Help / Frequently Asked Questions (FAQ)


    How do I access the satellite data?

  1. How do I register for access/my own username?
  2. Why do I see 'Restricted Access' for some images?
  3. Who can access restricted products?
  4. Has my password stopped working?
  5. What datasets are available?

  6. Which sensors are available?
  7. What images are available?
  8. What products are available from different satellite sensors?
    1. AVHRR
    2. SeaWiFS
    3. MODIS
    4. MERIS

    Information about EO products:

  9. Why are there large areas of black (no-data) in these images?
  10. Why are there missing data for a specific date (YYYY-MM-DD)?
  11. What are the image filename formats?
    1. AVHRR and SeaWiFS filenames
    2. MODIS and MERIS filenames
  12. What systems do you use to process satellite images?
  13. What datum and ellipsoid do you use to map the data?
  14. Information about Level 3 composites:

  15. What is the composite browser directory structure?
  16. What are the composite image filename formats?
  17. MultiView website:

  18. Which products are available in MultiView for each sensor?
  19. Which model products are available in MultiView?
  20. Java Image Viewer:

  21. Why does nothing appear when I click on an image?
  22. How do I use the image viewer?
  23. What do I need to run the image viewer?
  24. How can I determine which version of Java is installed?
  25. Issues with Microsoft Java
  26. Issues with Apple Java
  27. How do I get Java?
  28. Why can't I access TIFF images?
  29. How do I extract data from images?

  30. How do I convert digital numbers to real-world values?
  31. How do I convert real-world values to digital numbers?
  32. Where can I find the slope and intercept values of an image?
  33. How do I convert latitude/longitude to image coordinates?
  34. How do I convert image coordinates to latitude/longitude?
  35. NetCDF files

  36. What are netCDF files and what are the benefits?
  37. What standards do NEODAAS' netCDF files comply with?
  38. What tools are available for accessing data in netCDF files?
  39. How can I access netCDF data in my own programs?
  40. Specific algorithms:

  41. MODIS NDVI/EVI
  42. MERIS TOA/BOA vegetation index
  43. MODIS chlorophyll OC5
  44. MODIS 90th percentile of chlorophyll OC5
  45. MERIS Inherent Optical Properties
  46. Further information:

  47. How to acknowledge the use of our data
  48. What is your privacy policy?
  49. Who holds the copyright on NEODAAS data?
  50. References

    How do I access the satellite data?

  1. How do I register for access/my own username?
  2. Registration only takes a few minutes and is possible here.


  3. Why do I see 'Restricted Access' for some images?
  4. After you have registered, you are given free access to certain images in our archive. Other products are restricted to certain groups of users. So if you see 'Restricted Access', it does not mean your password has stopped working; it just means that you are not authorised to view those particular products.

    Everyone can currently access these products:

    • Full-resolution images of Plymouth and Mount Etna areas.
    • Full-resolution AVHRR images more than 14 days old: up to 30 images for evaluation
    • Thumbnail-size individual scenes and composites.

    These products have restricted access:

    • Full-resolution scenes and composite maps except as listed above.
    • Requests for new areas or products which are not routinely processed

    If, after reading the question below, you feel that you have been denied access to an image incorrectly, please contact us.


  5. Who can access restricted products?
  6. There are several groups of users who have privileged access to some of the restricted products:

    • Any scientist, research group or project either supported by NERC (e.g. AMT project), or eligible for a NERC research grant or training award (i.e. UK-resident academic researchers).
    • Participants in projects which contribute to funding of RSG.
    • Research institutes, commercial companies or private customers who pay for processing specific products.
    • Scientists involved in collaborative research projects with RSG.
    • To access any SeaWiFS ocean colour products you must also be authorised by NASA (see below).

    If you are in the UK academic category, then you can apply for the data you need by filling in and posting to us a peer-review form (Word RTF format). If you are potentially in one of the other categories, please contact us for details, pricing quotations or to discuss research collaboration.


  7. Has my password stopped working?
  8. If you have read the two questions above, but still think that your password is not working, please check your password before you contact us. Please do not fill out another registration form. NB If you do still need to contact us about an access problem, please include your username and the full URL (http://.../.../) of the page you were trying to access, otherwise it is difficult for us to help.


    What datasets are available?

  9. Which sensors are available?

  10. SensorTime period available
    AVHRR (1.1 km)1997 - present day
    SeaWiFS (1.1 km)1997 - December 2004
    SeaWiFS (4.4 km)November 2005 - present day
    MODIS-Aqua2002 - present day
    MODIS-Aqua NASAOctober 2004 - present day
    MERISOctober 2004 - present day

    Table 1: Temporal availability for each sensor.


    Data from four different sensors are available through this site. The oldest of these is the AVHRR which was first launched in 1978 on-board the TIROS-N satellite. Subsequent launches of replacement satellites has allowed continual coverage from 1978 up to present day. Through this site we offer AVHRR sea surface temperature (SST) estimates at a spatial resolution of 4 km. The SeaWiFS sensor was launched in 1997 on-board the Orbview SeaStar satellite. SeaWiFS ocean colour products are available through this site including normalised water leaving radiance data (nLw), estimates of chlorophyll-a and K490 estimates all at a spatial resolution of 1 km. These data are no longer available to us in near-real time, however, an archive of data between 1997 and December 2004 is available. More recently NASA have launched two MODIS sensors known as MODIS Terra (launched in 1999) and MODIS Aqua (launched in 2002). On this website we refer to MODIS-Aqua data processed by NASA as 'MODIS-Aqua NASA', whereas 'MODIS-Aqua' refers to identical data that we have processed (these data are available up to 24 hours before the equivalent NASA data). The MODIS products include nLw, chlorophyll-a (for both case I and case II waters) and SST. Finally, we have recently obtained access to the MERIS data. This instrument was launched in 2002 onboard the European Space Agency's environmental monitoring satellite ENVISAT. These data are available at 1 km spatial resolution providing products including nLw and chlorophyll-a (for case 1 and case 2 waters).


  11. What images are available?
  12. search The easiest way to find the data you are looking for is to search the archive for individual satellite images for a particular area and time period. Follow the Data Portal help to select and browse the images you require.

    You can find images in the Products Browser if you already know the contract name under which they are stored. For example, the 'pa' area covers Plymouth, and those data are produced for the 'PACE' contract; so choose one of the satellite sensors (e.g. 'modis'), then click on the 'pace' directory, then the year, month and day of the satellite pass you require.

    There is also a large area covering most of NW Europe which is only available as a composite SST image. There are many other areas which are not being routinely processed, but have been processed for certain periods in the past. Check the lower part of the area list and the browser, to see if your region and dates of interest have already been processed. Requests for other areas within the receiving range of Dundee Satellite Station can be processed at 1 km resolution. Any region outside this range can be processed at a reduced resolution (4 km for AVHRR). Please contact us for details.


  13. What products are available from different satellite sensors?
    1. AVHRR
    2. The <product> part of image filenames specifies what property is represented by the image values:

      Band Code File Extension Product
      1-5 1-5 Raw satellite bands
      s sst Sea-surface temperature
      c cld Cloud mask (using hybrid method)
      t cld Cloud mask (using Thiermann method)
      l lnd Land-sea mask
      v wv Atmospheric water vapour
      v sva Satellite view angle
      z zen Solar zenith angle
      g,h r1,r2 Bidirectional reflectance for bands 1, 2
      i,j,k bt3,bt4,bt5 Brightness temperature for bands 3, 4, 5
      p,q,r ssts,ssta,sstp Partial SST: sea, atmosphere, combined

      All images are navigated and warped to Mercator projection. Land, clouds, and sunglint are usually masked out in black, though users should be aware that clouds may occasionally escape detection, and appear as unnaturally cold sea regions. In certain regions and conditions, the sea surface may become significantly warmer than the bulk SST during the day, so the earliest image in the morning usually gives the most reliable estimate.

      Weekly and monthly SST composite images are available for most areas. These are very useful for visualising dynamic structures and seasonal temperature distributions even in cloudy regions.

      A coloured version of each SST image ('...sstpcol.gif') is now produced automatically, so that most thermal structures can be seen immediately without additional enhancement. The colour palette is dynamically adjusted to visualise the temperature range on each image. This scheme is preferable to a fixed palette for images covering small geographic regions, as these usually have a small temperature range, though it does mean that colours may not be comparable between different images. We recommend the use of an image manipulation package for further contrast enhancement and magnification of regions of interest. We use XV for X Windows, and Paint Shop Pro for Windows.

