A - Papers appearing in refereed journals
Casal, G., Harris, P., Monteys, X., Hedley, J., Cahalane, C. and McCarthy, T. 2019. Understanding satellite-derived bathymetry using Sentinel 2 imagery and spatial prediction models. GIScience & Remote Sensing.
|Authors||Casal, G., Harris, P., Monteys, X., Hedley, J., Cahalane, C. and McCarthy, T.|
Optical satellite data is an eﬃcient and complementary method to hydrographic surveys for deriving bathymetry in shallow coastal waters. Empirical approaches (in particular, the models of Stumpf and Lyzenga) provide a practical methodology to derive bathymetric information from remote sensing. Recent studies, however, have focused on enhancing the performance of such empirical approaches by extending them via spatial information. In this study, the relationship between multibeam depth and Sentinel-2 image bands was analyzed in an optically complex environment using the spatial predictor of kriging with an external drift (KED), where its external drift component was estimated: a) by a ratio of log-transformed bands based on Stumpf’s model (KED_S) and b) by a log-linear transform based on Lyzenga’s model (KED_L). Through the calibration of KED models, the study objectives were: 1) to better understand the empirical relationship between Sentinel-2 multispectral satellite reﬂectance and depth, 2) to test the robustness of KED to derive bathymetry in a multitemporal series of Sentinel-2 images and multibeam data, and 3) to compare the performance of KED against the existing non-spatial models described by Stumpf et al. and Lyzenga. Results showed that KED could improve prediction accuracy with a decrease in RMSE of 89% and 88%, and an increase in R2 of 27% and 14%, over the Stumpf and Lyzenga models, respectively. The decrease in RMSE provides a worthwhile improvement in accuracy, where results showed eﬀective prediction of depth up to 6 m. However, the presence of higher concentrations of suspended materials, especially river plumes, can reduce this threshold to 4 m. As would be expected, prediction accuracy could be improved through the removal of outliers, which were mainly located in the channel of the river, areas inﬂuenced by the river plume, abrupt topography, but also very shallow areas close to the shoreline. These areas have been identiﬁed as conﬂictive zones where satellite-derived bathymetry can be compromised.
|Keywords||Bathymetry; Multispectral; Geostatistical modelling; Kriging|
|Year of Publication||2019|
|Journal||GIScience & Remote Sensing|
|Digital Object Identifier (DOI)||doi:10.1080/15481603.2019.1685198|
|Open access||Published as non-open access|
|Funder||Biotechnology and Biological Sciences Research Council|
|Funder project or code||S2N - Soil to Nutrition [ISPG]|
|Online||04 Nov 2019|
|Publication process dates|
|Accepted||20 Oct 2019|
|Publisher||Taylor & Francis|
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