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Atmosphere
Special Issues
Land Surface Temperature Retrieval Using Satellite Remote Sensing
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Land Surface Temperature Retrieval Using Satellite Remote Sensing

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

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 7586

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,

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 to retrieve 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. So far, many LST products have been generated from satellite data, such as the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and MODerate resolution Imaging Spectroradiometer (MODIS) LST products. 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 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:

  • 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 (5 papers)

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Research

17 pages, 21657 KiB  
Article
Investigation of the Efficiency of Satellite-Derived LST Data for Mapping the Meteorological Parameters in Istanbul
by Adalet Dervisoglu
Atmosphere 2023, 14(4), 644; https://doi.org/10.3390/atmos14040644 - 29 Mar 2023
Cited by 2 | Viewed by 1713
Abstract
Land surface temperature (LST) is an essential parameter for studying environmental and ecological processes and climate change at various scales. It is also valuable for studies of evapotranspiration, soil moisture conditions, surface energy balance, and urban heat islands. Since meteorological station data can [...] Read more.
Land surface temperature (LST) is an essential parameter for studying environmental and ecological processes and climate change at various scales. It is also valuable for studies of evapotranspiration, soil moisture conditions, surface energy balance, and urban heat islands. Since meteorological station data can provide a limited number of point data, satellite images that provide high temporal and spatial resolution LST data in large areas are needed to be used in all these applications. In this study, the usage of satellite-derived LST images was investigated in comparison with meteorological station data measurements in Istanbul, which has heterogeneous urban structures. LST data were obtained from Landsat 5 TM, Landsat 8 OLI/TIRS, and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images using the Google Earth Engine (GEE) cloud platform. The linear correlation analysis performed between Landsat LST and MODIS LST images gave a high correlation (r = 0.88). In the correlation analysis, hourly air temperature and soil temperature meteorology station data provided by the State Meteorological Service and LST values obtained from images taken from Landsat TM/TIRS and Terra MODIS were used. The correlations between air temperatures and Landsat LST ranged from 0.47–0.95 for 1987–2017 to 0.44–0.80 for MODIS LST for 2000–2017. The correlations between 5 cm soil temperatures and Landsat LST ranged from 0.76–0.93 for 2009–2017 to 0.22–0.61 for MODIS LST 2000–2017. In addition, linear regression models produced with meteorological parameters and LST values were applied to 2022 LST maps to show the spatial distribution of these parameters, and then, accuracy analyses were made. Full article
(This article belongs to the Special Issue Land Surface Temperature Retrieval Using Satellite Remote Sensing)
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18 pages, 4750 KiB  
Article
A Hybrid Approach for Extracting Large-Scale and Accurate Built-Up Areas Using SAR and Multispectral Data
by Rida Azmi, Jérôme Chenal, Hicham Amar, Cédric Stéphane Tekouabou Koumetio and El Bachir Diop
Atmosphere 2023, 14(2), 240; https://doi.org/10.3390/atmos14020240 - 25 Jan 2023
Cited by 1 | Viewed by 1342
Abstract
This article examines the use of multisensor data fusion for land classification in three Moroccan cities. The method employs a Random Forest classification algorithm based on multispectral, synthetic aperture radar (SAR), and derived land surface temperature (LST) data. The study compares the proposed [...] Read more.
This article examines the use of multisensor data fusion for land classification in three Moroccan cities. The method employs a Random Forest classification algorithm based on multispectral, synthetic aperture radar (SAR), and derived land surface temperature (LST) data. The study compares the proposed approach to existing datasets on impervious surfaces (Global Artificial Impervious Area—GAIA, Global Human Settlement Layer—GHSL, and Global 30 m Impervious Surfaces Dynamic Dataset—GIS30D) using traditional evaluation metrics and a common training and validation dataset. The results indicate that the proposed approach has a higher precision (as measured by the F-score) than the existing datasets. The results of this study could be used to improve current databases and establish an urban data hub for impervious surfaces in Africa. The dynamic information of impervious surfaces is useful in urban planning as an indication of the intensity of human activities and economic development. Full article
(This article belongs to the Special Issue Land Surface Temperature Retrieval Using Satellite Remote Sensing)
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21 pages, 5370 KiB  
Article
Evaluation of Fengyun-4A Detection Accuracy: A Case Study of the Land Surface Temperature Product for Hunan Province, Central China
by Jiazhi Fan, Qinzhe Han, Songqi Wang, Hailei Liu, Leishi Chen, Shiqi Tan, Haiqing Song and Wei Li
Atmosphere 2022, 13(12), 1953; https://doi.org/10.3390/atmos13121953 - 23 Nov 2022
Cited by 3 | Viewed by 1227
Abstract
Land surface temperature (LST) is an important parameter in determining surface energy balance and a fundamental variable detected by the advanced geostationary radiation imager (AGRI), the main payload of FY-4A. FY-4A is the first of a new generation of Chinese geostationary satellites, and [...] Read more.
