浏览全部资源
扫码关注微信
Published： 07 August 2021 ，
扫 描 看 全 文
吴骅，李秀娟，李召良，段四波，钱永刚.2021.高光谱热红外遥感：现状与展望.遥感学报，25（8）： 1567-1590
Wu H，Li X J，Li Z L，Duan S B and Qian Y G. 2021. Hyperspectral thermal infrared remote sensing： Current status and perspectives. National Remote Sensing Bulletin， 25（8）：1567-1590
高光谱热红外数据中蕴含着丰富的长波光谱信息，可以更精细的揭示地气耦合过程导致的辐射变化，反映热红外谱段特有的地物诊断特征，同时高光谱特性也可以为热红外关键特征参数的病态反演问题提供更合理的假设和约束条件，具有重要的研究价值和应用前景。高光谱热红外遥感技术自诞生起，在吸纳多光谱热红外遥感技术的基础上迅速发展，成为热红外遥感领域的重要研究方向和突破点。然而，当前高光谱热红外遥感存在着可用数据不足，处理方法传统，反演精度有限，应用难以有效实施等问题。为进一步明晰高光谱热红外遥感的研究进展和现存挑战，本文在高光谱热红外相关文献深入分析的基础上，梳理了高光谱热红外研究的发展脉络和热点，介绍了现有国内外主要的高光谱热红外传感器，分析了高光谱大气效应校正、地表温度和发射率分离以及地气关键特征参数一体化反演的现状和问题，总结了相关典型行业应用，展望了高光谱热红外的发展方向，以期为未来高光谱热红外研究工作的开展提供借鉴和帮助。
Hyperspectral thermal infrared data contains abundant long-wave spectral information
which can reveal radiation changes caused by the land-atmosphere coupling process more precisely and reflect the unique diagnostic characteristics of the thermal infrared spectrum. At the same time
the hyperspectral characteristics can also provide more reasonable assumptions and constraints for the ill-posed inversion of the key thermal infrared characteristic parameters. Therefore
the hyperspectral thermal infrared remote sensing has important research value and application prospect. Since its birth
hyperspectral thermal infrared remote sensing technology has developed rapidly on the basis of absorbing multispectral thermal infrared remote sensing technology
and has become an important research direction and breakthrough point of thermal infrared remote sensing research. However
there are some problems in the current hyperspectral thermal infrared remote sensing
such as lack of available data
traditional processing methods
limited inversion accuracies
and difficult implementation of the applications. To further clarify the research progress and existing challenges of hyperspectral thermal infrared remote sensing
based on the in-depth analysis of related literature
this paper sorts out the development process and hot spots of hyperspectral thermal infrared research
introduces the main hyperspectral thermal infrared sensors at home and abroad
and analyzes the current situation and problems of the atmospheric correction of hyperspectral atmospheric data
the separation of surface temperature and emissivity
and the integrated inversion of the key characteristic parameters of the land and atmosphere. Finally
the application of the relevant typical industries is summarized
and the future development direction of hyperspectral thermal infrared is prospected
so as to provide reference and help for the future research of hyperspectral thermal infrared remote sensing.
高光谱热红外遥感反演地表温度发射率大气廓线
hyperspectralthermal infraredremote sensing retrievalland surface temperatureemissivityatmospheric profile
Aires F, Chedin A, Scott N A and Rossow W B. 2002a. A regularized neural net approach for retrieval of atmospheric and surface temperatures with the IASI instrument. Journal of Applied Meteorology, 41(2): 144-159 [DOI: 10.1175/1520-0450(2002)041http://dx.doi.org/10.1175/1520-0450(2002)041]
Aires F, Rossow W B, Scott N A and Chedin A. 2002b. Remote sensing from the infrared atmospheric sounding interferometer instrument - 1. Compression, denoising, and first-guess retrieval algorithms. Journal of Geophysical Research-Atmospheres, 107(D22): 4619 [DOI: 10.1029/2001JD000955http://dx.doi.org/10.1029/2001JD000955]
Barsi J A, Barker J L and Schott J R. 2003. An atmospheric correction parameter calculator for a single thermal band earth-sensing instrument. IGARSS, 5, 3014-3016
Barducci A and I Pippi. 1996. Temperature and emissivity retrieval from remotely sensed images using the ‘grey body emissivity’ method. IEEE Transactions on Geoscience and Remote Sensing, 34: 681-695 [DOI: 10.1109/36.499748http://dx.doi.org/10.1109/36.499748]
Becker F and Li Z L. 1990. Towards a local split window method over land surfaces. International Journal of Remote Sensing, 11(3):369-393 [DOI: 10.1080/01431169008955028http://dx.doi.org/10.1080/01431169008955028]
Berk A, Anderson G P, Acharya P K, Bernstein L S, Muratov L, Lee J, Fox M, Adler-Golden S M, Chetwynd J H, Hoke M L, Lockwood R B, Cooley T W and Gardner J A. 2005. Modtran5: a reformulated atmospheric band model with auxiliary species and practical multiple scattering options. Proceedings of SPIE - The International Society for Optical Engineering, 5425, 341-347 [DOI: 10.1117/12.578758http://dx.doi.org/10.1117/12.578758]
Borel C C. 1997. Iterative retrieval of surface emissivity and temperature for a hyperspectral sensor. Office of Scientific & Technical Information Technical Reports, 1-5
Borel C C. 1998. Surface emissivity and temperature retrieval for a hyperspectral sensor//Proceedings of IEEE International Symposium on Geoscience and Remote Sensing (IGARSS). Seattle, WA:IEEE: 546-549 [DOI: 10.1109/IGARSS.1998.702966http://dx.doi.org/10.1109/IGARSS.1998.702966]
Borel C C. 2008. Error analysis for a temperature and emissivity retrieval algorithm for hyperspectral imaging data. International Journal of Remote Sensing, 29(17/18): 5029-5045 [DOI:10.1080/01431160802036540http://dx.doi.org/10.1080/01431160802036540]
Buitrago M F, Groen T A, Hecker C A and Skidmore A K. 2016. Changes in thermal infrared spectra of plants caused by temperature and water stress. ISPRS J. Photogramm. Remote Sens. 111, 22-31 [DOI: 10.1016/j.isprsjprs.2015. 11.003http://dx.doi.org/10.1016/j.isprsjprs.2015.11.003]
Cao X F, Li X Y, Luo Q, Liu S H, Li P and Liu X. 2021. Review of temperature profile inversion of satellite-borne infrared hyperspectral sensors. National Remote Sensing Bulletin, 25(2): 577-598
曹西凤, 李小英, 罗琪, 刘双慧, 李鹏, 刘欣. 2021. 星载红外高光谱传感器温度廓线反演综述. 遥感学报, 25(2): 577-598 [DOI: 10.11834/jrs.20210009http://dx.doi.org/10.11834/jrs.20210009]
Chalon G, Cayla F and Diebel D. 2001. IASI: an advance sounder for operational meteorology. Proceedings of the 52nd Congress of IAF. Toulouse.
