Near Real‐Time Mapping of All‐Sky Land Surface Temperature From GOES‐R Using Machine Learning Article Swipe
Sadegh Ranjbar
,
Danielle Losos
,
Sophie Hoffman
,
Shiva Arabi
,
Ankur R. Desai
,
Paul C. Stoy
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1029/2024jh000464
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1029/2024jh000464
Land surface temperature (LST) is crucial for understanding earth system processes. We expanded the Advanced Baseline Imager Live Imaging of Vegetated Ecosystems (ALIVE) framework to estimate LST in near‐real‐time for both cloudy and clear sky conditions at a five‐minute resolution. We compared two machine learning (ML) models, Long Short‐Term Memory (LSTM) networks and Gradient Boosting Regressor (GBR), using top‐of‐atmosphere observations from the Advanced Baseline Imager (ABI) on the GOES‐16 satellite against observations from hundreds of observation sites for a five‐year period. Long Short‐Term Memory outperformed GBR, especially at coarser resolutions and under challenging conditions, with a clear sky R 2 of 0.96 (RMSE 2.31K) and a cloudy sky R 2 of 0.83 (RMSE 4.10K) across CONUS, based on 10‐repeat Leave‐One‐Out Cross‐Validation (LOOCV). GBR maintained high accuracy and ran 5.3 times faster, with only a 0.01–0.02 R 2 drop. Feature importance revealed infrared bands were key in both models, with LSTM adapting dynamically to atmospheric changes, while GBR utilized more time information in cloudy conditions. A comparative analysis against the physically based ABI LST product showed strong agreement in winter, particularly under clear sky conditions, while also highlighting the challenges of summer LST estimation due to increased thermal variability. This study underscores the strengths and limitations of data‐driven models for LST estimation and suggests potential pathways for integrating ML models to enhance the accuracy and coverage of LST products.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1029/2024jh000464
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2024JH000464
- OA Status
- diamond
- References
- 99
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409805123
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4409805123Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1029/2024jh000464Digital Object Identifier
- Title
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Near Real‐Time Mapping of All‐Sky Land Surface Temperature From GOES‐R Using Machine LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-25Full publication date if available
- Authors
-
Sadegh Ranjbar, Danielle Losos, Sophie Hoffman, Shiva Arabi, Ankur R. Desai, Paul C. StoyList of authors in order
- Landing page
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https://doi.org/10.1029/2024jh000464Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2024JH000464Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2024JH000464Direct OA link when available
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Sky, Surface (topology), Remote sensing, Environmental science, Computer science, Computer graphics (images), Meteorology, Geology, Geography, Mathematics, GeometryTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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