地球科学进展 ›› 2016, Vol. 31 ›› Issue (5): 471 -480. doi: 10.11867/j.issn.1001-8166.2016.05.0471.

上一篇    下一篇

基于地面观测的异质性下垫面像元尺度地表温度模拟研究进展
彭志兴( ), 周纪 *( ), 李明松   
  1. 1.电子科技大学 资源与环境学院,四川 成都 611731
    2.电子科技大学 信息地学研究中心,四川 成都 611731
  • 收稿日期:2016-02-28 修回日期:2016-04-20 出版日期:2016-05-20
  • 通讯作者: 周纪 E-mail:scxhpzx@sina.com;jzhou233@uestc.edu.cn
  • 基金资助:
    国家自然科学基金面上项目“基于三维建模与组分发射辐射分离的异质性场景像元尺度表面温度模拟研究”(编号:41371341);国家重点基础研究发展计划项目“复杂地表遥感信息动态分析与建模”(编号:2013CB733406)资助

Review of Methods for Simulating Land Surface Temperature at the Pixel Scale Based on Ground Measurements over Heterogeneous Surface

Zhixing Peng( ), Ji Zhou *( ), Mingsong Li   

  1. 1.School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
    2.Information Geoscience Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
  • Received:2016-02-28 Revised:2016-04-20 Online:2016-05-20 Published:2016-05-10
  • Contact: Ji Zhou E-mail:scxhpzx@sina.com;jzhou233@uestc.edu.cn
  • About author:

    First author:Peng Zhixing(1991-), male, Xuanhan County, Sichuan Province, Master Student. Research areas include 3D scene reconstruction and validation of remotely sensed land surface temperature product.E-mail:scxhpzx@sina.com

    Corresponding author:Zhou Ji(1983-), male, Nanchong City, Sichuan Province, Associate Professor. Research areas include validation of remotely sensed land surface temperature and retrieval of land surface temperature based on thermal infrared and passive microwave remote sensing.E-mail:jzhou233@uestc.edu.cn

  • Supported by:
    Project supported by the National Natural Science Foundation of China “Surface temperature simulation at the pixel scale for heterogeneous surfaces based on 3D modeling and component emissions separation”(No.41371341);The Chinese State Key Basic Research Project “Dynamic analyses and modeling based on remote sensing information in relation to heterogeneous land surfaces”(No.2013CB733406)

遥感反演的地表温度是研究全球气候变化和区域水分与能量交换的关键参数,准确获取地表温度对诊断地球环境变化意义重大。然而在异质性下垫面,像元尺度地表温度相对真值获取困难,导致遥感反演的地表温度的不确定性难以准确评估,影响了遥感反演的地表温度的深入应用。梳理了基于地面观测数据的异质性下垫面像元尺度地表温度模拟研究进展,依据模型是否构建真实空间分布,归纳了修正几何投影模型、真实结构三维模型和其他模型等,并对比了几种模型的优缺点。最后,指出了异质性下垫面像元尺度地表温度模拟中尚待解决的问题,并探讨了以后的研究方向。

Remotely sensed Land Surface Temperature (LST) is a key parameter for studying the global climate changes and the exchanges of water and energy. Acquiring LST accurately is important to diagnose the change of environment on earth. Quantifying the uncertainty of remotely sensed LST is the first step of its application. However, due to the difficulties in obtaining the ground truth of LST at the pixel scale, it is difficult to validate the remotely sensed LST. Here, methods for simulating the LST at the pixel scale based on ground measurements over heterogeneous area were reviewed. From the way to construct the ground scene, these methods were classified into three types, including the Modified Geometric Projection model (MGP), realistic structural three-dimensional model, and other model. The advantages and disadvantages of these models were examined and compared. Finally, some issues in simulating LST at the pixel scale over heterogeneous area needed to be solved and on-going directions in the future were summarized.

