浏览全部资源
扫码关注微信
Published： 2019-11 ，
Accepted： 30 July 2018
扫 描 看 全 文
杨以坤, 历华, 孙林, 杜永明, 曹彪, 柳钦火, 朱金山. 2019. 高分五号全谱段光谱成像仪地表温度与发射率反演. 遥感学报, 23(6): 1132–1146
Yang Y K, Li H, Sun L, Du Y M, Cao B, Liu Q H and Zhu J S. 2019. Land surface temperature and emissivity separation from GF-5 visual and infrared multispectral imager data. Journal of Remote Sensing, 23(6): 1132–1146
地表温度在全球能量平衡和气候变化研究中具有重要意义。中国新一代高分辨率卫星高分五号卫星(GF-5)搭载的全谱段成像光谱仪有4个40 m空间分辨率的热红外波段，可以提供高空间分辨率的地表温度信息。本文提出了适用于全谱段成像光谱仪的温度与发射率分离TES(Temperature and Emissivity Separation)算法同时反演地表温度和发射率，为了提高大气校正精度，算法加入了水汽缩放WVS(Water Vapor Scaling)大气校正方法。首先利用Seebor V5.0全球大气廓线库构建模拟数据对算法精度进行了评价；然后利用张掖地区11景ASTER影像作为替代数据和同步的地面实测数据对算法精度进行了验证。模拟数据结果表明加入WVS方法后TES算法反演地表温度的RMSE由2.59 K降低到1.54 K，4个波段地表发射率的RMSE分别从0.122、0.12、0.102和0.037降低到0.042、0.04、0.028和0.026；地表验证结果表明本文算法反演的地表温度与站点实测值具有更好的一致性，平均Bias由1.08 K降低到0.47 K，RMSE由2.17 K降低到1.7 K；反演的各波段地表发射率与地面实测结果误差均小于1%。因此，本文提出的温度与发射率分离算法具有较高精度，可以利用GF-5数据获取高精度高空间分辨率的地表温度和发射率数据，服务于其他相关研究。
Land Surface Temperature (LST)
as a key parameter of surface physical processes at regional and global scales
is an important indicator of energy balance and climate change on the land surface. Land Surface Emissivity (LSE) is an intrinsic property of natural materials and is a key parameter of surface energy balance and mineral mapping. LSE is also an important input variable for LST retrieval. LST rapidly changes in space and time given the heterogeneity of land surface
and remote sensing in thermal infrared (TIR) provides a unique means of obtaining LST information at regional and global scales. GF-5 satellite
which was launched in 2018
is the fifth satellite in the national high-resolution Earth observation project of China. Visual and Infrared Multispectral Imager (VIMI) is a sensor onboard GF-5
which has four TIR channels centered at 8.20
8.63
10.80
and 11.95 μm with a spatial resolution of 40 m. In this study
we present a physics-based Temperature and Emissivity Separation (TES) algorithm (denoted as WVSTES algorithm) to retrieve LST and emissivity (LST&E) simultaneously from GF-5 VIMI data. The TES algorithm uses full radiative transfer simulations to isolate surface-emitted radiance and an emissivity calibration curve based on the variability in the surface radiance data to retrieve LST and spectral emissivity dynamically. Furthermore
an improved Water Vapor Scaling (WVS) model is adopted to improve the accuracy and stability of atmospheric correction for conditions with high atmospheric water vapor content. First
Seebor V5.0 atmospheric profile database and 81 emissivity spectra extracted from the ASTER spectral library were used to simulate WVS coefficients. Then
reanalysis data of the Modern Era Retrospective-analysis for Research and Applications (MERRA) and fast radiative transfer model RTTOV were used to perform the atmospheric correction of GF-5 TIR data. Second
atmospheric parameters
such as atmospheric transmittance
upwelling
and downwelling radiance
were adjusted through the WVS method. Finally
the LST&E were retrieved using the TES algorithm. Two methods were used to evaluate the accuracy of the proposed algorithm. The first method was used to evaluate the algorithm using simulated data constructed on Seebor profile database and MODTRAN 5.2 model. The second method was applied to validate the algorithm using 11 daytime simulated images from ASTER data acquired in the Heihe River Basin with the concurrent in situ LST&E measurements. First
