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Remote Sensing
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Advances in Thermal Infrared Remote Sensing II
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Advances in Thermal Infrared Remote Sensing II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: 30 August 2024 | Viewed by 2831

Special Issue Editors

Dr. Tian Hu

E-Mail Website
Guest Editor
Luxembourg Institute of Science and Technology, 4362 Esch-sur-Alzette, Luxembourg
Interests: thermal infrared remote sensing; ecohydroloy; ecosystem processes
Special Issues, Collections and Topics in MDPI journals
Dr. Mengmeng Wang

E-Mail Website
Guest Editor
Department of Remote Sensing Science, China University of Geosciences, Wuhan 430079, China
Interests: landsat surface temperature retrieval; local climate zone classification
Special Issues, Collections and Topics in MDPI journals
Dr. Vicente Burchard-Levine

E-Mail Website
Guest Editor
Institute of Agricultural Sciences, Spanish National Research Council (CSIC), 28006 Madrid, Spain
Interests: surface energy balance modeling; evapotranspiration; precision agriculture; ecohydrology
Special Issues, Collections and Topics in MDPI journals
Dr. Gilles Boulet

E-Mail Website
Guest Editor
CESBIO, Université de Toulouse, CNES, CNRS, IRD, UPS, 18 Avenue Edouard Belin, bpi 2801, CEDEX 9, 31401 Toulouse, France
Interests: water resources; semi arid lands; thermal infrared remote sensing; agrohydrology; ecohydrology
Special Issues, Collections and Topics in MDPI journals
Dr. Jean-Louis Roujean

E-Mail Website
Guest Editor
CESBIO, Université de Toulouse, CNES, CNRS, IRD, UPS, 18 Avenue Edouard Belin, bpi 2801, CEDEX 9, 31401 Toulouse, France
Interests: albedo; BRDF; agriculture; radiation forcing; modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Thermal infrared (TIR) remote sensing plays an increasingly important role in Earth observation, especially with the intensifying global warming and drying. TIR remote sensing captures longwave radiation from the land–atmosphere continuum and is sensitive to surface temperature and water stress conditions. Multiple sensors onboard different satellites have been launched to collect TIR images that are widely used in agricultural, environmental, and ecological applications, including the Landsat series, ASTER, MODIS, VIIRS, and SEVIRI. Pioneered by ECOSTRESS, future TIR missions aim to collect images with high spatio-temporal resolutions which would allow for an unprecedented opportunity for a wide range of applications such as agricultural irrigation, water resource management, and urban thermal environment monitoring.

This Special Issue aims to invite papers focusing on recent advances in TIR remote sensing, with the goal of facilitating a better utilization of future TIR missions. Topics may range from theoretic modeling and algorithm development to different applications.

Topics for this Special Issue include, but are not limited to, the following:

  • Land surface temperature retrieval and evaluation;
  • Thermal infrared radiative transfer modeling;
  • Surface energy balance modeling;
  • Evapotranspiration and water stress;
  • Surface radiation budget;
  • Ecosystem functioning;
  • Urban thermal environment;
  • Geologic exploration.

This Special Issue is the second edition of the Special Issue “Advances in Thermal Infrared Remote Sensing”.

Dr. Tian Hu
Dr. Mengmeng Wang
Dr. Vicente Burchard-Levine
Dr. Gilles Boulet
Dr. Jean-Louis Roujean
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • land surface temperature
  • evapotranspiration
  • thermal radiative transfer
  • surface energy balance
  • ecosystem functioning
  • urban thermal environment
  • future thermal infrared mission

Related Special Issue

Published Papers (3 papers)

