Progress report ofAOGEO Task Group 4:
Ocean, Coast, and Island
Task

Objectives of the TG4 OCI Task (since 2017)
This task, Ocean Coast and Islands (OCI) has the following objectives:
1) To provide a regional mechanism to advance and exploit synergies among the manyobservational programmes devoted to islands, coasts and oceans of the Asia-Oceania region;2) To articulate regional user needs from Earth Observations and raise awareness of thesocietal benefits of ocean observation;3) To seek to address gaps in user needs in the Asia-Oceania region to evolve a comprehensive
and integrated observation data or inventory system for the region;4) To continue development of, and cooperation for, a data inventory system, and facilitatesharing of data, tools and products, and5) To link with other GEO Initiatives (e.g. Blue Planet, GFOI, GEOGLAM and other AOGEOSStasks) to develop regional data hub and coordinate regional activities and integratedproducts.

TG4-OCI breakout
sessionfor developmentof data portal andsatellite ocean
color analysis
Date: September
15th, 2022
Time: 04:00-08:30
UTC, 13:00-17:30
in TokyoThis session will consist of two main agendas.
1. To develop a coastal in-situ ocean data portal using
Geonetwork,- further develop the geonetwork url, which wasinitialized in the last meeting on the Amazon Web Service
supported by JAMSTEC and make the site more useful anddiscuss the possible parameters to be used for search.
2. to discuss utilizations of satellite ocean color data (e.g.,
MODIS aqua, and GCOM) to algae blooms.
- focus on various methods of detections of algae
species spatially, and on a discussion of collaborations with insitu observations for reduction of uncertainty of detections ofalgae from satellite data.

Session-1:• Development of new coastal in-situ dataportal (K. Ando)• 2-1. Briefing what we have done after thelast call in October 2021 (K. Ando)• 2-2. New AWS site for geonetwork (F.
Akazawa)• 2-3. Practices on the new site on AWS (F.
Akazawa)• 2-4. Discussion on an alternate scheme tosearch data (K. Ando)Needs of in-situ ocean data networking
GOOS (Global Ocean Observing System)

Global GOOS is implemented by several components ofsurface buoy array, Argo floats and hydrographic shipbased observations Data are collected by eachcomponent, shared and disseminated.

Regional GOOS is implemented by member states andparticipating organizations usually cooperating throughGOOS Regional Alliances for coastal ocean services.
NEAR-GOOS, SEA-GOOS, IO-GOOS, and PI-GOOS are major regional
GOOS in the AP region

But the data collection in the Asia Oceania region has not been well achieved, due to jurisdictional issue (= Economic
Exclusive Zone), we have started from development of data inventory (portal)

(in-situ) Ocean Data Networking System Web Portal:
JAMSTEC

A InstituteDatabase-J1

Database-A1

B Univ.

Most recent version of concept of ocean
data networking

Database-B1

AInstitute

B Univ.

Database

Databasesystem

Metadata
systemMetadata
system

Users

Users

Users

JAMSTEC
Database-J2

Database-A2

Database

GEOSS-AP Ocean
data networking(Portal)
Users

Users

Users

Metadata
SystemOn AWS

Metadata

Only users accessed to JAMSTEC site can go to theothers
We felt necessity to update the data networking
systemDemonstration site on Amazon Web Service (AWS)

Making ownportal on AWS

In the session 1, several data portal sites
were developed on the AWS site by usingGeonetwork.

1. Agreed to develop test sites between Malaysia and Japan to demonstrate the new XML (parameter)
exchanges, and it could be done in 2022-2023
2. As a follow‒up action, we plan to copy the system into the local systems in some agencies and institutes

Utilization of Ocean Color Satellite Data
- Keywords: Ocean Surface Plankton, Harmful Algae Bloom, Eutrophication) New possible case todetect phytoplankton byusing MODIS data- Case study to detect
one specie of Karenia

Challenge to detect
phytoplankton by using
GCOM-C SGLI data

Karenia Selliformis

Sep 30th 2021, Hokkaido Newspaper(From the slides of Dr. Eko Siswanto)- Cases of Tokyo Bay,
Manila Bay, and Jakarta
BaySession-2:
Utilization ofSatellite Ocean
Color data

Session-2 Agenda:
1. HAB monitoring and early-warning in the Philippines:
challenges and opportunities (Aletta T. Yñiguez, Universityof the Philippines, Philippines)
2. Estimation of phytoplankton abundance using MODIS data
for monitoring fish kill events in Jakarta Bay (SamWouthuyzen, National Research and Innovation Agency,Indonesia)
3. Seasonal variability of MODIS-derived algal blooms in theupper Gulf of Thailand (Jutarak Luang-on, JAMSTEC)
4. Red tide detection by SGLI/GCOM-C (Joji Ishizaka, NagoyaUniversity)
5. Potentials of Google Earth Engine for ocean color studies:
The case of the Global Eutrophication Watch (Elígio deRaús Maúre, Northwest Pacific Region
Environmental Cooperation Center)
6. Discussion for collaboration (Eko Siswanto)

HAB monitoring and early-warning in the Philippines: challenges andopportunities - by Aletta T. Yniquez (University of Philippine)

Satellite data (ocean color) for large-scale assess●Pyrodinium bahamense●Alexandrium spp.
Gymnodinium catenatum●(From Prof Yniquezʼs PPT file)Site-specific models for harmful algal blooms
using machine learning and data inputPotentials of Google Earth Engine for ocean color studies: The caseof the Global Eutrophication Watch By Eligio Maure(NOWPAP/UNEP)- Marine Environmental Watch- Webinar for training- Global Eutrophication Watch(1998-2021)

H: High Eutrophic
L: Low
D: Decreasing
N: Neutral
I: Increasing(Bricker, S., et al. 2007. NOAA COP DAS No. 26. NCCOS, Silver Spring, MD. 328 pp.)

Next step: Ingestion of SGLI/GCOM data into the GEE- SGLI Level 2 mapped data (250 m resolution)

As eutrophic patches shrink, oligotrophic patches
appear to be increasing, especially in the 19982021 assessment.
(From Dr Maureʼs PPT file)Red tide detection by SGLI/GCOM-C
By Ishizaka (Nagoya University)

Can we discriminate Karenia from Diatom？

☆bbp-index from SGLI data:
K/D>10 Low for same Chl-a, Higher Diatom: Higher Scattering
Some hints to improve the algorithm

Karenia Selliformis(From Prof Ishizakaʼs PPT file)☆Fall 2021: able to detect but underestimate☆Sept., Oct. 2018−2020: a little overestimate☆Aug. 2022:No Karenia
Useful to some degree, but further challenge is needed.

FutureDirection• Develop more reliable datasets/maps ofocean color-based phytoplankton(discriminate species as much aspossible), chlorophyl, and eutrophicationarea, as a system• Collaborate with in-situ data providers ofphytoplankton, biology, biogeochemical
oceanography etc.
• Develop links and/or portal among datasystems including in-situ data systems• Articulate regional user needs fromocean color related data and raiseawareness of the societal benefits ofocean observation• Address gaps in user needs in the AsiaOceania region• Get feedbacks for improving systems