      A textual information file ('..._xx%.txt') contains details of the time, size and coordinates of each image. The percentage of sea area which is cloud-free is appended to the filename, to assist the user in finding clear images. Clouds are masked from the images using the folllowing tests:

      Bit Val Test [hybrid.pro v1.15 17/11/95]
      7 128 Thiermann T4 dynamic threshold
      6 64 Saunders T4 coherence threshold
      5 32 Less strict T4 coherence threshold
      4 16 DAY/DUSK: Saunders R2 dynamic threshold
      3 8 DAY: Roozekrans R2/R1 threshold
      NIGHT: Saunders/Roozekrans T3-T5 threshold (optional)
      2 4 DAY: Roozekrans R2 coherence threshold
      1 2 Cloud gap size threshold
      0 1 NIGHT/DUSK: Saunders/Roozekrans T4-T3 threshold

      Water vapour products (in g cm-2) are calculated using the equation:

      w = 1.96 (T4 - T5) cos sva

    3. SeaWiFS
    4. NEODAAS Level 1B to Level 2 conversion

      The conversion is performed using a modified version of the SeaDAS module l2gen. A typical command line would have the form:

      IDL>l2gen,ifile='L1A_file',ofile='L2_file',mskflg='CLDICE1',
         met1='NCEP.MET1',met2='NCEP.MET2',
         ozone1='EPTOMS.OZONE1',ozone2='EPTOMS.OZONE2',
         calhdf='SEAWIFS_SENSOR_CAL.TBL',
         calmod_flg=1,calmod_gain='(1.02,0.99,0.958,0.98,0.99,0.987,0.94,1.0)',
         calmod_off='(0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00)',/wait

      Processing methodology:

      • The current SeaWiFS instrument calibration file is used, but the gain settings are tweaked using the NASA vicarious calibration (calmod_gain). This method is currently being used by NASA for GAC processing.
      • The NEODAAS rather than SeaWiFS land and cloud masks are used.
      • If the current meteorlogical and ozone ancillary data files (available at the SeaWIFS ancillary data WWW page) are not available, the climatological meteorological file will be used and the nearest ozone file, which is up to 7 days old (if not available the climatological file), will be used.

      Viewable Product Files

      For SeaWiFS the <product> part of image filename is split into several parts of the form <resolution>_<product>_F<col>. The resolution can be of the form:

      Resolution Description
      HRPT Local Area Coverage (received at Dundee Satellite Receiving Station and processed by NEODAAS)
      GAC Global Area Coverage (processed by NASA)

      The standard products are as follows, and are explained in more detail in NASA's MSL12 documentation.

      Product Description
      nLw_xxx Normalised water-leaving radinace at xxx nm [mW cm-2 um-1 sr-1], ranges from 412 to 555 nm
      nasa_chlor_a In-water chlorophyll-a concentration calculated using the OC4 algorithm (O'Reilly, 2000), displayed using a fixed NASA chlorophyll palette. [mg m-3]
      chlor_a Same as nasa_chlor_a, but displayed using a fixed RSG colour palette (0.2 to 5.0 mg m-3).
      K_490 In-water diffuse attenuation coefficient at 490 nm [m-1]
      rgb Stretched colour composite composed of the 555, 510 and 443 nm wavebands.
      La_xxx Aerosol radiance at xxx nm [mW cm-2 um-1 sr-1] produced for 670 and 865 nm
      tau_865 Aerosol optical depth at 865 nm
      eps_87 Ratio of the aerosol radiances at 865 and 760 nm

      All images are navigated and warped to Mercator projection. The <col> part shows whether the gif file has had a colour palette applied. It is important to note that colour palette (except for the nasa_chlor_a product) is dynamically adjusted to visualise the value range on each image. This means that colours may not be comparable between different images.

      SeaWiFS Algorithms used within SeaDAS

      Chlorophyll algorithm: 
         OC4 version 4  (Maximum Band Ratio, 4th Order Polynomial)
         a = [0.366,-3.067,1.930,0.649,-1.532]
         R = ALOG10((Rrs443>Rrs490>Rrs510)/Rrs555)
         Chl a (ug/l) = 10.0^(a(0) + a(1)*R +  a(2)*R^2   + a(3)*R^3  + a(4)*R^4)
      K_490 algorithm: 
         K_490 = k490_1 + k490_2 * ( nLw_443 / nLw_555 )  k490_3
         Coefficients are: k490_1 0.022; k490_2 0.100; k490_3 -1.29966

      HDF Product Files

      The mapped HDF files are generated using the SeaDAS module bl2map.

    5. MODIS
    6. The standard MODIS products are listed in this table, and are explained in more detail in NASA's MSL12 documentation.

      Product Description
      nLw_xxx Normalised water-leaving radinace at xxx nm [mW cm-2 um-1 sr-1], ranges from 412 to 667 nm
      chlor_a In-water chlorophyll-a concentration calculated using the OC3 algorithm, displayed using a fixed NASA chlorophyll palette, or a fixed RSG palette (0.2 to 5.0 mg m-3). [mg m-3]
      K_490 In-water diffuse attenuation coefficient at 490 nm [m-1]
      nLw_RGB Stretched colour composite composed of the 551, 488 and 443 nm wavebands.
      tau_869 Aerosol optical depth at 869 nm
      eps_78 Ratio of the aerosol radiances at 869 and 748 nm
      angstrom_531 Atmospheric angstrom coefficient at 531 nm
      chl_oc5 In-water chlorophyll-a concentration calculated using the OC5 algorithm. (more info)
      p90_oc5 90th percentile of chlorophyll-a derived from OC5 algorithm. (more info)
      NDVI Normalised Difference Vegetative Index, a measure of vegetative cover (more info)

    7. MERIS
    8. Product Description
      nLw_xxx Normalised water-leaving radiance at xxx nm [mW cm-2 um-1 sr-1], ranges from 413 to 619 nm
      algal_1 In-water chlorophyll-a concentration calculated using the Case 1 algorithm, displayed using a fixed NASA chlorophyll palette, or a fixed RSG palette (0.2 to 5.0 mg m-3). [mg m-3]
      algal_2 In-water chlorophyll-a concentration calculated using the Case 2 neural network algorithm, displayed using a fixed NASA chlorophyll palette, or a fixed RSG palette (0.2 to 5.0 mg m-3). [mg m-3]
      nLw_RGB Stretched colour composite composed of the 560, 490 and 443 nm wavebands.
      aero_opt_thick Aerosol optical depth. Dimensionless.
      total_susp Total suspended material. Dimensionless.
      yellow_subs Yellow substance (also known as gelbstoff or coloured dissolved organic matter - CDOM) m-1
      toa_veg Vegetation index (using top-of-atmosphere radiance). Dimensionless. (more info)
      boa_veg Vegetation index (using bottom-of-atmosphere radiance). Dimensionless. (more info)
      cloud_top_press Cloud top pressure (hPa) (more info)
      cloud_albedo Cloud albedo. Dimensionless. (more info)
      a_XXX_pml Total absorption at XXX nm [m-1]. (more info).
      bb_XXX_pml Total backscatter at XXX nm [m-1]. (more info).
      aph_XXX_pml Absorption due to phytoplankton at XXX nm [m-1]. (more info).
      ady_XXX_pml Absorption due to detrital material and CDOM at XXX nm [m-1]. (more info).

    Information about EO products:

  14. Why are there large areas of black (no-data) in these images?
  15. Part (or all) of the scene may contain no-data (appearing black) due to the existence of cloud and other aerosols between the sensor and the target (ocean or land). This will result in missing data as the majority of the data that we process are from visible spectrum sensors (400nm - 700nm).


  16. Why are there missing data for a specific date (YYYY-MM-DD)?
  17. Data may be unavailable for the particular date and time (See "Why are there large areas of black (no-data) in these images?"). A further reason for lack of data is that the sensor did not pass over the region of interest. This occurs as the satellites have repeat cycles of 1-3 days (dependent on the satellite). This can mean that data are only available over a particular region (for a particular sensor) once every three days. MERIS data are most likely to be affected by this issue.


  18. What are the image filename formats?
    1. AVHRR and SeaWiFS filenames
    2. Image filenames generated by Panorama are of the form <date><time><area><product>.gif, where the <product> types are described in the AVHRR and SeaWiFS sections below.

    3. MODIS and MERIS filenames
    4. Image filenames for these sensors are of the following form, and the product types are described in the MODIS and MERIS sections.

