Expectations from satellite
observations
Osamu Ochiai, JAXAAOGEO Symposium
TG-2 and 3 joint session
November 3, 2019, Canberra, Australia

Committee on Earth Observation Satellites• CEOS was established in September, 1984 (G7 Summit)• 34 Space Agencies & 28 Associates with 170 Satellites• CEOS is the mechanism that brings these organisations together tocollaborate on missions, data systems, and global initiatives that benefit
society and align with their own Agency missions and priorities.
• CEOS is a PO of GEO• CEOS Objectives:• To optimize the benefits of space-based Earth observation through cooperation ofCEOS Agencies in mission planning and in the development of compatible data
products, formats, services, applications and policies• To aid both CEOS Agencies and the international user community by, among
other things, serving as the focal point for international coordination of spacebased Earth observation activities, including the Group on Earth Observations
and entities related to global change• To exchange policy and technical information to encourage complementarity andcompatibility among space-based Earth observation systems currently in service
or development, and the data received from them, as well as address issues ofcommon interest across the spectrum of Earth observation satellite missions

CEOS Carbon activity - history and Background•

GEO Carbon Report developed in June 2010 byteam led by Ciais et al. (GCP).

•CEOS Strategy for Carbon Observations fromSpace – written in response to above, completedin March 2014 – Wickland et al.

•

42 Actions identified in the report for specificresponse– first discussed at CEOS SIT TechnicalWorkshop in September 2013

•April 2014:Proposed establishment of a study
team to take forward the Actions and also
identify formal CEOS mechanism to manage
Actions.

•CEOS Plenary 2016: Agreed approach withdedicated pilots activities
http://ceos.org/home-2/the-ceos-carbon-strategy-space-satellites/

CEOS Plenary 2019, 14-16 October

3

2019 Refinement IPCC Guideline (GHG Inventory)[Old] 2006 IPCC Guidelines for GHG
Inventories
Volume 1 Chapter 6: Quality Assurance/Quality Control and Verification[New] Refinement to 2006 IPCC Guidelines for
GHG Inventories
Volume 1 Chapter 6: Quality Assurance/Quality
Control and Verification[6.10.2 Comparisons with atmosphericmeasurements]• Considering the limited monitoring
network currently available for many of
the greenhouse gases and the resulting
uncertainties in the model results, inverse
modeling is not likely to be frequently
applied as a verification tool of national
inventories in the near future. Even theavailability of satellite-borne sensors forgreenhouse gas concentration
measurements will not fully resolve thisproblem, due to limitations in spatial,vertical and temporal resolution (*).

• Delete: Descriptions about limitation on
availability of satellite observations (* the left)• Add: Many descriptions on usability and rolesof satellite data as a comparison tool ofinventories. Particularly, a new section of“Satellite Observations” are included.
• Improvement of estimation accuracy ofmodel by satellite data utilization at the areathat in-situ data is not ready fully.
• Prospects that satellite data estimation willquickly improve because of increase in thenumber of observations by new GHG
observation satellites (TROPOMI, GOSAT-2,GeoCarb, TanSat etc.)

A Decade-Long Global GHG Observation by GOSAT5

The Architecture Exploits the Evolving Fleetof CO2 and CH4 Satellites• Space agencies have supported several
pioneering space-based GHG sensors• SCIAMACHY on ESA’s ENVISAT• Japan’s GOSAT TANSO-FTS, NASA’s OCO-2, China’s TanSatAGCS, Feng Yun-3D GAS and Gaofen-5 GMI, Copernicus
Sentinel 5 Precursor TROPOMI, Japan’s GOSAT-2 TANSOFTS-2 and NASA’s ISS OCO-3• Others are under development• CNES MicroCarb, CNES/DLR MERLIN, NASA’s GeoCarb• Others are in the Planning stages• Japan’s GOSAT Follow-on, Copernicus CO2M6A System Approach is Adopted to Deliver
Atmospheric CO2 and CH4 Inventories

CEOS Plenary 2019, 14-16 October7

Developing Atmospheric
GHG Inventories

The CEOS GHG White Paper recommends the following approach:1.

Refine requirements for atmospheric flux inventories•2.

Foster collaboration between the space-based and groundbased GHG measurement and modeling communities and thebottom-up inventory and policy communities
Produce a prototype atmospheric CO2 and CH4 flux inventory
that is available in time to inform the bottom-up inventories
for the 2023 global Stocktake•
3.

