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Advances in Thermal Infrared Remote Sensing II

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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 (30 August 2024) | Viewed by 12428

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Dr. Tian Hu

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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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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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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
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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

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Advances in Thermal Infrared Remote Sensing in Remote Sensing (11 articles)

Published Papers (8 papers)

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Research

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21 pages, 12594 KiB

Open AccessArticle

Remotely Sensed Estimation of Daily Near-Surface Air Temperature: A Comparison of Metop and MODIS

by Zhenwei Zhang, Peisong Li, Xiaodi Zheng and Hongwei Zhang

Remote Sens. 2024, 16(20), 3754; https://doi.org/10.3390/rs16203754 - 10 Oct 2024

Viewed by 674

Abstract

The estimation of spatially resolved near-surface air temperature (NSAT) has been extensively performed in previous studies using satellite-derived land surface temperature (LST) from MODIS. However, there remains a need for estimating daily NSAT based on LST data from other satellites, which has important implications for integrating multi-source LST in estimating NSAT and ensuring the continuity of satellite-derived estimates of NSAT over long-term periods. In this study, we conducted a comprehensive comparison of LST derived from Metop with MODIS LST in the modeling and mapping of daily NSAT. The results show that Metop LST achieves consistent predictive performance with MODIS LST in estimating daily NSAT, and models based on Metop LST or MODIS LST have overall predictive performance of about 1.2–1.4 K, 1.5–2.0 K, and 1.8–1.9 K in RMSE for estimating Tavg, Tmax, and Tmin, respectively. Compared to models based on nighttime LST, daytime LST can improve the predictive performance of Tmax by about 0.26–0.28 K, while performance for estimating Tavg or Tmin using different schemes of LST is comparable. Models based on Metop LST also exhibit high consistency with models utilizing MODIS LST in terms of the variability in predictive performance across months, with RMSE of 1.03–1.82 K, 1.3–2.49 K, and 1.26–2.66 K for Tavg, Tmin, and Tmax, respectively. This temporal variability in performance is not due to sampling imbalance across months, which is confirmed by comparing models trained using bootstrapped samples in balance, and our results imply that sampling representativeness, complicated by retrieval gaps in LST, is an important issue when analyzing the variability in predictive performance for estimating NSAT. To fully assess the predictive capability of Metop LST in estimating daily NSAT, more studies need to be performed using different methods across areas with a range of scales and geographical environments. Full article

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

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21 pages, 7177 KiB

Open AccessArticle

Neural Network-Based Estimation of Near-Surface Air Temperature in All-Weather Conditions Using FY-4A AGRI Data over China

by Hai-Lei Liu, Min-Zheng Duan, Xiao-Qing Zhou, Sheng-Lan Zhang, Xiao-Bo Deng and Mao-Lin Zhang

Remote Sens. 2024, 16(19), 3612; https://doi.org/10.3390/rs16193612 - 27 Sep 2024

Viewed by 494

Abstract

Near-surface air temperature (T_a) estimation by geostationary meteorological satellites is mainly carried out under clear-sky conditions. In this study, we propose an all-weather T_a estimation method utilizing FY-4A Advanced Geostationary Radiation Imager (AGRI) and the Global Forecast System (GFS), along with additional auxiliary data. The method includes two neural-network-based T_a estimation models for clear and cloudy skies, respectively. For clear skies, AGRI LST was utilized to estimate the T_a (T_a,clear), whereas cloud top temperature and cloud top height were employed to estimate the T_a for cloudy skies (T_a,cloudy). The estimated T_a was validated using the 2020 data from 1211 stations in China, and the RMSE values of the T_a,clear and T_a,cloudy were 1.80 °C and 1.72 °C, while the correlation coefficients were 0.99 and 0.986, respectively. The performance of the all-weather T_a estimation model showed clear temporal and spatial variation characteristics, with higher accuracy in summer (RMSE = 1.53 °C) and lower accuracy in winter (RMSE = 1.88 °C). The accuracy in southeastern China was substantially better than in western and northern China. In addition, the dependence of the accuracy of the T_a estimation model for LST, CTT, CTH, elevation, and air temperature were analyzed. The global sensitivity analysis shows that AGRI and GFS data are the most important factors for accurate T_a estimation. The AGRI-estimated T_a showed higher accuracy compared to the ERA5-Land data. The proposed models demonstrated potential for T_a estimation under all-weather conditions and are adaptable to other geostationary satellites. Full article

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

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23 pages, 7944 KiB

Open AccessArticle

Spatial Downscaling of Nighttime Land Surface Temperature Based on Geographically Neural Network Weighted Regression Kriging

by Jihan Wang, Nan Zhang, Laifu Zhang, Haoyu Jing, Yiming Yan, Sensen Wu and Renyi Liu

