COVE

The CEOS Visualization Environment (COVE) Tool

Harnessing the Power of Satellite Data

The CEOS Visualization Environment (COVE) tool is a browser-based system that leverages Google-Earth to display satellite sensor coverage areas and identify coincidence scene locations.
Launch the COVE Tool

Welcome to the COVE Portal

The CEOS Visualization Environment (COVE) tool is a browser-based system that leverages Google-Earth to display satellite sensor coverage areas and identify coincidence scene locations. The NASA CEOS System Engineering Office (SEO) worked with the Committee on Earth Observing Satellites (CEOS) Working Group on Calibration and Validation to develop the COVE tool.

CEOS is currently operating and planning hundreds of Earth observation satellites. Standard Calibration and Validation (Cal/Val) exercises to compare near-simultaneous surface observations and identify corresponding image pairs are time-consuming and labor-intensive. The COVE suite of tools has been developed to make such tasks easier.

Recent Updates

Enhancements

  • 12/18/2012

    Precipitation Data

    Precipitation data overlays have been added to COVE based upon the 50-year GPCC VASClimO data set.

  • 11/04/2012

    Data Overlays

    COVE now features overlays for cloud data from ISCCP and ground cover data from GlobCover and MODIS.

  • 07/24/2012

    GeoSynchronous Satellites

    GeoSynchronous Satellites have been added to the COVE tool.

  • 01/03/2012

    External API

    COVE now supports an HTTP-based API, so you can access COVE programmatically. more details.

  • 12/21/2011

    STK Integration

    COVE displays can now be exported to STK scene files. To use, click "Export to STK" from the Import/Export tab.

  • 10/20/2011

    Beta Release

    The new COVE site has been released to Beta! New features include an improved layout and the Rapid Acquistion Tool, which makes it easy to generate a spreadsheet of position acquisitions.

Modeling Assumptions

    • No recent updates have been made to COVE's underlying modeling assumptions.