Satellite Remote Sensing of Ocean Surface Parameters: Physics-Based Methods and Machine Learning, and Validation

Dear Colleagues,

The ocean surface represents the dynamic interface where the atmosphere, hydrosphere, and biosphere interact; accurate observation of ocean surface conditions is essential for understanding climate variability, improving marine forecasting, supporting ecosystem assessment and maritime applications. Satellite remote sensing provides unique capabilities for continuous and large-scale observations of ocean environments, generating foundational datasets for oceanographic research and operational services. Traditional physical algorithms based on radiative transfer theory and geophysical model functions have long driven operational retrievals. However, existing methods still face challenges in addressing complex environmental variability, improving product accuracy, enhancing spatial and temporal resolution, integrating multi-source observations, and quantifying uncertainties. Recent advances in artificial intelligence (AI) and machine learning have introduced new opportunities to improve satellite ocean remote sensing through advanced retrieval methods, multi-sensor data fusion, and enhanced exploitation of satellite observations. Nevertheless, ensuring physical consistency, robustness, and reliable uncertainty characterization remains a key challenge for AI-driven methods. Therefore, there is an increasing need for innovative frameworks that effectively integrate physical knowledge, data-driven methodologies, and multi-platform observations to improve the accuracy, spatial and temporal resolution, consistency, and reliability of satellite-derived ocean surface parameter products.

This Special Issue aims to highlight recent advances in satellite remote sensing and validation of ocean surface parameters, with particular emphasis on the integration of physics-based knowledge, AI, and multi-source observations. The Special Issue focuses on innovative methodologies for improving satellite-derived ocean products, including retrieval, data fusion, product enhancement, and uncertainty characterization. We invite you to contribute your latest research findings to this Special Issue. Both original research articles and review papers will be accepted.

Research areas may include (but are not limited to) the following:

• Physics-driven and AI-based retrieval of sea surface temperature and salinity from microwave and thermal infrared radiometers;
• Synergistic estimation of ocean surface dynamics, including wind and wave characteristics, using scatterometers, altimeters, and synthetic aperture radar;
• Sea surface height and geostrophic current derivation from multi-mission satellite altimetry;
• AI-enhanced multi-sensor data fusion and high-resolution reconstruction of sea surface parameter;
• Physics-informed machine learning approaches for retrieval optimization, data gap filling, uncertainty quantification, and model generalization;
• Validation, evaluation, and intercomparison of satellite-derived sea surface parameters using in situ observations (buoys, drifters, ships, Argo) and numerical model outputs.

Dr. Wenyu Wang
Prof. Dr. Lanjie Zhang
Prof. Dr. Zhenzhan Wang
Guest Editors

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