中图分类号: 

图1 太阳光照阴影和传感器观测阴影的相互关系 [ 28 ]
Fig.1 The relationship between the actual shadow on the ground and shadow observed by the satellite sensor [ 28 ]
图2 在光照—观测几何条件下投影到规则格网上的面积图示 [ 16 ]
Fig.2 Schematic diagram of projected areas on the fine regular grid under a given illumination and viewing geometry [ 16 ]
图3 DART模型系统图示 [ 32 ]
Fig.3 Schematic diagram of the DART model [ 32 ]
图4 DART模型透视投影图示 [ 21 ]
Fig.4 Schematic diagram of the DART perspective projection [ 21 ]
表1 各种方法比较
Table 1 Comparison of different methods
表2 遥感LST升尺度应用
Table 2 Application of remotely sensed LST upscaling
[1] Sellers P J, Hall F G, Asrar G, et al.An overview of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE)[J]. Journal of Geophysical Research,1992, 97: 18 345-18 371.
[2] Huang Qiang, Chen Zishen.Regional study on the trends of extreme temperature and precipitation events in the Pearl River Basin[J]. Advances in Earth Science,2014, 29(8): 956-967.
[黄强, 陈子燊. 全球变暖背景下珠江流域极端气温与降水事件时空变化的区域研究[J]. 地球科学进展, 2014, 29(8): 956-967.]
[3] Zhou J, Chen Y, Zhang X, et al.Modeling the diurnal variations of urban heat island with multi-source satellite data[J]. International Journal of Remote Sensing,2013, 34: 7 568-7 588.
[4] Li Z, Tang B, Wu H, et al.Satellite-derived land surface temperature: Current status and perspectives[J]. Remote Sensing of Environment,2013, 131: 14-37.
[5] Wan Z.New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product[J]. Remote Sensing of Environment,2014, 140: 36-45.
[6] Trigo I F, Monteiro I T, Olesen F, et al.An assessment of remotely sensed land surface temperature[J]. Journal of Geophysical Research: Atmospheres,2008, 113(D17),doi:10.1029/2008JD010035.
[7] Prata F.Land Surface Temperature Measurement from Space: AATSR Algorithm Theoretical Basis (Contract report to ESA)[C]. Aspendale, Victoria, Australia: CSIRO Atmospheric Research, 2002.
[8] Kustas W P, Norman J M.Use of remote sensing for evapotranspiration monitoring over land surfaces[J]. Hydrological Sciences Journal,1996, 41(4): 495-515.
[9] Snyder W C, Wan Z, Zhang Y, et al.Requirements for satellite land surface temperature validation using a silt playa[J]. Remote Sensing of Environment,1997, 61: 279-289.
[10] Luan Haijun, Tian Qingjiu, Yu Tao,et al.Review of up-scaling of quantitative remote sensing[J]. Advances in Earth Science,2013, 28(6): 657-664.
[栾海军, 田庆久, 余涛, 等. 定量遥感升尺度转换研究综述[J]. 地球科学进展, 2013, 28(6): 657-664.]
[11] Hu Yunfeng, Xu Zhiying, Liu Yue, et al.A review of the scaling issues of geospatial data[J]. Advances in Earth Science,2013, 28(3): 297-304.
[胡云锋, 徐芝英, 刘越, 等. 地理空间数据的尺度转换[J]. 地球科学进展, 2013, 28(3): 297-304.]
[12] Wan Z, Zhang Y, Zhang Q, et al.Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data[J]. Remote Sensing of Environment,2002, 83: 163-180.
[13] Guillevic P C, Bork-Unkelbach A, Göttsche F M, et al.Directional viewing effects on satellite Land Surface Temperature products over sparse vegetation canopies—A multisensor analysis[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 99: 1-5.
[14] Pinheiro A C T, Privette J L, Mahoney R, et al. Directional effects in a daily AVHRR land surface temperature dataset over Africa[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(9): 1 941-1 954.
