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|CSDMS meeting abstract presentation=Thermal inertia derived from infrared imagery offers a valuable tool for remotely mapping the physical structure and water content of soil and regolith. Unlike dry or airless bodies, however, Earth's abundant water and dense atmosphere lead to dynamic thermophysical conditions that are a greater challenge to model. In this work, an approach was developed using field experiments to inform and fine-tune a thermophysical model of terrestrial sediment. A Markov chain Monte Carlo optimization scheme is used to calculate an inherent thermal inertia value with higher precision and less initial knowledge of the sediment than has previously been achieved remotely on Earth. A more accurate thermal model for Earth has broad applications from local UAV-based geophysical surveys to climate modeling to improving interpretations of materials on Mars. | |CSDMS meeting abstract presentation=Thermal inertia derived from infrared imagery offers a valuable tool for remotely mapping the physical structure and water content of soil and regolith. Unlike dry or airless bodies, however, Earth's abundant water and dense atmosphere lead to dynamic thermophysical conditions that are a greater challenge to model. In this work, an approach was developed using field experiments to inform and fine-tune a thermophysical model of terrestrial sediment. A Markov chain Monte Carlo optimization scheme is used to calculate an inherent thermal inertia value with higher precision and less initial knowledge of the sediment than has previously been achieved remotely on Earth. A more accurate thermal model for Earth has broad applications from local UAV-based geophysical surveys to climate modeling to improving interpretations of materials on Mars. | ||
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Revision as of 11:56, 20 May 2025
CSDMS 2025: Exploring Earth's Surface with Models, Data & AI
Standardizing Soil Thermal Inertia Derivations with Bayesian Statistics
Abstract
Thermal inertia derived from infrared imagery offers a valuable tool for remotely mapping the physical structure and water content of soil and regolith. Unlike dry or airless bodies, however, Earth's abundant water and dense atmosphere lead to dynamic thermophysical conditions that are a greater challenge to model. In this work, an approach was developed using field experiments to inform and fine-tune a thermophysical model of terrestrial sediment. A Markov chain Monte Carlo optimization scheme is used to calculate an inherent thermal inertia value with higher precision and less initial knowledge of the sediment than has previously been achieved remotely on Earth. A more accurate thermal model for Earth has broad applications from local UAV-based geophysical surveys to climate modeling to improving interpretations of materials on Mars.
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