Virtual Research

The earth science enterprise increasingly relies on the integration and synthesis of multivariate datasets from diverse observational platforms. NASA’s ocean salinity missions, that include Aquarius/SAC-D and the SPURS (Salinity Processes in the Upper Ocean Regional Study) field campaign, illustrate
the value of integrated observations in support of studies on ocean circulation, the water cycle, and climate. However, the inherent heterogeneity of resulting data and the disparate, distributed systems that serve them complicates their effective utilization for both earth science research and applications.
Key technical interoperability challenges include adherence to metadata and data format standards that are particularly acute for in-situ data and the lack of a unified metadata model facilitating archival and integration of both satellite and oceanographic field datasets. Here we report on efforts at the
PO.DAAC, NASA’s physical oceanographic data center, to extend our data management and distribution support capabilities for field campaign datasets such as those from SPURS. We also discuss value-added services, based on the integration of satellite and in-situ datasets, which are under development with a particular focus on DOMS. The distributed oceanographic matchup service (DOMS) implements a portable technical infrastructure and associated web services that will be broadly accessible via the PO.DAAC for the dynamic collocation of satellite and in-situ data, hosted by distributed data providers, in support of mission cal/val, science and operational applications.

Data challenges have become a method of promoting innovation within data-intensive applications; building or evolving user communities, and potentially developing sustainable commercial services. They can utilise the vast amounts of information (both in scope and volume) that’s available online, including reduced processing costs, that’s closely related to the themes of e-Science and open science.

The E-CEO project aimed to deliver a collaborative platform that, through Data Challenges, improves the adoption and outreach of new applications and methods to processes Earth Observation (EO) data. Underneath the backbone is a common environment where the applications can be developed, deployed and executed. Then, the results are published in a common visualization platform for their effective validation and evaluation.

The example challenge was based around the atmospheric correction (AC) of ocean colour: processing Level 1 top of atmosphere calibrated radiances/reflectances to Level 2 bottom of atmosphere calibrated radiances/reflectances and derived products. In input data was Medium Resolution Imaging Spectrometer (MERIS) Level 1 full resolution files and matchups were extracted for sites held within the NASA bio-Optical Marine Algorithm Data set (NOMAD), with processors tested including the ESA BEAM Toolbox Simplified Method for Atmospheric Correction (SMAC), NASA SeaDAS toolbox and prototype MERIS processor (MEGS). The results showed that, overall, a collaborative platform can be used to simplify the process of comparing different processors. Once the different participant’s software is uploaded and connected to the data packages, the processing runs automatically and collects information about the computing performance. Analysing the satellite versus in-situ matchups is quick to run and adjust, but it’s important to continue to have human involvement in this step as otherwise statistics may be incorrectly interpreted.

The Geoscience and Remote Sensing Society [1] of the IEEE [2] seeks to advance science and technology in geoscience, remote sensing and related fields using conferences, education, and other resources. The Image Analysis and Data Fusion Technical Committee (IADFTC) [3] of the GRSS serves as a global, multi-disciplinary, network for geospatial data fusion, with the aim of connecting people and resources, educating students and professionals, and promoting the best practices in data fusion applications.

Encouraging young scientists and researchers to invest in Remote Sensing and pursuing a scientific career in the field has always been a priority for the GRS Society in general, and the IADFTC in particular. This is accomplished at various levels, including organizing a yearly Data Fusion Contest promoting progress on fusion and analysis methodologies for multisource remote sensing data; also, facilitating the dissemination and testing of new methods and algorithms for Remotely Sensed data processing, envisaging different applications.

In this context, an important initiative recently started by the GRSS is the “Standardized Algorithm and Data Evaluation" (SADE) web site [4], providing a set of community data sets and algorithm evaluation standards for use by the Earth observation community to support research, development, and testing of algorithms for remote sensing data products. The SADE website is conceived for remote sensing scientists, students, and professionals, who wish to evaluate the performances of their image analysis methods on freely available data against undisclosed test samples. The web site is undergoing further development, which will make it even more user-friendly and flexible, and hopefully also more effective in triggering curiosity and interest. The full paper will report about the original idea, the website, its history, and its features, in an effort to support further steps towards “Science 2.0” in the Remote Sensing arena.

References

[1] The IEEE Geoscience and Remote Sensing Society (GRSS) web site [Online]. Available: http://www.grss-ieee.org/

[2] The Institute of Electrical and Electronics Engineers (IEEE) web site [Online]. Available: http://www.ieee.org/

[3] Web site of the IEEE GRSS Image Analysis and Data Fusion Technical Committee (IADFTC) [Online]. Available: http://www.grss-ieee.org/community/technical-committees/data-fusion/

[4] The Standardized Algorithm and Data Evaluation website, beta version [Online]. Available: http://hdsade.ticinumaerospace.com/

ForM@Ter is the French solid Earth data centre project. It was launched in 2012 by the French national space agency (CNES) and the National Centre for Scientific Research (CNRS), with the active participation of the National institute for geographical and forestry information (IGN). Currently, it relies on the contributions of scientists from more than 20 French Earth science laboratories. Its perimeter, designed with the scientific community, focus on the determination of the shape and movements of the Earth surface (ForM@Ter: Formes et Mouvements de la Terre) with the objective to federate a wide variety of scientific areas (earthquake cycle, tectonics, morphogenesis, volcanism, erosion dynamics, mantle rheology, geodesy) and to offer many interfaces with other thematics, such as glaciology or snow evolution. The project aims at providing a national cooperative platform to facilitate data access, provide processing tools and value-added products with support for non-expert users. However its challenge in the evolving context of the current and forthcoming national and international e-infrastructures, is to design a non-redundant service based on interoperations with existing services, and to cope with highly complex data flows due to the granularity of the data and its associated knowledge. For this purpose, the services offer by the pole will be built based on the needs expressed by the scientific community.

The first thematic developed focuses on SAR and optical imagery data. The services that will be implemented, like the Ground Deformation Monitoring (GDM) service, will be presented here. GDM will be based on a CNES computing infrastructure hosting Sentinels products, and will offer catalogue access, HPC facilities and thematic computation services on inSAR and optical imagery. Computation services will give scientists access to DTM, displacement map time-series, quality indicators and modelling tools. GDM is aimed at serving a wide panel of scientific fields, such as earthquake cycle studies, tectonics, volcanism, erosion dynamics, or anthropogenic deformations. It will contribute to the ESFRI EPOS research infrastructure implementation with an implication in the WP 12 -Satellite data-.

The growing number and increasing diversity of Earth observations (EO) data offer an enormous potential of synergy confronting scientists with unprecedented perspectives to extract information. Unfortunately, this boost in data availability leads to substantial methodological challenges. Today’s researchers still encounter multiple difficulties to consistently assemble EO data across different domains of the Earth system (for instance, combining aquatic and terrestrial data, or socio-ecological data with biogeophysical processes). The aim of ESA’s STSE project CAB-LAB STSE is building a consistent Earth System Data cube ( http://earthsystemdatacube.net/) in order to concatenate a wide range of EOs in a hyper-data cube that is easily accessible by a wide audience. Our goal is to tap into the full potential of a data rich word by revising both: data access opportunities as well as developing a new suite of data analytic tools that can simultaneously explore heterogeneous global data streams. Our grand challenge is to extract characteristic biosphere-atmosphere system trajectories and understand where for example climatological or human induced extreme anomalies influence this complex feedback system. In this presentation we present a prototype of the Earth System Data Cube and first application cases that reveal the potential of a synergistic approach to data streams in the Earth system.