[15] Rasmussen M O, Pinheiro A C, Proud S R, et al.Modeling angular dependences in Land Surface Temperature from the SEVIRI instrument onboard the geostationary Meteosat Second Generation satellites[J]. IEEE Transactions on Geoscience and Remote Sensing,2010, 48(8): 3 123-3 133.
[16] Ermida S L, Trigo I F, Dacamara C C, et al.Validation of remotely sensed surface temperature over an oak woodland landscape—The problem of viewing and illumination geometries[J].Remote Sensing of Environment,2014, 148: 16-27.
[17] Zhou J, Li J, Zhang L, et al.Intercomparison of methods for estimating land surface temperature from a Landsat-5 TM image in an arid region with low water vapour in the atmosphere[J].International Journal of Remote Sensing,2012, 33: 2 582-2 602.
[18] Zhou J, Li M, Liu S, et al.Validation and performance evaluations of methods for estimating land surface temperatures from ASTER data in the middle reach of the Heihe River basin, northwest China[J].Remote Sensing,2015, 7: 7 126-7 156.
[19] Pinheiro A C, Privette J L, Guillevic P.Modeling the observed angular anisotropy of land surface temperature in a savanna[J]. IEEE Transactions on Geoscience and Remote Sensing,2006, 44(4): 1 036-1 047.
[20] Guillevic P C, Privette J L, Coudert B, et al.Land Surface Temperature product validation using NOAA’s surface climate observation networks-Scaling methodology for the Visible Infrared Imager Radiometer Suite (VIIRS)[J]. Remote Sensing of Environment,2012, 124: 282-298.
[21] Yin T, Lauret N, Gastellu-Etchegorry J-P. Simulating images of passive sensors with finite field of view by coupling 3-D radiative transfer model and sensor perspective projection[J]. Remote Sensing of Environment,2015, 162: 169-185.
[22] Chen J, Leblanc S G. four-scale bidirectional reflectance model based on canopy architecture[J]. IEEE Transactions on Geoscience and Remote Sensing,1997, 35(5): 1 316-1 337.
[23] Franklin J, Michaelson J, Strahler A H.Spatial analysis of density dependent pattern in coniferous forest stands[J]. Vegetatio,1985, 64: 29-36.
[24] Serra J.Image Analysis and Mathematical Morphology[C]. London, New York: Academic, 1982.
[25] Strahler A H, Jupp D L B. Modeling bidirectional reflectance of forests and woodlands using Boolean models and geometric optics[J]. Remote Sensing of Environment,1990, 34(3): 153-166.
[26] Liu J, Melloh R A, Woodcock C E, et al.The effect of viewing geometry and topography on viewable gap fractions through forest canopies[J]. Hydrological Processes, 2004, 18: 3 595-3 607.
[27] Li X, Strahler A H.Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: Effect of crown shape and mutual shadowing[J]. IEEE Transactions on Geoscience and Remote Sensing,1992, 30(2): 276-292.
[28] Li Xiaowen, Wang Jindi.Optical Remote Sensing Model and Structure Parameters of Vegetation[M]. Beijing: Science Press, 1995.
[李小文, 王锦地. 植被光学遥感模型与植被结构参数化[M]. 北京: 科学出版社, 1995.]
[29] Rasmussen M O, Gottsche F M, Olesen F S, et al.Directional effects on land surface temperature estimation from meteosat second generation for savanna landscapes[J]. IEEE Transactions on Geoscience and Remote Sensing,2011, 49(11): 4 458-4 468.
