Generating a 30 m Hourly Land Surface Temperatures Based on Spatial Fusion Model and Machine Learnin...
Generating a 30 m Hourly Land Surface Temperatures Based on Spatial Fusion Model and Machine Learning Algorithm
About this item
Full title
Author / Creator
Su, Qin , Yao, Yuan , Chen, Cheng and Chen, Bo
Publisher
Switzerland: MDPI AG
Journal title
Language
English
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Publication information
Publisher
Switzerland: MDPI AG
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Scope and Contents
Contents
Land surface temperature (LST) is a critical parameter for understanding climate change and maintaining hydrological balance across local and global scales. However, existing satellite LST products face trade-offs between spatial and temporal resolutions, making it challenging to provide all-weather LST with high spatiotemporal resolution. In this...
Alternative Titles
Full title
Generating a 30 m Hourly Land Surface Temperatures Based on Spatial Fusion Model and Machine Learning Algorithm
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_45447b113fac4cf3a4a54e1f3d5462e5
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_45447b113fac4cf3a4a54e1f3d5462e5
Other Identifiers
ISSN
1424-8220
E-ISSN
1424-8220
DOI
10.3390/s24237424