Chang S J , Sheng Z, Du H D, Ge W and Zhang W. 2020. A channel selection method for hyperspectral atmospheric infrared sounders based on layering. Atmospheric Measurement Techniques, 13(2): 629-644 [DOI: 10.5194/amt-13-629-2020http://dx.doi.org/10.5194/amt-13-629-2020]
Chaumat L, Standfuss C, Tournier B, Armante R and Scott N A. 2012. 4A/OP reference documentation (NOVELTIS). [http://4aop.noveltis.com/sites/4aop.noveltis.loc/files/NOV-3049-NT-1178v4.3.pdfhttp://4aop.noveltis.com/sites/4aop.noveltis.loc/files/NOV-3049-NT-1178v4.3.pdf]
Chen M S, Qian Y G, Wang N, Ma L L, Li C R and Tang L L. 2016.A temperature and emissivity retrieval algorithm based on atmospheric absorption feature from hyperspectral thermal infrared data. Journal of Infrared and Millimeter Waves, 35(5): 617:624
陈梦说, 钱永刚, 王宁, 马灵玲, 李传荣, 唐伶俐. 2016. 基于大气吸收线特征的高光谱热红外数据地表温度/发射率反演算法. 红外与毫米波学报, 35(5): 617:624 [DOI: 10.11972/j.issn.1001-9014.2016.05.017http://dx.doi.org/10.11972/j.issn.1001-9014.2016.05.017]
Chen M S, Ni L, Jiang X G and Wu H. 2019. Retrieving atmospheric and land surface parameters from at-sensor thermal infrared hyperspectral data with artificial neural network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7), 2409-2416 [DOI:10.1109/JSTARS.2019.2904992http://dx.doi.org/10.1109/JSTARS.2019.2904992]
Cheng J, Xiao Q, Li X W, Liu Q H, Du Y M and Nie A X. 2007. Multi-layer perceptron neural network based algorithm for simultaneous retrieving temperature and emissivity from hyperspectral FTIR dataset. IEEE International Geoscience and Remote Sensing Symposium, 4383-4385 [DOI: 10.1109/IGARSS.2007.4423824http://dx.doi.org/10.1109/IGARSS.2007.4423824]
Cheng J, Liu Q H, Li X W, Xiao Q, Liu Q, and Du Y M. 2008. Correlation-based temperature and emissivity separation algorithm. Science in China Series D-Earth Sciences, 51(3):357-369 [DOI: 10.1007/s11430-008-0022-7http://dx.doi.org/10.1007/s11430-008-0022-7]
Cheng J, Liang S L, Wang J D and Li X W. 2010. A stepwise refining algorithm of temperature and emissivity separation for hyperspectral thermal infrared data. IEEE Transactions on Geoscience and Remote Sensing, 48(3):1588-1597 [DOI: 10.1109/TGRS.2009.2029852http://dx.doi.org/10.1109/TGRS.2009.2029852]
Cheng J, Liang S L, Liu Q H and Li X W. 2011. Temperature and emissivity separation from ground-based MIR hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 49(4): 1473-1484 [DOI: 10.1109/TGRS.2010.2076818http://dx.doi.org/10.1109/TGRS.2010.2076818]
Cristóbal J, Jimenez-Munoz J C, Prakash A, Mattar C, Skokovic D and Sobrino J A. 2018. An improved single-channel method to retrieve land surface temperature from the Landsat-8 thermal band. Remote Sensing, 10(3): 1-14 [DOI: 10.3390/rs10030431http://dx.doi.org/10.3390/rs10030431]
Coll C, Galve J M, Niclos R, Valor E and Barbera M J. 2019. Angular variations of brightness surface temperatures derived from dual-view measurements of the Advanced Along-Track Scanning Radiometer using a new single band atmospheric correction method. Remote Sensing of Environment, 223:274-290 [DOI: 10.1016/j.rse.2019.01.021http://dx.doi.org/10.1016/j.rse.2019.01.021]
Cooper D I and Asrar G. 1989. Evaluating atmospheric correction models for retrieving surface temperatures from the AVHRR over a tallgrass prairie. Remote Sensing of Environment, 27(1): 93-102 [DOI: 10.1016/0034-4257(89)90040-0http://dx.doi.org/10.1016/0034-4257(89)90040-0]
Cui Y J, Li J, Wang Y Y, Iiu Y M, Chen Z and Du J G. Application of gas remote sensing technique to earthquake monitoring. Advances in Earth Science, 30(02),284-294
崔月菊, 李静, 王燕艳, 刘永梅, 陈志, 杜建国. 2015. 遥感气体探测技术在地震监测中的应用. 地球科学进展, 30(02),284-294
Dai J J, Złao L X. Jiang Q, Wang H Y and Liu T Y. 2020. Review of thermal infrared spectroscopy applied in geological ore exploration. Acta Geologica Sinica, 94(8): 2520-2533
代晶晶, 赵龙贤, 姜琪, 王海宇, 刘婷玥. 2020. 热红外高光谱技术在地质找矿中的应用综述. 地质学报，94(8): 2520-2533 [DOI: 10. 19762/j. cnki. dizhixuebao. 2020172http://dx.doi.org/10.19762/j.cnki.dizhixuebao.2020172]
