{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T22:36:57Z","timestamp":1768775817662,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,5]],"date-time":"2021-11-05T00:00:00Z","timestamp":1636070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFC1522703"],"award-info":[{"award-number":["2020YFC1522703"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771385"],"award-info":[{"award-number":["41771385"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41622107"],"award-info":[{"award-number":["41622107"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Emissivity information derived from thermal infrared (TIR) hyperspectral imagery has the advantages of both high spatial and spectral resolutions, which facilitate the detection and identification of the subtle spectral features of ground targets. Despite the emergence of several different TIR hyperspectral imagers, there are still no universal spectral emissivity measurement standards for TIR hyperspectral imagers in the field. In this paper, we address the problems encountered when measuring emissivity spectra in the field and propose a practical data acquisition and processing framework for a Fourier transform (FT) TIR hyperspectral imager\u2014the Hyper-Cam LW\u2014to obtain high-quality emissivity spectra in the field. This framework consists of three main parts. (1) The performance of the Hyper-Cam LW sensor was evaluated in terms of the radiometric calibration and measurement noise, and a data acquisition procedure was carried out to obtain the useful TIR hyperspectral imagery in the field. (2) The data quality of the original TIR hyperspectral imagery was improved through preprocessing operations, including band selection, denoising, and background radiance correction. A spatial denoising method was also introduced to preserve the atmospheric radiance features in the spectra. (3) Three representative temperature-emissivity separation (TES) algorithms were evaluated and compared based on the Hyper-Cam LW TIR hyperspectral imagery, and the optimal TES algorithm was adopted to determine the final spectral emissivity. These algorithms are the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm, the improved Advanced Spaceborne Thermal Emission and Reflection Radiometer temperature and emissivity separation (ASTER-TES) algorithm, and the Fast Line-of-sight Atmospheric Analysis of Hypercubes-IR (FLAASH-IR) algorithm. The emissivity results from these different methods were compared to the reference spectra measured by a Model 102F spectrometer. The experimental results indicated that the retrieved emissivity spectra from the ISSTES algorithm were more accurate than the spectra retrieved by the other methods on the same Hyper-Cam LW field data and had close consistency with the reference spectra obtained from the Model 102F spectrometer. The root-mean-square error (RMSE) between the retrieved emissivity and the standard spectra was 0.0086, and the spectral angle error was 0.0093.<\/jats:p>","DOI":"10.3390\/rs13214453","type":"journal-article","created":{"date-parts":[[2021,11,7]],"date-time":"2021-11-07T20:42:54Z","timestamp":1636317774000},"page":"4453","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Field-Based High-Quality Emissivity Spectra Measurement Using a Fourier Transform Thermal Infrared Hyperspectral Imager"],"prefix":"10.3390","volume":"13","author":[{"given":"Lyuzhou","family":"Gao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2945-2708","authenticated-orcid":false,"given":"Liqin","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Printing and Packaging, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9446-5850","authenticated-orcid":false,"given":"Yanfei","family":"Zhong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoyang","family":"Jia","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MGRS.2018.2889610","article-title":"Longwave infrared hyperspectral imaging: Principles, progress, and challenges","volume":"7","author":"Manolakis","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3084","DOI":"10.1080\/01431161.2012.716540","article-title":"Land surface emissivity retrieval from satellite data","volume":"34","author":"Li","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3155","DOI":"10.1109\/JSTARS.2020.2999057","article-title":"Mineral Identification and Mapping by Synthesis of Hyperspectral VNIR\/SWIR and Multispectral TIR Remotely Sensed Data with Different Classifiers","volume":"13","author":"Ni","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","first-page":"10","article-title":"Airborne infrared-hyperspectral mapping for detection of gaseous and solid targets","volume":"7665","author":"Puckrin","year":"2010","journal-title":"SPIE Def. Secur. Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhu, X., Cao, L., Wang, S., Gao, L., and Zhong, Y.J.R.S. (2021). Anomaly Detection in Airborne Fourier Transform Thermal Infrared Spectrometer Images Based on Emissivity and a Segmented Low-Rank Prior. Remote Sens., 13.","DOI":"10.3390\/rs13040754"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2016.08.013","article-title":"Review of studies on tree species classification from remotely sensed data","volume":"186","author":"Fassnacht","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, H., Wu, K., Xu, H., and Xu, Y.J.R.S. (2021). Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks. Remote Sens., 13.","DOI":"10.3390\/rs13163117"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1109\/36.700995","article-title":"A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images","volume":"36","author":"Gillespie","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.rse.2015.05.019","article-title":"Improved surface temperature estimates with MASTER\/AVIRIS sensor fusion","volume":"167","author":"Grigsby","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.rse.2016.04.023","article-title":"A water vapor scaling model for improved land surface temperature and emissivity separation of MODIS thermal infrared data","volume":"182","author":"Malakar","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2012.12.008","article-title":"Satellite-derived land surface temperature: Current status and perspectives","volume":"131","author":"Li","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_12","first-page":"102357","article-title":"Cross-calibration of Chinese Gaofen-5 thermal infrared images and its improvement on land surface temperature retrieval","volume":"101","author":"Ye","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, J., Duan, S.-B., Zhang, X., Wu, P., Huang, C., Leng, P., and Gao, M.J.R.S. (2020). Evaluation of Seven Atmospheric Profiles from Reanalysis and Satellite-Derived Products: Implication for Single-Channel Land Surface Temperature Retrieval. Remote Sens., 12.","DOI":"10.3390\/rs12050791"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1109\/36.602541","article-title":"A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS\/MODIS data","volume":"35","author":"Wan","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2583","DOI":"10.1364\/OE.382813","article-title":"New land surface temperature retrieval algorithm for heavy aerosol loading during nighttime from Gaofen-5 satellite data","volume":"28","author":"Zhao","year":"2020","journal-title":"Opt. Express"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"22440","DOI":"10.1364\/OE.25.022440","article-title":"Optical design, laboratory test, and calibration of airborne long wave infrared imaging spectrometer","volume":"25","author":"Yuan","year":"2017","journal-title":"Opt. Express"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hackwell, J.A., Warren, D.W., Bongiovi, R.P., Hansel, S.J., Hayhurst, T.L., Mabry, D.J., Sivjee, M.G., and Skinner, J.W. (1996, January 4\u20139). LWIR\/MWIR imaging hyperspectral sensor for airborne and ground-based remote sensing. Proceedings of the Imaging Spectrometry II, Denver, CO, USA.","DOI":"10.1117\/12.258057"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, H., Xiao, Q., Li, H., and Zhong, B. (2011, January 9\u201311). Temperature and emissivity separation algorithm for TASI airborne thermal hyperspectral data. Proceedings of the 2011 International Conference on Electronics, Communications and Control (ICECC), Ningbo, China.","DOI":"10.1109\/ICECC.2011.6066288"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Montembeault, Y., Lagueux, P., Farley, V., Villemaire, A., and Gross, K.C. (2010, January 14\u201316). Hyper-Cam: Hyperspectral IR imaging applications in defence innovative research. Proceedings of the 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Reykjavik, Iceland.","DOI":"10.1109\/WHISPERS.2010.5594890"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.14358\/PERS.79.12.1099","article-title":"Temperature and Emissivity Separation from Thermal Airborne Hyperspectral Imager (TASI) Data","volume":"79","author":"Yang","year":"2013","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"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 Sens., 9.","DOI":"10.3390\/rs9050454"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1364\/AO.35.001679","article-title":"Portable Fourier transform infrared spectroradiometer for field measurements of radiance and emissivity","volume":"35","author":"Korb","year":"1996","journal-title":"Appl. Opt."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Salvaggio, C., and Miller, C.J. (2001, January 16\u201319). Methodologies and protocols for the collection of midwave and longwave infrared emissivity spectra using a portable field spectrometer. Proceedings of the Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, Orlando, FL, USA.","DOI":"10.1117\/12.437046"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"10981","DOI":"10.3390\/s111110981","article-title":"Thermal infrared spectrometer for Earth science remote sensing applications-instrument modifications and measurement procedures","volume":"11","author":"Christoph","year":"2011","journal-title":"Sensors"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3995","DOI":"10.3390\/rs4123995","article-title":"A hyperspectral thermal infrared imaging instrument for natural resources applications","volume":"4","author":"Schlerf","year":"2012","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4149","DOI":"10.1109\/JSTARS.2020.3010092","article-title":"New Airborne Thermal-Infrared Hyperspectral Imager System: Initial Validation","volume":"13","author":"Liu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yousefi, B., Sojasi, S., Castanedo, C.I., Beaudoin, G., Huot, F., Maldague, X.P., Chamberland, M., and Lalonde, E. (2016, January 18\u201321). Emissivity retrieval from indoor hyperspectral imaging of mineral grains. Proceedings of the Thermosense: Thermal Infrared Applications XXXVIII, Baltimore, MD, USA.","DOI":"10.1117\/12.2224379"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/LGRS.2008.2006005","article-title":"Longwave thermal infrared spectral variability in individual rocks","volume":"6","author":"Balick","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Farley, V., Belzile, C., Chamberland, M., Legault, J.