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Kick off meeting. February 17th, 2011

QUAlity aware VIsualisation for the Global Earth QUAlity aware VIsualisation for the Global Earth Observation system of systemsObservation system of systems

GEOVIQUA workshop

February, the 18th, 2011 Barcelona

S&T experiences on data quality in Remote Sensing

Joost Smeets

Contents

• Introduction to S&T background- experiences• Overall approach to data quality control in Remote Sensing• Types of data quality information• Data quality concepts• Quality Information and Action Protocol

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Relevant S&T data quality projects

• Development of routine calibration and quality monitoring systems for ESA Earth Observation satellite missions– Atmospheric sensors on ENVISAT (GOMOS, MIPAS, SCIAMACHY)– Earth Explorer missions: AEOLUS (wind speed), SWARM

(magnetic field), CRYOSAT-2 (ice monitoring)

• IDEAS project – Harmonizing quality monitoring of different missions– Initial work on standardization for quality monitoring

• GECA project: Generic Environment for Cal/Val Analysis– Intercomparison tool chain of satellite data vs. correlative data– Development of Quality Information and Action Protocol (QIAP) – Contribution to major evolution of Generic Earth Observation

Metadata Standard (GEOMS)

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Organisation of data quality control for remote sensing

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QualityMonitoring

Missionplanning

Missionoperations

ProcessingArchiving

ProcessingArchiving

ProcessingArchiving

ProcessingArchiving

Product QC

Product QC

Product QC

Anomaly investigationCalibration processingCalibration monitoringLong-term monitoring

Configuration monitoring

Data flow monitoringPDGS performance

Image: ESA

Data quality control loop

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Image: ESA

Description of quality information

• Examples of quality information– Uncertainty represented by a standard deviation– A data validity flag that flags measurement data as valid/invalid– A combination of flags that signify special conditions on the data or

the processing algorithms

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Description of quality information

• Examples of quality information (continued)– Secondary measurements and corresponding rules to derive quality

of primary measurement• Discard measurement if cloud detected (filtering)

• Measurements at solar zenith angles>80° should be discarded

• Model based uncertainty Ɛx=f(x,y,z,...)

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Description of quality information

• Examples of quality information (continued)– Disclaimers

• Inform users on data quality aspects that are not in the product (non-integrated, typically becomes available only after data product has been delivered to users)

• Disclaimer example

– Plots (example from GOMOS monthly report)

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Quality concepts – processing level

• Quality issues for different processing levels:– Level 0: Accumulation of downlinked data into “Raw” instrument data

products• Corrupted data issues (communication), Saturation of measurements ,

spikes, Signal-to-Noise Ratio, telemetry data

– Level 1: Processing into calibrated (intermediate) physical quantities • Application of calibration parameters and processing algorithms to raw

data• Updating of calibration parameters from special measurements (on-board

calibration)

– Level 2+: Processing into derived physical quantities that the user is interested in• All kinds of processing, combination of data products

• Lower-level data quality indicators influence higher level data quality in a non-trivial way

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Quality concepts – data version

• Several values for a measurement at a specific time and place may exist due to different processing– Near real time processing: faster delivery of data to users at the

expense of data quality– Consolidated processing: after an additional period, more accurate

data become available due to more information about instrument characterisation (calibration)

– Reprocessing: after even longer periods, intercomparison/ground based validation may lead to additional knowledge that is used to reprocess raw data into a new version of the measurement.

• A concept is needed to identify different versions of measurement data

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Quality concepts – data version

• Possible definition: “A data-version associated with measurement data is a textual identifier indicating a common configuration for these measurement data, for example because the data have been generated using the same data processor within the same processing centre and using the same procedure”

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Quality concepts -- metadata

• Different levels of specification of (quality) metadata– At measurement level (requires access to actual product data)– At product level (can be provided through catalogues)– At dataset level (e.g. for dataset selection)

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Quality concepts -- QIAP

• QIAP= Quality Information and Action Protocol

• QIAP study and implementation has been part of GECA project (Generic Environment for Cal/Val Analysis)

• Concept: moment of discovery of quality information with respect to data availability:– Integrated quality information: quality information that is included in the

data processing and delivered with the data– Non-integrated: data quality information that becomes available only

after a data product has been delivered to users

• QIAP is a means of:– Informing users about non-integrated data quality– Providing users with a correction/filtering mechanism to be applied to

data products for improving data quality. Note: the correction should be able to be performed automatically by SW.

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Quality concepts -- QIAP

• Issues found during QIAP design:– Different notions of what quality information is/should be– As a consequence the scope of QIAP was initially unclear

• Therefore the QIAP design included a study that:– Captured different views on quality information– Defined clear vocabulary for describing quality information concepts

that guided the final decision on the scope of QIAP

• Meanwhile QIAP has been successfully implemented as:– A defined protocol– A (pilot)

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Quality concepts QIAP

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QIAP-server

ProductProductProductProductProductProduct

QualityInformation

database

ProductProductProductProductDataData

Quality information Quality informationCorrection/filtering info

Initial products Corrected products (data)

User