Du Y M, Li H, Cao B, Bian Z and Su Z. 2020. A Modified interactive spectral smooth temperature emissivity separation algorithm for low-temperature surface. IEEE Transactions on Geoscience and Remote Sensing, PP(99): 1-11 [DOI: 10.1109/TGRS.2020.2982960http://dx.doi.org/10.1109/TGRS.2020.2982960]
Ellicott E, Vermote E, Petitcolin F and Hook S J. 2009. Validation of a new parametric model for atmospheric correction of thermal infrared data. IEEE Transactions on Geoscience and Remote Sensing, 47(1), 295-311 [DOI: 10.1109/TGRS.2008.2006182http://dx.doi.org/10.1109/TGRS.2008.2006182]
Galfalk M, Olofsson G, Crill P and Bastviken D. 2016. Making methane visible. Nature Climate Change, 6(4):426-430 [DOI：10.1038/nclimate2877http://dx.doi.org/10.1038/nclimate2877]
Galve J M, Sanchez J M, Coll C and Villodre J. 2018. A New Single-Band Pixel-by-Pixel Atmospheric Correction Method to Improve the Accuracy in Remote Sensing Estimates of LST. Application to Landsat 7-ETM+. Remote Sensing, 10(6): 1-19 [DOI: 10.3390/rs10060826http://dx.doi.org/10.3390/rs10060826]
Gillespie A R, 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 [DOI: 10.1109/36.700995http://dx.doi.org/10.1109/36.700995]
Goetz A F H, Vane G, Solomon J E and Rock B N. 1985. Imaging spectrometry for earth remote sensing. Science, 228(4704): 1147-1153 [DOI: 10.1126/science.228.4704.1147http://dx.doi.org/10.1126/science.228.4704.1147]
Goetz A F H. 2009. Three decades of hyperspectral remote sensing of the earth: a personal view. Remote Sensing of Environment, 113(S1): S5-S16 [DOI: 10.1016/j.rse.2007.12.014http://dx.doi.org/10.1016/j.rse.2007.12.014]
Gu D G, Gillespie A R, Kahle A B and Palluconi F D. 2000. Autonomous atmospheric compensation (AAC) of high resolution hyperspectral thermal infrared remote-sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 38(6): 2557-2570 [DOI: 10.1109/36.885203http://dx.doi.org/10.1109/36.885203]
Hsiao T C, Fereres E, Acevedo E and Henderson D W. 1976. Water Stress and Dynamics of Growth and Yield of Crop Plants. In Water and Plant Life SE - 18; Lange, O.L.,
Kappen, L., Schulze, E.-D., Eds.; Ecological Studies; Springer: Berlin/Heidelberg,Germany, Volume 19, pp. 281-305
Hocking J, Rayer P J, Rundle D, Saunders R W, Matricardi M, Geer A, Brunel P and Vidot J. 2013. RTTOV v11 Users Guide, NWP-SAF report, Met. Office, Exeter, UK
Hu X, Tian S F, Ding L L and Zhou J J. 2015. Comparison of two emissivity inversion methods for airborne hyperspectral thermal infrared data. Journal of Remote Sensing, (02), 000302-318
胡骁, 田淑芳, 丁雷龙, 周家晶. 2015. 航空高光谱热红外数据的两种发射率反演方法对比. 遥感学报, (02), 000302-318 [DOI: 10.11834/jrs.20153348]
Hulley G C and Hook S J. 2009. Intercomparison of versions 4, 4.1 and5 of the MODIS land surface temperature and emissivity products and validation with laboratory measurements of sand samples from the Namib Desert, Namibia. Remote Sensing of Environment, 113(6): 1313-1318 [DOI: 10.1016/j.rse.2009.02.018http://dx.doi.org/10.1016/j.rse.2009.02.018]
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 [DOI: 10.1109/TGRS.2010.2063034http://dx.doi.org/10.1109/TGRS.2010.2063034]
Hulley G C, Duren R M, Hopkins F M, Hook S J, Vance N, Guillevic P, Johnson W R, Eng B T, Mihaly J M, Jovanovic V M, Chazanoff S L, Staniszewski Z K, Kuai L, Worden J, Frankenbergb C, Rivera G, Aubrey A D, Miller C E, Malakar N K., Sánchez Tomás J M and Holmes K T. 2016. High spatial resolution imaging of methane and other trace gases with the airborne hyperspectral thermal emission spectrometer (HyTES). Atmos. Meas. Tech. 9, 2393-2408 [DOI: 10. 5194/amt-9-2393-2016http://dx.doi.org/10.5194/amt-9-2393-2016]
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 [DOI: 10.1109/TGRS.2016. 2611566http://dx.doi.org/10.1109/TGRS.2016.2611566]
Jacob F, Petitcolin F, Schmugge T, Vermote E, French A and Ogawa K. 2004. Comparison of land surface emissivity and radiometric temperature derived from MODIS and ASTER sensors, Remote Sensing of Environment, 90(2): 137-152 [DOI: 10.1016/j.rse.2003.11.015http://dx.doi.org/10.1016/j.rse.2003.11.015]
Jiang D M, Cao S Q and Qu Y M. 2010. A neural networks approach to retrieval of atmospheric temperature profile from high spectral resolution infrared measurements. 26(6): 819-824
