Artificial Intelligence and Satellite Remote Sensing for Environmental Monitoring

Dear Colleagues,

Artificial intelligence (AI) and satellite remote sensing (optical, SAR, thermal, hyperspectral) are reshaping how we observe the environment across land, inland waters, coasts, and the open ocean. This Special Issue welcomes contributions that combine satellite data with AI (e.g., deep learning, foundation models, physics-informed ML) or statistical modeling (e.g., geospatial analysis) to advance environmental monitoring, change detection, and decision support at scale. We also welcome rigorous statistical modeling approaches that do not rely solely on machine learning methods, provided that they advance environmental monitoring with satellite data. We invite contributions addressing terrestrial, inland, and marine water systems, such as forest health, agricultural monitoring, water quality monitoring, and marine pollution detection. While its scope is broad and this Special Issue is open to all environmental domains, we place a particular emphasis on marine ecosystems.

Within this area of emphasis, we encourage studies on marine pollution and coastal dynamics, including the detection and monitoring of marine litter (e.g., floating plastics), phytoplankton phenology monitoring, oil-spill detection and characterization (including SAR-based approaches), coastline evolution and erosion, and maritime target detection under challenging conditions (cloud cover, variable sea states, near-shore clutter).

Topics of interest include (but are not limited to) the following:

- Environmental monitoring across domains: water quality; phytoplankton dynamics and biomass monitoring; pollution detection; habitat/vegetation and land-use/land-cover mapping; hazards and disasters; object/material detection; and long-term trend and change analysis.

- Marine/coastal/inland waters applications: floating-plastic detection; oil-spill mapping; phytoplankton and phenology metrics monitoring; shoreline and coastal morphology change; ship/target detection and tracking; Sargassum and floating algae monitoring using AI or statistical pipelines.

- AI methods and pipelines for environmental monitoring: deep learning and computer vision; self-/weakly supervised learning; transfer learning and domain adaptation; physics-informed modeling; uncertainty quantification; explainable AI; multi-sensor fusion (optical–SAR–thermal–hyperspectral); and scalable/operational workflows.

We particularly encourage submissions that adopt reproducible and comparable practices, including open-source code and pipelines, benchmark datasets, standardized reporting/metrics, and cross-regional validation frameworks. Given the societal relevance of environmental monitoring, we also welcome studies that explore policy implications, pathways to operational uptake, community engagement/participatory approaches, and the ethical dimensions of AI.

Dr. Ioannis Kakogeorgiou
Dr. Katerina Kikaki
Dr. Konstantinos Karantzalos 
Guest Editors

Manuscript Submission Information

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