      MYYYYJJJ.HHMM.aa.product.sensor.DDmmmYYHHMM.version.YYYYJJJHHMM.palette.filetype

      where each filename component is explained as follows:


      Filename component Explanation
      M Mapped.
      YYYYJJJ.HHMM Acquisition date and time, given as year, Julian day and time HH:MM in UTC.
      aa Short area code.
      product Product name
      sensor Sensor code:
      MYO: Aqua-MODIS acquired from NASA;
      MYD: Aqua-MODIS acquired from Dundee;
      MOD: Terra-MODIS acquired from Dundee;
      MER: MERIS reduced resolution (1.2km);
      MES: MERIS full resolution (300m) from ESA Level 2;
      MEF: MERIS full resolution (300m) from ESA Level 1 processed using SeaDAS.
      DDmmmYYHHMM Human-readable acquisition date using abbreviated month names, and time (HH:MM UTC).
      version Processing version.
      YYYYJJJHHMM Processing date and time.
      palette Palette and annotation type:
      rsg_chl: RSG Chlorophyll Colour - 0.2 to 5.0 mg;
      nasa_col: NASA Fixed Colour;
      rsg_grey: Greyscale, non-annotated;
      rsg_comb: RGB colour combination.
      filetype png: 8-bit PNG image with palette

  19. What systems do you use to process satellite images?
  20. For AVHRR and SeaWiFS we use a system called Panorama, developed at RSG using C, UNIX and IDL. A description of Panorama was published in this paper. The system for MODIS and MERIS is described in this paper. The systems are not currently available for purchase.


  21. What datum and ellipsoid do you use to map the data?
  22. Panorama uses a sphere of diameter 6370718 meters to approximate the Earth's surface. This is used as the datum baseline for all mapped processing. The regional Molodensky offsets between the sphere and a WGS 84 ellipsoid based datum are available on request.


    Information about Level 3 composites:

  23. What is the composite browser directory structure?
  24. Navigate to the data you require using the the directory links at the top of the page. The composite products are organised in the following directory structure:

    sensor/dataset/area-code/period/product/year

    where each directory component is explained as follows:


    Directory component Explanation
    sensor modis, meris, seawifs, or avhrr.
    dataset time_series. If instead of a discrete weekly/monthly time-series you need rolling n-day composites, use MultiView.
    area-code Short area code, see Data Portal.
    period Compositing period: weekly or monthly.
    Suffix _all means all images during each day; _night means only the earliest night-time image each day.
    product Product name, e.g. sst, chlor_a.
    year YYYY

  25. What are the composite image filename formats?
  26. All level 3 composite files are named according to this format:

    MYYYYJJJ-YYYYJJJ.aa.product.sensor.composite-type.DDmmmYY-DDmmmYY.version.YYYYJJJTTTT.palette.filetype

    where each filename component is explained as follows:


    Filename component Description
    M Mapped.
    YYYYJJJ-YYYYJJJ Composite date range, using year and Julian day.
    aa Short area code.
    product Product name.

    Only for front maps:
    stepX: minimum DN step size across front.
    sensor Sensor code:
    MYO: Aqua-MODIS acquired from NASA
    MYD: Aqua-MODIS acquired from Dundee
    MER: MERIS
    SEA: SeaWiFS
    AVH: AVHRR
    composite-type L3_: level 3 data, followed by...
    median: per-pixel median of all valid data
    mean: per-pixel mean of all valid data
    vp: valid pixel count

    Only for front maps:
    fcomp: composite front map (gradient/persistence/proximity)
    fcomp_comb: composite front map combined with source data
    fcomp_mosaic: mosaic of current and previous front maps
    fpersist: front gradient weighted by persistence
    cp: clear pixel count
    DDmmmYY-DDmmmYY Composite date range, using abbreviated month names.
    version Processing version.
    YYYYJJJTTTT Processing date and time.
    palette Palette and annotation type:
    rsg_chl: RSG Chlorophyll Colour - 0.2 to 5.0 mg;
    rsg_col: RSG Fixed Colour - same palette applied to whole time-series;
    nasa_col: NASA Fixed Colour;
    rsg_varcol: RSG Variable Colour - NB colour palette highlights a different range of values on each image to enhance structures, and hence colours are not comparable across time-series;
    rsg_grey: Greyscale, non-annotated.
    filetype png, gif: 8-bit image with palette
    8bit.gz: 8-bit data array
    info: metadata file.

    MultiView website:

  27. Which products are available in MultiView for each sensor?

  28. SensorProductDescription (units)
    AVHRRSSTSea surface temperature estimates. (°C)
       
    SeaWiFSChlorophyll (OC4v4)Chlorophyll (chlor_a) concentration estimates (mg m-3).
    SeaWiFSTrue ColourSimulated true colour (This is a combination of three normalised water leaving radiances at 442 nm, 490 nm and 555nm) (dimensionless).
    SeaWiFSSPM Proxy (555nm)Normalised water leaving radiance at 551nm. (mW cm-2 um-1 sr-1)
    SeaWiFSTurbidityIn-water diffuse attenuation coefficient (Kd) at 490 nm (m-1).
    SeaWiFSAerosol opt. thickAerosol optical thickness at 865 nm (dimensionless).
    SeaWiFSAerosol EpsilonEpsilon of aerosol correction at 765 nm (dimensionless).
    SeaWiFSAerosol AngstromAngstrom coefficient, 510 to 865 nm (dimensionless).
    SeaWiFSTrue colour (L1)Level 1B (geolocated and calibrated) top-of-atmosphere composite (note: always in a geographic projection) (dimensionless).
    SeaWiFSLevel 0 full passLevel 0 composite image (generated from 3 spectral bands), shown in the satellite projection (dimensionless).
    SeaWiFSKarenia HABHarmful algal bloom spectral classifier output (Miller et al, International Journal of Remote Sensing 2006).
       
    MODISChlorophyll (OC3M)Chlorophyll concentration estimates (an analogue of the SeaWiFS algorithm - chl_oc3) (mg m-3)
    MODISChlorophyll (OC5)Chlorophyll concentration estimates (chl_oc5 algorithm) (mg m-3)
    MODISTrue ColourSimulated true colour (This is a combination of three normalised water leaving radiances at 443 nm, 488 nm and 551 nm) (dimensionless).
    MODISSPM Proxy (551nm)Normalised water leaving radiance at 551nm (mW cm-2 um-1 sr-1)
    MODISTurbidity (K_490)In-water diffuse attenuation coefficient (Kd) at 490 nm (m-1).
    MODISAerosol opt. thickAerosol optical thickness at 869 nm (dimensionless).
    MODISAerosol EpsilonEpsilon of aerosol correction at 865 nm (dimensionless).
    MODISAerosol AngstromAngstrom coefficient, 531 to 869 nm (dimensionless).
    MODISSSTSea surface temperature estimates (°C).
    MODISTrue colour (L1G)Level 1B (geolocated and calibrated) top-of-atmosphere composite (dimensionless).
    MODISLevel 0 full passLevel 0 composite image (generated from 3 spectral bands), shown in the satellite projection (dimensionless).
    MODISPML:a (443 nm)Total absorption at 443 nm (Smyth et al, Applied Optics 2003) (m).
    MODISPML:aph (443 nm)Absorption due to phytoplankton at 443 nm (Smyth et al, Applied Optics 2003) (m).
    MODISPML:ady (443 nm)Absorption due to gelbstoff and detrital material at 443 nm (Smyth et al, Applied Optics 2003) (m).
    MODISPML:bb (551 nm)Total backscatter at 551 nm (Smyth et al, Applied Optics 2003) (m).
    MODISPrimary productionNet primary production (Smyth et al Journal of Geophysical Research 2005) (mgC m-2 day-1).
    MODISKarenia HABHarmful algal bloom spectral classifier output (Miller et al, International Journal of Remote Sensing 2006).
    MODISNDDINormalised difference dust index (dimensionless)
       
    MERISChlorophyll (algal_1)Chlorophyll concentration estimates for case 1 water (mg m-3).
    MERISChlorophyll (algal_2)Chlorophyll concentration estimates for case 2 waters (mg m-3).
    MERISTrue ColourSimulated true colour (This is a combination of three normalised water leaving radiances at 442 nm, 490 nm and 560nm) (dimensionless).
    MERISRadiance (nLw 560)Normalised water leaving radiance at 560nm (mW cm-2 um-1 sr-1).
    MERISAerosol op. thickA measurement the opacity of the aerosol layers at 865 nm (dimensionless)
    MERISYellow substanceMeasurement of the gelbstoff absorption (m-1).
    MERISSPMA measurement of the suspended sediments concentration (Log10(g m-3))
    MERISTOA vegetationTop of atmosphere vegetation indices (dimensionless).

    Table 2: Available products for each sensor.



