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

Deadline for manuscript submissions: closed (5 September 2023) | Viewed by 31655

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Special Issue Editors

Dr. Tian Hu

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Guest Editor

Luxembourg Institute of Science and Technology, 4362 Esch-sur-Alzette, Luxembourg
Interests: thermal infrared remote sensing; ecohydroloy; ecosystem processes
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Dr. Mengmeng Wang

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Guest Editor

Department of Remote Sensing Science, China University of Geosciences, Wuhan 430079, China
Interests: landsat surface temperature retrieval; local climate zone classification
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Dr. Vicente Burchard-Levine

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Institute of Agricultural Sciences, Spanish National Research Council (CSIC), 28006 Madrid, Spain
Interests: surface energy balance modeling; evapotranspiration; precision agriculture; ecohydrology
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Dr. Gilles Boulet

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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
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Dr. Jean-Louis Roujean

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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:

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.

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

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Related Special Issue

Advances in Thermal Infrared Remote Sensing II in Remote Sensing (8 articles)

Published Papers (11 papers)

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20 pages, 7825 KiB

Open AccessArticle

Improving HJ-1B/IRS LST Retrieval of the Generalized Single-Channel Algorithm with Refined ERA5 Atmospheric Profile Database

by Guoqin Zhang, Dacheng Li, Hua Li, Zhaopeng Xu, Zhiheng Hu, Jian Zeng, Yi Yang and Hui Jia

Remote Sens. 2023, 15(21), 5092; https://doi.org/10.3390/rs15215092 - 24 Oct 2023

Cited by 2 | Viewed by 1357

Abstract

Land surface temperature (LST) is a fundamental variable of environmental monitoring and surface equilibrium. Although the HJ-1B infrared scanner (IRS) has accumulated many observations, further application of HJ-1B/IRS is limited by the lack of LST products. This study refined the ERA5 atmospheric profile database, instead of the widely used traditional TIGR atmospheric profile database, and simulated the coefficients of the generalized single-channel (GSCs) algorithms to improve LST retrieval. GSCs can be divided into the GSC_w and GSC_wT algorithms, depending on whether the input is atmospheric water vapor content (WVC) or in situ near-surface air temperature and WVC. Land surface emissivity (LSE) was obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) and vegetation/snow cover products. Then, the retrieved LSTs were evaluated using the LSTs from the RTE algorithm, TIGR_w/TIGR_wT profiles, and in situ near-surface air temperature from the HiWATER experiment in China from 2012 to 2014. The bias (root mean square error (RMSE)) values are displayed as ERA5_wT < RTE < ERA5_w < TIGR_wT < TIGR_w. The accuracy of ERA5_wT, with a bias (RMSE) of 0.02 K (2.30 K), is higher than that of RTE, with a bias (RMSE) of 0.74 K (2.47 K). The accuracy of RTE is preferable to that of ERA5_w, with a bias (RMSE) of 0.89 K (2.48 K), followed by TIGR_wT, with a bias (RMSE) of −1.18 K (2.50 K), and then, TIGR_w, with a bias (RMSE) of 1.60 K (2.77 K). In summary, the accuracy of LST obtained by GSC from the refined ERA5 atmospheric profiles is higher than that obtained from the TIGR profiles. The accuracy of LST obtained by GSC_wT is greater than that obtained by GSC_w. The accuracy of LST obtained using in situ near-surface air temperature is higher than that obtained using ERA5 air temperature. The accuracy of LSE_ASTER is slightly better than that of LSE_MOD21. The aforementioned conclusions can provide scientific support to generate HJ-1B/IRS LST products. Full article

(This article belongs to the Special Issue Advances in Thermal Infrared Remote Sensing)

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25 pages, 16718 KiB

Open AccessArticle

Global Satellite Monitoring of Exothermic Industrial Activity via Infrared Emissions

by Christopher D. Elvidge, Mikhail Zhizhin, Tamara Sparks, Tilottama Ghosh, Stephen Pon, Morgan Bazilian, Paul C. Sutton and Steven D. Miller

Remote Sens. 2023, 15(19), 4760; https://doi.org/10.3390/rs15194760 - 28 Sep 2023

