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Bathymetric Modeling from Time Series of Multispectral Satellite Images by Using Google Earth Engine: Understanding Error Distribution by Depth
Muhammad F.a, Sakti A.D.a, Adytia D.b, Sidiq T.P.a, Windupranata W.a, Poerbandonoa
a Institut Teknologi Bandung, Hydrography Research Group, Faculty of Earth Sciences and Technology, Bandung, Indonesia
b Telkom University, School of Computing, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 IEEE.Bathymetric data could be extracted by means of remote sensing techniques from satellite sensors known as satellite derived bathymetry (SDB). This study applies the use of remote sensing data for extraction of bathymetry within a specified time range. Logarithmic and linear analytical methods are applied to Sentinel 2A and Landsat 8 imagery to retrieve bathymetry data. We intend to analyse the pattern of error in regards to depth and epoch. The collection of satellite image, the corresponding storage, and processing makes use of Google Earth Engine (GEE). The result shows that the root mean square error (RMSE) of depth is ranging from 1.6 m to 5.4 m from both sources of imageries. Better accuracy is obtained by applying logarithmic method to Landsat imagery.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Analytical method,Error distributions,Logarithmic methods,Multispectral satellite image,Remote sensing data,Remote sensing techniques,Root mean square errors,Satellite sensors[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Bathymetry,Error pattern,Google Earth Engine,Satellite Derived Bathymetry[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]ACKNOWLEDGMENT The authors would like to thank Telkom University for holding the International Conference on Data Science and Its Applications on data science (IcoDSA). Dr. Arnida Lailatul Latifah is acknowledged for her comments on this work. The DG of Higher Education Ministry of Education and Culture, Indonesia funds the work under World Class Research scheme 2020.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICoDSA50139.2020.9213080[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]