  29. Which model products are available in MultiView?

  30. ModelProductDescription
    MRCS modelChlorophyll compositeChlorophyll (mg m-3) model output from the Met Office.
    MRCS vs MODISPercentage differenceChlorophyll percentage difference between MRCS and Aqua NASA.
    MRCS modelModel SST compositeSST (°C) model output from the Met Office.
    MRCS vs observationsModel - satellite SST difference compositeModel - SST (°C) composite from AVHRR. Positive -> model warmer.
    MRCS modelModel salinity compositeSalinity (PSU) model output from the Met Office.
    MRCS vs observationsReceiver Operator Characteristic (ROC)Receiver Operator Characteristic (ROC) curve. Points above 1:1 represent model predictive skill.
    MRCS vs observationsEquitable threat score (ETS)Threat score minus those values that were correct due to chance alone.
    MRCS vs observationsOdds ratio (OR)A measure of dependency between forecast and observations (product of the odds for forecasting the event correctly and the odds for forecasting the non-event correctly)
    MRCS vs observationsBiasRatio of the frequency of the observed events and the frequency of the forecast events.
    MRCS vs observationsKappa coefficientCohen's kappa measures the agreement between two raters (takes into account the agreement occurring by chance).
    MRCS vs observationsWavelet MSEHAAR wavelet mean squared error (MSE) as per Casati et al (2004), Meteorol. Appl. 11, 141-154
    MRCS vs observationsWavelet SSHAAR wavelet skill score (SS) as per Casati et al (2004), Meteorol. Appl. 11, 141-154
    MRCS vs observationsEOF principal component Empirical orthogonal function principal component (also known has PCA principal component).
    MRCS vs observationsEOF eigenvectors Empirical orthogonal function eigenvectors (also known has PCA eigenvectors). These descibe the timing and sign (+ve or -ve) of variations which correspond with the component images.
    MRCS vs observationsEOF variance Empirical orthogonal function cumulative variance (also known has PCA cumulative variance). This shows the cumulative variance in the dataset described by each component.

    Table 3: Available model products.



    Java Image Viewer:

  31. Why does nothing appear when I click on an image?
  32. When you click on an image, the Java image viewer should load. If it does not, then you need to install a Java plugin for your browser (further information)

  33. How do I use the image viewer?
  34. When you select an image the Java image viewer will load showing a window onto your selected data. The scroll bars on the sides of the image will allow you to move around. Across the top of the viewer are a series of menus: file, view, zoom and overlay. The details of these are described below.

    File

    Save as..: allows you save the current display as an image (supported formats include gif, png, bmp, jpeg, wbmp and ascii). If you did not accept the Java certificate the first time the image viewer loaded then this functionality will be disabled.

    Copy: allows you to copy the image to your clipbaord. If you did not accept the Java certificate the first time the image viewer loaded then this functionality will be disabled.

    View

    Interactive stretch: This tool is very simple yet remarkably powerful. It allows you to stretch and slide the colour palette which is helpful for viewing the fine detail in the images. The tool consists of a box with a diagonal line passing through it. This line represents the colour palette. When it passes through both the bottom-left and top-right corners of the box the palette is identical to the one stored in the image. If you drag the mouse around a bit and you will quickly get the idea. Right clicking will toggle the 'Interactive Stretch' tool on and off.

    Pixel values : This allows you to determine the parameters of the pixel directly underneath the mouse cursor. Parameters displayed are latitude, longitude, DN value, geophysical parameter (if appropriate) and its scale bar colour. Left clicking with the mouse on the image will cause the 'Pixel Values' tool window to toggle on and off.

    Colour table :This allows you to select and view a palette from a predefined selection.

    Zoom

    This menu allows you zoom to one of five predefined levels, or to zoom to a custom level. Also the 'Fit' option will zoom the image to that it fits exactly within the available space. Alternatively you can zoom using the mouse wheel or by selecting a box around the desired area selectin the left mouse button and dragging. You can also zoom in and out using the mouse wheel if you have one, and you can zoom in to a specific area by dragging the left mouse button to draw the desired box.

    Overlay

    This menu will vary depending on the image you are viewing, however the 'Grid' option is always available. The Grid option will overlay a labelled lattitude/longitude grid onto the image. The labels of this grid will 'follow' the currently viewed area. Some examples of the overlays are the Coastlines which draw the lines where the land and sea meet.

  35. What do I need to run the image viewer?
  36. You will need Sun's Java Runtime Environment which can be downloaded at the Sun Microsystems. You will need at least version 1.4.2 to be able to run the Image Viewer. The Image Viewer is a signed applet. This means that it has some features that are not normally available to applets and it needs your approval to enable these features. These features including 'Copy to clipboard' and Saving the image. Before the image viewer starts you may be asked if you trust the applet and wish to accept the certificate. If you choose 'Yes' or 'Always' these features will be enabled. If you answer 'No' then you will still be able to use the image viewer but these features will not work.

  37. How can I determine which version of Java is installed?
  38. The following applet will give you information about your version of Java.

  39. Issues with Microsoft Java
  40. Microsoft ceased development on Microsoft Java and it is no longer supplied with Micorsoft Windows (WinXP/SP1 and later do not include Java). We recommend the use of Sun's Java using their JRE. The minimum version required for the Java image viewer on this site is v1.4.2.

    If you are accessing Java from a PC or a Mac then you will need to use J2SE. If you are using a mobile phone or a PDA you will probably need to use J2ME. However, please note that although every effort has been made to ensure that these pages will work under a mobile phone or PDA envionment, no guarantees are given. The Java referred to here should not be confused with JavaScript or Java Server pages as these are different technologies.


  41. Issues with Apple Java
  42. This is an excellent JVM (it is Sun's JVM with some special Mac features added) however there are a few non-technical issues to be aware of. The main one is that since Apple's JVM is so tightly integrated into MacOS X, it is often the case that you need to upgrade OS X to be able to update the JVM. Apple have recently released v5.0 of their JVM, however you need OS X 10.4 to be able to install it. The minimum required JVM version is v1.4.2 which is packaged with OS X 10.3 and later. JVM 1.4.2 may be available on OS X 10.2 but I have been unable to confirm this (it is definitely not available for 10.1 and earlier).


  43. How do I get Java?
  44. Windows Users:

    You can download Sun's JVM 5.0 from here. It is up to you whether you choose the offline installation or the online version. For slow connections it is probably best to choose the offline installation so that you can back it up on to CD.

    Linux Users:

    You can download Sun's JVM 5.0 from here. If your distribution uses RedHat Package (i.e. you install things with an .rpm file) then select the 'RPM in self extracting file'. Otherwise select the normal 'self extracting file'.

    Mac OS X Users:

    You can download Apple's JVM 5.0 from here, you will need Mac OS X 10.4 to use it. Users of Mac OS X 10.3 (possibly 10.2 but we're not sure) can use Mac OS X instead.


  45. Why can't I access TIFF images?
  46. Before loading the image viewer you should install the JAI-ImageIO optional package. This is not a requirement however you will gain access to more image formats if it installed, most notably the TIFF format (which we plan to extend into GeoTIFF in the future). You can download the extension from Sun Microsystems. Mac users must download it from Apple instead.


    How do I extract data from images?

  47. How do I convert digital numbers to real-world values?
  48. Real-world values (e.g. chlorophyll concentration) can be extracted from the imagery by downloading the black and white PNGs or 8bit files and applying the appropriate conversion equation with the slope and intercept values found in the info file (seen when you display an image). Most of the products have a linear scaling applied (nLw_xxx, La_xxx, tau_865 and eps_87) and the conversion equation will be:

    value = (DN * slope) + intercept

    For other products (e.g. nasa_chlor_a, K_490) which are log scaled the conversion equation will be:

    value = 10^[(DN * slope) + intercept]

    Note that for log scaled products, because the intercept is given in log space the actual value that is the minimum that could be represented in the file will be 10^intercept.

    DN is the Digital Number within the 8bit, PNG or GIF file, which will be in the range 0 to 255. We use 'indexed' or 'palletised' 8-bit PNG and GIF files, so that these can be loaded as DNs into most languages or image analysis systems. For instance in Matlab, use the syntax:

    [DN, rgb] = imread(filename)
    which reads the data into array 'DN' and the colour pallet RGB values into 'rgb'. We often use 0 (black) for pixels with no data and 255 (white) for annotation.

  49. How do I convert real-world values to digital numbers?
  50. If you have a real-world value and wish to know what digital number it would be represented by in the image, here are the reverse equations for linear and log scaling respectively (then round to the nearest integer):

    DN = (value - intercept) / slope
    DN = [log10(value) - log10(intercept)] / slope

    NB: In the intercept column of the table below, where the scale is logarithmic, the values shown have already had their base-10 logarithm computed. Therefore, in the equation substitute the entire 'log10(intercept)' term for the value in the table.

  51. Where can I find the slope and intercept values of an image?
  52. The following table lists the scaling type and the scaling parameters normally used for the common products. However you should always check the .info file corresponding to the image, as these values may be different for particular areas.

