Machine Learning for Atmospheric and Remote Sensing Research

Dear Colleagues,

Over the past few years, artificial intelligence (AI) and machine learning (ML) have revolutionized the field of remote sensing and atmospheric sciences. The availability of high-resolution satellites, aerial, and ground-based sensors has led to an unprecedented volume and complexity of environmental data. Simultaneously, rapid advancements in ML/AI—deep learning, transfer learning, and data-driven modeling in particular—are unlocking new possibilities for extracting timely insights, automating complex analyses, and improving predictive capabilities for weather, climate, and atmospheric events.

This Special Issue is particularly relevant due to the following challenges:

• Global Environmental Challenges: Climate change, extreme weather, pollution, and resource management are growing in urgency and require robust, scalable, and data-driven approaches for environmental monitoring and decision support.
• Technological Convergence: Recent breakthroughs have taken place in computer vision, natural language processing, and sensor technology to enable the more accurate and comprehensive retrieval of atmospheric parameters and geospatial features.
• Data Availability: The expansion of open-access satellite missions, Earth observation programs, and environmental datasets has empowered the community to develop, test, and deploy innovative ML/AI solutions.
• Emergence of Trustworthy AI: The call for explainable, transparent, and reliable AI models has grown, especially in critical applications such as disaster response and air quality monitoring.

By addressing these trends, this Special Issue aims to bring together pioneering work leveraging ML and AI to meet current and future challenges in atmospheric and remote sensing sciences. Researchers are encouraged to submit contributions that not only demonstrate technical novelty but also advance practical impacts in monitoring, understanding, and managing our environment.

Topics of Interest:

• ML/AI algorithms for satellite, airborne, UAV, or ground-based remote sensing data analysis;
• Deep learning techniques for atmospheric parameter retrieval, weather forecasting, and event detection;
• ML-empowered environmental monitoring, air quality analysis, climate change studies, and land cover classification;
• Explainable, trustworthy, and robust AI methods for geospatial and atmospheric data;
• Data fusion, transfer learning, and multi-modal data integration strategies;
• ML/AI approaches for limited-data scenarios (few-shot, semi-supervised, and self-supervised learning);
• The real-time or edge processing of remote sensing data for atmospheric applications;
• Open datasets, benchmarks, and reproducible research in the domain;
• Application case studies: disaster monitoring, extreme event forecasting, pollution tracking, and resource management.

Dr. Amit Kumar Mishra
Dr. Michał Lupa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• machine learning
• artificial intelligence
• deep learning
• remote sensing
• atmospheric applications
• earth observation
• environmental monitoring
• data fusion
• climate science
• air quality
• satellite data
• explainable AI
• weather forecasting
• anomaly detection
• edge computing
• geospatial analysis