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Research

18 pages, 5077 KiB  
Article
Estimation and Evaluation of 15 Minute, 40 Meter Surface Upward Longwave Radiation Downscaled from the Geostationary FY-4B AGRI
by Limeng Zheng, Biao Cao, Qiang Na, Boxiong Qin, Junhua Bai, Yongming Du, Hua Li, Zunjian Bian, Qing Xiao and Qinhuo Liu
Remote Sens. 2024, 16(7), 1158; https://doi.org/10.3390/rs16071158 - 27 Mar 2024
Viewed by 651
Abstract
Surface upward longwave radiation (SULR) is one of the four components of surface net radiation. Geostationary satellites can provide high temporal but coarse spatial resolution SULR products. Downscaling coarse SULR to a higher resolution is important for fine-scale thermal condition monitoring. Statistical regression [...] Read more.
Surface upward longwave radiation (SULR) is one of the four components of surface net radiation. Geostationary satellites can provide high temporal but coarse spatial resolution SULR products. Downscaling coarse SULR to a higher resolution is important for fine-scale thermal condition monitoring. Statistical regression downscaling is widely used due to its simplicity and is built on the assumption that the thermal parameter like land surface temperature (LST) or SULR has a relationship with the related surface factors like the normalized difference vegetation index (NDVI), and the relationship remains unchanged in any scales. In this study, to establish the relationship between SULR and the related surface factors, we chose the multiple linear regression (MLR) model and five surface factors (i.e., the modified normalized difference water index (MNDWI), normalized difference built-up and soil index (NDBSI), NDVI, normalized moisture difference index (NMDI), and urban index (UI)) to drive the downscaling process. Additionally, a step-by-step downscaling strategy was applied to reach the 100-fold increase in spatial resolution, transitioning the estimated SULR from 4 km of the advanced geostationary radiation imager (AGRI) onboard FengYun-4B (FY-4B) satellite to 40 m of the visual and infrared multispectral imager (VIMI) in infrared spectrum onboard GaoFen5-02 (GF5-02). Finally, we evaluated the downscaling results by comparing the downscaled SULR values with the in situ measured SULR and GF5-02-calculated SULR, and the root mean square errors (RMSEs) were 19.70 W/m2 and 24.86 W/m2, respectively. Throughout this MLR-based step-by-step downscaling method (high-frequency data from FY-4B and high spatial resolution data from GF5-02), high spatiotemporal SULR (15 min temporal resolution, 40 m spatial resolution) were successfully generated instead of coarse spatial resolution ones from the FY-4B satellite or a coarse temporal resolution one from the GF5-02 satellite, relieving the above-mentioned conflict to some extent. Full article
(This article belongs to the Special Issue Advances in Thermal Infrared Remote Sensing II)
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28 pages, 24992 KiB  
Article
The Potential of Using SDGSAT-1 TIS Data to Identify Industrial Heat Sources in the Beijing–Tianjin–Hebei Region
by Yanmei Xie, Caihong Ma, Yindi Zhao, Dongmei Yan, Bo Cheng, Xiaolin Hou, Hongyu Chen, Bihong Fu and Guangtong Wan
Remote Sens. 2024, 16(5), 768; https://doi.org/10.3390/rs16050768 - 22 Feb 2024
Cited by 1 | Viewed by 624
Abstract
It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with [...] Read more.
It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with low combustion temperatures and small scales. In this study, a new industrial heat source identification and classification model using SDGSAT-1 TIS and Landsat 8/9 Operational Land Imager (OLI) data was proposed to improve the accuracy and granularity of industrial heat source recognition. First, multiple features (thermal and optical features) were extracted using SDGSAT-1 TIS and Landsat 8/9 OLI data. Second, an industrial heat source identification model based on a support vector machine (SVM) and multiple features was constructed. Then, industrial heat sources were generated and verified based on the topological correlation between the identification results of the production areas and Google Earth images. Finally, the industrial heat sources were classified into six categories based on point-of-interest (POI) data. The new model was applied to the Beijing–Tianjin–Hebei (BTH) region of China. The results showed the following: (1) Multiple features enhance the differentiation and identification accuracy between industrial heat source production areas and the background. (2) Compared to active-fire-point (ACF) data (375 m) and Landsat 8/9 thermal infrared sensor (TIRS) data (100 m), nighttime SDGSAT-1 TIS data (30 m) facilitate the more accurate detection of industrial heat source production areas. (3) Greater than 2~6 times more industrial heat sources were detected in the BTH region using our model than were reported by Ma and Liu. Some industrial heat sources with low heat emissions and small areas (53 thermal power plants) were detected for the first time using TIS data. (4) The production areas of cement plants exhibited the highest brightness temperatures, reaching 301.78 K, while thermal power plants exhibited the lowest brightness temperatures, averaging 277.31 K. The production areas and operational statuses of factories could be more accurately identified and monitored with the proposed approach than with previous methods. A new way to estimate the thermal and air pollution emissions of industrial enterprises is presented. Full article
(This article belongs to the Special Issue Advances in Thermal Infrared Remote Sensing II)
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29 pages, 835 KiB  
Article
Physically Based Thermal Infrared Snow/Ice Surface Emissivity for Fast Radiative Transfer Models
by Nicholas R. Nalli, Cheng Dang, James A. Jung, Robert O. Knuteson, E. Eva Borbas, Benjamin T. Johnson, Ken Pryor and Lihang Zhou
Remote Sens. 2023, 15(23), 5509; https://doi.org/10.3390/rs15235509 - 27 Nov 2023
Viewed by 1144
Abstract
Accurate thermal infrared (TIR) fast-forward models are critical for weather forecasting via numerical weather prediction (NWP) satellite radiance assimilation and operational environmental data record (EDR) retrieval algorithms. The thermodynamic and compositional data about the surface and lower troposphere are derived from semi-transparent TIR [...] Read more.
Accurate thermal infrared (TIR) fast-forward models are critical for weather forecasting via numerical weather prediction (NWP) satellite radiance assimilation and operational environmental data record (EDR) retrieval algorithms. The thermodynamic and compositional data about the surface and lower troposphere are derived from semi-transparent TIR window bands (i.e., surface-sensitive channels) that can span into the far-infrared (FIR) region under dry polar conditions. To model the satellite observed radiance within these bands, an accurate a priori emissivity is necessary for the surface in question, usually provided in the form of a physical or empirical model. To address the needs of hyperspectral TIR satellite radiance assimilation, this paper discusses the research, development, and preliminary validation of a physically based snow/ice emissivity model designed for practical implementation within operational fast-forward models such as the U.S. National Oceanic and Atmospheric Administration (NOAA) Community Radiative Transfer Model (CRTM). To accommodate the range of snow grain sizes, a hybrid modeling approach is adopted, combining a layer scattering model based on the Mie theory (viz., the Wiscombe–Warren 1980 snow albedo model, its complete derivation provided in the Appendices) with a specular facet model. The Mie-scattering model is valid for the smallest snow grain sizes typical of fresh snow and frost, whereas the specular facet model is better suited for the larger sizes and welded snow surfaces typical of aged snow. Comparisons of the model against the previously published spectral emissivity measurements show reasonable agreement across zenith observing angles and snow grain sizes, and preliminary observing system experiments (OSEs) have revealed notable improvements in snow/ice surface window channel calculations versus hyperspectral TIR satellite observations within the NOAA NWP radiance assimilation system. Full article
(This article belongs to the Special Issue Advances in Thermal Infrared Remote Sensing II)
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