The project »BAthymetry SErvice platform« (BASE-platform) addresses the lack of available

up-to-date, high-resolution bathymetry data in many areas of the world. With the

increasing number of earth observation satellites, e.g. by the ongoing deployment

of ESA’s Sentinel fleet, remote sensing data of the oceans are widely available. Three

sources of satellite information are combined in BASE-platform: optical, synthetic aperture

radar (SAR) and

altimetry data. BASEplatform’s

ambition is

to use these data for

creating bathymetric

maps and supply them

to end users via a

bathymetry data portal,

where data will be

available off-the-shelf

as well as on demand.

Adequate metadata will

be provided along with

the bathymetry so usability

by the end user is

ensured

Hartmann, Knut

Lewis, Simon

Martinez, Bernart

Schaap, Dick

Thornton, Tim

Verlaan, Martin

Wiehle, Stefan

Institute of Transport Research:Publications

1/8 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Deriving the bathymetry from combined satellite data  HYDRO 2016 Rostock-Warnemünde, Germany, 08 – 10 November 2016 The BASE-platform project: Deriving the bathymetry from combined satellite data   Stefan WIEHLE, Bernat MARTINEZ, Knut HARTMANN, Martin VERLAAN, Tim THORNTON, Simon LEWIS, Dick SCHAAP   Key words: Remote Sensing, Earth Observation, Oceanography, Bathymetry, Data fusion  SUMMARY   The project “BAthymetry SErvice platform“ (BASE-platform) addresses the lack of available up-to-date, high resolution bathymetry data in many areas of the world. With the increasing number of earth observation satellites, e.g. by the ongoing deployment of ESAs Sentinel fleet, remote sensing data of the oceans is widely available. BASE-platform’s ambition is to use this data for creating bathymetric maps and supply them to end users.  Three sources of satellite information are combined in BASE-platform: optical, Synthetic Aperture Radar (SAR), and altimetry data. From optical satellite images, the water depth can be obtained by analysing the spectral changes of the seafloor. This method works in optically shallow waters only, where the seafloor contributes a detectable part of the measured signal. Farther from the coast, SAR bathymetry is used which detects changes of wave lengths in the ocean, indicating changes of the underlying bathymetry due to the shoaling effect. Information about deeper areas is then acquired from altimetry data. Altimetry satellites use radar signals to determine the height of the ocean surface below them. Changes in the bathymetry cause gravimetric distortions which influence the height of the sea surface; this allows a reproduction of underlying bathymetric features.  Additional input is gathered from crowd sourced data, providing depth information from a large number of ships and small craft along their regular tracks. These in-situ measurements are also used for the calibration of EO data. With tidal modelling, all data are corrected for the tides during their respective acquisition time. By combining all these sources, a merged bathymetry product can then be created.  A major point of BASE-platform is the distribution to the user by a bathymetry data portal, where data will be available off-the-shelf as well as on demand. Adequate metadata will be provided along with the bathymetry so usability by the end user is ensured.  1. INTRODUCTION  Bathymetry is the measurement of the topography underwater. While the topography of land is rather well-know and was only recently determined world-wide in high resolution with the TanDEM-X radar satellite mission (Krieger et al., 2007), there is currently no possibility to measure the bathymetry worldwide with a single remote sensing instrument. However, with the ongoing expenses of the offshore industry, e.g. the construction of offshore wind parks, there is an increasing demand for accurate bathymetric data. In-situ measurements performed 2/8 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Deriving the bathymetry from combined satellite data  HYDRO 2016 Rostock-Warnemünde, Germany, 08 – 10 November 2016 by ships equipped with echo sounders can produce very high resolution bathymetric data, but their high operational costs make them economically unsuitable for covering larger areas. A coarse bathymetry with a resolution of about 900m is offered on platforms like Gebco for free, but resolution and possibly actuality are not sufficient for commercial usage.  The BASE-platform project fills this gap by combining different data sources that are all available worldwide: three types of satellite data: optical, Synthetic Aperture Radar (SAR), altimetry. This is enhanced by crowd-sourced echo sounding data and sea level modelling to correct variances in water levels. While all these existed individually before, BASE-platform merges these technologies, allowing for a wide coverage and highly accurate data. Within the project, existing data generation processes are automated to become part of a processing chain that requires minimal human interaction. Via the BASE-platform data portal, end user can then obtain a cost effective bathymetry product with a high resolution.  2. DATA SOURCES  This section describes the different sources of data used in BASE-platform. Figure 1 provides an overview on their respective applicable depths.   Figure 1: Depth ranges of the different techniques combined in BASE-platform. For remote sensing, optical bathymetry can cover shallow waters, followed by SAR bathymetry for medium depths, while Altimeter bathymetry covers the deepest areas. Crowd sourced bathymetry can be applied everywhere where ships can go, although sensor range generally does not extend 2000m depth. Corrections from hydrodynamic modelling are not limited to depth ranges, but their effect will usually be strongest near the coast.  2.1. Optical Satellite Data  For the successful use of optical bathymetry, the sea bottom must contribute a detectable part to the signal measured by the satellite sensor (Heege et al., 2008). The depth of these so called optical shallow waters varies from <10m in regions like the North Sea to about 30m, e.g. in Caribbean waters. The environmental conditions of the recording strongly vary over time, hence, sophisticated correction algorithms have to be applied. These will remove, e.g., 3/8 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Deriving the bathymetry from combined satellite data  HYDRO 2016 Rostock-Warnemünde, Germany, 08 – 10 November 2016 atmospheric effects, adjacency effects when land is nearby, sun glint on the sea surface or water refraction. An example of optical bathymetry is given in Fig. 2. Multiple satellites are used for data acquisition, including ESAs Sentinel-2 and Landsat 8.   Figure 2: Example of a satellite derived bathymetry product based on Landsat 8 imagery for the Kalpeni Island, Lakshadweep region, India.  2.2. Synthetic Aperture Radar Satellite Data  While radar beams cannot penetrate the ocean surface to directly determine the underlying bathymetry, the bathymetry causes the so called shoaling effects which changes wave parameters at the sea surface. This shoaling effects makes waves become shorter and steeper when approaching shallower waters, hence, a direct relation between changes in wavelength and depth exists. With SAR, the ocean waves can be depicted independent of sunlight or weather conditions. The wavelengths are then determined using the Fast Fourier Transform (FFT) on small subsections of the acquired radar image (Pleskachevsky et al., 2011). Due to recent improvements, artefacts like ships, sand banks or wave breaking zones can be filtered out, allowing an automatic and consistent analysis of the scenes. The algorithm for SAR bathymetry is sketched in Fig. 3, an example result is shown in Fig. 4.  The data used are primarily acquired by DLRs TerraSAR-X mission and ESAs Sentinel-1 mission, but data from other satellites can also be used for this method.  4/8 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Deriving the bathymetry from combined satellite data  HYDRO 2016 Rostock-Warnemünde, Germany, 08 – 10 November 2016  Figure 3: Algorithm for tracking wave rays: by computing the Fast Fourier Transformation (FFT) for a sub-image, a 2D image spectrum is retrieved in wave number space indicating wavelength and wave direction. Starting in open waters, the box for the FFT is moved in the wave direction, and a new FFT is computed. Data filtering is taken into account for the wave direction (cross sea) and wavelength (wind sea and wind streaks). The procedure is repeated until the corner points of the FFT box reach the shoreline (A); an example of one wave ray (B).   