混合光谱分解的城市热岛空间格局分析——以兰州市中心城区为例[J].干旱区地理,2015,38(1):111-119.]

[20]Xu Hanqiu.Retrieval of the Reflectance and Land Surface Temperature of the Newly-Launched Landsat 8 Satellite[J].Chinese Journal of Geophyiscs,2015,58(3):741-747.[徐涵秋.新型Landsat 8卫星影像的反射率和地表温度反演[J].地球物理学报,2015,58(3):741-747.]

[21]Du C,Ren H,Qin Q,et al.A Practical Split-window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data[J].Remote Sensing,2015,7:647-665.

[22]Song Ting,Duan Zheng,Liu Junzhi,et al.Comparison of Four Algorithms to Retrieve Land Surface Temperature Using Landsat 8 Satellite[J].Journal of Remote Sensing,2015,19(3):452-464.[宋挺,段峥,刘军志,等 Landsat 8数据地表温度反演算法对比[J].遥感学报,2015,19(3):451-464.]

[23]Wang F,Qin Z,Song C,et al.An Improved Mono-window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data[J].Remote Sensing,2015,7:4268-4289.

[24]Qin Zhihao,Li Wenjuan,Zhang Minghua,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 & Resources,2003,(2):37-43.[覃志豪,李文娟,张明华,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.]

[25]Sobrino J A,Jiménez-Muoz J C,Sòria G,et al.Land Surface Emissivity Retrieval from Different VNIR and TIR Sensors[J].IEEE Transactions on Geoscience and Remote Sensing,2008,46:316-327.

[26]Bertoldi G,Notarnicola C,Leitinger G,et al.Topographical and Ecohydrological Controls on Land Surface Temperature in an Alpine Catchment[J].Ecohydrology,2010,3:189-204.

[1] 汪子豪,秦其明,孙元亨. 基于BP神经网络的地表温度空间降尺度方法[J]. 遥感技术与应用, 2018, 33(5): 793-802.
[2] 李军,龚围,辛晓洲,高阳华. 重庆地表温度的遥感反演及其空间分异特征[J]. 遥感技术与应用, 2018, 33(5): 820-829.
[3] 施佩荣,陈永富,刘华,吴云华. 基于分割评价函数的多尺度分割参数的选择[J]. 遥感技术与应用, 2018, 33(4): 628-637.
[4] 钟函笑,边金虎,李爱农. Landsat-8 OLI与Sentinel-2 MSI山区遥感影像辐射一致性研究[J]. 遥感技术与应用, 2018, 33(3): 428-438.
[5] 史新,周买春. 基于Landsat 8数据的3种地表温度反演算法在三河坝流域的对比分析[J]. 遥感技术与应用, 2018, 33(3): 465-475.
[6] 郑明亮,黄方,张鸽. 基于TsHARP模型和STITFM算法的地表温度影像融合研究[J]. 遥感技术与应用, 2018, 33(2): 275-283.
[7] 赵航,陈方, 张美美. 基于改进C-V模型的冰湖轮廓提取方法研究[J]. 遥感技术与应用, 2018, 33(1): 177-184.
[8] 张雅,尹小君,王伟强. 基于Landsat 8 OLI遥感影像的天山北坡草地地上生物量估算[J]. 遥感技术与应用, 2017, 32(6): 1012-1021.
[9] 何炳伟,赵伟,李爱农,冯文兰,谭剑波,雷光斌,南希. 基于Landsat 8遥感影像的新旧城区热环境特征对比研究-以成都市为例[J]. 遥感技术与应用, 2017, 32(6): 1141-1150.
[10] 王猛猛,何国金,张兆明,王桂周,尹然宇,龙腾飞. 基于Landsat 8 TIRS数据的大气水汽含量反演劈窗算法[J]. 遥感技术与应用, 2017, 32(1): 166-172.
[11] 吴志杰,何国金,王猛猛,傅娇凤,邹丹. 南方丘陵区植被覆盖度遥感估算与时空变化研究—以福建省永定县为例[J]. 遥感技术与应用, 2016, 31(6): 1201-1208.
[12] 李炳亚,潘剑君,夏超,陈昕,隋传嘉. 基于空间位置关系的山地湖泊水体提取方法研究[J]. 遥感技术与应用, 2016, 31(5): 983-993.
[13] 南希,李爱农,陈昱,邓伟. 竖版中国数字山地图(1∶670万)的设计与编制[J]. 遥感技术与应用, 2016, 31(3): 451-458.
[14] 李爱农,尹高飞,靳华安,边金虎,赵伟. 山地地表生态参量遥感反演的理论、方法与问题[J]. 遥感技术与应用, 2016, 31(1): 1-11.
[15] 雷光斌,李爱农,谭剑波,张正健,边金虎,靳华安,赵伟,曹小敏. 基于多源多时相遥感影像的山地森林分类决策树模型研究[J]. 遥感技术与应用, 2016, 31(1): 31-41.