Underwater Remote Sensing: Status, New Challenges and Opportunities

Dear Colleagues,

According to recent estimates, approximately 70 % of Earth’s oceans remain underexplored by modern standards, yet gaining detailed insights into seafloor structure, coastal habitats, vegetation, mineral deposits, and more is essential for our understanding of the planet. Submerged ecosystems, especially those protected under the Marine Strategy Frameworks, are critical to monitor and understand, particularly the coastal zone ecosystems. While optical and lidar technologies continue to advance, active acoustics remain indispensable for deep, turbid, and dynamic underwater environments.

Multifrequency multibeam echosounders (MBES) represent the cutting edge in acoustic remote sensing, providing high-resolution bathymetry, backscatter intensity, and water column data across multiple frequencies. These sensors deliver dense point clouds, with narrow beamforming and snippet data capturing temporal backscatter variations. Coastal habitat mapping benefits especially from multifrequency MBES, as recent studies show that combining frequencies significantly improves discrimination of substrates. Moreover, morphometric indices, derived from bathymetry, offer valuable habitat descriptors and high-dimensional feature sets, including multispectral backscatter and geomorphometric derivatives, that improve classification accuracy when feature selection is optimized.

Machine learning is transforming the field of underwater remote sensing. Researchers have adapted terrestrial LiDAR research methods to segment seabed features from vegetation, sediments, and structural features using echoes directly on raw MBES point clouds.

These acoustic techniques complement optical sensors. Integrated workflows now fuse data from MBES, LiDAR, and satellite constellations, as well as hyperspectral imagery to build enriched models, particularly in shallow and transitional environments. Optical sensing methods are currently gaining popularity due to their exceptionally wide range of spatial and spectral resolutions, which extends from submillimeter (remotely operated or autonomous underwater vehicles) to several meters (e.g., Pixxel's Fireflies, Hyperion, etc.). Some satellites, such as WorldView, provide images with a very high spatial resolution (~30 cm) but only possess a few broad spectral bands, making it unclear whether they could be utilized in seafloor classification. Moreover, LiDAR is a mature technology with a high spatial resolution; however, the recorded data requires careful processing. Additionally, the high deployment costs of airborne LiDAR could limit its applicability in some regions of the world.

We encourage original research, comparative studies, algorithm development, field campaigns, and large-scale case studies that prioritize advanced processing methodologies enabled by modern acquisition technologies, either through acoustic or optical marine remote sensing.

We eagerly anticipate your high-impact contributions.

Prof. Dr. Yuri Rzhanov
Dr. Elias Fakiris
Guest Editors

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• multibeam sonar
• synthetic aperture sonar
• airborne lidar
• underwater vehicles
• hyperspectral satellites
• satellite-derived bathymetry
• satellite-based bathymetric measurements