Coordinate ongoing missions and atmospheric inversion efforts
to produce a best-effort inventory
Use lessons learned from the prototype flux inventory torefine requirements•

A future, purpose-built, operational, atmospheric inventory
system•Improved atmospheric GHG inventories to support the 2028
global Stocktake and beyond

8

CEOS GHG Roadmap Timeline
ParisAgreement
2015

2017

2019

Global Stock
Take 1
using inventories
through 2021
2021
2023

Prototypeatmospheric
CO2/CH4
inventory
Prototype
RefinedInventory
atmosphericrequirement
GHG
srequirements

CEOS GHG
Whitepaper

CEOS Plenary 2019, 14-16 October

Global StockTake 2
using inventories
through 2026
2026
2028Refinedatmospheric
GHG
inventory
InitialOperational
GHG
Constellation
Deployment92019 Refinement IPCC Guideline (AFOLU)
2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories on AFOLU(Agriculture Forestry and Other Land Use)
Volume 4 (AFOLU), Chapter 2, Page 2.20, BOX 2.0D (NEW) REMOTE SENSING TECHNOLOGIES

Optical, Synthetic Aperture Radar (SAR) and Light
Detection and Ranging (Lidar) sensors are available
currently as remote sensing data sources forproducing biomass density maps.
• SAR and LiDAR are active sensors available as airborne and space borne instruments whose derived
metrics are used to predict height, volume orbiomass of woody plants and trees.
• Referring missions: Landsat, Sentinel-1/2, ALOS-1/2,BIOMASS, NISAR, GEDI, ICESAT-2, Rapideye, andSPOT•10Biomass missions
Biomass data11

Case study: Forest above-ground biomass map development
Biomass observation using active sensors
LiDAR
LiDAR waveform can be used for
accurate estimation of above-groundbiomass.
Spatial distribution is limited to discrete
footprint points.

SARALOS-2/PALSAR-2

ALOS/PALSAR HV σ0

ICESat/GLAS[Yu & Saatchi, 2016]

HV polarization image can be used forestimation of above-ground biomass.
Signal saturation occurs at high biomass
forests.

Above-ground biomass

Polarization image ratio, image texture,
time-series
coherence,and time-seriesdata
data areused for saturation point enhancement.

Case study: Forest above-ground biomass map development
Analysis method
ICESat/GLAS

AGB measurement

ALOS-2/PALSAR-2

2016 time-series data

- Statistics- polarization image ratio- image texture[Hayashi et al., 2015]

Waveform analysis
method development

Applying it to theother footprints
Training/Validation data

- adjusted AGB according to the time-lag- divided 9:1 for training and validation

Machinelearning(Random Forest)

Abovegroundbiomassmap

Case study: Forest above-ground biomass map development
PALSAR-2 time-series data
BorneoData used:
PALSAR-2/ScanSAR image
HV/HH time-series image
Acquisition: 9-times in 2016
Spatial resolution: 50m

Case study: Forest above-ground biomass map development
Training/validation data
BorneoData used:
ICESat/GLAS
About 80,000 footprints
Acquisition: 2003-2008Case study: Forest above-ground biomass map development
Validation of AGB estimation models
Single HV image-7Time-series data
Abov e-ground biomass: PALSAR-2 (Mg ha-1)

Cycle-53

PALSAR-2 γ0HV (dB)

-9-11-13-15-17-19-210100

200

300

400500

GLAS above-ground biomass (Mg ha-1)

Signal saturation points = 130-160 Mg ha-1

500

400

300

200

10000100

200

300

400500Above-ground biomass: GLAS (Mg ha-1)

Signal saturation points = 280 Mg ha-1
Root-measn-square error = 62.8 Mg ha-1

Case study: Forest above-ground biomass map development
Above-ground biomass map

Case Study : Land-cover map development and regional-scale carbon budget estimation
Land-cover Map
2015

2016

Accuracy (2016 map)
User’s accuracy (forest): 91.2%
Producer’s accuracy (forest): 80.6%
Overall accuracy: 74.3%

Case Study : Land-cover map development and regional-scale carbon budget estimation
Area of each land-cover category
Unit: 1,000 ha

Case Study : Land-cover map development and regional-scale carbon budget estimation
Carbon budget estimation: 2015-2016

Carbon budgettC/ha/year

BorneoCentralWest
Kalimantan Kalimantan

SarawakCase Study : Peatland subsidence monitoring and local-scale carbon budget estimation
Peatland is a major carbon source in Borneo

Posa et al., 2011

Getty Images

Indonesian CO2 emissions

ESA website

Indonesia GHG Abatement Cost CurveCase Study : Peatland subsidence monitoring and local-scale carbon budget estimation
Study site: Peatland receiving development pressure
Before deforestation

40m flux tower

Provided by Prof. Hirano (Hokkaido Univ.)

