earth online
  • All Categories (31)
  • Data (3)
  • Events (2)
  • Tools (3)
  • Activities (3)
  • Campaigns (1)
  • Documents (19)
  • Document - Proceedings

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    EO-Summer-School-2-Overview-of-land-surface-parameters-from-EO.pdf

    Overview of land surface parameters from Earth Observation

  • Activity - General activities

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    MUltisource data package tools and SErvices (MUSE)

    The MUSE project aims to develop and integrate a tool to simultaneously manipulate geospatial data products, satellite, model and in-situ data.

  • Event - Workshop

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    ESA/MERIS Workshop 2008

    The ESA/MERIS Workshop 2008 covered the use of Envisat/MERIS data and the wider ESA-NASA-NOAA plans and collaboration.

  • Tools - Visualisation

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    EVDC Orbit Prediction Tool

    The EVDC Orbit Prediction and Overpass Tool generates and visualises satellite's overpasses.

  • Data - External Data (Restrained)

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    GRACE-A and GRACE-B Level 1B, Level 1B combined and Level 2 Data Products

    Level-1A Data Products are the result of a non-destructive processing applied to the Level-0 data at NASA/JPL. The sensor calibration factors are applied in order to convert the binary encoded measurements to engineering units. Where necessary, time tag integer second ambiguity is resolved and data are time tagged to the respective satellite receiver clock time. Editing and quality control flags are added, and the data is reformatted for further processing. The Level-1A data are reversible to Level-0, except for the bad data packets. This level also includes the ancillary data products needed for processing to the next data level. The Level-1B Data Products are the result of a possibly destructive, or irreversible, processing applied to both the Level-1A and Level-0 data at NASA/JPL. The data are correctly time-tagged, and data sample rate is reduced from the higher rates of the previous levels. Collectively, the processing from Level-0 to Level-1B is called the Level-1 Processing. This level also includes the ancillary data products generated during this processing, and the additional data needed for further processing. The Level-2 data products include the static and time-variable (monthly) gravity field and related data products derived from the application of Level-2 processing at GFZ, UTCSR and JPL to the previous level data products. This level also includes the ancillary data products such as GFZ's Level-1B short-term atmosphere and ocean de-aliasing product (AOD1B) generated during this processing. GRACE-A and GRACE-B Level-1B Data Product: Satellite clock solution [GA-OG-1B-CLKDAT, GB-OG-1B-CLKDAT, GRACE CLKDAT]: Offset of the satellite receiver clock relative to GPS time, obtained by linear fit to raw on-board clock offset estimates GPS flight data [GA-OG-1B-GPSDAT, GB-OG-1B-GPSDAT, GRACE GPSDAT]: Preprocessed and calibrated GPS code and phase tracking data edited and decimated from instrument high-rate (10 s (code) or 1 s (phase)) to low-rate (10 s) samples for science use (1 file per day, level-1 format) Accelerometer Housekeeping data [GA-OG-1B-ACCHKP, GB-OG-1B-ACCHKP, GRACE ACCHKP]: Accelerometer proof-mass bias voltages, capacitive sensor outputs, instrument control unit (ICU) and sensor unit (SU) temperatures, reference voltages, primary and secondary power supply voltages (1 file per day, level-1 format) Accelerometer data [GA-OG-1B-ACCDAT, GB-OG-1B-ACCDAT, GRACE ACCDAT]: Preprocessed and calibrated Level-1B accelerometer data edited and