Remote Sens. 2024, 16(14), 2542; https://doi.org/10.3390/rs16142542 - 10 Jul 2024

Viewed by 886

Abstract

Land surface temperature (LST) has a wide application in Earth Science-related fields, and spatial downscaling is an important method to retrieve high-resolution LST data. However, existing LST downscaling methods have difficulties in simultaneously constructing and expressing spatial non-stationarity, spatial autocorrelation, and complex non-linearity during the LST downscaling process, which limits the performance of the models. Moreover, there is a lack of research on high-resolution nighttime land surface temperature (NLST) reconstruction based on spatial downscaling, which does not meet the data needs for urban-scale nighttime urban heat island (UHI) studies. Therefore, this study combined Geographically Neural Network Weighted Regression (GNNWR) with Area-to-Point Kriging interpolation (ATPK) to propose a Geographically Neural Network Weighted Regression Kriging (GNNWRK) model for NLST downscaling. To verify the model’s generality and robustness, this study selected four study areas with different landform and climate type for NLST spatial downscaling experiments. The GNNWRK was compared with four benchmark downscaling methods, including TsHARP, Random Forest, Geographically Weighted Regression, and GNNWR. The results show that compared to these four benchmark methods, the GNNWRK method has higher accuracy in NLST downscaling, with a maximum Pearson’s Correlation Coefficient (Pcc) of 0.930 and a minimum Root Mean Square Error (RMSE) of 0.886 K. Moreover, the validation based on MODIS NLST data and ground-measured NLST data also indicates that the GNNWRK model can obtain more accurate, high-resolution NLST with richer and more detailed texture. This enhances the potential of NLST in studying the effects of urban nighttime heat islands at a finer scale. Full article

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

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16 pages, 4995 KiB

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Correcting an Off-Nadir to a Nadir Land Surface Temperature Using a Multitemporal Thermal Infrared Kernel-Driven Model during Daytime

by Qiang Na, Biao Cao, Boxiong Qin, Fan Mo, Limeng Zheng, Yongming Du, Hua Li, Zunjian Bian, Qing Xiao and Qinhuo Liu

Remote Sens. 2024, 16(10), 1790; https://doi.org/10.3390/rs16101790 - 18 May 2024

Cited by 1 | Viewed by 1199

Abstract

Land surface temperature (LST) is a fundamental parameter in global climate, environmental, and geophysical studies. Remote sensing is an essential approach for obtaining large-scale and frequently updated LST data. However, due to the wide field of view of remote sensing sensors, the observed LST with diverse view geometries suffers from inconsistency caused by the thermal radiation directionality (TRD) effect, which results in LST products being incomparable, especially during daytime. To address this issue and correct current off-nadir LSTs to nadir LSTs, a semi-physical time-evolved kernel-driven model (TEKDM) is proposed, which depicts multitemporal TRD patterns during the daytime. In addition, we employ a Bayesian optimization method to calibrate seven unknown parameters in the TEKDM. Validation results using the U.S. Climate Reference Network (USCRN) sites show that the RMSE (MBE) for GOES-16 and MODIS off-nadir LST products is reduced from 3.29 K (−2.0 K) to 2.34 K (−0.02 K), with an RMSE reduction of 0.95 K (29%) and a significant reduction in systematic bias. Moreover, the proposed method successfully eliminates the angular and temporal dependence of the LST difference between the satellite off-nadir LST and in situ nadir LST. In summary, this study presents a feasible approach for estimating the high-accuracy nadir LST, which can enhance the applicability of LST products in various domains. Full article

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

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18 pages, 5077 KiB

Open AccessArticle

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

Cited by 3 | Viewed by 1292

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 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

Open AccessArticle

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 2 | Viewed by 2248

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 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

Open AccessArticle

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

Cited by 2 | Viewed by 1859

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 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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Review

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19 pages, 1037 KiB

Open AccessReview

Advances in Thermal Infrared Remote Sensing Technology for Geothermal Resource Detection

by Sen Wang, Wei Xu and Tianqi Guo

Remote Sens. 2024, 16(10), 1690; https://doi.org/10.3390/rs16101690 - 9 May 2024

Cited by 1 | Viewed by 2872

Abstract

This paper discusses thermal infrared (TIR) remote sensing technology applied to the delineation of geothermal resources, a significant renewable energy source. The technical characteristics and current status of TIR remote sensing is discussed and related to the integration of geological structure, geophysical data, and geochemical analyses. Also discussed are surface temperature inversion algorithms used to delineate anomalous ground-surface temperatures. Unlike traditional geophysical and geochemical exploration methods, remote sensing technology exhibits considerable advantages in terms of convenience and coverage extent. The paper addresses the major challenges and issues associated with using TIR remote sensing technology in geothermal prospecting. Full article

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

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