Cited by 4 | Viewed by 2379

Abstract

This paper reports on the first daily global monitoring program for natural gas flaring and industrial sites producing waste heat based on satellite observed infrared emissions. The Visible Infrared Imaging Radiometer Suite (VIIRS) collects nightly global infrared data in spectral bands ranging from near infrared (NIR) to longwave infrared (LWIR), providing a unique capability to detect and characterize infrared emitters at night. The VIIRS nightfire (VNF) algorithm identifies infrared (IR) emitters with multiple spectral bands and calculates the temperature, source area, and radiant heat via Planck curve fitting and physical laws. VNF data are produced nightly and extend from 2012 to the present. The most common infrared emitter is biomass burning, which must be filtered out. Industrial IR emitters can be distinguished from biomass burning based on temperature and persistence. The initial filtering to remove biomass burning was performed with 15 arc second grids formed from eleven years of VIIRS data, spanning 2012–2022. The locations and shapes of the remaining features were used to guide the generation of super-resolution pixel center clouds. These data clouds were then analyzed to define bounding vectors for single emitters and to split larger clusters into multiple emitters. A total of nearly 20,000 IR emitters were identified; each was assigned an identification number, and the type of emitter was recorded. Nightly temporal profiles were produced for each site, revealing activity patterns back to 2012. Nightly temporal profiles were kept current with weekly updates. Temporal profiles from individual sites were aggregated by country to form monthly profiles extending back to 2012. The nightly and monthly temporal profiles were suitable for analyzing industrial production, identifying disruption events, and tracking recovery. The data could also be used in tracking progress in energy conservation and greenhouse gas emission inventories. Full article

(This article belongs to the Special Issue Advances in Thermal Infrared Remote Sensing)

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21 pages, 7362 KiB

Open AccessArticle

Evaluation of the Radiometric Calibration of ZY1-02E Thermal Infrared Data

by Honggeng Zhang, Hongzhao Tang, Xining Liu, Xianhui Dou, Yonggang Qian, Wei Chen and Kun Li

Remote Sens. 2023, 15(15), 3905; https://doi.org/10.3390/rs15153905 - 7 Aug 2023

Cited by 1 | Viewed by 1664 | Correction

Abstract

Following the launch of the ZY1-02E satellite, the thermal infrared sensor aboard the satellite experienced alterations in the space environment, leading to varying degrees of attenuation in some components. The laboratory calibration accuracy could not satisfy the demands of quantitative production, and a certain degree of deviation was observed in on-orbit calibration. To accurately characterize the on-orbit radiation properties of thermal infrared remote sensing payloads, an absolute radiometric calibration campaign was carried out at the Ulansuhai Nur and Baotou calibration sites in Inner Mongolia in July 2022. This paper outlines the processes of onboard calibration and vicarious calibration for the ZY1-02E satellite, comparing the outcomes of onboard calibration with those of vicarious calibration. The onboard calibration method involved internal calibration, while the vicarious calibration method utilized an on-orbit absolute radiometric calibration technique based on various natural features that were not constrained by satellite–Earth spectrum matching requirements. Calibration coefficients were acquired, and the absolute radiometric calibration results of on-orbit vicarious and onboard calibration were compared, analyzed, and verified using the radiance computed from measured data and the reference sensor data. The accuracy of on-orbit absolute vicarious calibration was determined, and the causes for the decline in the radiation calibration accuracy on the orbiting satellite were examined. The findings revealed that the vicarious calibration results exhibited a lower percentage of radiance deviation compared with the onboard calibration results, meeting the quantitative requirements of remote sensing data. These results were significantly better than those obtained from onboard blackbody calibration, offering a data foundation for devising satellite calibration plans and enhancing calibration algorithms. In the future, the developmental trend of on-orbit radiometric calibration technology will encompass high-precision and slow-attenuation onboard calibration techniques, as well as high-frequency and simplified-step vicarious calibration methods. Full article

(This article belongs to the Special Issue Advances in Thermal Infrared Remote Sensing)

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23 pages, 5262 KiB

Open AccessArticle

Evolution Patterns of Cooling Island Effect in Blue–Green Space under Different Shared Socioeconomic Pathways Scenarios

by Ziwu Pan, Zunyi Xie, Na Ding, Qiushuang Liang, Jianguo Li, Yu Pan and Fen Qin

Remote Sens. 2023, 15(14), 3642; https://doi.org/10.3390/rs15143642 - 21 Jul 2023