    NB Special case for AVHRR SST data only: Currently it is not possible to determine from the filename for AVHRR SST products what should be the slope and intercept, as three different scales are used depending on the area code. If there is an '.info' text file with the image, take the slope and intercept from that, otherwise use this table. If your area code does not appear in this table then you should use the 'warm range' values, but ask us to check this if you expect SST values below 5°C. (Last updated 08/10/2012)

    Sensor Product Scaling Type Slope Intercept Area codes
    AVHRR sst or sstp, 'cold range' Linear 0.1 -3.0 fi,ns,wa,ig,ng,fn,ie,bs,sv,nh,lb,im,sj,em,zm, uk,ah,mn,kt,mr,er,bq,jm,if,b9,ax,oa,jr,js,rw
    AVHRR sst or sstp, 'wide range' Linear 0.15 -3.0 bin,ys,rc,ia,la,00,am
    AVHRR sst or sstp, 'warm range' Linear 0.1 +5.0 All other areas

    For all other products (not AVHRR SST):

    Sensor Product Scaling Type Slope Intercept
    AATSRsst_combLinear0.15-2
    AATSRsst_comb_meanLinear1.180
    AATSRsst_nadirLinear0.15-2
    AMSRELow_res_sstLinear0.15-2
    ASARsigma0Linear0.0780
    ASARwind_speedLinear0.1560
    AVHRR1Linear10
    AVHRR2Linear10
    AVHRR3Linear10
    AVHRR4Linear10
    AVHRR5Linear10
    AVHRR6Linear10
    AVHRRaries1Linear10
    AVHRRaries2Linear10
    AVHRRaries3Linear10
    AVHRRaries4Linear10
    AVHRRaries5Linear10
    AVHRRariesbt3Linear0.01-100
    AVHRRariesbt4Linear0.01-100
    AVHRRariesbt5Linear0.01-100
    AVHRRbt3Linear0.01-30
    AVHRRbt4Linear0.01-30
    AVHRRbt5Linear0.01-30
    AVHRRcldLinear10
    AVHRRcmedLinear0.1-3
    AVHRRcsstdLinear0.1-10
    AVHRRcsstpLinear0.1-3
    AVHRRcsstpfinLinear0.1-3
    AVHRRpfmedLinear0.15-3
    AVHRRpfsstpLinear0.15-3
    AVHRRpfsstpfinLinear0.15-3
    AVHRRr1Linear0.10
    AVHRRr2Linear0.10
    AVHRRwsstLinear0.15-3
    AVHRRwsstpLinear0.15-3
    BINARYaatsr_sstLinear0.15-2
    BINARYanalysed_sstLinear1.180
    BINARYCO2fluxLinear0.117-15
    BINARYDpco2Linear1.176-150
    BINARYgascoefSW06Linear7.8e-050.0001
    BINARYkSW06Linear0.1960
    BINARYmod_SST_tLinear10
    BINARYpCO2_airLinear1.176-150
    BINARYpCO2_swLinear1.176-150
    BINARYpressureLinear0.39216950
    BINARYsalinityLinear0.07820
    BINARYsea_ice_coverageLinear10
    BINARYsolubilityLinear0.000310.01
    BINARYSST_tLinear10
    BINARYu10windLinear10
    BINARYvCO2_airLinear10
    BINARYwind_tLinear10
    MERISa_412_pmlLogarithmic0.01-1.852
    MERISa_413_pmlLogarithmic0.01-1.852
    MERISa_443Logarithmic0.01-1.852
    MERISa_443_pmlLogarithmic0.01-1.852
    MERISa_490_pmlLogarithmic0.01-1.852
    MERISa_510_pmlLogarithmic0.01-1.852
    MERISa_555_pmlLogarithmic0.01-1.852
    MERISa_560_pmlLogarithmic0.01-1.852
    MERISa_620_pmlLogarithmic0.01-1.852
    MERISadg_413_pmlLogarithmic0.01-1.852
    MERISadg_443_pmlLogarithmic0.01-1.852
    MERISadg_490_pmlLogarithmic0.01-1.852
    MERISadg_510_pmlLogarithmic0.01-1.852
    MERISadg_560_pmlLogarithmic0.01-1.852
    MERISadg_620_pmlLogarithmic0.01-1.852
    MERISaero_opt_thickLinear0.0150
    MERISalgal_1Logarithmic0.015-2
    MERISalgal_1_landLogarithmic0.015-2
    MERISalgal_1_meanLogarithmic0.015-2
    MERISalgal_2Logarithmic0.015-2
    MERISalgal_2_landLogarithmic0.015-2
    MERISaot_865Linear0.0020
    MERISaph_412_pmlLogarithmic0.01-1.852
    MERISaph_413_pmlLogarithmic0.01-1.852
    MERISaph_443_pmlLogarithmic0.01-1.852
    MERISaph_490_pmlLogarithmic0.01-1.852
    MERISaph_510_pmlLogarithmic0.01-1.852
    MERISaph_555_pmlLogarithmic0.01-1.852
    MERISaph_560_pmlLogarithmic0.01-1.852
    MERISaph_620_pmlLogarithmic0.01-1.852
    MERISbb_412_pmlLogarithmic0.0118-3
    MERISbb_413_pmlLogarithmic0.0118-3
    MERISbb_443_pmlLogarithmic0.0118-3
    MERISbb_490_pmlLogarithmic0.0118-3
    MERISbb_510_pmlLogarithmic0.0118-3
    MERISbb_555_pmlLogarithmic0.0118-3
    MERISbb_560Logarithmic0.01-1.852
    MERISbb_560_pmlLogarithmic0.0118-3
    MERISbb_620_pmlLogarithmic0.0118-3
    MERISbbp_413_pmlLogarithmic0.0118-3
    MERISbbp_443_pmlLogarithmic0.0118-3
    MERISbbp_490_pmlLogarithmic0.0118-3
    MERISbbp_510_pmlLogarithmic0.0118-3
    MERISbbp_560_pmlLogarithmic0.0118-3
    MERISbbp_619_pmlLogarithmic0.0118-3
    MERISbbp_620_pmlLogarithmic0.0118-3
    MERISboa_vegLinear0.01680
    MERISc2Logarithmic0.015-2
    MERISc2to20Logarithmic0.015-2
    MERISchl_oc5Logarithmic0.015-2
    MERISchlor_aLogarithmic0.015-2
    MERISchlor_a_2Logarithmic0.015-2
    MERISclargeLogarithmic0.015-2
    MERIScloud_albedoLinear0.0040
    MERIScloud_top_pressLinear3.920
    MERISconc_chl_oc4Logarithmic0.015-2
    MERISf2Linear0.0039215690
    MERISf2to20Linear0.0039215690
    MERISflargeLinear0.0039215690
    MERISkd_490Logarithmic0.011176-2
    MERISKd_490Logarithmic0.011176-2
    MERISl2_flagsLinear10
    MERISnLw_412Linear0.020
    MERISnLw_413Linear0.020
    MERISnLw_443Linear0.020
    MERISnLw_490Linear0.020
    MERISnLw_510Linear0.020
    MERISnLw_560Linear0.020
    MERISnLw_619Linear0.020
    MERISnLw_620Linear0.020
    MERISnLw_665Linear0.020
    MERISnLw_681Linear0.020
    MERISnLw_709Linear0.020
    MERISnLw_754Linear0.020
    MERISphotosyn_rad_meanLinear7.8430
    MERISRrs_412Linear0.00020
    MERISRrs_413Linear0.00020
    MERISRrs_443Linear0.00020
    MERISRrs_490Linear0.00020
    MERISRrs_510Linear0.00020
    MERISRrs_560Linear0.00020
    MERISRrs_619Linear0.00020
    MERISRrs_620Linear0.00020
    MERISRrs_665Linear0.00020
    MERISRrs_681Linear0.00020
    MERISspmiLinear0.10
    MERIStoa_vegLinear0.0040
    MERIStotal_suspLogarithmic0.013-2
    MERIStotal_susp_landLogarithmic0.01-2
    MERIStsm_clarkLogarithmic0.01-2
    MERISyellow_subsLogarithmic0.00784314-3
    MERISyellow_subs_land Logarithmic0.00784314-3
    MODIS469_EDGESWATH_BLinear10
    MODIS469_HIGLINT_BLinear10
    MODIS555_EDGESWATH_GLinear0.620
    MODIS555_HIGLINT_GLinear0.620
    MODIS645_EDGESWATH_RLinear0.480
    MODIS645_HIGLINT_RLinear0.480
    MODISa_412_pmlLogarithmic0.01-1.852
    MODISa_443_pmlLogarithmic0.01-1.852
    MODISa_469_pmlLogarithmic0.01-1.852
    MODISa_488_pmlLogarithmic0.01-1.852
    MODISa_531_pmlLogarithmic0.01-1.852
    MODISa_547_pmlLogarithmic0.01-1.852
    MODISadg_412_carderLogarithmic0.01-1.852
    MODISadg_412_giopLogarithmic0.01-1.852
    MODISadg_412_pmlLogarithmic0.01-1.852
    MODISadg_412_qaaLogarithmic0.01-1.852
    MODISadg_443_carderLogarithmic0.01-1.852
    MODISadg_443_giopLogarithmic0.01-1.852
    MODISadg_443_pmlLogarithmic0.01-1.852
    MODISadg_443_qaaLogarithmic0.01-1.852
    MODISadg_469_pmlLogarithmic0.01-1.852
    MODISadg_488_pmlLogarithmic0.01-1.852
    MODISadg_531_pmlLogarithmic0.01-1.852
    MODISadg_547_pmlLogarithmic0.01-1.852
    MODISady_443_pmlLogarithmic0.01-1.852
    MODISangstrom_531Linear0.0062-0.1
    MODISaot_869Linear0.0020
    MODISaph_412_pmlLogarithmic0.01-1.852
    MODISaph_443_pmlLogarithmic0.01-1.852
    MODISaph_469_pmlLogarithmic0.01-1.852
    MODISaph_488_pmlLogarithmic0.01-1.852
    MODISaph_531_pmlLogarithmic0.01-1.852
    MODISaph_547_pmlLogarithmic0.01-1.852
    MODISbb_412_pmlLogarithmic0.0118-3
    MODISbb_443_pmlLogarithmic0.0118-3
    MODISbb_469_pmlLogarithmic0.0118-3
    MODISbb_488_pmlLogarithmic0.0118-3
    MODISbb_531_pmlLogarithmic0.0118-3
    MODISbb_547_pmlLogarithmic0.0118-3
    MODISbbp_412_pmlLogarithmic0.0118-3
    MODISbbp_443_pmlLogarithmic0.0118-3
    MODISbbp_469_pmlLogarithmic0.0118-3
    MODISbbp_488_pmlLogarithmic0.0118-3
    MODISbbp_510_pmlLogarithmic0.0118-3
    MODISbbp_531_pmlLogarithmic0.0118-3
    MODISbbp_547_pmlLogarithmic0.0118-3
    MODISbricaudLogarithmic0.015-2
    MODISBT_3959Linear0.062
    MODISBT_7325Linear0.062
    MODISc2Logarithmic0.015-2
    MODISc2to20Logarithmic0.015-2
    MODIScalciteLogarithmic0.012192-4.3
    MODISCDOMLinear0.02767-0.02767
    MODISchl-aLogarithmic0.015-2
    MODISchl_medoc3Logarithmic0.015-2