the GF-5 at-sensor radiances were simulated using the MODTRAN 5.2 model with 9136 atmospheric profiles and 81 ASTER emissivity spectra to evaluate the simulated data. Second
the errors in atmospheric correction were simulated in terms of the total atmospheric water vapor content uncertainties
and the atmospheric profile was adjusted with a scaling factor of 1.2 in MODTRAN to simulate the errors. Finally
the standard TES and WVSTES algorithms were evaluated using the simulated data. Validation results using the simulated data show that LST RMSE reduces from 2.59 K to 1.54 K
and LSE RMSEs in the four bands reduce from 0.122
0.12
0.10
and 0.037 to 0.042
0.040
0.028
and 0.026
respectively
when the WVS model is applied. The validation results using the simulated GF-5 images show that the LST retrieved using the WVSTES algorithm agrees well with in situ LST data. The LST average bias reduces from 1.08 K to 0.47 K
and the RMSE reduces from 2.17 K to 1.70 K. The error between the retrieved emissivity and ground measured data is basically less than 1%. The abovementioned results indicate that the proposed WVSTES algorithm can retrieve accurate LST&E results
which can be used to obtain the LST&E with high accuracy and spatial resolution from the GF-5 VIMI data.
遥感地表温度地表发射率高分五号(GF-5)WVS温度与发射率分离
remote sensingland surface temperatureland surface emissivityGF-5WVSTES
Borbas E E, Seemann S W, Huang H L, Li J and Menzel W P. 2005. Global profile training database for satellite regression retrievals with estimates of skin temperature and emissivity//Proceeding of International ATOVS Study Conference. Beijing, China: ATOVS: 763-770.
Cheng J, Liang S L, Yao Y J and Zhang X T. 2013. Estimating the optimal broadband emissivity spectral range for calculating surface longwave net radiation. IEEE Geoscience and Remote Sensing Letters, 10(2): 401–405
Coll C, Caselles V, Valor E, Niclòs R, Sánchez J M, Galve J M and Mira M. 2007. Temperature and emissivity separation from ASTER data for low spectral contrast surfaces. Remote Sensing of Environment, 110(2): 162–175
Dash P, Göttsche F M, Olesen F S and Fischer H. 2002. Land surface temperature and emissivity estimation from passive sensor data: theory and practice-current trends. International Journal of Remote Sensing, 23(13): 2563–2594
Du C, Ren H Z, Qin Q M, Meng J J and Zhao S H. 2015. A practical split-window algorithm for estimating land surface temperature from Landsat 8 data. Remote Sensing, 7(1): 647–665
范斌, 陈旭, 李碧岑, 赵艳华. 2017. " 高分五号”卫星光学遥感载荷的技术创新. 红外与激光工程, 46(1): 8–14
Fan B, Chen X, Li B C and Zhao Y H. 2017. Technical innovation of optical remote sensing payloads onboard GF-5 satellite. Infrared and Laser Engineering, 46(1): 8–14
Francois C and Ottle C. 1996. Atmospheric corrections in the thermal infrared: global and water vapor dependent split-window algorithms-applications to ATSR and AVHRR data. IEEE Transactions on Geoscience and Remote Sensing, 34(2): 457–470
Gillespie A, Rokugawa S, Hook S J, Matsunaga T and Kahle A B. 1999. Temperature/Emissivity Separation Algorithm Theoretical Basis Document, Version 2.4. Pasadena, CA: Jet Propulsion Laboratory.