蒋德明，曹思沁，屈佑铭. 2010. 利用神经网络方法从高光谱分辨率红外遥感资料反演大气温度廓线. 热带气象学报, 26(6): 819-824
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): 4688 [DOI: 10.1029/2003JD003480http://dx.doi.org/10.1029/2003JD003480]
Jiménez-Muñoz J C, Cristobal J, Sobrino J A, Soria G, Ninyerola M and Pons X. 2009. Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data. IEEE Transactions on Geoscience and Remote Sensing, 47(1): 339-349 [DOI: 10.1109/TGRS.2008.2007125http://dx.doi.org/10.1109/TGRS.2008.2007125]
Jiménez-Muñoz J C and Sobrino J A. 2010. A single-channel algorithm for land-surface temperature retrieval from ASTER data. IEEE Geoscience and Remote Sensing Letters, 7(1): 176-179 [DOI: 10.1109/LGRS.2009.2029534http://dx.doi.org/10.1109/LGRS.2009.2029534]
Jin Y L. 2018. Research on Gas Monitoring Technology Based on Infrared Hyperspectral Imaging. Harbin Engineering University(金亚亮. 2018. 基于红外高光谱成像的气体监测技术研究.哈尔滨工程大学)
Kanani K, Poutier L, Nerry F and Stoll M P. 2007. Directional effects consideration to improve out-doors emissivity retrieval in the 3-13 um domain. Optics Express. 15(19): 12464:12482 [DOI: 10.1364/OE.15.012464http://dx.doi.org/10.1364/OE.15.012464]
Kuai L, Worden J R, Li K F, Hulley G C, Hopkins F M, Miller C E, Hook S J, Duren R M and Aubrey A D. 2016. Characterization of anthropogenic methane plumes with the hyperspectral thermal emission spectrometer (HyTES): a retrieval method and error analysis. Atmos. Meas. Tech. 9, 3165-3173 [DOI: 10.5194/ amt-9-3165-2016http://dx.doi.org/10.5194/amt-9-3165-2016]
Laio F, Porporato A, Ridolfi L and Rodriguez-Iturbe I. 2001. Plants in water-controlled ecosystems: active role in hydrologic processes and response to water stress. Advances in Water Resources, 24(7), 707-723 [DOI：10.1016/S0309-1708(01)00005-7http://dx.doi.org/10.1016/S0309-1708(01)00005-7]
Lan X, Zhao E, Li Z L, Labed J and Nerry F. 2019. An improved linear spectral emissivity constraint method for temperature and emissivity separation using hyperspectral thermal infrared data. Sensors, 19(24):5552 [DOI: 10.3390/s19245552http://dx.doi.org/10.3390/s19245552]
Lan X Y. 2020. Land surface temperature retrieval from hyperspectral thermal infrared data. Université De Strasbourg
Li C, Tian S, Li S and Yin M. 2016. Temperature and emissivity separation via sparse representation with thermal airborne hyperspectral imager data. Journal of Applied Remote Sensing, 10(4):042003 [DOI:10.1117/1.JRS.10.042003http://dx.doi.org/10.1117/1.JRS.10.042003]
Li J, Weisz E and Zhou D. 2007. Physical retrieval of surface emissivity spectrum from hyperspectral infrared radiances. Geophysical Research Letters, 34(16): L16812 [DOI: 10.1029/2007GL030543http://dx.doi.org/10.1029/2007GL030543]
Li Z L, Stoll M P, Zhang R H, Jia L and Su Z B. 2001. On the separate retrieval of soil and vegetation temperatures from ATSR data. Science in China Series D: Earth Sciences, 44(2): 97-111 [DOI:10.1007/BF02879653http://dx.doi.org/10.1007/BF02879653]
Li Z L, Tang B H, Wu H, Ren H, Yan G, Wan Z, Trigo I F and Sobrino J A. 2013. Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment, 131, 14-37 [DOI: 10.1016/j.rse.2012.12.008http://dx.doi.org/10.1016/j.rse.2012.12.008]
Li Z L, Duan S B, Tang B H, Wu H, Ren H Z, Yan G J, Tang R L and Leng P. 2016. Review of methods for land surface temperature derived from thermal infrared remotely sensed data. Journal of Remote Sensing, 20(5): 899-920
李召良, 段四波, 唐伯惠, 吴骅, 任华忠, 阎广建, 唐荣林, 冷佩. 2016. 热红外地表温度遥感反演方法研究进展. 遥感学报, 20(5): 899-920 [DOI:10.11834/jrs.20166192http://dx.doi.org/10.11834/jrs.20166192]
Luz B R and Crowley J K. 2010. Identification of plant species by using high spatial and spectral resolution thermal infrared (8.0-13.5 mu m) imagery. Remote Sensing of Environment, 114(2): 404-413 [DOI: 10.1016/j.rse.2009.09.019http://dx.doi.org/10.1016/j.rse.2009.09.019]
Ma X L, Wan Z M, Moeller C C, Menzel W P, Gumley L E and Zhang Y L. 2000. Retrieval of geophysical parameters from moderate resolution imaging spectroradiometer thermal infrared data: evaluation of a two-step physical algorithm. Applied Optics, 39(20), 3537 [DOI: 10. 1364/AO.39.003537http://dx.doi.org/10.1364/AO.39.003537]
