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Algorithms Exploration of Land Surface Temperature Retrieval from Satellites Data
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Algorithms Exploration of Land Surface Temperature Retrieval from Satellites Data

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

Deadline for manuscript submissions: 15 June 2026 | Viewed by 1824

Special Issue Editor

Prof. Dr. Caixia Gao

E-Mail Website
Guest Editor
National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: thermal infrared; hyperspectral; quantitative remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Satellite-derived land surface temperature (LST) data are critical for climate studies, urban heat island analysis, and agricultural monitoring. This process relies on algorithms that convert thermal infrared (TIR) radiation measured by satellites into accurate land surface temperature (LST) estimates, addressing challenges such as atmospheric interference and surface emissivity. In recent decades, significant advancements have been made in the theoretical understanding and methodological approaches for satellite data. Various LST retrieval algorithms have been developed from thermal infrared data, such as the single-channel and split-window/dual-window algorithms, which require known LSEs. Others, like the temperature and emissivity separation algorithm and the physics-based day/night algorithm, necessitate up-front atmospheric correction. Despite the capabilities of physics-based TIR models in describing electromagnetic wave interactions with complex surfaces, accurately and stably retrieving surface parameters from limited satellite observations remains a challenging task. In recent years, with the advancement of artificial intelligence (AI), the integration of AI with physical models, particularly through the incorporation of deep learning technologies, has the potential to significantly enhance the interpretation of remote sensing images and information extraction capabilities, marking a pivotal direction for future research. However, those issues, including spatial discontinuity caused by cloud cover, spatiotemporal incomparability due to wide-field scanners and anisotropy, as well as instantaneous characteristics, have limited the broader application of thermal infrared remote sensing.

This Special Issue focuses on “Algorithms Exploration of Land Surface Temperature Retrieval from Satellites Data”. Potential topics include but are not limited to the following:

  • Approaches to dealing with thermal infrared remote sensing data, such as atmospheric effect correction, land surface temperature, and emissivity separation;
  • Downscaling techniques to improve the spatial resolution of LST products;
  • Land surface temperature reconstruction under cloud-covered areas;
  • Studies on the validation of land surface temperature products.

Prof. Dr. Caixia Gao
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • thermal infrared sensors
  • atmospheric effect correction
  • land surface temperature and emissivity separation
  • land surface temperature downscaling
  • land surface temperature validation
  • artificial intelligence

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Published Papers (3 papers)

