Registration only takes a few minutes and is possible here.
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.
If, after reading the question below, you feel that you have been denied access to an image incorrectly, please contact us.
There are several groups of users who have privileged access to some of the restricted products:
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.
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.
Sensor | Time 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-Aqua | 2002 - present day |
MODIS-Aqua NASA | October 2004 - present day |
MERIS | October 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).
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.
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
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:
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.
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
The mapped HDF files are generated using the SeaDAS module bl2map.
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) |
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). |
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).
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.
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.
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 |
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.
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.
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 |
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. |
Sensor | Product | Description (units) |
---|---|---|
AVHRR | SST | Sea surface temperature estimates. (°C) |
SeaWiFS | Chlorophyll (OC4v4) | Chlorophyll (chlor_a) concentration estimates (mg m^{-3}). |
SeaWiFS | True Colour | Simulated true colour (This is a combination of three normalised water leaving radiances at 442 nm, 490 nm and 555nm) (dimensionless). |
SeaWiFS | SPM Proxy (555nm) | Normalised water leaving radiance at 551nm. (mW cm^{-2} um^{-1} sr^{-1}) |
SeaWiFS | Turbidity | In-water diffuse attenuation coefficient (Kd) at 490 nm (m^{-1}). |
SeaWiFS | Aerosol opt. thick | Aerosol optical thickness at 865 nm (dimensionless). |
SeaWiFS | Aerosol Epsilon | Epsilon of aerosol correction at 765 nm (dimensionless). |
SeaWiFS | Aerosol Angstrom | Angstrom coefficient, 510 to 865 nm (dimensionless). |
SeaWiFS | True colour (L1) | Level 1B (geolocated and calibrated) top-of-atmosphere composite (note: always in a geographic projection) (dimensionless). |
SeaWiFS | Level 0 full pass | Level 0 composite image (generated from 3 spectral bands), shown in the satellite projection (dimensionless). |
SeaWiFS | Karenia HAB | Harmful algal bloom spectral classifier output (Miller et al, International Journal of Remote Sensing 2006). |
MODIS | Chlorophyll (OC3M) | Chlorophyll concentration estimates (an analogue of the SeaWiFS algorithm - chl_oc3) (mg m^{-3}) |
MODIS | Chlorophyll (OC5) | Chlorophyll concentration estimates (chl_oc5 algorithm) (mg m^{-3}) |
MODIS | True Colour | Simulated true colour (This is a combination of three normalised water leaving radiances at 443 nm, 488 nm and 551 nm) (dimensionless). |
MODIS | SPM Proxy (551nm) | Normalised water leaving radiance at 551nm (mW cm^{-2} um^{-1} sr^{-1}) |
MODIS | Turbidity (K_490) | In-water diffuse attenuation coefficient (Kd) at 490 nm (m^{-1}). |
MODIS | Aerosol opt. thick | Aerosol optical thickness at 869 nm (dimensionless). |
MODIS | Aerosol Epsilon | Epsilon of aerosol correction at 865 nm (dimensionless). |
MODIS | Aerosol Angstrom | Angstrom coefficient, 531 to 869 nm (dimensionless). |
MODIS | SST | Sea surface temperature estimates (°C). |
MODIS | True colour (L1G) | Level 1B (geolocated and calibrated) top-of-atmosphere composite (dimensionless). |
MODIS | Level 0 full pass | Level 0 composite image (generated from 3 spectral bands), shown in the satellite projection (dimensionless). |
MODIS | PML:a (443 nm) | Total absorption at 443 nm (Smyth et al, Applied Optics 2003) (m). |
MODIS | PML:aph (443 nm) | Absorption due to phytoplankton at 443 nm (Smyth et al, Applied Optics 2003) (m). |
MODIS | PML:ady (443 nm) | Absorption due to gelbstoff and detrital material at 443 nm (Smyth et al, Applied Optics 2003) (m). |
MODIS | PML:bb (551 nm) | Total backscatter at 551 nm (Smyth et al, Applied Optics 2003) (m). |
MODIS | Primary production | Net primary production (Smyth et al Journal of Geophysical Research 2005) (mgC m^{-2 } day^{-1}). |
MODIS | Karenia HAB | Harmful algal bloom spectral classifier output (Miller et al, International Journal of Remote Sensing 2006). |
MODIS | NDDI | Normalised difference dust index (dimensionless) |
MERIS | Chlorophyll (algal_1) | Chlorophyll concentration estimates for case 1 water (mg m^{-3}). |
MERIS | Chlorophyll (algal_2) | Chlorophyll concentration estimates for case 2 waters (mg m^{-3}). |
MERIS | True Colour | Simulated true colour (This is a combination of three normalised water leaving radiances at 442 nm, 490 nm and 560nm) (dimensionless). |
MERIS | Radiance (nLw 560) | Normalised water leaving radiance at 560nm (mW cm^{-2} um^{-1} sr^{-1}). |
MERIS | Aerosol op. thick | A measurement the opacity of the aerosol layers at 865 nm (dimensionless) |
MERIS | Yellow substance | Measurement of the gelbstoff absorption (m^{-1}). |
MERIS | SPM | A measurement of the suspended sediments concentration (Log_{10}(g m^{-3})) |
MERIS | TOA vegetation | Top of atmosphere vegetation indices (dimensionless). |
Table 2: Available products for each sensor.