    MODISchl_oc2Logarithmic0.015-2
    MODISchl_oc3Logarithmic0.015-2
    MODISchl_oc488Logarithmic0.015-2
    MODISchl_oc5Logarithmic0.015-2
    MODISchl_oc5plusflagsLogarithmic0.015-2
    MODISchlor_aLogarithmic0.015-2
    MODISchlor_a_2Logarithmic0.015-2
    MODISchlor_a_3Logarithmic0.015-2
    MODISchlor_a_500m_pmlLogarithmic0.015-2
    MODISchlor_acompLogarithmic0.015-2
    MODISchlor_MODISLogarithmic0.015-2
    MODISclargeLogarithmic0.015-2
    MODIScp_oc3Linear0.020.01
    MODISct_oc3Linear0.020.01
    MODISc_to_chl_oc3Linear10
    MODISeps_78Linear0.010
    MODISev_000Linear0.480
    MODISev_001Linear0.620
    MODISev_002Linear10
    MODISEVILinear0.0040
    MODISf2Linear0.0039215690
    MODISf2to20Linear0.0039215690
    MODISflargeLinear0.0039215690
    MODISfront_step2_sstLinear10
    MODISfront_step4_sstLinear10
    MODIShab_kareniaLinear10
    MODIShab_karenia_screenLinear10
    MODIShvis531Linear0.20
    MODISiparLinear1.5e-050
    MODISK_490Logarithmic0.011176-2
    MODISKd_490Logarithmic0.011176-2
    MODISl2_flagsLinear10
    MODISlda_karenia_harmfulLinear0.003952569-0.003952569
    MODISlda_karenia_harmlessLinear0.003952569-0.003952569
    MODISlda_karenia_nobloomLinear0.003952569-0.003952569
    MODISlda_karenia_regr_nob_habLinear0.005025126-0.005025126
    MODISlda_karenia_rgbLinear10
    MODISlda_karenia_rgb_chlLinear10
    MODISlda_karenia_unknownLinear0.003952569-0.003952569
    MODISlda_phaeo_harmfulLinear0.003952569-0.003952569
    MODISlda_phaeo_harmlessLinear0.003952569-0.003952569
    MODISlda_phaeo_nobloomLinear0.003952569-0.003952569
    MODISlda_phaeo_regr_nob_habLinear0.005025126-0.005025126
    MODISlda_phaeo_rgbLinear10
    MODISlda_phaeo_rgb_chlLinear10
    MODISlda_phaeo_unknownLinear0.003952569-0.003952569
    MODISLt_1240Linear0.062
    MODISLt_1640Linear0.062
    MODISLt_2130Linear0.062
    MODISLt_469Linear0.066
    MODISLt_555Linear0.062
    MODISLt_645Linear0.062
    MODISLt_859Linear0.062
    MODISNDDILinear0.006270
    MODISNDDI_cLinear0.006270
    MODISNDVILinear0.0040
    MODISNDVI_cLinear0.0040
    MODISnLwLinear0.020
    MODISnLw_412Linear0.020
    MODISnLw_443Linear0.020
    MODISnLw_469Linear0.020
    MODISnLw_469_pmlLinear0.020
    MODISnLw_488Linear0.020
    MODISnLw_488_500m_pmlLinear0.020
    MODISnLw_490Linear0.020
    MODISnLw_510Linear0.020
    MODISnLw_531Linear0.020
    MODISnLw_547Linear0.020
    MODISnLw_551Linear0.020
    MODISnLw_555Linear0.020
    MODISnLw_555_pmlLinear0.020
    MODISnLw_667Linear0.020
    MODISnLw_678Linear0.020
    MODISnLw_748Linear0.020
    MODISnLw_869Linear0.020
    MODISoc_l2_flagsLinear10
    MODISp90_oc5Logarithmic0.015-2
    MODISp90_oc5_compLinear10
    MODISparLinear0.30480
    MODISpicLogarithmic0.012192-4.3
    MODISppLogarithmic0.00752
    MODISpsc_oc3Linear10
    MODISqual_sstLinear10
    MODISRrs_412Linear0.00020
    MODISRrs_443Linear0.00020
    MODISRrs_469Linear0.00020
    MODISRrs_488Linear0.00020
    MODISRrs_531Linear0.00020
    MODISRrs_547Linear0.00020
    MODISRrs_555Linear0.00020
    MODISRrs_645Linear0.00020
    MODISRrs_667Linear0.00020
    MODISRrs_678Linear0.00020
    MODISRrs_859Linear0.00020
    MODISspmiLinear0.10
    MODISsstLinear0.15-3
    MODISsst4Linear0.15-3
    MODISsst_l2_flagsLinear10
    MODIStau_869Linear0.0020
    MODISTau_869Linear0.0020
    MODISTOALR_469Linear0.00310
    MODISTOALR_555Linear0.00360
    MODISTOALR_645Linear0.00350
    MODIStsm_clarkLogarithmic0.01-2
    MODISvvis531Linear0.20
    MODISzeu_mbLinear0.80
    NETCDFaatsr_meris_ppLogarithmic0.0072
    NETCDFC_mean_ssLinear0.0003920
    NETCDFDpco2Linear1.176-150
    NETCDFflux_diffLinear0.157-20
    NETCDFflux_H06Linear0.00235-0.3
    NETCDFflux_LM86Linear0.00235-0.3
    NETCDFflux_M01Linear0.00235-0.3
    NETCDFflux_N00Linear0.00235-0.3
    NETCDFflux_W92Linear0.00235-0.3
    NETCDFflux_WM99Linear0.00235-0.3
    NETCDFflux_WoolfLinear0.00235-0.3
    NETCDFfr_velLinear0.03920
    NETCDFkbLinear0.00780
    NETCDFkdLinear0.00780
    NETCDFktLinear0.00780
    NETCDFku_mean_ssLinear0.0003920
    NETCDFku_sigma0Linear0.1960
    NETCDFku_wind_spLinear0.080
    NETCDFOFLinear0.00235-0.3
    NETCDFOFA1Linear0.003920
    NETCDFOFA4Linear0.011760
    NETCDFOK3Linear0.00780
    NETCDFsalinityLinear0.07820
    NETCDFsea_ice_coverageLinear0.00390
    NETCDFsig_wv_htLinear0.0390
    RA2ku_mod_wind_sp_uLinear0.1180
    RA2ku_mod_wind_sp_vLinear0.1180
    RA2ku_sigma0Linear0.196-25
    RA2ku_sig_wv_htLinear0.1180
    RA2ku_wind_spLinear0.1180
    SeaWiFSa_412_pmlLogarithmic0.01-1.852
    SeaWiFSa_443_pmlLogarithmic0.01-1.852
    SeaWiFSa_490_pmlLogarithmic0.01-1.852
    SeaWiFSa_510_pmlLogarithmic0.01-1.852
    SeaWiFSa_555_pmlLogarithmic0.01-1.852
    SeaWiFSa_670_pmlLogarithmic0.01-1.852
    SeaWiFSadg_412_gsmLogarithmic0.01-1.852
    SeaWiFSadg_412_pmlLogarithmic0.01-1.852
    SeaWiFSadg_443_gsmLogarithmic0.01-1.852
    SeaWiFSadg_443_pmlLogarithmic0.01-1.852
    SeaWiFSadg_490_gsmLogarithmic0.01-1.852
    SeaWiFSadg_490_pmlLogarithmic0.01-1.852
    SeaWiFSadg_510_gsmLogarithmic0.01-1.852
    SeaWiFSadg_510_pmlLogarithmic0.01-1.852
    SeaWiFSadg_555_gsmLogarithmic0.01-1.852
    SeaWiFSadg_555_pmlLogarithmic0.01-1.852
    SeaWiFSadg_670_gsmLogarithmic0.01-1.852
    SeaWiFSadg_670_pmlLogarithmic0.01-1.852
    SeaWiFSaph_412_pmlLogarithmic0.01-1.852
    SeaWiFSaph_443_pmlLogarithmic0.01-1.852
    SeaWiFSaph_490_pmlLogarithmic0.01-1.852
    SeaWiFSaph_510_pmlLogarithmic0.01-1.852
    SeaWiFSaph_555_pmlLogarithmic0.01-1.852
    SeaWiFSaph_670_pmlLogarithmic0.01-1.852
    SeaWiFSbb_412_pmlLogarithmic0.0118-3
    SeaWiFSbb_443_pmlLogarithmic0.0118-3
    SeaWiFSbb_490_pmlLogarithmic0.0118-3
    SeaWiFSbb_510_pmlLogarithmic0.0118-3
    SeaWiFSbb_555_pmlLogarithmic0.0118-3
    SeaWiFSbb_670_pmlLogarithmic0.0118-3
    SeaWiFSbbp_412_pmlLogarithmic0.0118-3
    SeaWiFSbbp_443_pmlLogarithmic0.0118-3
    SeaWiFSbbp_490_pmlLogarithmic0.0118-3
    SeaWiFSbbp_510_pmlLogarithmic0.0118-3
    SeaWiFSbbp_555_pmlLogarithmic0.0118-3
    SeaWiFSbbp_670_pmlLogarithmic0.0118-3
    SeaWiFSc2Logarithmic0.015-2
    SeaWiFSc2to20Logarithmic0.015-2
    SeaWiFSchl_oc488Logarithmic0.015-2
    SeaWiFSchl_oc5Logarithmic0.015-2
    SeaWiFSchl_oc5plusflagsLogarithmic0.015-2
    SeaWiFSchlor_aLogarithmic0.015-2
    SeaWiFSclargeLogarithmic0.015-2
    SeaWiFScocco_anom_BYLinear10
    SeaWiFSeps_78Linear0.010
    SeaWiFSf2Linear0.0039215690
    SeaWiFSf2to20Linear0.0039215690
    SeaWiFSflargeLinear0.0039215690
    SeaWiFSK_490Logarithmic0.02-2
    SeaWiFSl2_flagsLinear10
    SeaWiFSnasa_chlor_aLogarithmic0.015-2
    SeaWiFSnLw_412Linear0.020
    SeaWiFSnLw_412_FLinear0.020
    SeaWiFSnLw_443Linear0.020
    SeaWiFSnLw_443_anomLinear10
    SeaWiFSnLw_443_FLinear0.020
    SeaWiFSnLw_490Linear0.020
    SeaWiFSnLw_490_FLinear0.020
    SeaWiFSnLw_510Linear0.020
    SeaWiFSnLw_510_anomLinear10
    SeaWiFSnLw_510_FLinear0.020
    SeaWiFSnLw_555Linear0.020
    SeaWiFSnLw_555_anomLinear10
    SeaWiFSnLw_555_FLinear0.020
    SeaWiFSnLw_670Linear0.020
    SeaWiFSnLw_670_FLinear0.020
    SeaWiFSRrs_412Linear0.00020
    SeaWiFSRrs_443Linear0.00020
    SeaWiFSRrs_490Linear0.00020
    SeaWiFSRrs_510Linear0.00020
    SeaWiFSRrs_555Linear0.00020
    SeaWiFSRrs_670Linear0.00020
    SeaWiFStau_865Linear0.0020
    VIIRSaot_862Linear0.0020
    VIIRSc2Logarithmic0.015-2
    VIIRSc2to20Logarithmic0.015-2
    VIIRSchl_oc488Logarithmic0.015-2
    VIIRSchl_oc5Logarithmic0.015-2
    VIIRSchlor_aLogarithmic0.015-2
    VIIRSclargeLogarithmic0.015-2
    VIIRSf2Linear0.0039215690
    VIIRSf2to20Linear0.0039215690
    VIIRSflargeLinear0.0039215690
    VIIRSKd_490Logarithmic0.011176-2
    VIIRSl2_flagsLinear10
    VIIRSpicLogarithmic0.012192-4.3
    VIIRSRrs_410Linear0.00020
    VIIRSRrs_443Linear0.00020
    VIIRSRrs_486Linear0.00020
    VIIRSRrs_551Linear0.00020
    VIIRSRrs_671Linear0.00020
    VIIRSspmiLinear0.10