Figure 4: Results of SAR bathymetry around Jersey, Channel Islands. The two sub images from the SAR scene demonstrate the changes in ocean wave length resulting from the difference in water depth.  2.3. Altimetry Satellite Data   5/8 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Deriving the bathymetry from combined satellite data  HYDRO 2016 Rostock-Warnemünde, Germany, 08 – 10 November 2016  Figure 5: Bathymetry from satellite altimeter workflow. Three inputs are combined during the workflow, sea surface height from satellite altimeter, the EGM08 model and ship soundings.  Adding bathymetric ship soundings to the procedure improves the results in two different ways. On one hand a smoothed version of the bathymetric surface (isostatically compensated component) can be estimated by filtering an interpolated surface from the soundings. On the other hand, gravity to bathymetry ratio grids can be estimated. This avoids defining the unknown seafloor density variations which have great influence in gravity anomalies.    Figure 6: Bathymetry from satellite altimeter in Mauritius Islands  For this methodology, data from Cryosat-2 along with ancillary data from other satellites is used.  6/8 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Deriving the bathymetry from combined satellite data  HYDRO 2016 Rostock-Warnemünde, Germany, 08 – 10 November 2016 2.4. Crowd Sourced Bathymetry Data  While regular seafloor mapping campaigns are expensive, crowd sourced bathymetry (CSB) asks vessels to log position and depth data while they carry out their normal activities. The gathered data are periodically uploaded to generate a bathymetric data product. Many vessels were already gathered for the TeamSurv platform during the previous CoSuDEC project, now reaching about 300 vessels plus other vessels like research ships. For BASE-platform, CSB data are used as in-situ measurements and offer a way of calibrating the results obtained via remote sensing.  2.5. Sea level modelling  All of the methods previously described measure the distance between the sea surface and the sea floor at their respective time of measurement. However, this water depth is strongly influenced by tidal and meteorological variations. Tidal amplitudes are often amplified near the coast, which may reach several meters of tidal range, even up to 14.5m in the Bay of Fundy.  As permanently installed tidal gauges are mostly too scarce for global tidal interpolation, numerical hydrodynamic models are applied. Observations from tide gauges and satellite altimetry are included in these models to improve accuracy. For the BASE-platform project, this modelling allows to calculate the correct chart datum. According to the standard of the International Hydrographic Organization (IHO), this is based on the Lowest Astronomical Tide (LAT), and conversions to other reference systems like Mean Sea Level (MSL) and geoids are also possible with these hydrodynamic models.  3. WORKFLOW  A current hindrance for widely available bathymetric data from satellite measurements is the amount of manual steps involved in the acquisition and high-level preparation of the data. For many satellites, acquisitions must be scheduled several days ahead of time and it cannot be guaranteed that the ordered scenes are useable for the respective algorithms (e.g. cloud cover for optical images, no waves for SAR, etc.). With ESAs Sentinel fleet, this is solved as acquisitions are provided regularly without the need for previous ordering. The remaining part, optimizing the workflow of the bathymetry data generation and distribution, is a main target of the BASE-platform project. For this, all currently applied algorithms have to be most widely automated. This can include steps like determination of parameters depending on scene location or acquisition time, evaluation of required filters and in which sections of a scene these must be used, or masking land and sea in areas where no land mask is available or the land mask is wrong. However, most data extracted from an individual acquisition is not directly useable for a user; they need to be corrected for tidal variations and the coordinates and data format must be adapted to fit the user’s needs. This workflow is shown in Fig. 7.  7/8 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Deriving the bathymetry from combined satellite data  HYDRO 2016 Rostock-Warnemünde, Germany, 08 – 10 November 2016  Figure 7: Workflow of BASE-platform. The different data sources are corrected by sea level modelling and are offered on the server individually and as a merged product. Data from GEBCO and EMODNET are used for initialization; a feedback of low resolution data is also possible. Users can download off-the-shelf products or request new acquisitions via the BASE-platform web portal. Furthermore, within the BASE-platform project a combined bathymetry from all available sources is offered, so different sources must be merged to a single product first. All these steps also need to run as automatically as possible. One aspect therein is the inclusion of the data in a web portal, where users can directly select a region and their desired delivery format, among other parameters. This ensures accessing the data is very simple for any end user.   As sketched in Fig. 7, creating the merged bathymetry may also use freely available data from the GEBCO and EMODnet portals. While EMODnet offers a better resolution from a variety of sources with coverage limited to European waters, GEBCO offers coarser resolutions with worldwide coverage. As data on these portals may be several years old and, hence, may be found to be imprecise, BASE-platform can deliver low resolution data to these portals.  4. SUMMARY  The BASE-platform project presented in this paper aims to deliver satellite derived bathymetry data to end users. Optical, SAR and altimetry satellite data, which each have their strength in different depths, are included in the project. It builds upon existing algorithms which are automated for easier and faster use. A high quality of the data is achieved by inclusion of sea level modelling and crowd sourced bathymetry data. The delivery of the data 8/8 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Deriving the bathymetry from combined satellite data  HYDRO 2016 Rostock-Warnemünde, Germany, 08 – 10 November 2016 is done via a web portal where users can personalize delivery options to have the new data fit into their existing workflow.    REFERENCES  DIXON, Timothy,  NARAGHI, Manuher, McNUTT, Marcia; SMITH, S.M.: Bathymetric prediction from Seasat altimeter data, J. Geophys. Res. 88, 1563-1571, 1983.  HEEGE, Thomas; KOBRYN, Halina, HARVEY Matthew: How can I map littoral sea bottom properties and bathymetry? In: Fitoka E & Keramitsoglou I 2008 (editors). Inventory, assessment and monitoring of Mediterranean Wetlands: Mapping wetlands using Earth Observation techniques. EKBY & NOA. MedWet publication. (scientific editor Riddiford NJ), 2008.  KRIEGER, Gerhard; MOREIRA, Alberto; FIEDLER, Hauke; HAJNSEK, Irena; WERNER, Marian; YOUNIS, Marwan; ZINK Manfred, TanDEM-X: A Satellite Formation for High-Resolution SAR Interferometry, IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 11, pp. 3317-3341, Nov. 2007. doi: 10.1109/TGRS.2007.900693  PLESKACHEVSKY, Andrey, LEHNER, Susanne; HEEGE, Thomas; MOTT,C.: Synergy of Optical and Synthetic Aperture Radar Satellite Data for Underwater Topography Estimation the in Coastal Areas, Ocean Dynamics, Vol.  61 (12), pp. 2099-2120, 2011.  Links: www.base-platform.com, last accessed on Aug. 24, 2017 www.gebco.net, last accessed on Aug. 24, 2017 www.emodnet.eu, last accessed on Aug. 24, 2017  BIOGRAPHICAL NOTES Stefan Wiehle finished his PhD in Physics at the Technische Universität Braunschweig in 2014. Since then, he is researcher at DLR Maritime Safety and Security Lab in Bremen.  CONTACTS  Stefan Wiehle DLR Maritime Safety and Security Lab Bremen Henrich-Focke-Str. 4 28199 Bremen Germany Tel. +49 421 24420 1863 Fax +49 421 59702 421  Email: stefan.wiehle@dlr.de Web site: dlr.de    This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 687323.  

