Satellite Remote Sensing and Machine Learning for Advanced Atmospheric Monitoring and Forecasting
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 654
Editor
Interests: satellite remote sensing; satellite meteorology; satellite climatology; GIS analysis; atmospheric environment
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Satellite remote sensing has emerged as a cornerstone of modern atmospheric science and weather and climate monitoring, driven by continuous advances in sensor technology and data processing capabilities. Numerous satellite missions such as Sentinel-5P and Meteosat provide high-resolution, multi-spectral observations that enable systematic monitoring of the Earth’s atmosphere in many different spatiotemporal scales. These observations support the analysis of atmospheric composition, cloud dynamics, precipitation systems, and extreme weather events, all of which are central to understanding the atmosphere, the weather variability and the long-term climate change. Towards this, natural and anthropogenic emissions of aerosols and trace gases significantly influence radiative forcing, cloud microphysics, and the hydrological cycle. Accurate detection and quantification of these constituents from space are therefore essential. However, the increasing volume, complexity, and heterogeneity of satellite datasets pose substantial analytical challenges.
Machine learning offers transformative opportunities for advanced atmospheric monitoring and forecasting. By enabling automated feature extraction, nonlinear pattern recognition, data fusion, and uncertainty quantification, machine learning techniques enhance retrieval algorithms, improve spatiotemporal analyses, and strengthen predictive models. The integration of satellite remote sensing with data-driven approaches thus represents a powerful framework for next-generation atmospheric research, operational forecasting, and climate resilience strategies.
Dr. Stavros Kolios
Guest Editor
Manuscript Submission Information
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Keywords
- satellite remote sensing
- atmospheric monitoring
- machine learning
- aerosols
- radiative forcing
- cloud microphysics
- trace gases
- spatiotemporal analysis
- extreme weather events
- climate forecasting
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