Considerations for a Coordinated Data Acquisition Strategy under the GEO GLAM

Initiative

Yves CrevierCanadian Space Agency

Towards a GLAM System of Systems• High-level policy framework• Clearly articulated thematic priorities • Precise definition and certification of observation needs• Clearly articulated science plan (open science questions and goals)• Confirmed willingness of data providers:

– Government agencies (members of CEOS, CGMS)– Involvement of commercial sector sensor operators (commercial and hybrid

model)• Matching observational requirements to data provider’s capabilities –

manage expectations• Definition of an implementation strategy

– finite horizon– definition of key milestones– coordinated planning, acquisition– processing and creation of standardized product framework

Policy Framework Anchor• Positive reception of concept paper on

Strengthening Global Agricultural Monitoring by the G20

• Implementation of the Ag-01 Global Agriculture Monitoring and Early Warning in GEO Workplan– Task implementation is supported by the Global

Agricultural Monitoring Community of Practice – Component C1: A global monitoring system of systems

for agricultural production, famine early-warning, food security, and land use change.

High Level requirements – 3-tier

• Global agricultural areas – 1km to 250m resolution, several snapshots during growing season

• National agricultural areas – priority countries to be defined, 30m resolution, every cycles during growing season

• Site specific or sampling framework – Priority sites tbd (JECAM?), 1-5m resolution, every cycles during growing season

Thematic Priorities Borrowed from JECAM

• Crop Identification and Crop Area Estimation– The type, location and extent of crops are fundamental information required for policy,

food security programs and other agri-environmental health investigations. Methods are based on optical and radar imagery.

• Crop Condition/Stress– Collected in real, or near real time throughout the growing season. Generally these are

based on optical imagery to produce crop parameters (i.e. NDVI, LAI, FPAR) that are indicative of primary production and crop condition. Radar can also be applied to assess parameters associated with crop structure and measure soil moisture.

• Yield Prediction and Forecasting– Critical parameter required for food security policy and programs. This information is

required along with crop type and extent to forecast food production estimates. Early prediction of crop yield can support proactive response to regional food shortages. Yield models use both optical and radar data along with other biophysical characteristics (i.e. soil moisture, climate condition)

Shared ResponsibilitiesTask Responsibility

Coordinated mission planning, EO data acquisitions and distribution in response to operational needs*CEOS and Commercial data providers roles to be confirmed

data provider responsibility

Need for data inter-calibration, information extraction algorithms and methods, and product development -

Ag EO science community (JECAM)

Need for product standardization and assimilation process in support of the transparency concept (homogeneous, robust, responsive approach, reliable and defensible information) –

Ag CoP responsibility

CEOS Lucca Statement

CEOS Lucca StatementCEOS Agencies have decided at the recent plenary in Lucca to

focus on the following priority initiatives which will constitute the core of their programme for the years ahead:

• ...• The definition and implementation of new activities in

support of sustainable development and environmental management– The Joint Experiment for Crop Agricultural Monitoring (JECAM)

and a potential response to G20 requirements for Global Agricultural Monitoring, as part of hunger relief and food security initiatives

• ...

Missions, Instruments and MeasurementsExtracted from the CEOS Mission, Instru-ments and Measure-Ments database.

Shows mission con-tinuity, and comple-mentarity for geo-physical measurementsrelevant to agriculture. Not intended to de-monstrate missioncapacity, data policy,data availability, etc.

Data Provider Complementary PortfolioCEOS CGMS Hybrid Model

(PPP)Commercial

Type of mission Often considered as public domain missions

Public domain missions Commercial and public domains

Commercial

Resolution Coarse and medium resolution

Coarse and medium resolution

Medium and high resolution

High resolution

Surface Imaging Earth surfaces Imaging the atmosphere Imaging Earth surfaces Imaging Earth surfaces

Spectrum Optical and microwave (active and passive)

Optical and microwave (active and passive)

Optical and microwave (active)

Optical and microwave (active)

Data policy Open data policy when possible(due to commercial partnerships, security)

Open data policy Open policy for science – restricted (operations, security)

Commercial policy

Why Coordination Among Data Providers

• High frequency of acquisition in a focused timeframe:– the growing cycle of major crops is about 100 days long and its

temporal monitoring requires high repetition rate (daily to weekly).

• High variability in Ag practices:– Land use in some regions presents highly fragmented patterns

and its spatial monitoring requires high resolution imagery (10 to 20m) with sampling at fine resolution (1 to 3m).

• Variable environment and crop type:– Crop identification and vegetation-dynamics monitoring require

specific spectral information (visible, near-infrared, mid-infrared, thermal infrared and, in areas of high cloud cover, microwave).

Data Coordination Strategy• Understand the requirements (data needs, frequency, regions of interest,

etc.) and science objectives• Develop integrated thematic scenarios in collaboration with Ag CoP in order

to understand the requirements (per crop? per country? per eco-region?) • Inventory of existing observation activities (by space agencies or thematic

activities (i.e. USDA Crop Explorer, FAO GIEWS, EC MARS, etc) • Assessment of gaps between current requirements and capabilities• Assessment of mission capabilities and organization priorities (thematic,

strategic, political, etc.)• Iterative development of observation scenarios as a function of 2 families of

variables:– Theme, crop type, timing, environmental conditions, frequency of observation, scale,

etc.– Sensor of choice, mission capabilities and constraints (space and ground segments),

organization priorities, data policy, funding, etc.

Integrated Thematic Coverages Scenarios

• Integrated as a function of crop type:– Pros: allows better definition of observation timing, instrument type, instrument

configuration, facilitate the development of training material– Cons: distributed training

• Integrated as a function of national needs:– Pros: single national point of contact, easier funding (bridge between EO and

funding)– Cons: multiple crop types involves multiple instruments and instrument

configuration, variable timing for acquisition, distributed research activities and wide scope and varied training activities

• Integrated as a function of eco-regions:– Pros: similar to crop type integration with finer definition based on environmental

characteristics– Cons: not necessarily well-aligned with national priorities

Multi-level Coordination• Among data providers (public/public and

public/commercial) to optimize the use of resources, distribute imaging load, and address specific imaging requirements

• Among data providers and end-users – understand the requirements and agree on thematic coverages

• Among GEO Initiatives – by crosslinking the requirements, there is an opportunity to develop integrated observation scenarios in support of land cover/land use strategic priority sectors

Space Agencies’ Reality• Attributes that will influence the participation of space agencies:

– Translation of state G20 objectives into national priorities, agency guidance and funding;

– Current and future satellite missions and related data policies; – Challenge to meet the “free, full and open exchange” principle of GEO– Mission capabilities and constraints that may have an influence on the data

collection and distribution; – Strategic objectives and policy drivers with respect to agriculture;– Programs to support the national/regional/international agriculture

communities. • GEO-GLAM scenarios should already to pay attention – be sensitive to

the costs inherent to the required data from space agencies and commercial data providers in support of the GEO-GLAM operations

Conclusions• Need to have scientifically endorsed and vetted

requirements• Critical to work closely with GEO GLAM for the

understanding of requirements and the development observation scenarios

• Build on the existing local, regional and continental monitoring activities and streamlining a set of objectives and required products

• Manage expectation and propose realistic contribution within a multi-initiative framework

• Do not under-estimate the efforts for data coordination and costs (even under the GEO data policy principles)