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Atmosphere
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Land Surface Temperature Retrieval Using Satellite Remote Sensing (2nd Edition)
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Land Surface Temperature Retrieval Using Satellite Remote Sensing (2nd Edition)

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biosphere/Hydrosphere/Land–Atmosphere Interactions".

Deadline for manuscript submissions: 3 June 2024 | Viewed by 2680

Special Issue Editors

Dr. Geng-Ming Jiang

E-Mail Website
Guest Editor
Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China
Interests: retrieval of geophysical parameters from satellite data; radiometric calibration of satellite instruments; radiative transfer modeling; deep learning and information extraction from digital images
Special Issues, Collections and Topics in MDPI journals
Dr. Bo-Hui Tang

E-Mail Website
Guest Editor
Faculty of Land Resource Engineering, Kunming University of Science and Technology (KUST), Kunming 650093, China
Interests: retrieval and validation of land surface temperature/emissivity; retrieval and validation of net surface radiation; radiative transfer modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a follow-up of the first Special Issue entitled "Land Surface Temperature Retrieval Using Satellite Remote Sensing" (https://www.mdpi.com/journal/atmosphere/special_issues/Land_Surface_Temperature_Retrieval) published in Atmosphere in 2022.

Land surface temperature (LST) is a good indicator of energy partitioning at the land surface–atmosphere boundary, and is sensitive to changing surface conditions. Satellite remote sensing provides opportunities to estimate global and continuous LSTs. The key challenges in retrieving LST using satellite remote sensing are the removal of the atmospheric attenuation, the decoupling between LST and land surface emissivity (LSE), and topography. Over the past four decades, dozens of LST retrieval algorithms have been developed and expanded from the traditional thermal infrared and hyperspectral infrared remote sensing to microwave remote sensing. Meanwhile, to fill the gaps in the derived LSTs, many scientists are devoted to the extension of LST retrievals under all-weather conditions. To date, many LST products have been generated from satellite data, such as the advanced spaceborne thermal emission reflection radiometer (ASTER) and the moderate resolution imaging spectroradiometer (MODIS). The validation of LST products is fundamental for their further applications. Additionally, the LSTs estimated from satellite data are inconsistent due to different observation local times and viewing zenith angles. To tackle the problems of inconsistency, the LSTs derived from satellite data should be temporally and angularly normalized. For these reasons, this Special Issue mainly aims to collect papers investigating updated algorithms for LST estimation, validation, temporal and angular normalization, and the correlation between LST and surface air temperature.

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

  • Decoupling between LST and LSE;
  • LST estimation from satellite infrared and microwave measurements;
  • Temporal and angular normalization of LSTs;
  • LST validation;
  • Correlation between LST and surface air temperature.

Dr. Geng-Ming Jiang
Dr. Bo-Hui Tang
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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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

  • radiative transfer modeling
  • land surface emissivity (LSE)
  • land surface temperature (LST)
  • LST retrieval algorithms
  • temporal and angular normalization of LSTs
  • LST validation
  • surface air temperature

Published Papers (3 papers)