Cited by 3 | Viewed by 1854

Abstract

Blue–green space refers to blue space (rivers and lakes) and green space (lawns and trees), which have the cooling island effect and are increasingly acknowledged as a potential and effective way to help alleviate the urban heat island effect. Scientific and flexible blue–green space planning is required, especially for medium- and large-scale urban agglomerations in the face of climate change. However, the temporal evolution and spatial patterns of the cooling island effect in the blue–green space under different future scenarios of climate change have not been fully investigated. This would impede long-term urban strategies for climate change adaptation and resilience. Here we studied the relationship between future climate change and blue–green spatial layout with Weather Research and Forecasting (WRF), based on the numerical simulation data of 15 global climate models under different extreme Shared Socioeconomic Pathway (SSP) scenarios. As a result, future changes in urban cooling island (UCI) magnitudes were estimated between historical (2015–2020) and future timelines: 2030s (2021–2040), 2050s (2041–2060), 2070s (2061–2080), and 2090s (2081–2100). Our results showed different land use types in blue and green space across the study area were predicted to present various changes in the next 80 years, with forest, grassland, and arable land experiencing the most significant land use transfer. The future UCI intensity of cities under SPP5-8.5 (12) was found to be lower than that under SPP2-4.5 (15), indicating that cities may be expected to experience decreases in UCI magnitudes in the future under SSP5-8.5. When there is no expansion of urban development land, we found that the conversion of different land use types into blue and green space leads to little change in future UCI intensity. While the area growth of forests and water bodies is proportional to the increase in UCI, the increase of farmland was observed to have the most significant impact on reducing the amplitude of urban UCI. Given that Huai’an City, Yancheng City, and Yangzhou City have abundant blue–green space, the urban cooling island effect was projected to be more significant than that of other cities in the study area under different SSP scenarios. The simulation results of the WRF model indicate that optimizing the layout of urban blue–green space plays an important role in modulating the urban thermal environment. Full article

(This article belongs to the Special Issue Advances in Thermal Infrared Remote Sensing)

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26 pages, 5485 KiB

Open AccessArticle

Analysis of Short-Term Drought Episodes Using Sentinel-3 SLSTR Data under a Semi-Arid Climate in Lower Eastern Kenya

by Peter K. Musyimi, Ghada Sahbeni, Gábor Timár, Tamás Weidinger and Balázs Székely

Remote Sens. 2023, 15(12), 3041; https://doi.org/10.3390/rs15123041 - 10 Jun 2023

Cited by 5 | Viewed by 2130

Abstract

This study uses Sentinel-3 SLSTR data to analyze short-term drought events between 2019 and 2021. It investigates the crucial role of vegetation cover, land surface temperature, and water vapor amount associated with drought over Kenya’s lower eastern counties. Therefore, three essential climate variables (ECVs) of interest were derived, namely Land Surface Temperature (LST), Fractional Vegetation Cover (FVC), and Total Column Water Vapor (TCWV). These features were analyzed for four counties between the wettest and driest episodes in 2019 and 2021. The study showed that Makueni and Taita Taveta counties had the highest density of FVC values (60–80%) in April 2019 and 2021. Machakos and Kitui counties had the lowest FVC estimates of 0% to 20% in September for both periods and between 40% and 60% during wet seasons. As FVC is a crucial land parameter for sequestering carbon and detecting soil moisture and vegetation density losses, its variation is strongly related to drought magnitude. The land surface temperature has drastically changed over time, with Kitui and Taita Taveta counties having the highest estimates above 20 °C in 2019. A significant spatial variation of TCWV was observed across different counties, with values less than 26 mm in Machakos county during the dry season of 2019, while Kitui and Taita Taveta counties had the highest estimates, greater than 36 mm during the wet season in 2021. Land surface temperature variation is negatively proportional to vegetation density and soil moisture content, as non-vegetated areas are expected to have lower moisture content. Overall, Sentinel-3 SLSTR products provide an efficient and promising data source for short-term drought monitoring, especially in cases where in situ measurement data are scarce. ECVs-produced maps will assist decision-makers with a better understanding of short-term drought events as well as soil moisture loss episodes that influence agriculture under arid and semi-arid climates. Furthermore, Sentinel-3 data can be used to interpret hydrological, ecological, and environmental changes and their implications under different environmental conditions. Full article

(This article belongs to the Special Issue Advances in Thermal Infrared Remote Sensing)

