ambiguity of quality in remote sensing data
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Ambiguity of Quality in Remote Sensing Data. Christopher Lynnes, NASA/GSFC Greg Leptoukh , NASA/GSFC Goddard Earth Sciences Data and Information Services Center. - PowerPoint PPT PresentationTRANSCRIPT
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Ambiguity of Quality in Remote Sensing Data
Christopher Lynnes, NASA/GSFCGreg Leptoukh, NASA/GSFC
Goddard Earth Sciences Data and Information Services Center
Funded by: NASA’s Advancing Collaborative Connections for Earth System Science (ACCESS) and Advanced Information Systems Technology (AIST)
programs
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Data Quality Goal
Describe data quality so that users understand: What it means for the data How to use quality information for
data selection and analysis Why so difficult?
Data Quality depends on intended use
Data Quality = Fitness for Purpose
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Satellite data swaths and grids
Level 2Swath
Level 3Grid
Temporal and Spatial Aggregatio
n
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A Survey of Data Quality at Different Levels of Aggregation
Type of Data
Data-Value Quality
Data-File Quality
Dataset* Quality
Level 2 Swath
Quality Control (QC) flags
Statistical summary of QC info
Statistical accuracy relative to validation sites
Level 3 Grid
•Cell std. dev.•Number of input values
Statistical summary of QC info
??
Increasing Aggregation
Incr
easin
g Ag
greg
atio
n
*Dataset = the assemblage of all the data values of the same type
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Contrasting dataset-level vs. data-value-level Use Cases
Give me just the good- quality data values
Tell me how good the dataset is
Data Provider
Quality Control
Quality Assessment
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Data-ValueQuality Control
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The Data Quality Screening Service: a straightforward example of data-value quality
controlMask based on quality
flags
Good quality data pixels
retained
Output file has the same format and structure as the input file, with fill values replacing the low-quality data
Original data array
AIRS* Total column
precipitable water
* Atmospheric Infrared Sounder
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Or, maybe not so straightforward...
AIRS* Quality Indicators
MODIS Atmosphere**
Confidence Flags0 Best1 Good2 Do Not Use
3 Very Good2 Good1 Marginal0 Bad?
?
* Atmospheric Infrared Sounder** Moderate Resolution ImagingSpectroradiometer
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Match up by recommendation?
AIRS Quality Indicators
MODIS Aerosols Confidence Flags
0 Best Data Assimilation
1 Good Climatic Studies2 Do Not
UseUse these flags in order to stay within expected
error bounds
3 Very Good2 Good1 Marginal0 Bad
3 Very Good2 Good1 Marginal0 Bad
Ocean Land
±0.05 ± 0.15 t ±0.03 ± 0.10 tOcean Land
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How do quality control indicators relate to dataset quality
assessment?AIRS Relative Humidity Comparison against Dropsonde with and without Applying PBest Quality Flag Filtering
Boxed data points indicate AIRS RH data with dry bias > 20%
From a study by Sun Wong (JPL) on specific humidity in the Atlantic Main Development Region for Tropical
Storms
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Quality Measures of Aggregated Data
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File-Level Quality Statistics may or may not be useful for data
selectionStudy Area
Percent Cloud Cover?
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Level 3 grid cell standard deviation is difficult to interpret due to its dependence
on magnitude
Mean
StandardDeviation
MODIS AerosolOptical Thicknessat 550 nm
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Potential Solution to Data Quality Ambiguity
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Solution Part 1: Harmonize Quality Terms
ISO 19115 Data Quality model Committee on Earth Observation Satellites
(CEOS) Quality Assurance for Earth Observations
(QA4EO) Working Group on Cal/Val (WGCV)
Federation of Earth Science Information Partners ESIP Information Quality Cluster
Quality Ontology Data Quality Screening Service: Data-value QC Aerostat: Level 2 Bias Multi-sensor Data Synergy Advisor: Bias,
accuracy
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Solution Part 2: Address more dimensions of Quality
Accuracy: bias + dispersion Accuracy of data with low-quality flags Accuracy of grid cell aggregations
Consistency: spatial, temporal, observing conditions
Completeness Temporal: Time range, diurnal coverage, revisit
frequency Spatial: Coverage and Grid Cell
Representativeness Observing conditions N.B.: Incompleteness affects accuracy via
sampling bias
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Solution Part 3: Address Fitness for Purpose Directly
Standardize terms of recommendation Enumerate more positive realms and
examples Enumerate negative examples
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Facets of Quality
Quality
QualityAssessment
QualityControl
Accuracy
Consist-ency
Complete-ness
BiasDispersionsam
pling
bias
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Backup Slides
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Neither pixel count nor standard deviation fully express how representative the grid
cell value isMODIS Aerosol Optical Thickness at 0.55 microns
Level 3 Grid AOT Mean
Level 2 Swath
Level 3 Grid AOT Standard Deviation
Level 3 Grid AOT Input Pixel Count0 1 2 0 10.5
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