The BASE-platform project: Deriving the bathymetry from combined satellite data

Martínez, Bernat

Hydraulic Engineering Repository

Article, Published VersionWiehle, Stefan; Martínez, Bernat; Hartmann, Knut; Verlaan, Martin;Thornton, Tim; Lewis, Simon; Schaap, DickThe BASE-platform project – Deriving the bathymetry fromcombined satellite dataHydrographische NachrichtenVerfügbar unter/Available at: https://hdl.handle.net/20.500.11970/107879Vorgeschlagene Zitierweise/Suggested citation:Wiehle, Stefan; Martínez, Bernat; Hartmann, Knut; Verlaan, Martin; Thornton, Tim; Lewis,Simon; Schaap, Dick (2017): The BASE-platform project – Deriving the bathymetry fromcombined satellite data. In: Hydrographische Nachrichten 108. Rostock: DeutscheHydrographische Gesellschaft e.V.. S. 20-23. https://doi.org/10.23784/HN108-03.Standardnutzungsbedingungen/Terms of Use:Die Dokumente in HENRY stehen unter der Creative Commons Lizenz CC BY 4.0, sofern keine abweichendenNutzungsbedingungen getroffen wurden. Damit ist sowohl die kommerzielle Nutzung als auch das Teilen, dieWeiterbearbeitung und Speicherung erlaubt. Das Verwenden und das Bearbeiten stehen unter der Bedingung derNamensnennung. Im Einzelfall kann eine restriktivere Lizenz gelten; dann gelten abweichend von den obigenNutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.Documents in HENRY are made available under the Creative Commons License CC BY 4.0, if no other license isapplicable. Under CC BY 4.0 commercial use and sharing, remixing, transforming, and building upon the materialof the work is permitted. In some cases a different, more restrictive license may apply; if applicable the terms ofthe restrictive license will be binding.20 Hydrographische NachrichtenSpace hydrographylevels. While all these existed individually before, BASE-platform merges these technologies, allow-ing for a wide coverage and highly accurate data. Within the project, existing data generation proc-esses are automated to become part of a process-ing chain that requires minimal human interaction. Via the BASE-platform data portal, end users can then obtain a cost effective bathymetry product with a high resolution.2 Data sourcesThis section describes the different sources of data used in BASE-platform. Fig. 1 provides an overview on their respective applicable depths.Three sources of satellite information are com-bined in BASE-platform: optical, synthetic aperture radar (SAR) and altimetry data. From optical satel-lite images, the water depth can be obtained by analysing the spectral changes of the seafloor. This method works in optically shallow waters only, where the seafloor contributes a detectable part of the measured signal. Farther from the coast, SAR bathymetry is used which detects changes of wavelengths in the ocean, indicating changes of the underlying bathymetry due to the shoaling effect. Information about deeper areas is then ac-1 IntroductionBathymetry is the measurement of the topography underwater. While the topography of land is rather well-known and was only recently determined worldwide in high resolution with the TanDEM-X radar satellite mission (Krieger et al. 2007), there is currently no possibility to measure the bathymetry worldwide with a single remote sensing instru-ment. However, with the ongoing expenses of the offshore industry, e.g. the construction of offshore wind parks, there is an increasing demand for ac-curate bathymetric data. In-situ measurements performed by ships equipped with echo sound-ers can produce very high-resolution bathymetric data, but their high operational costs make them economically unsuitable for covering larger areas. A coarse bathymetry with a resolution of about 900 m is offered on platforms like GEBCO for free, but resolution and possibly actuality are not suf-ficient for commercial usage.The BASE-platform project fills this gap by com-bining different data sources that are all available worldwide: three types of satellite data: optical, synthetic aperture radar (SAR), altimetry. This is en-hanced by crowdsourced echo sounding data and sea level modelling to correct variances in water An article by STEFAN WIEHLE, BERNAT MARTINEZ, KNUT HARTMANN, MARTIN VERLAAN, TIM THORNTON, SIMON LEWIS and DICK SCHAAP AuthorsDr. Stefan Wiehle is researcher at DLR Maritime Safety and Security Lab in Bremen, Germany.stefan.wiehle@dlr.deBernat Martinez: isardSAT, Barcelona, Catalunya, Spain; Knut Hartmann: EOMAP GmbH & Co. KG, Seefeld, Germany;Martin Verlaan: Deltares, Delft, The Netherlands;Tim Thornton: Smartcom Software, Stockbridge, United Kingdom;Simon Lewis: Find Mapping Ltd, London, United Kingdom;Dick Schaap: MARIS BV, Voorburg, The Netherlands.The project »BAthymetry SErvice platform« (BASE­platform) addresses the lack of avail­able up­to­date, high­resolution bathymetry data in many areas of the world. With the increasing number of earth observation satellites, e.g. by the ongoing deployment of ESA’s Sentinel fleet, remote sensing data of the oceans are widely available. Three sources of satellite information are combined in BASE­platform: optical, synthetic ap­erture radar (SAR) and altimetry data. BASE­platform’s ambition is to use these data for creating bathymetric maps and supply them to end users via a bathymetry data por­tal, where data will be available off­the­shelf as well as on demand. Adequate metadata will be provided along with the bathymetry so us­ability by the end user is ensured.The BASE-platform projectRemote sensing | Earth observation | Oceanography | Bathymetry | Data fusionDeriving the bathymetry from combined satellite dataFig 1: Depth ranges of the different techniques combined in BASE-platform. For remote sensing, optical bathymetry can cover shallow waters, followed by SAR bathymetry for medium depths, while altimeter bathymetry covers the deepest areas. Crowdsourced bathymetry can be applied everywhere where ships can go, although sensor range generally does not extend 2000 m depth. Corrections from hydrodynamic modelling are not limited to depth ranges, but their effect will usually be strongest near the coast21HN 108 — 10/2017Space hydrographyquired from altimetry data. Altimetry satellites use radar signals to determine the height of the ocean surface below them. Changes in the bathymetry cause gravimetric distortions which influence the height of the sea surface; this allows a reproduc-tion of underlying bathymetric features.Additional input is gathered from crowdsourced data, providing depth information from a large number of ships and small craft along their regular tracks. These in-situ measurements are also used for the calibration of earth observation data. With tidal modelling, all data are corrected for the tides during their respective acquisition time. By com-bining all these sources, a merged bathymetry product can then be created.2.1 Optical satellite dataFor the successful use of optical bathymetry, the sea bottom must contribute a detectable part to the signal measured by the satellite sensor (Heege et al. 2008). The depth of these so called optical shallow waters varies from <10  m in regions like the North Sea to about 30 m, e.g. in Caribbean wa-ters. The environmental conditions of the record-ing strongly vary over time, hence, sophisticated correction algorithms have to be applied. These will remove, e.g., atmospheric effects, adjacency effects when land is nearby, sunglint on the sea surface or water refraction. An example of optical bathymetry is given in Fig. 2. Multiple satellites are used for data acquisition, including ESA’s Senti-nel-2 and Landsat 8.2.2 Synthetic aperture radar satellite dataWhile radar beams cannot penetrate the ocean sur-face to directly determine the underlying bathym-etry, the bathymetry causes the so called shoaling effect which changes wave parameters at the sea surface. This shoaling effect makes waves become shorter and steeper when approaching shallower waters, hence, a direct relation between changes in wavelength and depth exists. With SAR, the ocean waves can be depicted independent of sunlight or weather conditions. The wavelengths are then determined using the Fast Fourier Transform (FFT) on small subsections of the acquired radar image (Pleskachevsky et al. 2011). Due to recent improve-ments, artefacts like ships, sandbanks or wave breaking zones can be filtered out, allowing an au-tomatic and consistent analysis of the scenes. The algorithm for SAR bathymetry is sketched in Fig. 3, an example result is shown in Fig. 4. The data used are primarily acquired by DLR’s TerraSAR-X mission and ESA’s Sentinel-1 mission, but data from other satellites can also be used for this method.Fig 2: Example of a satellite-derived bathymetry product based on Landsat 8 imagery for the Kalpeni Island, Lakshadweep region, IndiaFig 3: Algorithm for tracking wave rays: by computing the Fast Fourier Transform (FFT) for a