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Attention-Enhanced GAN for Spatial-Spectral Fusion and Chlorophyll-a Inversion in Chen Lake, China.

Chenxi Zeng1, Cheng Shang1,2, Yankun Wang1,2

  • 1School of Geosciences, Yangtze University, Wuhan 430100, China.

Sensors (Basel, Switzerland)
|April 14, 2026
PubMed
Summary

A new Multi-Scale-Attention-based Unsupervised Generative Adversarial Network (MSA-UGAN) effectively fuses Sentinel-3 Ocean and Land Colour Instrument (OLCI) spectral data with Sentinel-2 Multi-Spectral Instrument (MSI) spatial data. This advanced fusion enhances inland water quality monitoring capabilities.

Keywords:
Chlorophyll-a inversionSentinel-2 MSISentinel-3 OLCIUnsupervised GANspatial–spectral fusion

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Area of Science:

  • Remote Sensing
  • Water Quality Monitoring
  • Geospatial Analysis

Background:

  • Sentinel-3 Ocean and Land Colour Instrument (OLCI) provides valuable spectral data for water quality but has coarse spatial resolution, limiting its use in small water bodies.
  • Sentinel-2 Multi-Spectral Instrument (MSI) offers high spatial resolution but lacks the spectral detail of OLCI.
  • Spatial-spectral fusion techniques, particularly deep learning methods, are crucial for integrating data from different sensors but face challenges in fusing Sentinel-2 MSI and Sentinel-3 OLCI data.

Purpose of the Study:

  • To develop an effective method for fusing Sentinel-3 OLCI spectral data with Sentinel-2 MSI spatial data.
  • To improve the spatial-temporal resolution of satellite imagery for inland water quality monitoring.
  • To introduce a novel deep learning model, the Multi-Scale-Attention-based Unsupervised Generative Adversarial Network (MSA-UGAN), for enhanced spatial-spectral fusion.

Main Methods:

  • Proposed a Multi-Scale-Attention-based Unsupervised Generative Adversarial Network (MSA-UGAN) to integrate the spectral information from Sentinel-3 OLCI and the spatial resolution from Sentinel-2 MSI.
  • Conducted quantitative evaluations comparing MSA-UGAN against five benchmark methods, including traditional approaches (GS, SFIM, MTF-GLP) and deep learning models (SRCNN, UCGAN).
  • Applied the fused imagery for Chlorophyll-a inversion in Chen Lake to assess its practical utility in water quality monitoring.

Main Results:

  • MSA-UGAN demonstrated superior performance with the highest QNR (0.9709) and SSIM (0.9087) values, and the lowest spatial (DS = 0.0389) and spectral distortion (Dλ = 0.0252) compared to benchmark methods.
  • The model effectively preserved both the spatial details of Sentinel-2 MSI and the spectral features of Sentinel-3 OLCI data, with excellent ERGAS (2.2734) performance.
  • Chlorophyll-a inversion in Chen Lake using MSA-UGAN fused imagery revealed a spatial gradient (3.25–19.33 µg/L), highlighting nearshore concentrations likely linked to aquaculture.

Conclusions:

  • MSA-UGAN successfully generates high-spatial-resolution multispectral images by effectively fusing Sentinel-2 MSI and Sentinel-3 OLCI data.
  • The fused images are highly suitable for detailed inland water quality monitoring, providing crucial data for management and decision-making.
  • The developed method offers a significant advancement in leveraging multi-sensor satellite data for precision environmental management of inland water bodies.