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    A comparison of Landsat 8, RapidEye and Pleiades products for improving empirical predictions of satellite-derived bathymetry


    Cahalane, Conor and Magee, Aidan and Monteys, Xavier and Casal, Gema and Hanafin, J. and Harris, P. (2019) A comparison of Landsat 8, RapidEye and Pleiades products for improving empirical predictions of satellite-derived bathymetry. Remote Sensing of Environment, 233 (111414). ISSN 1879-0704

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    Abstract

    penetration of the water column. The resolution of bathymetric mapping and achievable horizontal and vertical accuracies vary but generally, all SDB outputs are constrained by sensor type, water quality and other environmental conditions. Efforts to improve accuracy include physics-based methods (similar to radiative transfer models e.g. for atmospheric/vegetation studies) or detailed in-situ sampling of the seabed and water column, but the spatial component of SDB measurements is often under-utilised in SDB workflows despite promising results suggesting potential to improve accuracy significantly. In this study, a selection of satellite datasets (Landsat 8, RapidEye and Pleiades) at different spatial and spectral resolutions were tested using a log ratio transform to derive bathymetry in an Atlantic coastal embayment. A series of non-spatial and spatial linear analyses were then conducted and their influence on SDB prediction accuracy was assessed in addition to the significance of each model's parameters. Landsat 8 (30m pixel size) performed relatively weak with the non-spatial model, but showed the best results with the spatial model. However, the highest spatial resolution imagery used – Pleiades (2m pixel size) showed good results across both non-spatial and spatial models which suggests a suitability for SDB prediction at a higher spatial resolution than the others. In all cases, the spatial models were able to constrain the prediction differences at increased water depths.

    Item Type: Article
    Additional Information: © 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/). Cite as: C. Cahalane, A. Magee, X. Monteys, G. Casal, J. Hanafin, P. Harris, A comparison of Landsat 8, RapidEye and Pleiades products for improving empirical predictions of satellite-derived bathymetry, Remote Sensing of Environment, Volume 233, 2019, 111414, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2019.111414. Research in this publication was funded under Geological Survey Ireland's Research Programme, Contract No. 2015-sc-024 and BBSRC core funding via grants BBS/E/C/000J0100; BBS/E/C/000I03320 and BBS/E/C/000I0330. The authors wish to thank the INFOMAR program, the Irish Marine Institute and the Geological Survey of Ireland for the validation datasets provided. RapidEye and Pleiades datasets were provided by the European Space Agency under Third Party Mission license. Landsat 8 images provided courtesy of the US Geological Survey.
    Keywords: Multispectral; Multi-platform; Geostatistics; LiDAR; Coastal;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
    Faculty of Social Sciences > Geography
    Item ID: 14156
    Identification Number: https://doi.org/10.1016/j.rse.2019.111414
    Depositing User: Conor Cahalane
    Date Deposited: 10 Mar 2021 15:00
    Journal or Publication Title: Remote Sensing of Environment
    Publisher: Elsevier
    Refereed: Yes
    Funders: Geological Survey Ireland
    URI:

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