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

A - Papers appearing in refereed journals

Cahalane, C, Magee, A, Monteys, X, Casal, G, 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, p. 111414.

AuthorsCahalane, C, Magee, A, Monteys, X, Casal, G, Hanafin, J and Harris, P.
Abstract

Satellite derived bathymetry (SDB) enables rapid mapping of large coastal areas through measurement of optical 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.

Keywordsmultispectral; multi-platform; geostatistics; LiDAR; coastal
Year of Publication2019
JournalRemote Sensing of Environment
Journal citation233, p. 111414
Digital Object Identifier (DOI)doi:10.1016/j.rse.2019.111414
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeThe North Wyke Farm Platform- National Capability [2017-22]
S2N - Soil to Nutrition - Work package 2 (WP2) - Adaptive management systems for improved efficiency and nutritional quality
S2N - Soil to Nutrition - Work package 3 (WP3) - Sustainable intensification - optimisation at multiple scales
Publisher's version
Copyright license
CC BY
Output statusPublished
Publication dates
Online13 Sep 2019
Publication process dates
Accepted06 Sep 2019
PublisherElsevier Science Inc
ISSN0034-4257

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