[30] Cao B, Liu Q, Du Y, et al.Modeling directional brightness temperature over mixed scenes of continuous crop and road: A case study of the Heihe River Basin[J].IEEE Geoscience and Remote Sensing Letters,2015, 12(2):234-238.
[31] Gastellu-Etchegorry J P, Demarez V, Pinel V, et al. Modeling radiative transfer in heterogeneous 3D vegetation canopies[J]. Remote Sensing of Environment,1996, 58: 131-156.
[32] Gastellu-Etchegorry J P, Yin T, Lauret N, et al. Discrete Anisotropic Radiative Transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes[J].Remote Sensing,2015, 7: 1 667-1 701.
[33] Guillevic P C, Gastellu-Etchegorry J P, Demarty J, et al. Thermal infrared radiative transfer within three-dimensional vegetation covers[J].Journal of Geophysical Research,2003, 108(D8):4 248.
[34] Soux A, Voogt J A, Oke T R.A model to calculate what a remote sensor ‘sees’ of an urban surface[J]. Boundary-Layer Meteorology,2004, 111(1): 109-132.
[35] Voogt J A.Assessment of an urban sensor view model for thermal anisotropy[J]. Remote Sensing of Environment,2008, 112: 482-495.
[36] Zhou Ji, Chen Yunhao, Li Jing, et al.Progress in thermal anisotropy of urban areas: A review[J]. Advances in Earth Science,2009, 24(5): 497-505.
[周纪, 陈云浩, 李京, 等. 城市区域热辐射方向性研究进展[J]. 地球科学进展, 2009, 24(5): 497-505.]
[37] Zhan Wenfeng, Zhou Ji, Ma Wei.Computer simulation of land surface thermal anisotropy based on realistic structure model: A review[J]. Advances in Earth Science,2009, 24(12): 1 309-1 317.
[占文凤,周纪,马伟. 基于真实结构的地表热辐射方向性计算机模拟研究进展[J].地球科学进展, 2009, 24(12): 1 309-1 317.]
[38] Ma Wei, Chen Yunhao, Zhan Wenfeng,et al.Thermal anisotropy model for simulated three dimensional urban targets[J].Journal of Remote Sensing,2013, 17(1): 62-76.
[马伟, 陈云浩, 占文凤, 等. 城市模拟目标的3维热辐射方向性模型[J].遥感学报, 2013, 17(1): 62-76.]
[39] Zhan Wenfeng, Chen Yunhao, Ma Wei,et al.FOV effect analysis in directional brightness temperature observations for urban targets[J]. Journal of Remote Sensing,2010,14(2): 372-386.
[占文凤, 陈云浩, 马伟, 等. 城市目标方向亮温观测的视场效应分析[J]. 遥感学报, 2010, 14(2): 372-386.]
[40] Zhan Wenfeng, Chen Yunhao, Ma Wei,et al.Impacts of different components’proportion determination methods on directional brightness temperature simulation for urban targets[J]. Geomatics and Information Science of Wuhan University, 2010, 35(4): 436-440.
[占文风, 陈云浩, 马伟, 等. 组分权重方法对城市目标方向亮温模拟的影响[J].武汉大学学报: 信息科学版, 2010, 35(4): 436-440.]
[41] Goel N S, Rozehnal I, Thompson R L.A computer graphics based model for scattering from objects of arbitrary shapes in the optical region[J]. Remote Sensing of Environment,1991, 36: 73-104.
[42] Qin W, Gerstl S A W. 3-D scene modeling of semidesert vegetation cover and its radiation regime[J]. Remote Sensing of Environment, 2000, 74(1): 145-162.
[43] Liu Q, Huang H, Qin W, et al.An extended 3-D radiosity-graphics combined model for studying thermal-emission directionality of crop canopy[J]. IEEE Transactions on Geoscience and Remote Sensing,2007, 45(9): 2 900-2 918.
[44] Huang H, Liu Q, Qin W.Thermal emission hot-spot effect of crop canopies—Part I: Simulation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2010, 3: 312-322.
[45] Yu T, Gu X, Tian G, et al.Modeling directional brightness temperature over a maize canopy in row structure[J]. IEEE Transactions on Geoscience and Remote Sensing,2004, 42(10): 2 290-2 304.