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Research

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26 pages, 4779 KB  
Article
A Day–Night-Differentiated Method for Sea Surface Temperature Retrieval with Emissivity Correction
by Caixia Gao, Qinghua Zhang, Yaru Meng, Yun Wang, Wan Li, Enyu Zhao and Yongguang Zhao
Remote Sens. 2026, 18(4), 604; https://doi.org/10.3390/rs18040604 - 14 Feb 2026
Viewed by 256
Abstract
Sea surface temperature (SST) is widely used to characterize marine productivity, environmental pollution, and climate variability, and is commonly derived from thermal infrared measurements obtained by optical satellite sensors. However, accurately retrieving large-scale SSTs remains challenging due to the complexity of air–sea coupling [...] Read more.
Sea surface temperature (SST) is widely used to characterize marine productivity, environmental pollution, and climate variability, and is commonly derived from thermal infrared measurements obtained by optical satellite sensors. However, accurately retrieving large-scale SSTs remains challenging due to the complexity of air–sea coupling processes and the difficulty of accurately obtaining key intermediate parameters. This study proposes a day–night-differentiated SST retrieval method with emissivity correction rather than treating it as a fixed value. Specifically, radiance characteristics from the mid-infrared band are integrated alongside those from thermal infrared bands. The retrieved SSTs are then validated against the MODIS SST product and in situ measurements. The results demonstrate strong consistency between the retrieved SST and the MODIS SST product, with overall root mean square errors (RMSEs) of 0.66 K and 0.82 K for daytime and nighttime, respectively. In winter the RMSEs improve to 0.37 K (day) and 0.42 K (night). In situ validation against Argo measurements in 2019 shows that the RMSEs of the retrieved SSTs are approximately 0.26 K for both day and night. This confirms the efficacy of the proposed SST retrieval approach, providing a feasible solution for high-precision SST retrieval in high-latitude regions with large view zenith angles. Full article
(This article belongs to the Special Issue Algorithms Exploration of Land Surface Temperature Retrieval from Satellites Data)
26 pages, 17855 KB  
Article
Deep Learning Retrieval and Prediction of Summer Average Near-Surface Air Temperature in China with Vegetation Regionalization
by Wenting Lu, Zhefan Li, Ya Wen, Shujuan Xie, Jiaming Ou, Jianfang Wang, Zhenhua Liu, Jiahe Si, Zheyu Gan, Yue Lyu, Zitong Ji, Qianyi Fang and Mingzhe Jin
Remote Sens. 2025, 17(18), 3209; https://doi.org/10.3390/rs17183209 - 17 Sep 2025
Viewed by 808
Abstract
Retrieving and predicting summer average near-surface air temperature (SANSAT) across China remain challenging due to the country’s complex topography and heterogeneous vegetation cover. This study proposes an innovative deep learning framework that incorporates vegetation regionalization to achieve high-precision spatiotemporal temperature retrieval and prediction. [...] Read more.
Retrieving and predicting summer average near-surface air temperature (SANSAT) across China remain challenging due to the country’s complex topography and heterogeneous vegetation cover. This study proposes an innovative deep learning framework that incorporates vegetation regionalization to achieve high-precision spatiotemporal temperature retrieval and prediction. Using MODIS land surface temperature, vegetation indices, weather station data (2000–2019) and other relevant datasets, we first apply GeoDetector to identify key influencing factors (e.g., nighttime surface temperature, elevation, vegetation index, and population density) within each vegetation region. Based on these findings, we develop a deep neural network (DNN) model, which achieves high accuracy in SANSAT retrieval (with validation R2 ranging from 0.90 to 0.97 and RMSE from 0.46 to 0.64 °C). Results indicate that temperature variations in the eastern monsoon region are primarily influenced by human activity and topography, whereas natural factors dominate in the western regions. Subsequently, using a Long Short-Term Memory (LSTM) network with an optimal seven-year time step, we predict SANSAT for 2020–2023, achieving R2 values of 0.71 in training and 0.69 in testing, which confirms the model’s high reliability in SANSAT prediction. The core innovation of this work lies in its vegetation-regionalized deep learning approach, which explicitly addresses landscape heterogeneity by customizing models to specific eco-climatic zones, thereby quantifying human-nature interactions more effectively than traditional, spatially uniform methods. This framework enhances the understanding of summer temperature dynamics and provides valuable spatial data to support applications in agricultural disaster prevention, ecological conservation, and carbon neutrality. Future research will incorporate multi-seasonal data and enhance the spatiotemporal resolution to further improve NSAT modeling. Full article
(This article belongs to the Special Issue Algorithms Exploration of Land Surface Temperature Retrieval from Satellites Data)
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Review

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42 pages, 5921 KB  
Review
Deep Learning for Spatio-Temporal Fusion in Land Surface Temperature Estimation: A Comprehensive Survey, Experimental Analysis, and Future Trends
by Sofiane Bouaziz, Adel Hafiane, Raphaël Canals and Rachid Nedjai
Remote Sens. 2026, 18(2), 289; https://doi.org/10.3390/rs18020289 - 15 Jan 2026
Viewed by 473
Abstract
Land Surface Temperature (LST) plays a key role in climate monitoring, urban heat assessment, and land–atmosphere interactions. However, current thermal infrared satellite sensors cannot simultaneously achieve high spatial and temporal resolution. Spatio-temporal fusion (STF) techniques address this limitation by combining complementary satellite data, [...] Read more.
Land Surface Temperature (LST) plays a key role in climate monitoring, urban heat assessment, and land–atmosphere interactions. However, current thermal infrared satellite sensors cannot simultaneously achieve high spatial and temporal resolution. Spatio-temporal fusion (STF) techniques address this limitation by combining complementary satellite data, one with high spatial but low temporal resolution, and another with high temporal but low spatial resolution. Existing STF techniques, from classical models to modern deep learning (DL) architectures, were primarily developed for surface reflectance (SR). Their application to thermal data remains limited and often overlooks LST-specific spatial and temporal variability. This study provides a focused review of DL-based STF methods for LST. We present a formal mathematical definition of the thermal fusion task, propose a refined taxonomy of relevant DL methods, and analyze the modifications required when adapting SR-oriented models to LST. To support reproducibility and benchmarking, we introduce a new dataset comprising 51 Terra MODIS-Landsat LST pairs from 2013 to 2024, and evaluate representative models to explore their behavior on thermal data. Full article
(This article belongs to the Special Issue Algorithms Exploration of Land Surface Temperature Retrieval from Satellites Data)
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