Earth observation imagery have traditionally been expensive, difficult to find and access, and required specialized skills and software to transform imagery into actionable information. This has limited adoption by the broader science community. Changes in cost of imagery and changes in computing technology over the last decade have enabled a new approach for how to organize, analyze, and share Earth observation imagery, broadly referred to as a data cube. The vision and promise of image data cubes is to lower these hurdles and expand the user community by making analysis ready data readily accessible and providing modern approaches to more easily analyze and visualize the data, empowering a larger community of users to improve their knowledge of place and make better informed decisions. Image data cubes are large collections of temporal, multivariate datasets typically consisting of analysis ready multispectral Earth observation data. Several flavors and variations of data cubes have emerged. To simplify access for end users we developed a flexible approach supporting multiple data cube styles, referencing images in their existing structure and storage location, enabling fast access, visualization, and analysis from a wide variety of web and desktop applications. We provide here an overview of that approach and three case studies.

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Most people face some level of water insecurity. Wise water management practices to address water security issues typically require data derived from a combination of observation and model data. This data has historically proven difficult to sustainably supply in many areas of the world. We present the design and development of a global, modeled streamflow data source for the Group on Earth Observation (GEO) Global Water Sustainability (GEOGloWS) implemented at the European Centre for Medium‐Range Weather Forecasts (ECMWF). This GEOGloWS ECMWF Streamflow Service (GEOGloWS Service) is a solution and prototype to sustainably address this need for data. The GEOGloWS Service centralizes computing and human resources to build a global hydrologic model and exposes data and mapping web services that allow users to consume the resulting data to meet their specific needs. The global hydrologic model produces global 15‐day ensemble streamflow forecasts and a historical simulation since January 1979. We present case studies in several countries and research environments which demonstrate the utility of the approach taken by the GEOGloWS Service. The case studies show how the global modeled data are being applied to make informed decisions and advance projects in ways that otherwise would not have been possible.

RESUMECette communication propose une grille de critères ergonomiques pour analyser la persuasion dans les interactions humain-machine. Etablie sur la base d'une bibliographie relevant des technologies persuasives, nous avons dégagé huit critères ergonomiques : crédibilité, privacité, personnalisation, attractivité, sollicitation, accompagnement, engagement, emprise. Après une introduction à la per suasion technologique, nous exposerons et expliquerons les éléments de cette grille en soulignant son intérêt pour la conception et l'évaluation des interfaces. En conclusion, nous discuterons de la nécessité de valider expérimentalement cette grille, travail entrepris grâce à une expérience menée sur 30 experts en ergonomie des interfaces. ABSTRACTThis communication proposes a grid of ergonomic criteria to analyse persuasion in human-computer interaction systems. Based on a bibliographical revue about persuasive technologies, we have identified 8 ergonomic criteria: credibility, privacy, personalization, attractiveness, solicitation, priming, commitment and ascendency. After an introduction about technological persuasion, we will present and explain elements from this grid by underlying its interest for interface design and e valuation. To conclude, we will discuss the requirement to validate this grid, work in progress in an experiment which gathered 30 experts in software ergonomic and HCI.

Bridging the Gap Between Remote Sensing and GIS; Redlands, California, 17–18 November 2010; Fifty remote sensing scientists and geographic information systems (GIS) experts attended a joint NASA–Environmental Science Research Institute (ESRI) workshop at the headquarters of ESRI. The purpose of the workshop was to bring together a diverse community of experts to explore benefits and barriers to the integration of remote sensing data into GIS. Remote sensing represents an ever‐expanding source of scientific data about our planet. Some individual NASA missions alone provide more than 1 terabyte of data per day. Remote sensing data can help solve problems in diverse applications, including disaster response, environmental planning, global change, insurance, and private investment. These data attain their greatest value when combined with other data from a variety of sources, yet this seemingly simple step is often very challenging.