  53. How do I convert latitude/longitude to image coordinates?
  54. The images we produce are in Mercator projection and there are a set of fairly simple formulae for converting latitude/longitude (lat/lon) positions to x/y pixel co-ordinates.

    • rows - number of rows in image
    • cols - number of columns in image
    • minlon/maxlon - minimum/maximum longitude of image (in decimal degrees)
    • minlat/maxlat - minimum/maximum latitude of image (in decimal degrees)
      Note that these are the coordinates of the CENTRE of the corner pixels.
    • DEGTORAD - conversion from degrees to radians (PI/180.0)
    • ln - natural log

    Calculate X position.

    FractX = (lon - minlon) / (maxlon - minlon)
    x = (cols - 1) * FractX

    Calculate Y position.

    Ymin = ln (tan (DEGTORAD * (45.0 + (minlat / 2.0))))
    Ymax = ln (tan (DEGTORAD * (45.0 + (maxlat / 2.0))))
    Yint = ln (tan (DEGTORAD * (45.0 + (lat / 2.0))))
    FractY = (Yint - YMin) / (YMax - YMin)
    y = (rows - 1) * (1.0 - FractY)

    The maximum/minimum latitude/longitude values and sizes are given when the images are viewed.

  55. How do I convert image coordinates to latitude/longitude?
  56. For converting from x/y pixel co-ordinates on a Mercator projection image to latitude/longitude (lat/lon) positions use:

    • rows - number of rows in image
    • cols - number of columns in image
    • minlon/maxlon - minimum/maximum longitude of image (in decimal degrees)
    • minlat/maxlat - minimum/maximum latitude of image (in decimal degrees)
      Note that these are the coordinates of the CENTRE of the corner pixels.
    • DEGTORAD - conversion from degrees to radians (PI/180.0)
    • RADTODEG - conversion from radians to degrees to radians
    • ln - natural log

    Calculate longitude.

    lonfract = x / (cols - 1)
    lon = minlon + (lonfract * (maxlon - minlon))

    Calculate latitude.

    latfract = 1.0 - (y / (rows - 1))
    Ymin = ln (tan (DEGTORAD * (45.0 + (minlat / 2.0))))
    Ymax = ln (tan (DEGTORAD * (45.0 + (maxlat / 2.0))))
    Yint = Ymin + (latfract * (Ymax - Ymin))
    lat = 2.0 * (RADTODEG * (arctan (exp (Yint))) - 45.0)

    The maximum/minimum latitude/longitude values and sizes are given when the images are viewed.

    Here is some information about the format of raw 8bit data files and how to use them.


    NetCDF files

  57. What are netCDF files and what are the benefits?
  58. NetCDF is a file format designed for storing array-based scientific data along with self-descriptive data. It is increasingly popular and may become the standard format in the Earth Observation field.

    The main benefits are:

    • Direct access to data - no scaling or other manual conversions ; the data are immediately available in their final form.
    • Built-in transparent compression (v4 only) - smaller files without having to unzip them to use them.
    • Self-describing - each file contains all the data needed to access it correctly (e.g. units, map projection, flags), as well as a wealth of other information (e.g. bounding box, provider contact details, ..).
    • Many analysis programs accept netCDF directly.
    • Software libraries available for a wide range of programming languages.