After deforestation

Case Study : Peatland subsidence monitoring and local-scale carbon budget estimation
PALSAR-2 data for InSAR analysis

Target areafor carbon budget estimation
Sentinel-2 (2018.08.30)

Acquisition date: 2017.09.26
2018.01.30
2018.02.27
2018.05.22

Area: 2,630 ha
History: Deforestation in May and Sep. 2017
Oil-palm planting in Jul. 2018

Case Study : Peatland subsidence monitoring and local-scale carbon budget estimation
Ground displacement distributions in Line-of-Sight (LOS) direction2017.09-2018.01

2018.01-2018.02

2018.02-2018.05

Case Study : Peatland subsidence monitoring and local-scale carbon budget estimation
Carbon budget estimation
Subsidence rate(cm yr-1)
AverageMaximum

Carbonemissions(tC ha-1 yr-1)

Sep. 2017 - Jan. 2018

-16.3

-32.4

43.1

Jan. 2018 - Feb. 2018

-27.8

-66.1

73.5

Feb. 2018 - May 2018

-10.3

-44.2

27.3

Whole period

-15.0

-31.0

39.7

InSAR pairCarbon emission = Subsidence volume × Soil bulk density × Soil carbon content× Contribution rate of oxidative peat decomposition to the whole subsidenceSummary and future plan
PALSAR-2 time-series data has an ability in above-ground biomass estimationand land-cover mapping.
PALSAR-2 InSAR technology has an ability to observe spatial distribution ofground subsidence in peatlands.
These abilities are effective in carbon budget estimation in a large-scale.
In the future, we will compare in-situ observation data (CO2 flux and peatdepth) with our results to clarify the reliability of InSAR observation.

Peat depth observation poleby The Sarawak Tropical Peat ResearchInstituteSustainable Forest Management:

Monitoring Forest Changes for More Than 20 Years
JAXA has released annual global mosaic and Forest / Non-Forest (FNF) map by SARs
JERS-1 (1996) ~ ALOS (2007-2010) ~ ALOS-2 (2014-2017) > Changes over 20 years

2017 FNF Map by ALOS-2
2017 ALOS-2

2009 ALOS

1996 JERS-1(only HH-pol.)Forest change between 2008
and 2016. (red: deforestation,light green: reforestation)

28

GCOM-C SGLI Global land-cover map

2019/08
Forest (very-closed)
Forest (closed)
Forest (closed-to-open) with dense understory
Forestveg. (closed-to-open) with sparse understory veg.
Forest (open) with dense [LAI>2.0] understory veg.
Forest (open) with sparse [0.5<LAI<2.0]
Forest (open)
understoryveg.with no [LAI<0.5] understory veg.
Forest (very-open) with dense [LAI>2.0]
Forest (very-open)with sparse [0.5<LAI<2.0]
understoryveg.
Forest(very-open)with no [LAI<0.5] understory
understoryveg.
Herbaceous veg. [2.0<LAI]
veg.
Herbaceous veg. [0.5<LAI<2.0]
Bare or sparse veg. [LAI<0.5]
Not-classified (other land covers or no-data)

29

CEOS Land Product Validation (LPV)
Biomass Subgroup• To compile a list of LPV biomass supersites with high-quality longterm
monitoring of forest aboveground biomass and existing, planned or logisticallyfeasible airborne data.
• To develop a CEOS Biomass Protocol for methodological standardization.

ForestGeo
NEON
TERN
ForestPlotsNeed data and knowledge platform(s) for
Satellite datacollaboration
Data and Knowledge Platform

In-situ data

Machinelearning

Abovegroundbiomassmap

Abovegroundbiomassmap

Validation
Standardization
Lidar data

Model data

Trusted&Reproducible
ResultBeyond Refinement of IPCC Guidelines
FutureThe Paris Agreement

Monitoring, Mitigation, Adaptation

Now

Ocean Carbon

2019
Refinement of IPCC
Guidelines
ModelSpaceIn-Situ based GHG
Observation
32

Discussion• Earth Observation Satellite data can be coordinated by individual
agency or through CEOS for data acquisition and provision• In-situ would be more challenge but could be more instrumental
in the GEO framework – how different In-situ networks can befacilitated and sustained in line with the GEOSS data sharing andmanagement principles• Integration between EO satellites and In-situ should becoordinated in the GEO framework as well – CEOS as EO Space
Agency’s coordination body is ready to implement for GHG andBiomass cases and data and knowledge platform(s) is necessary
to promote