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 [DOI: 10.1016/j.rse.2016.04.023http://dx.doi.org/10.1016/j.rse.2016.04.023]
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 [DOI: 10.1029/JC080i036p05113http://dx.doi.org/10.1029/JC080i036p05113]
Meerdink S K, Roberts D A, King J Y, Roth K L, Dennison P E, Amaral C H and Hook S J. 2016. Linking seasonal foliar traits to VSWIR-TIR spectroscopy across California ecosystems. Remote Sensing of Environment, 186: 322-338 [DOI: 10.1016/j.rse. 2016.08.003http://dx.doi.org/10.1016/j.rse.2016.08.003]
Menke W. 1984. Geophysical data analysis: discrete inverse theory. Columbia University, New York: Academic Press [DOI: 10.1016/B978-0-12-397160-9.00019-9http://dx.doi.org/10.1016/B978-0-12-397160-9.00019-9]
Neinavaz E, Skidmore A K , Darvishzadeh R and Groen T A. 2017. Retrieving vegetation canopy water content from hyperspectral thermal measurements. Agricultural and Forest Meteorology. 247, 365-375 [DOI：10.1016/J.AGRFORMET.2017.08.020http://dx.doi.org/10.1016/J.AGRFORMET.2017.08.020]
Ninomiya Y and Pu B. 2019. Thermal infrared multispectral remote sensing of lithology and mineralogy based on spectral properties of materials. ORE Geology Reviews, 108:54-72 [DOI: 10.1016/j.oregeorev.2018.03.012http://dx.doi.org/10.1016/j.oregeorev.2018.03.012]
Ni L, Xu H G and Zhou X M. 2020. Mineral identification and mapping by synthesis of hyperspectral VNIR/SWIR and multispectral TIR remotely sensed data with different classifiers, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 3155-3163 [DOI: 10.1109/JSTARS.2020.2999057http://dx.doi.org/10.1109/JSTARS.2020.2999057]
Norman, J M and Becker F. 1995. Terminology in thermal infrared remote sensing of natural surfaces. Agricultural and Forest Meteorology 77: 153-166 [DOI: 10.1016/0168-1923(95)02259-Zhttp://dx.doi.org/10.1016/0168-1923(95)02259-Z]
Pelta R and Ben-Dor E. 2019. An exploratory study on the effect of petroleum hydrocarbon on soils using hyperspectral longwave infrared imagery. Remote Sensing, 11(5): 1-15 [DOI: 10.3390/rs11050569http://dx.doi.org/10.3390/rs11050569]
Qin Z H, 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 [DOI：10.1080/0143116001000697 1http://dx.doi.org/10.1080/01431160010006971]
Qin Z H, LI W J, Zhang M H, Arnon K and Pedro B. 2003. Estimating of the essential atmospheric parameters of mono. window algorithm for land surface temperature retrieval from Landsat TM6. Remote Sensing for Land & Resources, 2: 37-43
覃志豪, LI W J, Zhang M H, Arnon K, Pedro B. 2003. 单窗算法的大气参数估计方法. 国土资源遥感, 2: 37-43
Rabier F, Fourrié N and Chafäi D. 2010. Channel selection methods for infrared atmospheric sounding interferometer radiances. Quarterly Journal of the Royal Meteorological Society, 128(581): 1011-1027 [DOI: 10.1256/0035900021643638http://dx.doi.org/10.1256/0035900021643638, 2010]
Raissouni N and Sobrino J A. 2000. Toward remote sensing methods for land cover dynamic monitoring: Application to morocco. International Journal of Remote Sensing, 21(2), 353-366 [DOI:10.1080/014311 600210876http://dx.doi.org/10.1080/014311600210876]
Ren H Z, Ye X, Nie J, Meng J J, Fan W J, Qin Q M, Liang Y Z and Liu H C. 2021. Retrieval of land surface temperature, emissivity, and atmospheric parameters from hyperspectral thermal infrared image using a feature-band linear-format hybrid algorithm. IEEE Transactions on Geoscience and Remote Sensing, 1-15 [DOI: 10.1109/TGRS.2020.3047381http://dx.doi.org/10.1109/TGRS.2020.3047381]
Rodgers C D. 1976. Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation. Reviews of Geophysics, 14(4): 609-624 [DOI: 10.1029/RG014i004p00609http://dx.doi.org/10.1029/RG014i004p00609]
Sabol D E, Gillespie A R, Abbott E and Yamada G. 2009. Field validation of the ASTER temperature-emissivity separation algorithm. Remote Sensing of Environment, 113: 2328-44 [DOI: 10.1016/j.rse.2009.06.008http://dx.doi.org/10.1016/j.rse.2009.06.008]
Salisbury J W. 1986. Preliminary measurements of leaf spectral reflectance in the 8—14 μm region. International Journal of Remote Sensing, 7(12), 1879-1886 [DOI:10.1080/01431168608948981http://dx.doi.org/10.1080/01431168608948981]