Land surface temperature (LST) is an important parameter in determining surface energy balance and a fundamental variable detected by the advanced geostationary radiation imager (AGRI), the main payload of FY-4A. FY-4A is the first of a new generation of Chinese geostationary satellites, and the detection product of the satellite has not been extensively validated. Therefore, it is important to conduct a comprehensive assessment of this product. In this study, the performance of the FY-4A LST product in the Hunan Province was authenticity tested with in situ measurements, triple collocation analyzed with reanalysis products, and impact analyzed with environmental factors. The results confirm that FY-4A captures LST well (R = 0.893, Rho = 0.915), but there is a general underestimation (Bias = −0.6295 °C) and relatively high random error (RMSE = 8.588 °C, ubRMSE = 5.842 °C). In terms of accuracy, FY-4A LST is more accurate for central-eastern, northern, and south-central Hunan Province and less accurate for western and southern mountainous areas and Dongting Lake. FY-4A LST is not as accurate as Himawari-8 LST; its accuracy also varies seasonally and between day and night. The accuracy of FY-4A LST decreases as elevation, in situ measured LST, surface heterogeneity, topographic relief, slope, or NDVI increase and as soil moisture decreases. FY-4A LST is also more accurate when the land cover is cultivated land or artificial surfaces or when the landform is a platform for other land covers and landforms. The conclusions drawn from the comprehensive analysis of the large quantity of data are generalizable and provide a quantitative baseline for assessing the detection capability of the FY-4A satellite, a reference for determining improvement in the retrieval algorithm, and a foundation for the development and application of future domestic satellite products. Full article
(This article belongs to the Special Issue Land Surface Temperature Retrieval Using Satellite Remote Sensing)
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15 pages, 5921 KiB  
Article
Assessing the Impact of Natural Conditions/Socioeconomic Indicators on the Urban Thermal Environment Based on Geographic Big Data
by Xiaolong Lu, Haihui Wang, Huanliang Chen and Shuai Gao
Atmosphere 2022, 13(12), 1942; https://doi.org/10.3390/atmos13121942 - 22 Nov 2022
Viewed by 1117
Abstract
Understanding correctly the factors influencing the urban thermal environment is a prerequisite and basis for formulating heat-island-effect mitigation policies and studying urban ecological issues. The rapid urbanization process has led to the gradual replacement of natural landscapes by products of socioeconomic activities, and [...] Read more.
Understanding correctly the factors influencing the urban thermal environment is a prerequisite and basis for formulating heat-island-effect mitigation policies and studying urban ecological issues. The rapid urbanization process has led to the gradual replacement of natural landscapes by products of socioeconomic activities, and although previous studies have shown that natural conditions and socioeconomic intensity can significantly influence land surface temperature (LST), few studies have explored the combined effects of both on LST, especially at a fine scale. Therefore, this study investigated the relationship between natural conditions/socioeconomic and summer daytime LST based on big data and a random forest (RF) algorithm using the city of Jinan as the study area. The results showed that the spatial pattern of LST, natural condition characteristics of the city, and socioeconomic characteristics are consistent in spatial pattern and have significant correlation. In the RF model, the fitted R2 of the regression model considering two influencing factors reaches 0.86, which is significantly higher than that of the regression model considering only one influencing factor. In the optimal regression model, topographic factors in natural conditions and socioeconomic factors in buildings and roads are very important factors influencing the urban thermal environment. Based on the results, strategies and measures for developing and managing measures related to the thermal environment are discussed in depth. The results can be used as a reference for mitigating urban heat islands in the study area or other cities with similar characteristics. Full article
(This article belongs to the Special Issue Land Surface Temperature Retrieval Using Satellite Remote Sensing)
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25 pages, 9547 KiB  
Article
Quantifying a Reliable Framework to Estimate Hydro-Climatic Conditions via a Three-Way Interaction between Land Surface Temperature, Evapotranspiration, Soil Moisture
by Mercedeh Taheri, Milad Shamsi Anboohi, Mohsen Nasseri, Mostafa Bigdeli and Abdolmajid Mohammadian
Atmosphere 2022, 13(11), 1916; https://doi.org/10.3390/atmos13111916 - 17 Nov 2022
Cited by 3 | Viewed by 1124
Abstract
Distributed hydrological models can be suitable choices for predicting the spatial distribution of water and energy fluxes if the conceptual relationships between the components are defined appropriately. Therefore, an innovative approach has been developed using a simultaneous formulation of bulk heat transfer theory, [...] Read more.
Distributed hydrological models can be suitable choices for predicting the spatial distribution of water and energy fluxes if the conceptual relationships between the components are defined appropriately. Therefore, an innovative approach has been developed using a simultaneous formulation of bulk heat transfer theory, energy budgeting, and water balance as an integrated hydrological model, i.e., the Monthly Continuous Semi-Distributed Energy Water Balance (MCSD-EWB) model, to estimate land surface hydrological components. The connection between water and energy balances is established by evapotranspiration (ET), which is a function of soil moisture and land surface temperature (LST). Thus, the developed structure is based on a three-way coupling between ET, soil moisture, and LST. The LST is obtained via the direct solution of the energy balance equation, and the spatiotemporal distribution of ET is presented using the computed LST and soil moisture through the bulk transfer method and water balance. In addition to the LST computed using the MCSD-EWB model, the LST products of ERA5-Land and MODIS are also utilized as inputs. The results indicate the adequate performance of the model in simulating LST, ET, streamflow, and groundwater level. Furthermore, the developed model performs better by employing the ERA5-Land LST than by using the MODIS LST in estimating the components. Full article
(This article belongs to the Special Issue Land Surface Temperature Retrieval Using Satellite Remote Sensing)
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