sub-image, a 2D image spectrum is retrieved in wave number space indicating wavelength and wave direction. Starting in open waters, the box for the FFT is moved in the wave direction, and a new FFT is computed. Data filtering is taken into account for the wave direction (cross sea) and wavelength (wind sea and wind streaks). The procedure is repeated until the corner points of the FFT box reach the shoreline (A); an example of one wave ray (B)Fig 4: Results of SAR bathymetry around Jersey, Channel Islands. The two sub-images from the SAR scene demonstrate the changes in ocean wavelength resulting from the difference in water depth22 Hydrographische NachrichtenAfterwards the residual heights are converted to slopes and interpolated into a grid. From the above surfaces the high-resolution component of gravity anomalies can be estimated from the east and north vertical deflection by solving the Laplace equation in the Fourier domain. Finally the estimation of gravity anomalies is the sum be-tween the recovered smoothed model (EGM08) and the high-resolution component. The work-flow for deriving bathymetry from satellite altim-etry data is given in Fig.  5, an example result is shown in Fig. 6.The gravity anomalies principle is applicable for topographic variations reaching from about 10 km (smaller variations create too little influence on the ocean surface) up to several hundred kilometres (larger variations are isostatically compensated and do not produce gravity variations).Adding bathymetric ship soundings to the procedure improves the results in two different ways. On one hand a smoothed version of the bathymetric surface (isostatically compensated component) can be estimated by filtering an in-terpolated surface from the soundings. On the other hand, gravity to bathymetry ratio grids can be estimated. This avoids defining the unknown seafloor density variations which have great influ-ence in gravity anomalies. For this methodology, data from Cryosat-2 along with ancillary data from other satellites was used.2.4 Crowdsourced bathymetry dataWhile regular seafloor mapping campaigns are expensive, crowdsourced bathymetry (CSB) asks vessels to log position and depth data while they carry out their normal activities. The gath-ered data are periodically uploaded to generate a bathymetric data product. Many vessels were already gathered for the TeamSurv platform dur-ing the previous CoSuDEC project, now reaching about 300 vessels plus other vessels like research ships. For BASE-platform, CSB data are used as in-situ measurements and offer a way of calibrating the results obtained via remote sensing.2.5 Sea level modellingAll of the methods previously described measure the distance between the sea surface and the seafloor at their respective time of measurement. However, this water depth is strongly influenced by tidal and meteorological variations. Tidal am-plitudes are often amplified near the coast, which may reach several metres of tidal range, even up to 14.5 m in the Bay of Fundy.As permanently installed tidal gauges are mostly too scarce for global tidal interpolation, numerical hydrodynamic models are applied. Observations from tide gauges and satellite altimetry are in-cluded in these models to improve accuracy. For the BASE-platform project, this modelling allows to calculate the correct chart datum. According to the standard of the International Hydrograph-2.3 Altimetry satellite dataSpace altimetry also employs radar waves, but uses a very different procedure than the SAR ap-proach described above. The altimetry method relies on the fact that topography on the seafloor creates gravity anomalies that tilt the ocean sur-face in ways that are measureable with a radar al-timeter (Dixon et al. 1983). From these, the underly-ing changes in bathymetry can be derived. The estimation of gravity anomalies starts with a smooth version of the geoid (EGM08) that can be used to apply the remove-compute-restore procedure widely used in geodesy. This allows the calculation of the rugosity over the smooth ge-oid model in a flat approach from altimetry data. Space hydrographyFig. 6: Bathymetry from satellite altimeter in Mauritius IslandsFig 5: Bathymetry from satellite altimeter workflow. Three inputs are combined during the workflow, sea surface height from satellite altimeter, the EGM08 model and ship soundingsHN 108 — 10/2017 23Space hydrographyReferencesDixon, Timothy; Manuher Naraghi; Marcia McNutt; S. M. Smith (1983): Bathymetric prediction from Seasat altimeter data; Journal of Geophysical Research; Vol. 88, pp. 1563–1571Heege, Thomas; Halina Kobryn; Matthew Harvey (2008): How can I map littoral sea bottom properties and bathymetry?; in: Eleni Fitoka; Iphigenia Keramitsoglou (eds.): Inventory, as-sessment and monitoring of Mediter-ranean Wetlands – Mapping wetlands using Earth Observation techniques; EKBY & NOA. MedWet publication; pp. 92–93Krieger, Gerhard; Alberto Moreira; Hauke Fiedler; Irena Hajnsek; Marian Werner; Marwan Younis; Manfred Zink (2007): TanDEM-X: A Satellite Formation for High-Resolution SAR Interferometry; IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, No. 11, pp. 3317–3341Pleskachevsky, Andrey; Susanne Lehner; Thomas Heege; C. Mott (2011): Synergy of Optical and Synthetic Aperture Radar Satellite Data for Underwater Topography Estimation the in Coastal Areas; Ocean Dynamics, Vol. 61, No. 12, pp. 2099–2120ic Organization (IHO), this is based on the lowest astronomical tide (LAT), and conversions to other reference systems like mean sea level (MSL) and geoids are also possible with these hydrodynamic models.3 WorkflowA current hindrance for widely available bathymetric data from satellite measurements is the amount of manual steps involved in the ac-quisition and high-level preparation of the data. For many satellites, acquisitions must be sched-uled several days ahead of time and it cannot be guaranteed that the ordered scenes are useable for the respective algorithms (e.g. cloud cover for optical images, no waves for SAR, etc.). With ESA’s Sentinel fleet, this is solved as acquisitions are provided regularly without the need for previ-ous ordering.The remaining part, optimising the workflow of the bathymetry data generation and distribution, is a main target of the BASE-platform project. For this, all currently applied algorithms have to be most widely automated. This can include steps like determination of parameters depending on scene location or acquisition time, evaluation of required filters and in which sections of a scene these must be used, or masking land and sea in areas where no land mask is available or the land mask is wrong.However, most data extracted from an indi-vidual acquisition is not directly useable for a user; they need to be corrected for tidal variations and the coordinates and data format must be adapted to fit the user’s needs. This workflow is shown in Fig. 7. Furthermore, within the BASE-platform project a combined bathymetry from all available sources is offered, so different sources must be merged to a single product first. All these steps also need to run as automatically as possible. One aspect therein is the inclusion of the data in a web portal, where users can directly select a region and their desired delivery format, among other parameters. This en-sures accessing the data is very simple for any end user. As sketched in Fig.  7, creating the merged bathymetry may also use freely available data from the GEBCO and EMODnet portals. While EMODnet offers a better resolution from a variety of sources with coverage limited to European waters, GEBCO offers coarser resolutions with worldwide cover-age. As data on these portals may be several years old and, hence, may be found to be imprecise, BASE-platform can deliver low-resolution data to these portals.4 SummaryThe BASE-platform project presented in this pa-per aims to deliver satellite-derived bathymetry data to end users. Optical, SAR and altimetry satellite data, which each have their strength in different depths, are included in the project. It builds upon existing algorithms which are au-tomated for easier and faster use. A high quality of the data is achieved by inclusion of sea level modelling and crowdsourced bathymetry data. The delivery of the data is done via a web portal where users can personalise delivery options to have the new data fit into their existing work-flow. “Fig. 7: Workflow of BASE-platform. The different data sources are corrected by sea level modelling and are offered on the server individually and as a merged product. Data from GEBCO and EMODnet are used for initialisation; a feedback of low-resolution data is also possible. Users can download off-the-shelf products or request new acquisitions via the BASE-platform web portalAcknowledgementsThe BASE-platform project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 687323.