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19 pages, 7225 KiB  
Article
Exploring the Dynamics of Land Surface Temperature in Jordan’s Local Climate Zones: A Comprehensive Assessment through Landsat Entire Archive and Google Earth Engine
by Khaled Hazaymeh, Mohammad Zeitoun, Ali Almagbile and Areej Al Refaee
Atmosphere 2024, 15(3), 318; https://doi.org/10.3390/atmos15030318 - 04 Mar 2024
Viewed by 746
Abstract
This study aimed to analyze the trend in land surface temperature (LST) over time using the entire archive of the available cloud-free Landsat images from 1986 to 2022 for Jordan and its nine local climate zones (LCZs). Two primary datasets were used (i) [...] Read more.
This study aimed to analyze the trend in land surface temperature (LST) over time using the entire archive of the available cloud-free Landsat images from 1986 to 2022 for Jordan and its nine local climate zones (LCZs). Two primary datasets were used (i) Landsat-5; -8 imagery, and (ii) map of LCZs of Jordan. All LST images were clipped, preprocessed, and checked for cloud contamination and bad pixels using the quality control bands. Then, time-series of monthly LST images were generated through compositing and mosaicking processes using cloud computing functions and Java scripts in Google Earth Engine (GEE). The Mann–Kendall (MK) test and Sen’s slope estimator (SSE) were used to detect and quantify the magnitude of LST trends. Results showed a warming trend in the maximum LST values for all LCZs while there was annual fluctuation in the trend line of the minimum LST values in the nine zones. The monthly average LST values showed a consistent upward trajectory, indicating a warming condition, but with variations in the magnitude. The annual rate of change in LST for the LCZs showed that the three Saharan zones are experiencing the highest rate of increase at 0.0184 K/year for Saharan Mediterranean Warm (SMW), 0.0185 K/year for Saharan Mediterranean Cool (SMC), and 0.0169 K/year for Saharan Mediterranean very Warm (SMvW), indicating rapid warming in these regions. The three arid zones came in the middle, with values of 0.0156 K/year for Arid Mediterranean Warm (AMW), 0.0151 for Arid Mediterranean very Warm (AMvW), and 0.0139 for Arid Mediterranean Cool (AMC), suggesting a slower warming trend. The two semi-arid zones and the sub-humid zone showed lower values at 0.0138, 0.0127, and 0.0117 K/year for the Semi-arid Mediterranean Cool (SaMC), Semi-arid Mediterranean Warm (SaMW) zones, and Semi-humid Mediterranean (ShM) zones, respectively, suggesting the lowest rate of change compared to other zones. These findings would provide an overall understanding of LST change and its impact in Jordan’s LCZs for sustainable development and water resources demand and management. Full article
(This article belongs to the Special Issue Land Surface Temperature Retrieval Using Satellite Remote Sensing (2nd Edition))
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16 pages, 3979 KiB  
Article
Retrieval of Plateau Lake Water Surface Temperature from UAV Thermal Infrared Data
by Ouyang Sima, Bo-Hui Tang, Zhi-Wei He, Dong Wang and Jun-Li Zhao
Atmosphere 2024, 15(1), 99; https://doi.org/10.3390/atmos15010099 - 12 Jan 2024
Viewed by 756
Abstract
The lake water surface temperature (LWST) is a critical parameter influencing lake ecosystem dynamics and addressing challenges posed by climate change. Traditional point measurement techniques exhibit limitations in providing comprehensive LWST data. However, the emergence of satellite remote sensing and unmanned aerial vehicle [...] Read more.
The lake water surface temperature (LWST) is a critical parameter influencing lake ecosystem dynamics and addressing challenges posed by climate change. Traditional point measurement techniques exhibit limitations in providing comprehensive LWST data. However, the emergence of satellite remote sensing and unmanned aerial vehicle (UAV) Thermal Infrared (TIR) technology has opened new possibilities. This study presents an approach for retrieving plateau lake LWST (p-LWST) from UAV TIR data. The UAV TIR dataset, obtained from the DJI Zenmuse H20T sensor, was stitched together to form an image of brightness temperature (BT). Atmospheric parameters for atmospheric correction were acquired by combining the UAV dataset with the ERA5 reanalysis data and MODTRAN5.2. Lake Water Surface Emissivity (LWSE) spectral curves were derived using 102 hand-portable FT-IR spectrometer (102F) measurements, along with the sensor’s spectral response function, to obtain the corresponding LWSE. Using estimated atmospheric parameters, LWSE, and UAV BT, the un-calibrated LWST was calculated through the TIR radiative transfer model. To validate the LWST retrieval accuracy, the FLIR Infrared Thermal Imager T610 and the Fluke 51-II contact thermometer were utilized to estimate on-point LWST. This on-point data was employed for cross-calibration and verification. In the study area, the p-LWST method retrieved LWST ranging from 288 K to 295 K over Erhai Lake in the plateau region, with a final retrieval accuracy of 0.89 K. Results demonstrate that the proposed p-LWST method is effective for LWST retrieval, offering technical and theoretical support for monitoring climate change in plateau lakes. Full article
(This article belongs to the Special Issue Land Surface Temperature Retrieval Using Satellite Remote Sensing (2nd Edition))
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16 pages, 8287 KiB  
Article
Validation of FY-4A/AGRI LST and High Temporal–Spatial Resolution Analysis of Surface Heat Resources in Hunan Province, Central China
by Jiazhi Fan, Hao Lin, Qinzhe Han, Leishi Chen, Shiqi Tan and Wei Li
Atmosphere 2023, 14(12), 1777; https://doi.org/10.3390/atmos14121777 - 30 Nov 2023
Viewed by 719
Abstract
Land surface temperature (LST) is a crucial parameter in climate and ecology, exerting significant influence on meteorological conditions, ecosystems, and human life. LST data sources are diverse, with remote sensing being the prevailing means of acquisition. FY-4A/AGRI offers high-quality LST products for East [...] Read more.
Land surface temperature (LST) is a crucial parameter in climate and ecology, exerting significant influence on meteorological conditions, ecosystems, and human life. LST data sources are diverse, with remote sensing being the prevailing means of acquisition. FY-4A/AGRI offers high-quality LST products for East Asia. We conducted a comprehensive evaluation and refined analysis of surface heat resources in Hunan Province, central China, over a two-year period using the 4 km/1 h resolution product in this study. The results demonstrate that the FY-4A LST product effectively captures surface temperature (R = 0.893), albeit with a relatively high error level (Bias = −6.295 °C; RMSE = 8.58 °C), particularly in capturing high LST values. The performance of this product is superior in the eastern flat terrain area of Hunan Province compared to its performance in the western mountainous region due to environmental conditions causing systematic errors that contribute to instability in detection deviation for this product. Surface heat resources are more abundant in eastern Hunan Province than in mountainous areas located west and southwardly, and the detailed distribution of them at finer scales is mainly influenced by terrain and climate conditions. There is no obvious seasonal difference in the distribution of heat resources except in winter, and rapid urbanization within Chang–Zhu–Tan urban agglomeration over two years has significantly altered the spatial distribution pattern of surface heat resources across Hunan Province. These findings provide a quantitative baseline for assessing FY-4A satellite’s detection capability while serving as a reference for further application of its LST products and establishing foundations for divisional classification and utilization strategies pertaining to surface heat resources within Hunan Province. Full article
(This article belongs to the Special Issue Land Surface Temperature Retrieval Using Satellite Remote Sensing (2nd Edition))
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