Gillespie A, Rokugawa S, Matsunaga T, Cothern J S, Hook S and Kahle A B. 1998. A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. IEEE Transactions on Geoscience and Remote Sensing, 36(4): 1113–1126
Gillespie A R, Abbott E A, Gilson L, Hulley G, Jiménez-Muñoz J C and Sobrino J A. 2011. Residual errors in ASTER temperature and emissivity standard products AST08 and AST05. Remote Sensing of Environment, 115(12): 3681–3694
Hocking J, Rayer P, Saunders R, Matricardi M, Geer A and Brunel P and Vidot J. 2015. RTTOV v11 Users Guide Doc ID: NWPSAF-MO-UD-028, available at: www.nwpsaf.eu/site/download/documentation/rtm/docs_rttov/users
Hook S J, Gabell A R, Green A A and Kealy P S. 1992. A comparison of techniques for extracting emissivity information from thermal infrared data for geologic studies. Remote Sensing of Environment, 42(2): 123–135
Huang C L, Xin L and Ling L. 2008. Retrieving soil temperature profile by assimilating MODIS LST products with ensemble Kalman filter. Remote Sensing of Environment, 112(4): 1320–1336
Hulley G C and Hook S J. 2011. Generating consistent land surface temperature and emissivity products between ASTER and MODIS data for earth science research. IEEE Transactions on Geoscience and Remote Sensing, 49(4): 1304–1315
Hulley G C and Hook S J. 2012. A radiance-based method for estimating uncertainties in the Atmospheric Infrared Sounder (AIRS) land surface temperature product. Journal of Geophysical Research: Atmospheres, 117(D20): D20117
Hulley G C, Hook S J and Baldridge A M. 2009. Validation of the North American ASTER land surface emissivity database (NAALSED) version 2.0 using pseudo-invariant sand dune sites. Remote Sensing of Environment, 113(10): 2224–2233
Hulley G C, Hughes C G and Hook S J. 2012. Quantifying uncertainties in land surface temperature and emissivity retrievals from ASTER and MODIS thermal infrared data. Journal of Geophysical Research: Atmospheres, 117(D23): D23113
Islam T, Hulley G C, Malakar N K, Radocinski R G, Guillevic P C and Hook S J. 2017. A physics-based algorithm for the simultaneous retrieval of land surface temperature and emissivity from VIIRS thermal infrared data. IEEE Transactions on Geoscience and Remote Sensing, 55(1): 563–576
Jiménez-Muñoz J C and Sobrino J A. 2003. A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research: Atmospheres, 108(D22): ACL 2
Jiménez-Muñoz J C, Sobrino J A, Mattar C, Hulley G and Göttsche F M. 2014. Temperature and emissivity separation from MSG/SEVIRI data. IEEE Transactions on Geoscience and Remote Sensing, 52(9): 5937–5951
晋锐, 李新, 阎保平, 罗万明, 李秀红, 郭建文, 马明国, 亢健, 张艳林. 2012. 黑河流域生态水文传感器网络设计. 地球科学进展, 27(9): 993–1005
Jin R, Li X, Yan B P, Luo W M, Li X H, Guo J W, Ma M G, Kang J and Zhang J L. 2012. Introduction of eco-hydrological wireless sensor network in the Heihe River Basin. Advances in Earth Science, 27(9): 993–1005
Kneizys F X, Robertson D C, Abreu L W, Acharya P, Anderson G P, Rothman L S, Chetwynd J H, Selby J E A, Shettle E P, Gallery W O, Berk A, Clough S A and Bernstein L S. 1996. The MODTRAN 2/3 Report and LOWTRAN 7 MODEL. Hanscom: Phillips Laboratory, Geophysics Directorate.
Kondratyev K Y. 1969. Radiation in the Atmosphere [Electronic Resource]. New York : Academic Press.