English

The BASE-platform project – Deriving the bathymetry from combined satellite data

1/8 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Deriving the bathymetry from combined satellite data  HYDRO 2016 Rostock-Warnemünde, Germany, 08 – 10 November 2016 The BASE-platform project: Deriving the bathymetry from combined satellite data   Stefan WIEHLE, Bernat MARTINEZ, Knut HARTMANN, Martin VERLAAN, Tim THORNTON, Simon LEWIS, Dick SCHAAP   Key words: Remote Sensing, Earth Observation, Oceanography, Bathymetry, Data fusion  SUMMARY   The project “BAthymetry SErvice platform“ (BASE-platform) addresses the lack of available up-to-date, high resolution bathymetry data in many areas of the world. With the increasing number of earth observation satellites, e.g. by the ongoing deployment of ESAs Sentinel fleet, remote sensing data of the oceans is widely available. BASE-platform’s ambition is to use this data for creating bathymetric maps and supply them to end users.  Three sources of satellite information are combined in BASE-platform: optical, Synthetic Aperture Radar (SAR) and altimetry data. From optical satellite images, the water depth can be obtained by analysing the spectral changes of the seafloor. This method works in optically shallow waters only, where the seafloor contributes a detectable part of the measured signal. Farther from the coast, SAR bathymetry is used which detects changes of wave lengths in the ocean, indicating changes of the underlying bathymetry due to the shoaling effect. Information about deeper areas is then acquired from altimetry data. Altimetry satellites use radar signals to determine the height of the ocean surface below them. Changes in the bathymetry cause gravimetric distortions which influence the height of the sea surface; this allows a reproduction of underlying bathymetric features.  Additional input is gathered from crowd sourced data, providing depth information from a large number of ships and small craft along their regular tracks. These in-situ measurements are also used for the calibration of EO data. With tidal modelling, all data are corrected for the tides during their respective acquisition time. By combining all these sources, a merged bathymetry product can then be created.  A major point of BASE-platform is the distribution to the user by a bathymetry data portal, where data will be available off-the-shelf as well as on demand. Adequate metadata will be provided along with the bathymetry so usability by the end user is ensured.  1. INTRODUCTION  Bathymetry is the measurement of the topography underwater. While the topography of land is rather well-know and was only recently determined world-wide in high resolution with the TanDEM-X radar satellite mission (Krieger et al., 2007), there is currently no possibility to measure the bathymetry worldwide with a single remote sensing instrument. However, with the ongoing expenses of the offshore industry, e.g. the construction of offshore wind parks, there is an increasing demand for accurate bathymetric data. In-situ measurements performed 2/8 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Deriving the bathymetry from combined satellite data  HYDRO 2016 Rostock-Warnemünde, Germany, 08 – 10 November 2016 by ships equipped with echo sounders can produce very high resolution bathymetric data, but their high operational costs make them economically unsuitable for covering larger areas. A coarse bathymetry with a resolution of about 900m is offered on platforms like Gebco for free, but resolution and possibly actuality are not sufficient for commercial usage.  The BASE-platform project fills this gap by combining different data sources that are all available worldwide: three types of satellite data: optical, Synthetic Aperture Radar (SAR), altimetry. This is enhanced by crowd-sourced echo sounding data and sea level modelling to correct variances in water levels. While all these existed individually before, BASE-platform merges these technologies, allowing for a wide coverage and highly accurate data. Within the project, existing data generation processes are automated to become part of a processing chain that requires minimal human interaction. Via the BASE-platform data portal, end user can then obtain a cost effective bathymetry product with a high resolution.  2. DATA SOURCES  This section describes the different sources of data used in BASE-platform. Figure 1 provides an overview on their respective applicable depths.   Figure 1: Depth ranges of the different techniques combined in BASE-platform. For remote sensing, optical bathymetry can cover shallow waters, followed by SAR bathymetry for medium depths, while Altimeter bathymetry covers the deepest areas. Crowd sourced bathymetry can be applied everywhere where ships can go, although sensor range generally does not extend 2000m depth. Corrections from hydrodynamic modelling are not limited to depth ranges, but their effect will usually be strongest near the coast.     3/8 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Deriving the bathymetry from combined satellite data  HYDRO 2016 Rostock-Warnemünde, Germany, 08 – 10 November 2016 2.1. Optical Satellite Data  For the successful use of optical bathymetry, the sea bottom must contribute a detectable part to the signal measured by the satellite sensor (Heege et al., 2008). The depth of these so called optical shallow waters varies from <10m in regions like the North Sea to about 30m, e.g. in Caribbean waters. The environmental conditions of the recording strongly vary over time, hence, sophisticated correction algorithms have to be applied. These will remove, e.g., atmospheric effects, adjacency effects when land is nearby, sun glint on the sea surface or water refraction. An example of optical bathymetry is given in Fig. 2.  Multiple satellites are used for data acquisition, including ESAs Sentinel-2 and Landsat 8.   Figure 2: Example of a satellite derived bathymetry product based on Landsat 8 imagery for the Kalpeni Island, Lakshadweep region, India.  2.2. Synthetic Aperture Radar Satellite Data  While radar beams cannot penetrate the ocean surface to directly determine the underlying bathymetry, the bathymetry causes the so called shoaling effects which changes wave parameters at the sea surface (Alpers and Hennings, 1983). This shoaling effects makes waves become shorter and steeper when approaching shallower waters, hence, a direct relation between changes in wavelength and depth exists. With SAR, the ocean waves can be depicted independent of sunlight or weather conditions. The wavelengths are then determined using the Fast Fourier Transform (FFT) on small subsections of the acquired radar image (Pleskachevsky et al., 2011). The algorithm for SAR bathymetry is sketched in Fig. 3. Due to recent improvements, artefacts like ships, sand banks or wave breaking zones can be filtered out, allowing an automatic and consistent analysis of the scenes.  The data used are primarily acquired by DLRs TerraSAR-X mission and ESAs Sentinel-1 mission, but data from other satellites can also be used for this method.  4/8 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Deriving the bathymetry from combined satellite data  HYDRO 2016 Rostock-Warnemünde, Germany, 08 – 10 November 2016  Figure 3: Algorithm for tracking wave rays: by computing the Fast Fourier Transformation (FFT) for a sub-image, a 2D image spectrum is retrieved in wave number space indicating wavelength and wave direction. Starting in open waters, the box for the FFT is moved in the wave direction, and a new FFT is computed. Data filtering is taken into account for the wave direction (cross sea) and wavelength (wind sea and wind streaks). The procedure is repeated until the corner points of the FFT box reach the shoreline (A); an example of one wave ray (B).  2.3. Altimetry Satellite Data  Space altimetry also employs radar waves, but use a very different procedure than the SAR approach described above. The altimetry method relies on the fact that topography on the seafloor creates gravity anomalies that tilt the ocean surface in ways that are measureable with a radar altimeter (Dixon et al., 1983). From these, the underlying changes in bathymetry can be derived.   The estimation of gravity anomalies starts with a smooth version of the geoid (egm08) that can be used to apply the remove-compute-restore procedure widely used in geodesy. This allows the calculation of the rugosity over the smooth geoid model in a flat approach from altimetry data. Afterwards the residual heights are converted to slopes and interpolated into a grid. From the above surfaces the high resolution component of gravity anomalies can be estimated from the east and north vertical deflection by solving the Laplace equation in the Fourier domain. Finally the estimation of gravity anomalies is the sum between the recovered smoothed model (egm08) and the high resolution component.  