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23 pages, 78594 KiB

Open AccessArticle

Simulation of Cooling Island Effect in Blue-Green Space Based on Multi-Scale Coupling Model

by Ziwu Pan, Zunyi Xie, Liyang Wu, Yu Pan, Na Ding, Qiushuang Liang and Fen Qin

Remote Sens. 2023, 15(8), 2093; https://doi.org/10.3390/rs15082093 - 16 Apr 2023

Cited by 9 | Viewed by 2789

Abstract

The mitigation of the urban heat island effect is increasingly imperative in light of climate change. Blue–green space, integrating water bodies and green spaces, has been demonstrated to be an effective strategy for reducing the urban heat island effect and enhancing the urban environment. However, there is a lack of coupled analysis on the cooling island effect of blue–green space at the meso-micro scale, with previous studies predominantly focusing on the heat island effect. This study coupled the single urban canopy model (UCM) with the mesoscale Weather Research and Forecasting (WRF) numerical model to simulate the cooling island effect of blue–green space in the Eastern Sea-River-Stream-Lake Linkage Zone (ESLZ) within the northern subtropical zone. In particular, we comparatively investigated the cooling island effect of micro-scale blue–green space via three mitigation strategies of increasing vegetation, water bodies, and coupling blue–green space, using the temperature data at the block scale within 100 m square of the urban center on the hottest day in summer. Results showed that the longitudinally distributed lakes and rivers in the city had a significant cooling effect on the ambient air temperature (Ta) at the mesoscale, with the largest cooling range occurring during the daytime and ranging from 1.01 to 2.15 °C. In contrast, a 5~20% increase in vegetation coverage or 5~15% increase in water coverage at the micro-scale was observed to reduce day and night Ta by 0.71 °C. Additionally, the most significant decrease in physiologically equivalent temperature (PET) was found in the mid-rise building environment, with a reduction of 2.65–3.26 °C between 11:00 and 13:00 h, and an average decrease of 1.25°C during the day. This study aims to guide the optimization of blue–green space planning at the meso-micro scale for the fast-development and expansion of new urban agglomerations. Full article

(This article belongs to the Special Issue Advances in Thermal Infrared Remote Sensing)

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18 pages, 6775 KiB

Open AccessArticle

A High-Resolution Land Surface Temperature Downscaling Method Based on Geographically Weighted Neural Network Regression

by Minggao Liang, Laifu Zhang, Sensen Wu, Yilin Zhu, Zhen Dai, Yuanyuan Wang, Jin Qi, Yijun Chen and Zhenhong Du

Remote Sens. 2023, 15(7), 1740; https://doi.org/10.3390/rs15071740 - 23 Mar 2023

Cited by 9 | Viewed by 3758

Abstract

Spatial downscaling is an important approach to obtain high-resolution land surface temperature (LST) for thermal environment research. However, existing downscaling methods are unable to sufficiently address both spatial heterogeneity and complex nonlinearity, especially in high-resolution scenes (<120 m), and accordingly limit the representation of regional details and accuracy of temperature inversion. In this study, by integrating normalized difference vegetation index (NDVI), normalized difference building index (NDBI), digital elevation model (DEM), and slope data, a high-resolution surface temperature downscaling method based on geographically neural network weighted regression (GNNWR) was developed to effectively handle the problem of surface temperature downscaling. The results show that the proposed GNNWR model achieved superior downscaling accuracy (maximum R² of 0.974 and minimum RMSE of 0.896 °C) compared to widely used methods in four test areas with large differences in topography, landforms, and seasons. We also achieved the best extracted and most detailed spatial textures. Our findings suggest that GNNWR is a practical method for surface temperature downscaling considering its high accuracy and model performance. Full article

(This article belongs to the Special Issue Advances in Thermal Infrared Remote Sensing)

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19 pages, 4365 KiB

Open AccessArticle

Seasonal Variations of the Relationship between Spectral Indexes and Land Surface Temperature Based on Local Climate Zones: A Study in Three Yangtze River Megacities

by Yang Xiang, Yongqi Tang, Zhihua Wang, Chucai Peng, Chunbo Huang, Yuanyong Dian, Mingjun Teng and Zhixiang Zhou