decimated from instrument high-rate (0.1 s) to low-rate (1s) samples for science use (1 file per day, level-1 format) Intermediate clock solution [GA-OG-1B-INTCLK, GB-OG-1B-INTCLK, GRACE INTCLK]: derived with GIPSY POD software (300 s sample rate) (1 file per day, GIPSY format) Instrument processing unit (IPU) Housekeeping data [GA-OG-1B-IPUHKP, GB-OG-1B-IPUHKP, GRACE IPUHKP]: edited and decimated from high-rate (TBD s) to low-rate (TBD s) samples for science use (1 file per day, level-1 format) Spacecraft Mass Housekeeping data [GA-OG-1B-MASDAT, GB-OG-1B-MASDAT, GRACE MASDAT]: Level 1B Data as a function of time GPS navigation solution data [GA-OG-1B-NAVSOL, GB-OG-1B-NAVSOL, GRACE NAVSOL]: edited and decimated from instrument high-rate (60 s) to low-rate (30 s) samples for science use (1 file per day, level-1 format) OBDH time mapping to GPS time Housekeeping data [GA-OG-1B-OBDHTM, GB-OG-1B-OBDHTM, GRACE OBDHTM]: On-board data handling (OBDH) time mapping data (OBDH time to receiver time Star camera data [GA-OG-1B-SCAATT, GB-OG-1B-SCAATT, GRACE SCAATT]: Preprocessed and calibrated star camera quaternion data edited and decimated from instrument high-rate (1 s) to low-rate (5 s) samples for science use (1 file per day, level-1 format) Thruster activation Housekeeping data [GA-OG-1B-THRDAT, GB-OG-1B-THRDAT, GRACE THRDAT]: GN2 thruster data used for attitude (10 mN) and orbit (40 mN) control GN2 tank temperature and pressure Housekeeping data [GA-OG-1B-TNKDAT, GB-OG-1B-TNKDAT, GRACE TNKDAT]: GN2 tank temperature and pressure data Oscillator frequency data [GA-OG-1B-USODAT, GB-OG-1B-USODAT, GRACE USODAT]: derived from POD product GRACE-A and GRACE-B Combined Level-1B Data Product Preprocessed and calibrated k-band ranging data [GA-OG-1B-KBRDAT, GB-OG-1B-KBRDAT, GRACE KBRDAT]: range, range-rate and range-acceleration data edited and decimated from instrument high-rate (0.1 s) to low-rate (5 s) samples for science use (1 file per day, level-1 format) Atmosphere and Ocean De-aliasing Product [GA-OG-1B-ATMOCN, GB-OG-1B-ATMOCN, GRACE ATMOCN]: GRACE Atmosphere and Ocean De-aliasing Product. GRACE Level-2 Data Product: GAC [GA-OG-_2-GAC, GB-OG-_2-GAC, GRACE GAC]: Combination of non-tidal atmosphere and ocean spherical harmonic coefficients provided as average over certain time span (same as corresponding GSM product) based on level-1 AOD1B product (1file per time span, level-2 format) GCM [GA-OG-_2-GCM, GB-OG-_2-GCM, GRACE GCM]: Spherical harmonic coefficients and standard deviations of the long-term static gravity field estimated by combination of GRACE satellite instrument data and other information for a dedicated time span (multiple years) and spatial resolution (1 file per time span, level-2 format) GAB [GA-OG-_2-GAB, GB-OG-_2-GAB, GRACE GAB]: Non-tidal ocean spherical harmonic coefficients provided as average over certain time span (same as corresponding GSM product) based on level-1 AOD1B product (1file per time span, level-2 format) GAD [GA-OG-_2-GAD, GB-OG-_2-GAD, GRACE GAD]: bottom pressure product - combination of surface pressure and ocean (over the oceans, and zero over land). Spherical harmonic coefficients provided as average over certain time span (same as corresponding GSM product) based on level-1 AOD1B product (1file per time span, level-2 format) GSM [GA-OG-_2-GSM, GB-OG-_2-GSM, GRACE GSM]: Spherical harmonic coefficients and standard deviations of the static gravity field estimated from GRACE satellite instrument data only for a dedicated time span (e.g. weekly, monthly, multiple years) and spatial resolution (1 file per time span, level-2 format).

  • Data - External Data (Restrained)

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    ADAM Surface Reflectance Database v4.0

