سابِق|تال

Hyperspectral thermal infrared remote sensing： Current status and perspectives

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：wuhua@igsnrr.ac.cn
  • Introduction：吴骅，1980年生，男，研究员，研究方向为热红外定量遥感、尺度转换和真实性检验。E-mail： wuhua@igsnrr.ac.cn
WU Hua12,  
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

LI Xiujuan12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

    Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

  • Email：lizhaoliang@caas.cn
  • Introduction：李召良，1964年生，男，研究员，研究方向为热红外遥感。E-mail： lizhaoliang@caas.cn
LI Zhaoliang123*,  
  • Affiliation：

    Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

DUAN Sibo3,  
  • Affiliation：

    Key Laboratory of Quantitative Remote Sensing Information Technology, Institute of Aerospace Information Innovation, Chinese Academy of Sciences, Beijing 100094, China

QIAN Yonggang4

ملخص

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.

مفهوم

hyperspectral;thermal infrared;remote sensing retrieval;land surface temperature;emissivity;atmospheric profile

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    Borel C C. 1997. Iterative retrieval of surface emissivity and temperature for a hyperspectral sensor. Office of Scientific & Technical Information Technical Reports, 1-5
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    Chalon G, Cayla F and Diebel D. 2001. IASI: an advance sounder for operational meteorology. Proceedings of the 52nd Congress of IAF. Toulouse.
  13. 13.
    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
  14. 14.
    Chaumat L, Standfuss C, Tournier B, Armante R and Scott N A. 2012. 4A/OP reference documentation (NOVELTIS). []
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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
  18. 18.
    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
  19. 19.
    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
  20. 20.
    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
  21. 21.
    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
  22. 22.
    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
  23. 23.
    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
  24. 24.
    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
  25. 25.
    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
  26. 26.
    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
  27. 27.
    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
  28. 28.
    Galfalk M, Olofsson G, Crill P and Bastviken D. 2016. Making methane visible. Nature Climate Change, 6(4):426-430
  29. 29.
    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
  30. 30.
    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
  31. 31.
    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
  32. 32.
    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
  33. 33.
    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
  34. 34.
    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.,
  35. 35.
    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
  36. 36.
    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
  37. 37.
    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
  38. 38.
    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
  39. 39.
    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
  40. 40.
    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
  41. 41.
    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
  42. 42.
    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
  43. 43.
    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
  44. 44.
    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
  45. 45.
    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
  46. 46.
    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)
  47. 47.
    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
  48. 48.
    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
  49. 49.
    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
  50. 50.
    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
  51. 51.
    Lan X Y. 2020. Land surface temperature retrieval from hyperspectral thermal infrared data. Université De Strasbourg
  52. 52.
    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
  53. 53.
    Li J, Weisz E and Zhou D. 2007. Physical retrieval of surface emissivity spectrum from hyperspectral infrared radiances. Geophysical Research Letters, 34(16): L16812
  54. 54.
    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
  55. 55.
    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
  56. 56.
    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
  57. 57.
    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
  58. 58.
    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
  59. 59.
    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
  60. 60.
    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
  61. 61.
    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
  62. 62.
    Menke W. 1984. Geophysical data analysis: discrete inverse theory. Columbia University, New York: Academic Press
  63. 63.
    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
  64. 64.
    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
  65. 65.
    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
  66. 66.
    Norman, J M and Becker F. 1995. Terminology in thermal infrared remote sensing of natural surfaces. Agricultural and Forest Meteorology 77: 153-166
  67. 67.
    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
  68. 68.
    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
  69. 69.
    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
  70. 70.
    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: , 2010]
  71. 71.
    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
  72. 72.
    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
  73. 73.
    Rodgers C D. 1976. Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation. Reviews of Geophysics, 14(4): 609-624
  74. 74.
    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
  75. 75.
    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
  76. 76.
    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
  77. 77.
    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
  78. 78.
    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]
  79. 79.
    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
  80. 80.
    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
  81. 81.
    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
  82. 82.
    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
  83. 83.
    Song C, Yin Q and Xie Y N. 2019. Development of channel selection methods for infrared atmospheric vertical sounding. Infrared, 40(6): 18-26
  84. 84.
    Tanner C B. 1963. Plant temperatures. Agronomy Journal, 55(2), 210-211
  85. 85.
    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
  86. 86.
    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
  87. 87.
    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
  88. 88.
    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
  89. 89.
    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
  90. 90.
    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
  91. 91.
    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
  92. 92.
    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
  93. 93.
    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
  94. 94.
    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
  95. 95.
    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
  96. 96.
    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
  97. 97.
    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
  98. 98.
    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
  99. 99.
    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
  100. 100.
    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
  101. 101.
    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
  102. 102.
    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
  103. 103.
    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
  104. 104.
    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-
  105. 105.
    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
  106. 106.
    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 .
  107. 107.
    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
  108. 108.
    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
  109. 109.
    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 .
  110. 110.
    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
  111. 111.
    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
  112. 112.
    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
  113. 113.
    Zhang B. 2016. Advancement of hyperspectral image processing and information extraction. Journal of Remote Sensing, 20(5): 1062-1090
  114. 114.
    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
  115. 115.
    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
  116. 116.
    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
  117. 117.
    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)
  118. 118.
    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
  119. 119.
    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
  120. 120.
    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

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website