Scafutto R D M, Lievens C, Hecker C, van der Meer F D and de Souza C R. 2021. Detection of petroleum hydrocarbons in continental areas using airborne hyperspectral thermal infrared data (SEBASS). Remote Sensing of Environment, 256,1-14 [DOI: 10.1016/j.rse.2021.112323http://dx.doi.org/10.1016/j.rse.2021.112323]
Shao H, Liu C, Xie F, Li C and Wang J. 2020. Noise-sensitivity analysis and improvement of automatic retrieval of temperature and emissivity using spectral smoothness. Remote Sensing, 12(14), 2295 [DOI:10.3390/rs12142 295http://dx.doi.org/10.3390/rs12142295]
Shenk W E and Curran R J. 1974. The detection of dust storms over land and water with satellite visible and infrared measurements. Monthly Weather Review, 102(12) [DOI: 10.1175/1520-0493(1974)1022.0.CO;2]
Snyder W C, Z Wan, Y Zhang and Feng Y Z. 1998. Classification- based emissivity for land surface temperature measurement from space. International Journal of Remote Sensing [DOI:10.1080/0143116982 14497http://dx.doi.org/10.1080/014311698214497]
Sobrino J A, Jiménez-Muoz J C and Paolini L. 2004. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90(4), 434-440 [DOI:10.1016/ j.rse.2004.02.003http://dx.doi.org/10.1016/j.rse.2004.02.003]
Sobrino J A and Jiménez-Muñoz J C. 2005. Land surface temperature retrieval from thermal infrared data: an assessment in the context of the Surface Processes and Ecosystem Changes Through Response Analysis (SPECTRA) mission. Journal of Geophysical Research-Atmospheres, 110(D16): D16103 [DOI: 10.1029/ 2004JD005588http://dx.doi.org/10.1029/2004JD005588]
Sobrino J A, Jimenez-Munoz J C, Zarco-Tejada P J, Sepulcre-Canto G and de Miguel E. 2006. Land surface temperature derived from airborne hyperspectral scanner thermal infrared data. Remote Sensing of Environment, 102(1-2):99-115 [DOI: 10.1016/j.rse.2006.02.001http://dx.doi.org/10.1016/j.rse.2006.02.001]
Song C, Yin Q and Xie Y N. 2019. Development of channel selection methods for infrared atmospheric vertical sounding. Infrared, 40(6): 18-26
宋慈, 尹球, 谢亚楠. 2019. 红外大气垂直探测通道优选方法的发展. 红外, 40(6): 18-26 [DOI: 10.3969/j.issn.1672-8785.2019.06.004http://dx.doi.org/10.3969/j.issn.1672-8785.2019.06.004]
Tanner C B. 1963. Plant temperatures. Agronomy Journal, 55(2), 210-211 [DOI:10.2134/agronj1963.0002196200 5500020043xhttp://dx.doi.org/10.2134/agronj1963.00021962005500020043x]
Thome K, Palluconi F, Takashima T and Masuda K. 1998. Atmospheric correction of ASTER, IEEE Transactions on Geoscience and Remote Sensing, 36(4): 1199-1211 [DOI: 10.1109/36.701026http://dx.doi.org/10.1109/36.701026]
Tong Q X, Zhang B and Zhang L F. 2016. Current progress of hyperspectral remote sensing in China. Journal of Remote Sensing, 20(5): 689-707
童庆禧, 张兵, 张立福. 2016.中国高光谱遥感的前沿进展，遥感学报, 20(05):689-707 [DOI:10.11834/jrs.20166264http://dx.doi.org/10.11834/jrs.20166264]
Tong Q X, Xue Y Q and Zhang L F. 2014. Progress in hyperspectral remote sensing science and technology in china over the past three decades. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1): 70-91 [DOI: 10.1109/ JSTARS.2013.2267204http://dx.doi.org/10.1109/JSTARS.2013.2267204]
Tong Q X, Meng Q Y and Yang H. 2018. Development and prospect of the remote sensing technology. City and Disaster Reduction, 06:2-11
童庆禧 孟庆岩 杨杭. 2018.遥感技术发展历程与未来展望，城市与减灾. (06):2-11
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 [DOI: 10.1109/36.911125http://dx.doi.org/10.1109/36.911125]
Tonooka H. 2005. Accurate atmospheric correction of ASTER thermal infrared imagery using the water vapor scaling method. IEEE Transactions on Geoscience and Remote Sensing, 43, 2778-2792 [DOI: 10.1109/TGRS.2005.857886http://dx.doi.org/10.1109/TGRS.2005.857886]
Tardy B, Rivalland V, Huc M, Hagolle O, Marcq S and Boulet G. 2016. A software tool for atmospheric correction and surface temperature estimation of landsat infrared thermal data. Remote Sensing, 8(9): 1-24 [DOI: 10.3390/rs8090696http://dx.doi.org/10.3390/rs8090696]
Ullah S, Schlerf M, Skidmore A K and Hecker C. 2012. Identifying plant species using mid-wave infrared (2.5—6 μm) and thermal infrared (8—14 μm) emissivity spectra. Remote Sensing of Environment, 118, 95-102 [DOI: 10.1016/j.rse.2011.11.008http://dx.doi.org/10.1016/j.rse.2011.11.008]