Li H, Liu Q H, Zhong B, Du Y M, Wang H S, Wang Q. 2010. A Single-channel algorithm for land surface temperature retrieval from HJ-1B/IRS data based on a Parametric Model//Proceedings of 2010 IEEE International Geoscience and Remote Sensing Symposium. Honolulu, HI, USA: IEEE: 2448-2451 [DOI: 10.1109/IGARSS.2010.5649801]
Li H, Liu Q H, Du Y M, Jiang J X and Wang H S. 2013. Evaluation of the NCEP and MODIS atmospheric products for single channel land surface temperature retrieval with ground measurements: a case study of HJ-1B IRS data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3): 1399–1408
Li H, Sun D L, Yu Y Y, Wang H Y, Liu Y L, Liu Q H, Du Y M, Wang H S and Cao B. 2014. Evaluation of the VIIRS and MODIS LST products in an arid area of Northwest China. Remote Sensing of Environment, 142: 111–121
Li X, Cheng G D, Liu S M, Xiao Q, Ma M G, Jin R, Che T, Liu Q H, Wang W Z, Qi Y, Wen J G, Li H Y, Zhu G F, Guo J W, Ran Y H, Wang S G, Zhu Z L, Zhou J, Hu X L and Xu Z W. 2013. Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design. Bulletin of the American Meteorological Society, 94(8): 1145–1160
Li Z L, Wu H, Wang N, Qiu S, Sobrino J A, Wan Z M, Tang B H and Yan G J. 2013. Land surface emissivity retrieval from satellite data. International Journal of Remote Sensing, 34(9/10): 3084–3127
刘超, 历华, 杜永明, 曹彪, 柳钦火, 孟翔晨, 胡友健. 2017. Himawari 8 AHI数据地表温度反演的实用劈窗算法. 遥感学报, 21(5): 702–714
Liu C, Li H, Du Y M, Cao B, Liu Q H, Meng X C and Hu Y J. 2017. Practical split-window algorithm for retrieving land surface temperature from Himawari 8 AHI data. Journal of Remote Sensing, 21(5): 702–714
Malakar N K and Hulley G C. 2016. A water vapor scaling model for improved land surface temperature and emissivity separation of MODIS thermal infrared data. Remote Sensing of Environment, 182: 252–264
Matricardi M, Chevallier F, Kelly G and Thépaut J N. 2004. An improved general fast radiative transfer model for the assimilation of radiance observations. Quarterly Journal of the Royal Meteorological Society, 130(596): 153–173
McMillin L M. 1975. Estimation of sea surface temperatures from two infrared window measurements with different absorption. Journal of Geophysical Research, 80(36): 5113–5117
Meng X C, Li H, Du Y M, Liu Q H, Zhu J S and Sun L. 2016. Retrieving land surface temperature from Landsat 8 TIRS data using RTTOV and ASTER GED//Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing, China: IEEE [DOI: 10.1109/IGARSS.2016.7730121]
Mu X H, Huang S, Ren H Z, Yan G J, Song W J and Ruan G Y. 2015. Validating GEOV1 fractional vegetation cover derived from coarse-resolution remote sensing images over croplands. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2): 439–446
Pierluissi J H, Jarem J M and Peng G S. 1985. Proposed molecular transmission band models for LOWTRAN//Proceedings of SPIE 0510, Infrared Technology X. San Diego, United States: SPIE [DOI: 10.1117/12.945019]
Qin Z, Karnieli A and Berliner P. 2001. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International Journal of Remote Sensing, 22(18): 3719–3746
Ren H Z, Ye X, Liu R Y, Dong J J and Qin Q M. 2018. Improving land surface temperature and emissivity retrieval from the Chinese Gaofen-5 satellite using a hybrid algorithm. IEEE Transactions on Geoscience and Remote Sensing, 56(2): 1080–1090
Rienecker M M, Suarez M J, Gelaro R, Todling R, Bacmeister J, Liu E, Bosilovich M G, Schubert S D, Takacs L, Kim G K, Bloom S, Chen J Y, Collins D, Conaty A, da Silva A, Gu W, Joiner J, Koster R D, Lucchesi R, Molod A, Owens T, Pawson S, Pegion P, Redder C R, Reichle R, Robertson F R, Ruddick A G, Sienkiewicz M and Woollen J. 2011. MERRA: NASA’s modern-era retrospective analysis for research and applications. Journal of Climate, 24(14): 3624–3648