    ADAM enables generating typical monthly variations of the global Earth surface reflectance at 0.1° spatial resolution (Plate Carree projection) and over the spectral range 240-4000 nm. The ADAM product is made of gridded monthly mean climatologies over land and ocean surfaces, and of a companion API toolkit that enables the calculation of hyperspectral (at 1 nm resolution over the whole 240-4000 nm spectral range) and multidirectional reflectances (i.e. in any illumination/viewing geometry) depending on user choices. The ADAM climatologies that feed the ADAM calculation tools are: For ocean: Monthly chlorophyll concentration derived from SeaWiFS-OrbView-2 (1999-2009); it is used to compute the water column reflectance (which shows large spectral variations in the visible, but is insignificant in the near and mid infrared). Monthly wind speed derived from SeaWinds-QuikSCAT-(1999-2009); it is used to calculate the ocean glint reflectance. For land: Monthly normalized surface reflectances in the 7 MODIS narrow spectral bands derived from FondsdeSol processing chain of MOD09A1 products (derived from Aqua and Terra observations), on which relies the modelling of the hyperspectral/multidirectional surface (soil/vegetation/snow) reflectance. Uncertainty variance-covariance matrix for the 7 spectral bands associated to the normalized surface reflectance. For sea-ice: Sea ice pixels (masked in the original MOD09A1 products) have been accounted for by a gap-filling approach relying on the spatial-temporal distribution of sea ice coverage provided by the CryoClim climatology for year 2005.

  • Tools - Analysis

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    Sentinel-3 Toolbox

    The Toolbox consists of a set of visualisation, analysis and processing tools for the exploitation of OLCI and SLSTR data.

  • Tools - Analysis

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    Sentinel-2 Toolbox

    The Toolbox consists of a rich set of visualisation, analysis and processing tools for the exploitation of optical high-resolution products including the Sentinel-2 MSI sensor.

  • Data - Campaigns (Open)

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    THERMOPOLIS

    The THERMOPOLIS 2009 campaign mainly served the DUE “Urban Heat islands (UHI) and Urban Thermography (UT) Project”

  • Campaign

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    THERMOPOLIS

    The THERMOPOLIS 2009 campaign mainly served the DUE “Urban Heat islands (UHI) and Urban Thermography (UT) Project”

  • Activity - Quality

    SnowPEx

    SnowPEx is an intercomparison and validation exercise

  • Document - Algorithms Theoretical Baseline Document

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    cawa-requirements-baseline.pdf

    CAWA (Advanced Clouds, Aerosols and WAter vapour products for Sentinel-3/OLCI) requirements baseline document provides information about the physical background, technical structure and the functional principle of the CAWA total column water vapour retrieval as defined within the CAWA project, which aims to the development and improvement of advanced atmospheric retrieval algorithms for the Envisat/MERIS and Sentinel-3/OLCI mission.

  • Activity - Quality

    SEOM CAWA

    The Advanced Clouds, Aerosols and WAter vapour products for Sentinel-3/OLCI project aims to develop and improve the advanced atmospheric retrieval algorithms developed for MERIS and OLCI instruments.

  • Event - Workshop

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    CEOS-IVOS Workshop on Inter-comparison of Large Scale Optical and Infrared Sensors

    The 2004 workshop was an opportunity to present and exchange experiences and knowledge from work on inter-comparing large scale optical sensors at different product levels.

  • Document - Algorithms Theoretical Baseline Document

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    cawa-algorithm-theoretical-basis-water-vapor.pdf

    This document provides information about the physical background, technical structure and the functional principle of the CAWA total column water vapour retrieval as defined within the SEOM CAWA 'advanced Clouds, Aerosols and WAter vapour products for Sentinel-3/OLCI' project.

  • Document - Proceedings

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    EO-Summer-School-2-Earth-Observation-techniques-and-the-carbon-cycle.pdf

    Earth Observation Measurements in Terrestrial Carbon Cycle Science

  • Document - Proceedings

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    Advances-in-Atmospheric-Science-and-Applications.pdf

    Proceedings in Advances in Atmospheric Science and Applications held in 18-22 June 2012 in Bruges, Belgium.

  • Document - General Reference

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    BIOMASS-Report-for-Mission-Selection-An-Earth-Explorer-to-observe-forest-biomass.pdf

    The objective of the BIOMASS Mission is to determine the global distribution of forest biomass by reducing the uncertainty in the calculation of carbon stock and fluxes associated with the terrestrial biosphere.

  • Document - Proceedings

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    EO-Summer-School-4-New-Satellite-Technologies-Applications-and-Reanalysis.pdf

    This presentation from ESA's EO Summer School 4 describes new technologies, applications and reanalysis for earth observation

  • Document - Proceedings

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    EO-Summer-School-4-Basic-concepts-from-EO-measurements-to-surface-albedo-estimates-1.pdf

    This presentation from ESA's EO Summer School 4 describes representing interactions between radiation and Earth's surface in large-scale models (part 1)