Van der Meijde M, Knox N M, Cundill S L, Noomen M, van der Werff H M A and Hecker C. 2013. Detection of hydrocarbons in clay soils: A laboratory experiment using spectroscopy in the mid- and thermal infrared. International Journal of Applied Earth Observation and Geoinformation, 23, 384-388 [DOI: 10.1016/j.jag.2012.11.001http://dx.doi.org/10.1016/j.jag.2012.11.001]
Veraverbeke S, Dennison P, Gitas I, Hulley G, Kalashnikova O , Katagis T, Kuai L, Meng R, Roberts D and Stavros N. 2018. Hyperspectral remote sensing of fire: State-of-the-art and future perspectives. Remote Sensing of Environment, 216: 105-121 [DOI: 10.1016/j.rse.2018.06.020http://dx.doi.org/10.1016/j.rse.2018.06.020]
Wan Z M and Dozier J. 1996. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geoscience & Remote Sensing, 34(4), 892-905 [DOI:10.1109/36.508406http://dx.doi.org/10.1109/36.508406]
Wan Z M and Li Z L. 1997. A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 35(4), 980-996 [DOI:10.1109/ 36.602541http://dx.doi.org/10.1109/36.602541]
Wang J Y and Li C L. 2021. Development and prospect of hyperspectral imager and its application. Chinese Journal of Space Science. 41(01): 22-33
王建宇，李春来. 2021. 高光谱遥感成像技术的发展与展望. 空间科学学报，41(01): 22-33
Wang N, Wu H, Nerry F, Li C R and Li Z L. 2011. Temperature and emissivity retrievals from hyperspectral thermal infrared data using linear spectral emissivity constraint. IEEE Transactions on Geoscience and Remote Sensing, 49(4), p.1291-1303 [DOI: 10.1109/TGRS.2010.2062527http://dx.doi.org/10.1109/TGRS.2010.2062527]
Wang N, Li Z L, Tang B H, Zeng F and Li C R. 2013. Retrieval of atmospheric and land surface parameters from satellite-based thermal infrared hyperspectral data using a neural network technique. International Journal of Remote Sensing, 34(9-10), 3485-3502 [DOI:10.1080/ 01431161.2012.716536http://dx.doi.org/10.1080/01431161.2012.716536]
Wang N, Qian Y G, Wu H, Ma L L , Tang L L and Li C R. 2019. Evaluation and comparison of hyperspectral temperature and emissivity separation methods influenced by sensor spectral properties. International journal of remote sensing, 40(5-6), 1693-1708 [DOI: 10.1080/01431161.2018.1484963http://dx.doi.org/10.1080/01431161.2018.1484963]
Wang R S, Gan F P, Yan B K, Yang S M and Wang Q H. 2010. Hyperspectral mineral mapping and its application. Remote Sensing for Land & Resources, (01): 1:13
王润生, 甘甫平, 闫柏琨, 杨苏明, 王青华. 2010. 高光谱矿物填图技术与应用研究.国土资源遥感, (01): 1-13 [DOI: 10.3724/SP.J.1146.2009.01622]
Wang X H, Qiu S, Jiang X G, Ouyang X Y and Li Z L. 2010. Land surface temperature and emissivity retrieval from hyperspectral thermal infrared data. Arid Land Geography, 33(3):419-426
王新鸿, 邱实, 姜小光, 欧阳晓莹, 李召良. 2010. 高光谱热红外数据反演地表温度与发射率方法研究. 干旱区地理, 33(3): 419:426 [DOI: CNKI:SUN:GHDL.0.2010-03-018http://dx.doi.org/CNKI:SUN:GHDL.0.2010-03-018]
Windahl E and De Beurs K. 2016. An intercomparison of Landsat land surface temperature retrieval methods under variable atmospheric conditions using in situ skin temperature. International Journal of Applied Earth Observation and Geoinformation, 51, 11-27 [DOI: 10.1016/j.jag.2016.04.003http://dx.doi.org/10.1016/j.jag.2016.04.003]
Yan B K, Liu S W, Wang R S, Gan F P, Chen W T and Yang S M. 2006. Quantitative inversion of the SIO, content in surface rocks using thermal infrared remote sensing. Geological Bulletin of China, 25(5) 639-
643闫柏琨, 刘圣伟, 王润生, 甘甫平, 陈伟涛, 杨苏明. 2006. 热红外遥感定量反演地表岩石的SiO2含量. 地质通报, (5):639-643
Yang H, Zhang L F, Zhang X W, Fang C H and Tong Q X. 2011. Algorithm of emissivity spectrum and temperature separation based on TASI data. Journal of Remote Sensing, 15(6), 1248-1264
杨杭, 张立福, 张学文, 房丛卉, 童庆禧. 2011. Tasi数据的温度与发射率分离算法. 遥感学报, 15(6), 1248-1264 [DOI: 10.11834/jrs.20110380http://dx.doi.org/10.11834/jrs.20110380]
Yang J J, Duan S B, Zhang X, Wu P and Gao M. 2020. Evaluation of seven atmospheric profiles from reanalysis and satellite-derived products: Implication for single-channel land surface temperature retrieval. Remote Sensing, 12(5): 1-24 [DOI:10.3390/rs12050791http://dx.doi.org/10.3390/rs12050791].