    To see an example of the metadata content of a netcdf, click here.

    For more information, see the netCDF home page and the netCDF FAQ.

  59. What standards do NEODAAS' netCDF files comply with?
  60. NEODAAS' netCDF files use netCDF version 4 (version 3 available on request) and comply with the key remote sensing standards - the Climate and Forecast (CF) Conventions (version 1.4 at the time of writing this FAQ) and the Unidata NetCDF discovery metadata convention. These should make it simple to import netCDF EO data into your applications.

  61. What tools are available for accessing data in netCDF files?
  62. Unidata maintains a large list of software supporting netCDF, although netCDF support is increasingly common in standard analysis packages - please check your user documentation if Unidata does not include your package.

    The following is a shortlist of particularly useful or relevant programs:

    • Panoply is a viewer that plots longitude-latitude and latitude-vertical gridded data. Features include: Slice specific latitude-longitude, latitude-vertical, or time-latitude arrays from multidimensional variables; Two arrays may be combined in one plot by differencing, summing, or averaging ; Lon-lat data may be plotted as global maps (using any of over 75 map projections) or as zonal average plots, overlaying continent outlines or masks on lon-lat plots. This viewer is simple to use and multi-platform.
    • Ocean Data View (ODV) is a software package for the interactive exploration, analysis and visualization of oceanographic and other geo-referenced profile or sequence data. It is particularly suitable for comparing satellite data with in-situ measurements, although it is not capable of handling very large datasets.
    • ncview is a quick visual browser for netCDF files, allowing you to view simple movies of the data, view along various dimensions, take a look at the actual data values, change color maps, invert the data, etc. A fast quick viewer, though aimed at UNIX users.
    • IDV (Integrated Data Viewer) is a Java viewer that displays a variety of netCDF files, particularly well formatted, geolocated datasets, allowing slicing and probing of multidimensional/multi-band data.
    • The Climate Data Analysis Tool (CDAT) provides tools to diagnose, validate, and intercompare large observational and global climate model data sets. The package is comprehensive but complex.
    • Climate Data Operators >350 scriptable operators to manipulate and analyse climate data files. These tools are very useful, particularly if you are comfortable with a command line interface. A partial overview of some of the categories:
      • File information (info, sinfo, diff, ...) and operations (copy, cat, merge, split*, ...)
      • Mathematical functions (arithmetic, sqrt, exp, log, sin, cos, ...)
      • Comparision (eq, ne, le, lt, ge, gt, ...)
      • Field, vertical and time range statistics
      • Field, vertical and time range interpolation
    • is a package of command line operators that work on generic netCDF or HDF4 files, including ensemble/record averaging, interpolation and a variety of manipulation functions (attribute editing; extract, cut, paste, print data)
    • NCAR Command Language (NCL) is an intepreted programming language for scientific data analysis and visualization including >600 functions for use specifically with climate and model data computing, empirical orthogonal functions, Fourier coefficients, wavelets, singular value decomposition, 1-, 2-, and 3-dimensional interpolation, approximation, and regridding, and computer analysis of scalar and vector global geophysical quantities. The visualizations are publication-quality and highly customizable.

  63. How can I access netCDF data in my own programs?
  64. A tutorial for all netCDF-supporting languages is beyond the scope of this FAQ, but the Unidata site provides links to libraries for many languages, including Ada, C, C++, Fortran, IDL, Java, MATLAB, Perl, Python, R, Ruby and Tcl/Tk. NEODAAS mostly uses the C, Python and Java interfaces, although the Fortran, MATLAB (and Octave) and R interfaces are also used.

    We have a small amount of example code available. Choose your language below to download the sample code we have available. Note that you will usually need the basic netCDF libraries from Unidata installed as well as a specific netCDF library for the language you are using. Note also that netCDF files can be organised in many different ways, so our sample code is likely to have to be adapted by you in order to read your specific netCDF file.

    For programming assistance and sample code, please contact us.

    Specific algorithms:

  65. MODIS NDVI/EVI
  66. This consists of two products, NDVI and EVI. For more information, see the MODIS Land Team website at http://modis-land.gsfc.nasa.gov/ or for algorithm details, see the ATBD at http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf NDVI has the usual definition of (NIR-RED)/(NIR+RED), while EVI is defined as 2(NIR-RED)/(L+NIR+C1*RED+c2*BLUE), where L, C1 and C2 are parameters. This is claimed to have improved resistance to effects of atmosphere and background, e.g. soil. Both have a practical range of 0 (no vegetation) to 1 (maximum cover). We have implemented NASA's vegetation index retrieval package, and performed some basic validation of our results against those produced by NASA. The scattergram shows an example NDVI comparison for a scene with high NDVI variability. The small differences seen are probably due to differences between software versions, or between instrument calibration files.

    At the moment no cloud clearing is possible on individual images, but where images overlap in composites we select the pixel with the highest vegetation index, which is more likely to be cloud free.

    Below is an example image of MODIS NDVI:

  67. MERIS TOA/BOA vegetation index
  68. This also consists of two products, the TOA vegetation index and the BOA vegetation index. For more information visit the Meris website at http://envisat.esa.int/instruments/meris/ or for algorithm details see the ATBDs at http://envisat.esa.int/instruments/meris/atbd/atbd_mgvi_jrc.pdf and http://envisat.esa.int/instruments/meris/atbd/atbd_2_22.pdf The TOA vegetation index, or Meris Global Vegetation Index (MGVI) is similar to NDVI but claims a better correlation with FAPAR, the fraction of absorbed photosynthetically active radiation, an in situ measure of vegetation activity. It also has a practical range of 0 to 1. The BOA vegetation index, or Meris Terrestrial Chlorophyll Index (MTCI) aims to represent canopy chlorophyll content. Hence it does not have a theoretical maximum value, but practical values range from 0 to about 4.2. Cloud clearing is integrated into these algorithms. Below are example images of MERIS TOA and BOA vegetation index:

  69. MODIS chlorophyll OC5
  70. This is a published case 2 waters chlorophyll-a estimation algorithm.

    • Gohin, F., Druon, J.N. & Lampert, L. (2002) A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International Journal of Remote Sensing, 23(8), 1639-1661.

  71. MODIS 90th percentile of chlorophyll OC5
  72. Reference:

    • Gohin F., B. Saulquin, H. Oger-Jeanneret, L. Lozac'h, L. Lampert, A. Lefebvre, P. Riou and F. Bruchon (2008). Towards a better assessment of the ecological status of coastal waters using satellite-derived chlorophyll-a concentrations. Remote Sensing of Environment, 112(8), 3329-3340.

  73. MERIS Inherent Optical Properties
  74. These measurements are derived using the PML Inherent Optical Property model (Smyth, T.J., Moore, G.F., Hirata, T. & Aiken, J. (2006) Semianalytical model for the derivation of ocean color inherent optical properties: description, implementation, and performance assessment. Applied Optics, 45(31), 8116-8131. Download paper (PDF 3 MB)

    Further information:

  75. How to acknowledge the use of our data
  76. To do so please include :

    `The authors thank the NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS) for supplying data for this study'

    in your publication and then email NEODAAS with the details. The service relies on users' publications as one measure of success.

  77. What is your privacy policy?
  78. Our privacy policy is detailed here.


  79. Who holds the copyright on NEODAAS data?
  80. Our copyright status is covered in the terms of use here.


  81. References
  82. AVHRR HRPT satellite images are obtained via a fast Internet link from Dundee Satellite Receiving Station, by special arrangement.

    The SST equation coefficients were obtained from NOAA Polar Orbiter Data User's Guide, NOAA KLM User's Guide, NOAA Satellite Information System, and various other sources.

    Thiermann V, Ruprecht E. A method for the detection of clouds using AVHRR infrared observations. Int. J. Remote Sensing 13(10):1829-1841, 1992.

    Saunders RW, Kriebel KT. An improved method for detecting clear sky and cloudy radiances from AVHRR data. Int. J. Remote Sensing 9(1):123-150, 1988.

    Roozekrans JN, Prangsma GJ. Processing and application of digital AVHRR imagery for land and sea surfaces. Royal Netherlands Meteorological Institute (KNMI), 1988.

    Dalu G. Satellite remote sensing of atmospheric water vapour. Int. J. Remote Sensing 7(9):1089-1097, 1986.

    McClain, C.R. 1997. 'SeaWiFS Bio-optical Mini-workshop (SeaBAM) Overview' or SeaBAM WWW page.

    O'Reilly, J.E. and co-authors, 2000: "Ocean color chlorophyll a algorithms for SeaWiFS, OC2, and OC4: Version 4." In: J.E. O'Reilly and co-authors, SeaWiFS Postlaunch Calibration and Validation Analyses, Part 3. NASA Tech. Memo. 2000-206892, Vol. 11, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 9-23.