Work

Advancing spatio-temporal fusion with spectral super-resolution for enhancing remote sensing of marine debris and landfills

Year: 2026

Type: article

Abstract: Monitoring environmental challenges such as landfill expansion and marine debris accumulation requires high-resolution satellite imagery with enhanced spectral, spatial, and temporal details. However, remote sensing systems often face trade-offs between these resolutions, limiting their effectiveness in detecting and analyzing dynamic environmental changes. To overcome this limitation, we propose a novel Spatio-Temporal Fusion (STF) framework that integrates Sentinel-2 and PlanetScope satellite datasets, improving image quality through spectral super-resolution. This study systematically evaluates deep learning-based STF methods, focusing on Conditional Generative Adversarial Networks (cGANs) and Multi-Stage Spectral-wise Transformers (MST++). Results indicate that instance normalization-based cGANs enhance spectral fidelity but introduce noise, while MST++ models offer superior structural reconstruction and generalization. By retraining models with an expanded dataset, their robustness significantly improved, with MST++ v2 emerging as the most reliable. The integration of STF methodologies enhances environmental monitoring by enabling more precise and frequent observations of critical issues such as plastic pollution, landfill management, and marine debris detection. The ability to generate high-resolution, multi-sensor fused images provides a powerful tool for sustainable resource management and environmental decision-making. This research highlights the potential of STF frameworks to advance Earth Observation applications and support global sustainability efforts, offering innovative solutions for addressing pressing environmental concerns.

Source: European Journal of Remote Sensing

DOI: 10.1080/22797254.2026.2718171

Authors: Maria Kremezi, Vassilia Karathanassi, Nicolò Taggio, Antonello Aiello, Giulio Ceriola

Institutions: National Technical University of Athens, Planetek Italia

Language: English

Topic: Advanced Image Fusion Techniques

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

SDGs: Responsible consumption and production, Climate action

Open Access status: gold

Estimated APC paid: $1,060