The gravity anomalies principle is applicable for topographic variations reaching from about 10km (smaller variations create too little influence on the ocean surface) up to several hundred kilometres (larger variations are isostatically compensated and do not produce gravity variations).  5/8 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Deriving the bathymetry from combined satellite data  HYDRO 2016 Rostock-Warnemünde, Germany, 08 – 10 November 2016  Figure 4: Bathymetry from satellite altimeter workflow. Three inputs are combined during the workflow, sea surface height from satellite altimeter, the EGM08 model and ship soundings.  Adding bathymetric ship soundings to the procedure improves the results in two different ways. On one hand a smoothed version of the bathymetric surface (isostatically compensated component) can be estimated by filtering an interpolated surface from the soundings. On the other hand, gravity to bathymetry ratio grids can be estimated. This avoids defining the unknown seafloor density variations which have great influence in gravity anomalies.    Figure 5: Bathymetry from satellite altimeter in Mauritius Islands  For this methodology, data from Cryosat-2 along with ancillary data from other satellites is used.  6/8 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Deriving the bathymetry from combined satellite data  HYDRO 2016 Rostock-Warnemünde, Germany, 08 – 10 November 2016 2.4. Crowd Sourced Bathymetry Data  While regular seafloor mapping campaigns are expensive, crowd source bathymetry (CSB) asks vessels to log position and depth data while they about their normal activities. The gathered data are periodically uploaded to generate a bathymetric data product. Many vessels were already gathered for the TeamSurv platform during the previous CoSuDEC project, now reaching about 300 vessels plus other vessels like research ships. For BASE-platform, CSB data are used as in-situ measurements and offer a way of calibrating the results obtained via remote sensing.  2.5. Sea level modelling  All of the methods previously described measure the distance between the sea surface and the sea floor at their respective time of measurement. However, this water depth is strongly influenced by tidal and meteorological variations. Tidal amplitudes are often amplified near the coast, which may reach several meters of tidal range, even up to 14.5m in the Bay of Fundy.  As permanently installed tidal gauges are mostly too scarce for global tidal interpolation, numerical hydrodynamic models are applied. Observations from tide gauges and satellite altimetry are included in these models to improve accuracy. For the BASE-platform project, this modelling allows to calculate the correct chart datum. According to the standard of the International Hydrographic Organization (IHO), this is based on the Lowest Astronomical Tide (LAT), and conversions to other reference systems like Mean Sea Level (MSL) and geoids are also possible with these hydrodynamic models.  3. WORKFLOW  A current hindrance for widely available bathymetric data from satellite measurements is the amount of manual steps involved in the acquisition and high-level preparation of the data. For many satellites, acquisitions must be scheduled several days ahead of time and it cannot be guaranteed that the ordered scenes are useable for the respective algorithms (e.g. cloud cover for optical images, no waves for SAR, etc.). With ESAs Sentinel fleet, this is solved as acquisitions are provided regularly without the need for previous ordering. The remaining part, optimizing the workflow of the bathymetry data generation and distribution, is a main target of the BASE-platform project. For this, all currently applied algorithms have to be most widely automated. This can include steps like determination of parameters depending on scene location or acquisition time, evaluation of required filters and in which sections of a scene these must be used, or masking land and sea in areas where no land mask is available or the land mask is wrong. However, most data extracted from an individual acquisition is not directly useable for a user; they need to be corrected for tidal variations and the coordinates and data format must be adapted to fit the user’s needs. This workflow is shown in Fig. 6.   7/8 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Deriving the bathymetry from combined satellite data  HYDRO 2016 Rostock-Warnemünde, Germany, 08 – 10 November 2016   Figure 6: Workflow of BASE-platform. The different data sources are corrected by sea level modelling and are offered on the server individually and as a merged product. Data from Gebco and EMODNET are used for initialization; a feedback of low resolution data is also possible. Users can download off-the-shelf products or request new acquisitions via the BASE web portal.  Furthermore, within the BASE-project a combined bathymetry from all available sources is offered, so different sources must be merged to a single product first. All these steps also need to run as automatically as possible. One aspect therein is the inclusion of the data in a web portal, where users can directly select a region and their desired delivery format, among other parameters. This ensures accessing the data is very simple for any end user.   As sketched in Fig. 6, creating the merged bathymetry may also use freely available data from the Gebco and EMODnet portals. While EMODnet offers a better resolution from a variety of sources with coverage limited to European waters, Gebco offers coarser resolutions with worldwide coverage. As data on these portals may be several years old and, hence, may be found to be imprecise, BASE-platform can deliver low resolution data to these portals.  4. SUMMARY  The BASE-platform project presented in this paper aims to deliver satellite derived bathymetry data to end users. Optical, SAR and altimetry satellite data, which each have their strength in different depths, are included in the project. It builds upon existing algorithms which are automated for easier and faster use. A high quality of the data is achieved by inclusion of sea level modelling and crowd sourced bathymetry data. The delivery of the data is done via a web portal where users can personalize delivery options to have the new data fit into their existing workflow.  8/8 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Deriving the bathymetry from combined satellite data  HYDRO 2016 Rostock-Warnemünde, Germany, 08 – 10 November 2016    REFERENCES  ALPERS, Werner;  HENNINGS, Ingo: A theory of the imaging mechanism of underwater bottom topography by real and synthetic aperture radar, Journal of Geophysical Research, 99, pp. 10 529-10 546, 1983.  DIXON, Timothy,  NARAGHI, Manuher, McNUTT, Marcia; SMITH, S.M.: Bathymetric prediction from Seasat altimeter data, J. Geophys. Res. 88, 1563-1571, 1983.  HEEGE, Thomas; KOBRYN, Halina, HARVEY Matthew: How can I map littoral sea bottom properties and bathymetry? In: Fitoka E & Keramitsoglou I 2008 (editors). Inventory, assessment and monitoring of Mediterranean Wetlands: Mapping wetlands using Earth Observation techniques. EKBY & NOA. MedWet publication. (scientific editor Riddiford NJ), 2008.  KRIEGER, Gerhard; MOREIRA, Alberto; FIEDLER, Hauke; HAJNSEK, Irena; WERNER, Marian; YOUNIS, Marwan; ZINK Manfred, TanDEM-X: A Satellite Formation for High-Resolution SAR Interferometry, IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 11, pp. 3317-3341, Nov. 2007. doi: 10.1109/TGRS.2007.900693  PLESKACHEVSKY, Andrey, LEHNER, Susanne; HEEGE, Thomas; MOTT,C.: Synergy of Optical and Synthetic Aperture Radar Satellite Data for Underwater Topography Estimation the in Coastal Areas, Ocean Dynamics, Vol.  61 (12), pp. 2099-2120, 2011.  Links: www.base-platform.com, last accessed on Aug. 17, 2016 www.gebco.net, last accessed on Aug. 17, 2016 www.emodnet.eu, last accessed on Aug. 17, 2016  BIOGRAPHICAL NOTES Stefan Wiehle finished his PhD in Physics at the Technische Universität Braunschweig in 2014. Since then, he is researcher at DLR Maritime Safety and Security Lab in Bremen.  CONTACTS  Stefan Wiehle DLR Maritime Safety and Security Lab Bremen Henrich-Focke-Str. 4 28199 Bremen Germany Tel. +49 421 24420 1863 Fax +49 421 59702 421  Email: stefan.wiehle@dlr.de Web site: dlr.de    This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 687323.  