Sabol D E Jr, Gillespie A R, Abbott E and Yamada G. 2009. Field validation of the ASTER temperature-emissivity separation algorithm. Remote Sensing of Environment, 113(11): 2328–2344
Sobrino J A, Jiménez-Muñoz J C and Paolini L. 2004. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90(4): 434–440
Sobrino J A, Jimenez-Munoz J C, Soria G, Romaguera M, Guanter L, Moreno J, Plaza A and Martinez P. 2008. Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing, 46(2): 316–327
Sobrino J A and Julien Y. 2016. Exploring the validity of the long-term data record v4 database for land surface monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8): 3607–3614
Sun D and Pinker R T. 2007. Retrieval of surface temperature from the MSG-SEVIRI observations: Part I. Methodology. International Journal of Remote Sensing, 28(23): 5255–5272
Tonooka H. 2001. An atmospheric correction algorithm for thermal infrared multispectral data over land-a water-vapor scaling method. IEEE Transactions on Geoscience and Remote Sensing, 39(3): 682–692
Tonooka H. 2005a. Accurate atmospheric correction of ASTER thermal infrared imagery using the WVS method. IEEE Transactions on Geoscience and Remote Sensing, 43(12): 2778–2792
Tonooka H. 2005b. Atmospheric correction of MODIS thermal infrared bands by water vapor scaling method//Proceedings of SPIE 5979, Remote Sensing of Clouds and the Atmosphere X. Bruges, Belgium: SPIE [DOI: 10.1117/12.629290]
Valor E and Caselles V. 1996. Mapping land surface emissivity from NDVI: application to European, African, and South American areas. Remote Sensing of Environment, 57(3): 167–184
Wan Z M. 2014. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sensing of Environment, 140: 36–45
Wan Z M and Dozier J. 1996. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, 34(4): 892–905
Wang H S, Xiao Q, Li H and Zhong B. 2011. Temperature and emissivity separation algorithm for TASI airborne thermal hyperspectral data//Proceedings of 2011 International Conference on Electronics, Communications and Control. Ningbo, China: IEEE: 1075-1078 [DOI: 10.1109/ICECC.2011.6066288]
Xu Z W, Liu S M, Li X, Shi S J, Wang J M, Zhu Z L, Xu T R, Wang W Z and Ma M G. 2013. Intercomparison of surface energy flux measurement systems used during the HiWATER-MUSOEXE. Journal of Geophysical Research: Atmospheres, 118(23): 13140–13157
Yamaguchi Y, Kahle A B, Tsu H, Kawakami T and Pniel M. 1998. Overview of advanced spaceborne thermal emission and reflection radiometer (ASTER). IEEE Transactions on Geoscience and Remote Sensing, 36(4): 1062–1071
杨贵军, 孙晨红, 历华. 2015. 黑河流域ASTER与MODIS融合生成高分辨率地表温度的验证. 农业工程学报, 31(6): 193–200
Yang G J, Sun C H and Li H. 2015. Verification of high-resolution land surface temperature by blending ASTER and MODIS data in Heihe River Basin. Transactions of the Chinese Society of Agricultural Engineering, 31(6): 193–200
Ye X, Ren H Z, Liu R Y, Qin Q M, Liu Y and Dong J J. 2017. Land surface temperature estimate from chinese gaofen-5 satellite data using split-window algorithm. IEEE Transactions on Geoscience and Remote Sensing, 55(10): 5877–5888
Zhou J, Zhan W F, Hu D Y and Zhao X. 2010. Improvement of mono-window algorithm for retrieving land surface temperature from HJ-1B satellite data. Chinese Geographical Science, 20(2): 123–131
相关文章
相关作者
相关机构
京公网安备11010802024621