Yin M, Tian S F and Li S J. 2016. Atmospheric compensation based on combined autonomous atmospheric compensation algorithms. Journal of Remote Sensing, 20(3): 450-458
尹梅, 田淑芳, 李士杰. 2016. AAC算法的大气校正复合改进算法. 遥感学报, 20(3): 450-458 [DOI: 10.11834/jrs.20165131http://dx.doi.org/10.11834/jrs.20165131]
Young S J, Johnson B R and Hackwell J A. 2002. An in-scene method for atmospheric compensation of thermal hyperspectral data. Journal of Geophysical Research Atmospheres, 107 (D24), ACH-1-ACH 14-20 [DOI: 10.1029/2001JD001266http://dx.doi.org/10.1029/2001JD001266]
Yu P P, C X Shi, L Yang and S Shan. 2020. A new temperature channel selection method based on singular spectrum analysis for retrieving atmospheric temperature profiles from FY-4A/GIIRS. Advances in Atmospheric Sciences, 37(7): 735-750 [DOI: 10.1007/s00376-020-9249-9http://dx.doi.org/10.1007/s00376-020-9249-9].
Yu X, Guo X and Wu Z. 2014. Land surface temperature retrieval from Landsat 8 TIRS-Comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sensing, 6, 9829-9852 [DOI: 10.3390/rs6109829http://dx.doi.org/10.3390/rs6109829]
Zavodsky B T, Chou S H and Jedlovec G J. 2012. Improved regional analyses and heavy precipitation forecasts with assimilation of atmospheric infrared sounder retrieved thermodynamic profiles. IEEE Transactions on Geoscience and Remote Sensing, 50(11): 4243-4251 [DOI: 10.1109/TGRS.2012.2194158http://dx.doi.org/10.1109/TGRS.2012.2194158]
Zeng Q C, Dong C H, Peng G B, Zhao S X, Fang Z Y and Jiao M Y. 2007. Duststorms and the related disasters. Climate and environmental research, 12(3):225-226
曾庆存，董超华，彭公炳，赵思雄，方宗义，矫梅燕. 2007. 沙尘暴及相关的自然灾害. 气候与环境研究, 12(3):225-226 [DOI: 10.3969/j.issn.1006-9585.2007.03.002http://dx.doi.org/10.3969/j.issn.1006-9585.2007.03.002]
Zhang B. 2016. Advancement of hyperspectral image processing and information extraction. Journal of Remote Sensing, 20(5): 1062-1090
张兵. 2016. 高光谱图像处理与信息提取前沿. 遥感学报, 20(5): 1062-1090 [DOI: 10.11834/jrs.20166179http://dx.doi.org/10.11834/jrs.20166179]
Zhang P, Zhang X Y, Hu X Q, Qi J and Dong C H. 2007. Satellite Remote Sensing and Analysis of a Dust Event in 2006. Climatic and Environmental Research, 12(3): 302-308
张鹏, 张兴赢, 胡秀清, 齐瑾, 董超华. 2007. 2006年一次沙尘活动的卫星定量遥感和分析研究.气候与环境研究, 12(3): 302-308 [DOI: 10.3969/j.issn.1006-9585.2007.03.011http://dx.doi.org/10.3969/j.issn.1006-9585.2007.03.011]
Zhang P, Wang C, Chen L, Bai W G, Qi C L and Qi J. 2018. Current status of satellite-based dust aerosol remote sensing and some issues to be concerned. Meteorological monthly. 44(06): 725-736
张鹏, 王春姣, 陈林, 白文广, 漆成莉, 齐瑾. 沙尘气溶胶卫星遥感现状与需要关注的若干问题. 气象, 44(06): 725-736 [DOI：10.7519/j.issn.1000-0526.2018.06.001http://dx.doi.org/10.7519/j.issn.1000-0526.2018.06.001]
Zhang Y Z, Wu H, Jiang X G, Jiang Y Z, Liu Z X and Nerry F. 2017. Land surface temperature and emissivity retrieval from field-measured hyperspectral thermal infrared data using wavelet transform. Remote Sensing, 9(5), 454 [DOI:10.3390/rs9050454http://dx.doi.org/10.3390/rs9050454]
Zhou A M. 2017. Atmospheric temperature and humidity profiles retrieval from hyperspectral infrared simulation data based on FY-4. Nan Jing: Nanjing University of Information Science & Technology, 1:10 (周爱明. 2017. 基于风云四号高光谱红外模拟资料反演大气温湿廓线试验研究. 南京: 南京信息工程大学: 1-10)
Zhou S G and Cheng J. 2018. A multi-scale wavelet-based temperature and emissivity separation algorithm for hyperspectral thermal infrared data. International journal of remote sensing, 39(21-22), 8092-8112 [DOI: 10.1080/01431161.2018.1482019http://dx.doi.org/10.1080/01431161.2018.1482019]
Zhou L, Goldberg M, Barnet C, Cheng Z, Sun F, Wolf W, King T, Liu X, Sun H and Divakarla M. 2008. Regression of surface spectral emissivity from hyperspectral instruments. IEEE Transactions on Geoscience and Remote Sensing, 46(2), 328-333 [DOI: 10.1109/TGRS.2007.912712http://dx.doi.org/10.1109/TGRS.2007.912712]
Zhou X M, Wang N and Wu H. 2012. Comparison of two methods for atmospheric correction of hyperspectral thermal infrared data. Journal of Remote Sensing, 16(04): 796-808
周孝明, 王宁, 吴骅. 2012. 两种高光谱热红外数据大气校正方法的分析与比较，遥感学报, 16(04): 796-808
相关文章
相关作者
相关机构
京公网安备11010802024621