1/6 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Bathymetry from combined satellite and crowd-sourced data The BASE-platform project Bathymetry from combined satellite and crowd-sourced data  Stefan WIEHLE, Bernat MARTINEZ, Knut HARTMANN, Martin VERLAAN, Tim THORNTON, SuiWin HUI, Dick SCHAAP   Key words: Remote Sensing, Earth Observation, Oceanography, Bathymetry, Data fusion  1. Introduction Bathymetric data of the oceans are often coarse or deprecated, often dating back to pre-GPS times or sometimes even to lead-line measurements of the 19th century. Using multiple satellite technologies supported by crowd-sourced data, BASE-platform derives the bathymetry in many areas worldwide, providing current, high resolution data which can be easily accessed via an online web portal.   2. Overview The BASE-platform project was created by the need for better and up-to-date bathymetry datasets which were inexpensive to create. The traditional method of ship soundings delivers accurate measurements, but at due to the effort of ship and crew, these data come at a very high price.  Modern Earth observation satellites, on the other hand, offer data cheaply or, for example with ESA’s Sentinel fleet, even for free. During the BASE-platform project, existing technologies to derive bathymetry from such satellite data were further refined, automated and merged with crowd-sourced data and water level modelling to provide the most reliable data possible. These data allow very cost effective surveys or pre-studies, reducing costs for following multibeam surveys or even replace them completely. Five sources of information are combined in the project: Optical satellite images are used for coastal bathymetry up to 30m depth. The depth range from about 10m to 70m is covered by Synthetic Aperture Radar (SAR) acquisitions. Data from altimeter satellites cover the deeper and deepest waters. Crowd-sourced bathymetry recruits vessels tracking their position and depth sounder data while sailing along their regular routes. The fifth source of information is tidal modelling, which is used to correct all the previous data for tidal effects and translate them to a common reference system. 2/6 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Bathymetry from combined satellite and crowd-sourced data  Figure 1: Different techniques combined in BASE-platform with their applicable depth range.  3. Optical data For the successful use of optical bathymetry, the sea bottom must contribute a detectable part to the signal measured by the satellite sensor. The depth of these so called optical shallow waters varies from <10m in regions like the North Sea to about 30m, e.g. in Caribbean waters. The environmental conditions of the recording strongly vary over time, hence, sophisticated correction algorithms have to be applied. These will remove, e.g., atmospheric effects, adjacency effects when land is nearby, sun glint on the sea surface or water refraction.  Figure 2: Example of a satellite derived bathymetry product based on Landsat 8 imagery for the Kalpeni Island, Lakshadweep region, India.  4. SAR data While radar beams cannot penetrate the ocean surface to directly determine the underlying bathymetry, the bathymetry causes the so called shoaling effects which changes wave parameters at the sea surface. This shoaling effects makes waves become shorter and steeper when approaching shallower waters, hence, a direct relation between changes in wavelength and depth exists. With SAR, the ocean waves can be depicted independent of sunlight or weather conditions. The wavelengths are then determined using the Fast Fourier Transform (FFT) on small subsections of the acquired radar image. Due to recent improvements, artefacts like ships, sand banks or wave breaking zones can be filtered out automatically, allowing a consistent analysis of the scenes. 3/6 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Bathymetry from combined satellite and crowd-sourced data  Figure 3: Results of SAR bathymetry around Jersey, Channel Islands. The two sub images from the SAR scene demonstrate the changes in ocean wave length resulting from the difference in water depth.  5. Altimeter data Space altimetry also employs radar waves, but uses a very different procedure than the SAR approach described above. The altimetry method relies on the fact that topography on the seafloor creates gravity anomalies that tilt the ocean surface in ways that are measureable with a radar altimeter. From these, the underlying changes in bathymetry can be derived.  The estimation of gravity anomalies starts with a smooth version of the geoid (egm08) that can be used to apply the remove-compute-restore procedure widely used in geodesy. This allows the calculation of the rugosity over the smooth geoid model in a flat approach from altimetry data. Afterwards the residual heights are converted to slopes and interpolated into a grid. From the above surfaces the high resolution component of gravity anomalies can be estimated from the east and north vertical deflection by solving the Laplace equation in the Fourier domain. Finally the estimation of gravity anomalies is the sum between the recovered smoothed model (egm08) and the high resolution component. The gravity anomalies principle is applicable for topographic variations reaching from about 10km up to several hundred kilometres. Smaller variations create too little influence on the ocean surface while larger variations are isostatically compensated and do not produce gravity variations Adding bathymetric ship soundings to the procedure improves the results in two different ways. On one hand a smoothed version of the bathymetric surface (isostatically compensated component) can be estimated by filtering an interpolated surface from the soundings. On the other hand, gravity to bathymetry ratio grids can be estimated. This avoids defining the unknown seafloor density variations which have great influence in gravity anomalies.  