What question did this study set out to answer?

The research aims to assess how spectral and spatial resolutions affect shallow-water monitoring for improved mapping accuracy.

March 24, 2026Open Access

Spatial-spectral resolution analysis using drone hyperspectral and satellite multispectral imagery for shallow coastal water monitoring

Key Points

The research aims to assess how spectral and spatial resolutions affect shallow-water monitoring for improved mapping accuracy.
Evaluated hyperspectral, multispectral, and RGB data across different spatial resolutions.
Simulated multispectral and RGB configurations from a hyperspectral dataset.
Developed empirical and machine learning models for bathymetry and benthic mapping.
Applied methods at Las Canteras beach for real-world analysis.
Hyperspectral data achieved the highest accuracy with MAE of 0.15 m and 94% accuracy.
Multispectral data provided a strong performance with MAE of 0.16 m and 93% accuracy.
RGB data was less reliable for benthic classification, showing an MAE of 0.24 m and 83% accuracy.
Subspace-KNN method outperformed others for bathymetry estimation.

Abstract

An accurate assessment of how spectral and spatial resolution influence coastal mapping remains a critical challenge for shallow-water monitoring. This study evaluates and compares hyperspectral (97 bands), multispectral (8 bands), and RGB (3 bands) data, with different spatial resolutions (10 cm and 2 m), to determine the most suitable spectral–spatial configuration for shallow-water mapping. For this, the effects of spectral and spatial resolution are isolated by simulating 8-band multispectral and 3-band RGB configurations at 10 cm and 2 m from a single 97-band hyperspectral drone dataset. This allows for comparisons between different resolutions without considering changes in temporal, atmospheric, water column, or image capture, among others acquisition-related factors. A comprehensive methodology for processing was developed using empirical and machine learning models for bathymetry estimation (Sigmoid, Subspace-KNN) and benthic mapping (SVM, FNN). The developed framework was applied at an urban sandy beach sheltered by a natural reef with rich marine biodiversity (Las Canteras beach, Gran Canaria, Spain). Results show that hyperspectral data achieved the highest accuracy (MAE, 0.15 m; accuracy, 94%), while multispectral data offered an excellent balance between resolution and performance (MAE, 0.16 m; accuracy, 93%). RGB data was acceptable for bathymetry but unreliable for benthic classification in complex habitats (MAE, 0.24 m; accuracy, 83%). Subspace-KNN outperformed empirical models for bathymetry, and FNN improved substrate discrimination. In addition, a comparative analysis between 2016 and 2023 imagery, comparing real WorldView-2 imagery (2016; 2 m and 8 bands), and drone imagery with the same resolutions emulated (2023; 2 m and 8 bands), suggests an approximate 7,200 m² reduction in marine vegetation that may be influenced with anthropogenic pressures and thermal increase. This approach provides a reproducible and adaptable tool for sustainable coastal management.

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