-F., and Schwantes, K.R. (2004, January 2\u20134). Development and testing of a hyperspectral imaging instrument for field spectroscopy. Proceedings of the Imaging Spectrometry X, Denver, CO, USA.","DOI":"10.1117\/12.567741"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2158","DOI":"10.1109\/36.957278","article-title":"Sensitivity of iterative spectrally smooth temperature\/emissivity separation to algorithmic assumptions and measurement noise","volume":"39","author":"Ingram","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shao, H., Liu, C., Li, C., Wang, J., and Xie, F. (2020). Temperature and Emissivity Inversion Accuracy of Spectral Parameter Changes and Noise of Hyperspectral Thermal Infrared Imaging Spectrometers. Sensors, 20.","DOI":"10.3390\/s20072109"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, N., Qian, Y.G., Ma, L.L., Tang, L.L., and Li, C.R. (2016, January 21\u201324). Influence of Sensor Spectral Properties on Temperature and Emissivity Separation for Hyperspectral Thermal Infrared Data. Proceedings of the2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (Whispers), Los Angeles, CA, USA.","DOI":"10.1109\/WHISPERS.2016.8071775"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shao, H.L., Liu, C.Y., Xie, F., Li, C.L., and Wang, J.Y. (2020). Noise-sensitivity Analysis and Improvement of Automatic Retrieval of Temperature and Emissivity Using Spectral Smoothness. Remote Sens., 12.","DOI":"10.3390\/rs12142295"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1109\/LGRS.2011.2163699","article-title":"Surface Emissivity Retrieval From Airborne Hyperspectral Scanner Data: Insights on Atmospheric Correction and Noise Removal","volume":"9","author":"Sobrino","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Borel, C.C. (1998, January 6\u201310). Surface emissivity and temperature retrieval for a hyperspectral sensor. Proceedings of the IGARSS\u201998. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No. 98CH36174), Seattle, WA, USA.","DOI":"10.1109\/IGARSS.1998.702966"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hernandez-Baquero, E.D.S., and John, R. (2000, January 24\u201326). Atmospheric compensation for surface temperature and emissivity separation. Proceedings of the Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, Orlando, FL, USA.","DOI":"10.1117\/12.410364"},{"key":"ref_37","unstructured":"Borel, C.C. (April, January 31). ARTEMISS\u2014An algorithm to retrieve temperature and emissivity from hyper-spectral thermal image data. Proceedings of the 28th Annual GOMACTech Conference, Hyperspectral Imaging Session, Tampa, FL, USA."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, N., Tang, B.H., Li, C.R., and Li, Z.L. (2010, January 25\u201330). A Generalized Neural Network for Simultaneous Retrieval of Atmospheric Profiles and Surface Temperature from Hyperspectral Thermal Infrared Data. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5651405"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1109\/TGRS.2010.2062527","article-title":"Temperature and emissivity retrievals from hyperspectral thermal infrared data using linear spectral emissivity constraint","volume":"49","author":"Wang","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"24761","DOI":"10.1364\/OE.20.024761","article-title":"Practical retrieval of land surface emissivity spectra in 8\u201314 \u03bcm from hyperspectral thermal infrared data","volume":"20","author":"Wu","year":"2012","journal-title":"Opt. Express"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Adler-Golden, S., Conforti, P., Gagnon, M., Tremblay, P., and Chamberland, M. (2014, January 24\u201327). Remote sensing of surface emissivity with the telops Hyper-Cam. Proceedings of the 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lausanne, Switzerland.","DOI":"10.1109\/WHISPERS.2014.8077616"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Farley, V., Valli\u00e8res, A., Chamberland, M., Villemaire, A., and Legault, J.-F. (2006). Performance of the FIRST: A Long-Wave Infrared Hyperspectral Imaging Sensor, SPIE.","DOI":"10.1117\/12.689487"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, M., Zheng, S., Li, X., and Qin, X. (2014, January 26\u201328). A new image denoising method based on Gaussian filter. Proceedings of the 2014 International Conference on Information Science, Electronics and Electrical Engineering, Sapporo, Japan.","DOI":"10.1109\/InfoSEEE.2014.6948089"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1016\/j.sigpro.2005.02.002","article-title":"Savitzky\u2013Golay smoothing and differentiation filter for even number data","volume":"85","author":"Luo","year":"2005","journal-title":"Signal Process."},{"key":"ref_45","unstructured":"Borel, C.C. (1997, January 6\u20138). Iterative retrieval of surface emissivity and temperature for a hyperspectral sensor. Proceedings of the Proceedings for the First JPL Workshop on Remote Sensing of Land Surface Emissivity, Pasadena, CA, USA."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Adler-Golden, S.M., Conforti, P., Gagnon, M., Tremblay, P., and Chamberland, M. (2014, January 6\u20137). Long-wave infrared surface reflectance spectra retrieved from Telops Hyper-Cam imagery. Proceedings of the SPIE Defense + Security, Baltimore, MD, USA.","DOI":"10.1117\/12.2050446"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4453\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:26:37Z","timestamp":1760167597000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4453"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,5]]},"references-count":46,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13214453"],"URL":"https:\/\/doi.org\/10.3390\/rs13214453","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,5]]}}}