[46] Du Y, Liu Q, Chen L, et al.Modeling directional brightness temperature of the winter wheat canopy at the ear stage[J].IEEE Transactions on Geoscience and Remote Sensing,2007, 45(11): 3 721-3 739.
[47] Ma Hongzhang, Liu Sumei, Sun Genyun, et al.Three-dimensional simulation model for thermalradiation directivity of nonuniform canopy: A case study of corn canopy[J]. Journal of Remote Sensing,2016, 20(3): 374-381.
[马红章, 刘素美, 孙根云, 等. 非匀一冠层热辐射方向性3维模型构建——以玉米冠层为例[J]. 遥感学报, 2016, 20(3): 374-381.]
[48] Guillevic P C, Biard J, Hulley G C, et al.Validation of land surface temperature products derived from the Visible Infrared Imager Radiometer Suite (VIIRS) using ground-based and heritage satellite measurements[J]. Remote Sensing of Environment,2014, 154: 19-37.
[49] Coudert B, Ottlé C, Boudevillain B, et al.Contribution of thermal infrared remote sensing data in multiobjective calibration of a dual-source odel[J]. Journal of Hydrometeorology,2006, 7(3): 404-420.
[50] Wang Lu, Hu Yueming, Zhao Yingshi, et al.Remote sensing scale transformation of soil moisture based on block Kriging[J].Journal of Geo-Information Science,2012, 14(4): 465-473.
[王璐, 胡月明, 赵英时, 等. 克里格法的土壤水分遥感尺度转换[J]. 地球信息科学学报, 2012, 14(4): 465-473.]
[51] Ge Y, Liang Y, Wang J, et al.Upscaling sensible heat fluxes with area-to-area regression kriging[J]. IEEE Transactions on Geoscience and Remote Sensing Letters,2015, 12(3): 656-660.
[52] Zhou G, Wang B, Zhou J.Automatic registration of tree point clouds from terrestrial LiDAR scanning for reconstructing the ground scene of vegetated surfaces[J].IEEE Geoscience and Remote Sensing Letters,2014, 11(9): 1 654-1 658.
[53] Xie Rong, Liu Yawen, Li Xiangxiang.Key technologies of Earth observation satellite data integration system under big data environment[J].Advances in Earth Science,2015, 30(8): 855-862.
[谢榕, 刘亚文, 李翔翔. 大数据环境下卫星对地观测数据集成系统的关键技术[J]. 地球科学进展, 2015, 30(8): 855-862.]
[1] 王忠静,石羽佳,张腾. TRMM遥感降水低估还是高估中国大陆地区的降水?[J]. 地球科学进展, 2021, 36(6): 604-615.
[2] 崔林丽, 史军, 杜华强. 植被物候的遥感提取及其影响因素研究进展[J]. 地球科学进展, 2021, 36(1): 9-16.
[3] 吴佳梅,彭秋志,黄义忠,黄亮. 中国植被覆盖变化研究遥感数据源及研究区域时空热度分析[J]. 地球科学进展, 2020, 35(9): 978-989.
[4] 董治宝, 吕萍, 李超. 火星风沙地貌研究方法[J]. 地球科学进展, 2020, 35(8): 771-788.
[5] 刘元波, 吴桂平, 赵晓松, 范兴旺, 潘鑫, 甘国靖, 刘永伟, 郭瑞芳, 周晗, 王颖, 王若男, 崔逸凡. 流域水文遥感的科学问题与挑战[J]. 地球科学进展, 2020, 35(5): 488-496.
[6] 刘磊,翁陈思,李书磊,胡帅,叶进,窦芳丽,商建. 太赫兹波被动遥感冰云研究现状及进展[J]. 地球科学进展, 2020, 35(12): 1211-1221.
[7] 李浩杰,李弘毅,王建,郝晓华. 河冰遥感监测研究进展[J]. 地球科学进展, 2020, 35(10): 1041-1051.
[8] WangJingfeng,刘元波,张珂. 最大熵增地表蒸散模型:原理及应用综述[J]. 地球科学进展, 2019, 34(6): 596-605.
[9] 陈泽青,刘诚,胡启后,洪茜茜,刘浩然,邢成志,苏文静. 大气成分的遥感监测方法与应用[J]. 地球科学进展, 2019, 34(3): 255-264.
[10] 冉有华,李新. 中国多年冻土制图:进展、挑战与机遇[J]. 地球科学进展, 2019, 34(10): 1015-1027.
[11] 陈云浩, 吴佳桐, 王丹丹. 广义地表热辐射方向性计算机模拟研究进展[J]. 地球科学进展, 2018, 33(6): 555-567.
[12] 肖雄新, 张廷军. 基于被动微波遥感的积雪深度和雪水当量反演研究进展[J]. 地球科学进展, 2018, 33(6): 590-605.
[13] 栾海军, 田庆久, 章欣欣, 聂芹, 朱晓玲. 定量遥感地表参数尺度转换研究趋势探讨[J]. 地球科学进展, 2018, 33(5): 483-492.
[14] 宋晓谕, 高峻, 李新, 李巍岳, 张中浩, 王亮绪, 付晶, 黄春林, 高峰. 遥感与网络数据支撑的城市可持续性评价:进展与前瞻[J]. 地球科学进展, 2018, 33(10): 1075-1083.
[15] 王建, 车涛, 李震, 李弘毅, 郝晓华, 郑照军, 肖鹏峰, 李晓峰, 黄晓东, 钟歆玥, 戴礼云, 李红星, 柯长青, 李兰海. 中国积雪特性及分布调查[J]. 地球科学进展, 2018, 33(1): 12-15.
阅读次数
全文


摘要