GeoViQua: Earth Observation data quality and visualization

The Earth is monitored by a vast, diverse and growing array of instruments.  Three thousand autonomous floats roam the oceans, each providing vital information about the ocean interior every ten days, giving roughly ten thousand new hydrographic profiles each month.

Earth observing satellites sense a huge range of properties from soil moisture, through land cover and ocean salinity to atmospheric chemistry.  Two million observations from these satellites are assimilated into ocean forecast models every day.  Numerical modelling activities produce even more voluminous datasets.  For example, the latest iteration of the Climate Model Intercomparison Project (CMIP), which will underpin the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, is expected to involve 2.5 petabytes of distributed climate data.

The Soil Moisture and Ocean Salinity satellite (SMOS).

Image credit European Space Agency.

Advances in information technology have made it easier than ever for scientists, policymakers and other users to access these data streams using the Internet.  But how can a user decide which of the many datasets best fits his or her needs?  We need to do a better job at considering the quality of data in our modern infrastructures.

Quality may be defined simply as “fitness for purpose” and therefore different users will have different perceptions of what “quality” means.  Concerns of accuracy, consistency, resolution, coverage and provenance all form part of the slippery notion of quality.

The GeoViQua project (http://www.geoviqua.org) aims to enable the Global Earth Observation System of Systems (GEOSS) to provide improved quality information to its users.  Following principles laid down by the QA4EO (Quality Assurance for Earth Observation) group, GeoViQua is developing standards and technologies to enable users to make better choices about data. 

The Reading e-Science Centre is leading the project’s initiative in visualising quality information.  This is a multifaceted topic, including:

·     How can uncertainties in data best be represented in visualizations?  The use of error bars in line plots is well-established, but it is also well-known that the problem of uncertainty visualization is much more difficult for multidimensional data.  Many techniques have been developed – which are best for Earth Observation data?

·     How can quality information be conveyed through standard web services, notably those defined by the Open Geospatial Consortium?

·     How can a user gain a rapid understanding of the quality of a dataset?  GeoViQua is contributing to the concept of the “GEO label”, a quality “kitemark” that will be attached to datasets in catalogues to summarize their quality.  But given that different users have different perceptions of quality, how can this information be summarized in a way that is useful to all?

GeoViQua’s work will be applicable much more widely than the GEOSS alone and will draw on previous work in data quality and visualization in fields outside Earth Observation. The e-Research South consortium is an ideal forum for exchanging relevant ideas and expertise from across different fields.

  The first image from SMOS – how can we represent the inherent uncertainties?

Image credit European Space Agency

By Jon Blower