PIML-SM: Physics-informed machine learning to estimate surface soil moisture from multi-sensor satellite images by leveraging swarm intelligence

Singh, A. orcid.org/0000-0001-6270-9355 and Gaurav, K. (2024) PIML-SM: Physics-informed machine learning to estimate surface soil moisture from multi-sensor satellite images by leveraging swarm intelligence. IEEE Transactions on Geoscience and Remote Sensing, 62. 4416913. ISSN 0196-2892

Abstract

Metadata

Item Type: Article
Authors/Creators:
Keywords: Improved integral equation model (I2EM), neural networks, physics-informed machine learning (PIML), Sentinel-1/2, soil moisture, swarm intelligence
Dates:
  • Published: 20 November 2024
  • Published (online): 20 November 2024
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Applied Mathematics (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 16 Dec 2024 11:43
Last Modified: 16 Dec 2024 17:06
Published Version: https://ieeexplore.ieee.org/document/10758874
Status: Published
Publisher: IEEE
Identification Number: 10.1109/tgrs.2024.3502618
Open Archives Initiative ID (OAI ID):

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