Remote Sens. 2023, 15(4), 870; https://doi.org/10.3390/rs15040870 - 4 Feb 2023

Cited by 11 | Viewed by 2550

Abstract

Urban heat islands are representative problems in urban environments. The impact of spectral indexes on land-surface temperature (LST) under different urban forms, climates, and functions is not fully understood. Local climate zones (LCZs) are used to characterize heterogeneous cities. In this study, we quantified the contribution of three cities to high-temperature zones and surface urban heat island intensity (SUHII) across LCZs and seasons, used Welch and Games–Howell tests to analyze the difference in LST, then described the spatial pattern characteristics of LST, and used a geographically weighted regression model to analyze the relationship between spectral indexes and LST. The results showed that compact midrise, compact low-rise (LCZ 3), large low-rise (LCZ 8), heavy industry (LCZ 10), and bare rock or paved (LCZ E) contributed greatly to high-temperature zones and had strong SUHII. There were 92–98% significant differences between different LCZs. The spatial aggregation of LST gradually weakened with a decrease in temperature. The modified normalized difference water index (MNDWI) in most LCZs of all seasons for Wuhan could reduce LST well, while MNDWI only had cooling effects in winter for Nanjing and Shanghai. Normalized difference vegetation index (NDVI) in most LCZs performed a cooling role during summer and transition seasons (spring and autumn), while it showed a warming effect in winter. The cooling effect of NDVI in open building types was stronger than that of compact building types, while the cooling effect of MNDWI was better in compact building types than in open building types. With the increase of normalized difference built-up index (NDBI), all LCZs showed warming effects, and the magnitude of LST increase varied in different cities and seasons. These results contribute further insight into thermal environment in heterogeneous urban areas. Full article

(This article belongs to the Special Issue Advances in Thermal Infrared Remote Sensing)

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24 pages, 9038 KiB

Open AccessArticle

Analysis of Space-Based Observed Infrared Characteristics of Aircraft in the Air

by Jiyuan Li, Huijie Zhao, Xingfa Gu, Lifeng Yang, Bin Bai, Guorui Jia and Zengren Li

Remote Sens. 2023, 15(2), 535; https://doi.org/10.3390/rs15020535 - 16 Jan 2023

Cited by 9 | Viewed by 3343

Abstract

The space-based infrared observatory of aircraft in the air has the advantages of wide-area, full-time, and passive detection. The optical design parameters for space-based infrared sensors strongly rely on target observed radiation, but there is still a lack of insight into the causes of aircraft observation properties and the impact of instrument performance. A simulation model of space-based observed aircraft infrared characteristics was constructed for this provision, coupling the aircraft radiance with background radiance and instrument performance effects. It was validated by comparing the model predictions to data from both space-based and ground-based measurements. The validation results reveal the alignment between measurements and model predictions and the dependence of overall model accuracy on the background. Based on simulations, the radiance contributions of aircraft and background are quantitatively evaluated, and the detection spectral window for flying aircraft and its causes are discussed in association with instrumental performance effects. The analysis results indicate that the target-background (T-B) contrast is higher in the spectral ranges where aircraft radiation makes an important contribution. The background radiance plays a significant role overall, while the observed radiance at 2.5–3μm is mainly from skin reflection and plume radiance. The skin-reflected radiation absence affects the model reliability, and its reduction at nighttime reduces the T-B contrast. The difference in T-B self-radiation and the stronger atmospheric attenuation for background contribute to the higher contrast at 2.7 μm compared to the other spectral bands. Full article

(This article belongs to the Special Issue Advances in Thermal Infrared Remote Sensing)

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Review

Jump to: Research, Other

33 pages, 6948 KiB

Open AccessReview

Satellite-Derived Land Surface Temperature Dynamics in the Context of Global Change—A Review

by Philipp Reiners, José Sobrino and Claudia Kuenzer

Remote Sens. 2023, 15(7), 1857; https://doi.org/10.3390/rs15071857 - 30 Mar 2023

Cited by 32 | Viewed by 6431

Abstract

Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter than 25 years. The following suggestions were made to increase the utilization of LST time series in climate research: The prolongation of the time series by, e.g., using AVHRR LST, the better representation of LST under clouds, the comparison of LST to traditional climate change measures, such as air temperature and reanalysis variables, and the extension of the validation to heterogenous sites. Full article

(This article belongs to the Special Issue Advances in Thermal Infrared Remote Sensing)

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