“Remote sensing data in operational marine applications“

Rivo Uiboupin rivo.uiboupin@msi.ttu.ee

Remote sensing workgroup Marine Systems Institute

Tallinn University of Technology

Introduction

• Practical examples on how MSI has used EO data in different marine applications.

– (1) bulk/batch processing of EO data for spatial planning and background information (marine wind, sea ice, water quality);

– (2) of EO data in operational application (water quality);

– (3) improvement of operational forecast models based on EO data (sea level, waves)

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Bulk processing for marine spatial planning: wind

GORWIND- Gulf of Riga as a Resource fo Wind Energy

Mean wind speed at 10m height - 2007-2010 - Envisat/ASAR - CMOD5 algorithm

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• Ice dynamics

• Winter scenarios

Gulf of Riga MODIS data 2002-2011

Bulk processing for marine spatial planning: ice

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Mild Medium Severe

Ice probability

Length of Ice season

Gulf of Riga MODIS data 2002-2011

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Bulk processing:background information to set refference levels

Mean TSM concentration in July retrieved from ENVISA/MERIS data (2006-2010)

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Water quality parameters

• MERIS and MODIS

• Total Suspended matter

• Chlorophyll a

• Modified Case-2 regional algorithm (Doerffer & Schiller 2007)

• Empirical algorithms for MODIS due to the loss of ENVISAT

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Bulk processing Total Suspended Matter (TSM)

April May June

July August September

Monthly mean TSM maps (2006-2010). Maps are used to set the background TSM values in diffeerent regions (e.g. in the contecst of dredging operations in ports)

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ESA/PECS Project „Environmental monitoring of harbor dredging“

Mean Chl a in July calculatedfrom MERIS

data (2006 - 2010)

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Chlorophyll a

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Total Suspended matter (TSM)

Mean TSM in June calculated from MERIS

data (2006 - 2010)

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Dredging impact

Background TSM concentration in July

TSM concentration during dredging operations

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• Amount of dredged material 5250 m3

• TSM concentration increased in an area of 1 km2

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ESA/PECS Project „Environmental monitoring of harbor dredging“

Difference from background showing area influenced by dredging (treshold value 2.1 mg/l )

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ESA/PECS project „Environmental monitoring of harbor dredging“

Online monitoring of harbour dredging

- Remote sensing - In situ - model

Sentinel-2 would be very welcome for this coastal application.

Improvement of operational forecast models based on EO data

• Hydrodynamic model (HIROMB), running at Marine Systems Institute (EST) operationally – Sea level

– MyOcean altimetry products (Jason)

• 1nm wave model SWAN covering Baltic Sea („tuned“ by TUT/MSI) is running in Foreca Ltd (FIN) – Wave direction

– Wavelength

– TerraSAR-X

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Supressing sea level bias from model using

satellite altimetry products

Altimetry

MODEL

BIAS

Comparison of satellite SLA

data during Nov 2013

IAP - Integrated Application Program (ESA)

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• Satellite altimetry -MyOcean SSH products

• Hydrodynamic model- BS01, Hiromb

• Bias was found between model and altimetry SSH products

• Applying bias correction on model data - bias was subtracted from the sea level boundary conditions while extracting the boundary from BS01 model for the EST model. This procedure was repeated before each computation cycle i.e. before each day in our tests.

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Altimetry data assimilation

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Date

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Obsevations

Reference model run

Test 2

Test 3

Test 4

Test 5

Test 6

Time

Experiment with six different methods for sea level data assimilation

Goal to supress bias from modelled sea level

Sea level highly dependent on boundary

Satellite altimetry assimilation (Test 6) gives good results 14.04.2014 rivo.uiboupin@msi.ttu.ee 15

Wave model (SWAN) validation using high res. wave fields from TerraSAR-X imagery

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Examples: Tallinn TerraSAR-X v. SWAN wave model

Wavelength

NAVIGATE-Advanced wave forecast for safe navigation of small vessels. (EUROSTARS/EU)

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Conclusions

• Looking forward to Sentinel data, to use it in the applications. – bulk/batch processing of EO data for spatial

planning and background information

– EO data in operational application

– improvement of operational forecast models based on EO data

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Acknowledgements

• Staff of MSI: – Liis Sipelgas

– Laura Raag

– Sander Rikka

– Priidik Lagemaa

– Victor Alari

• Partners: – Apprise,

– Regio

– Foreca

• Organisations/programmes: – ESA/PECS

– ESA/IAP

– EUROSTARS programme

– Interreg

– HIROMB consortia

• Projects: – GORWIND

– NAVIGATE

– Environmental monitoring of harbour dredging

– Coastal Flood Warning System for Baltic Sea

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