4/6 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Bathymetry from combined satellite and crowd-sourced data   Figure 4: Bathymetry from satellite altimeter in Mauritius Islands  6. Crowd-sourced data While regular seafloor mapping campaigns are expensive, crowd sourced bathymetry (CSB) asks vessels to log position and depth data while they carry out their normal activities. The gathered data are periodically uploaded to generate a bathymetric data product. Many vessels were already gathered for the TeamSurv platform during the previous CoSuDEC project, now reaching about 300 vessels plus other vessels like research ships. For BASE-platform, CSB data are used as in-situ measurements and offer a way of calibrating the results obtained via remote sensing.  7. Modelling All of the methods previously described measure the distance between the sea surface and the sea floor at their respective time of measurement. However, this water depth is strongly influenced by tidal and meteorological variations. Tidal amplitudes are often amplified near the coast, which may reach several meters of tidal range, even up to 14.5m in the Bay of Fundy. As permanently installed tidal gauges are mostly too scarce for global tidal interpolation, numerical hydrodynamic models are applied. Observations from tide gauges and satellite altimetry are included in these models to improve accuracy. For the BASE-platform project, this modelling allows to calculate the correct chart datum. According to the standard of the International Hydrographic Organization (IHO), this is based on the Lowest Astronomical Tide (LAT), and conversions to other reference systems like Mean Sea Level (MSL) and geoids are also possible with these hydrodynamic models.  8. Bringing the data to the user Most data extracted from an individual acquisition is not directly useable for a user; they need to be corrected for tidal variations and the coordinates and data format must be adapted so it can be directly loaded into the user’s GIS tool. For more convenience, a combined bathymetry from all available sources is offered, where different sources are merged to a single product. This allows using a single dataset within the user’s area of interest, even if it stretches from the coastline to deep waters and all sources were required for its generation. 5/6 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Bathymetry from combined satellite and crowd-sourced data With cheap data and low effort thanks to automated processing, BASE-platform can offer newly derived bathymetry data at prices of just a few Euro per square kilometre (or even only 1 cent in the case of altimetry data). All data is offered in the BASE-platform web portal, which is an easy-to-use platform where available data can be downloaded off-the-shelf. This includes also four free datasets from the project’s trial regions: German Wadden Sea, Channel Islands, Balearics and Mauritius. Since data has not yet been created for all areas of the world, users can also enquire for new data to be created for their respective region of interest.   Figure 5: Workflow of BASE-platform. The different data sources are corrected by sea level modelling and are offered on the portal individually and as a merged product.   Ressources DIXON, Timothy,  NARAGHI, Manuher, McNUTT, Marcia; SMITH, S.M.: Bathymetric prediction from Seasat altimeter data, J. Geophys. Res. 88, 1563-1571, 1983.  HEEGE, Thomas; KOBRYN, Halina, HARVEY Matthew: How can I map littoral sea bottom properties and bathymetry? In: Fitoka E & Keramitsoglou I 2008 (editors). Inventory, assessment and monitoring of Mediterranean Wetlands: Mapping wetlands using Earth Observation techniques. EKBY & NOA. MedWet publication. (scientific editor Riddiford NJ), 2008.  PLESKACHEVSKY, Andrey, LEHNER, Susanne; HEEGE, Thomas; MOTT,C.: Synergy of Optical and Synthetic Aperture Radar Satellite Data for Underwater Topography Estimation the in Coastal Areas, Ocean Dynamics, Vol.  61 (12), pp. 2099-2120, 2011.  This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 687323.  6/6 S. Wiehle, B. Martinez, K. Hartmann, M. Verlaan, T. Thornton, S. Lewis, D. Schaap The BASE-platform project: Bathymetry from combined satellite and crowd-sourced data  Links: www.base-platform.com   Multimedia: BASE-platform workshop:  https://www.youtube.com/watch?v=8HodaWSS2J8  BIOGRAPHICAL NOTES Stefan Wiehle finished his PhD in physics at the Technische Universität Braunschweig in 2014. Since then, he is researcher at DLR Maritime Safety and Security Lab in Bremen.  CONTACTS  Stefan Wiehle DLR Maritime Safety and Security Lab Bremen Henrich-Focke-Str. 4 28199 Bremen Germany Tel. +49 421 24420 1863 Fax +49 421 59702 421  Email: stefan.wiehle@dlr.de Web site: dlr.de 

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The BASE-platform project: Deriving the bathymetry from combined satellite data

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