Model | Product | Description |
---|---|---|
MRCS model | Chlorophyll composite | Chlorophyll (mg m^{-3}) model output from the Met Office. |
MRCS vs MODIS | Percentage difference | Chlorophyll percentage difference between MRCS and Aqua NASA. |
MRCS model | Model SST composite | SST (°C) model output from the Met Office. |
MRCS vs observations | Model - satellite SST difference composite | Model - SST (°C) composite from AVHRR. Positive -> model warmer. |
MRCS model | Model salinity composite | Salinity (PSU) model output from the Met Office. |
MRCS vs observations | Receiver Operator Characteristic (ROC) | Receiver Operator Characteristic (ROC) curve. Points above 1:1 represent model predictive skill. |
MRCS vs observations | Equitable threat score (ETS) | Threat score minus those values that were correct due to chance alone. |
MRCS vs observations | Odds 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 observations | Bias | Ratio of the frequency of the observed events and the frequency of the forecast events. |
MRCS vs observations | Kappa coefficient | Cohen's kappa measures the agreement between two raters (takes into account the agreement occurring by chance). |
MRCS vs observations | Wavelet MSE | HAAR wavelet mean squared error (MSE) as per Casati et al (2004), Meteorol. Appl. 11, 141-154 |
MRCS vs observations | Wavelet SS | HAAR wavelet skill score (SS) as per Casati et al (2004), Meteorol. Appl. 11, 141-154 |
MRCS vs observations | EOF principal component | Empirical orthogonal function principal component (also known has PCA principal component). |
MRCS vs observations | EOF 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 observations | EOF 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.
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)
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.
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.
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.
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.
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.
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.
The following applet will give you information about your version of Java.
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.
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).
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.
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'.
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.
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.
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.
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 = [log_{10}(value) - log_{10}(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 'log_{10}(intercept)' term for the value in the table.
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 |
---|---|---|---|---|
AATSR | sst_comb | Linear | 0.15 | -2 |
AATSR | sst_comb_mean | Linear | 1.18 | 0 |
AATSR | sst_nadir | Linear | 0.15 | -2 |
AMSRE | Low_res_sst | Linear | 0.15 | -2 |
ASAR | sigma0 | Linear | 0.078 | 0 |
ASAR | wind_speed | Linear | 0.156 | 0 |
AVHRR | 1 | Linear | 1 | 0 |
AVHRR | 2 | Linear | 1 | 0 |
AVHRR | 3 | Linear | 1 | 0 |
AVHRR | 4 | Linear | 1 | 0 |
AVHRR | 5 | Linear | 1 | 0 |
AVHRR | 6 | Linear | 1 | 0 |
AVHRR | aries1 | Linear | 1 | 0 |
AVHRR | aries2 | Linear | 1 | 0 |
AVHRR | aries3 | Linear | 1 | 0 |
AVHRR | aries4 | Linear | 1 | 0 |
AVHRR | aries5 | Linear | 1 | 0 |
AVHRR | ariesbt3 | Linear | 0.01 | -100 |
AVHRR | ariesbt4 | Linear | 0.01 | -100 |
AVHRR | ariesbt5 | Linear | 0.01 | -100 |
AVHRR | bt3 | Linear | 0.01 | -30 |
AVHRR | bt4 | Linear | 0.01 | -30 |
AVHRR | bt5 | Linear | 0.01 | -30 |
AVHRR | cld | Linear | 1 | 0 |
AVHRR | cmed | Linear | 0.1 | -3 |
AVHRR | csstd | Linear | 0.1 | -10 |
AVHRR | csstp | Linear | 0.1 | -3 |
AVHRR | csstpfin | Linear | 0.1 | -3 |
AVHRR | front_step2_sstp | Linear | 1 | 0 |
AVHRR | front_step4_sstp | Linear | 0.001 | 0 |
AVHRR | pfmed | Linear | 0.15 | -3 |
AVHRR | pfsstp | Linear | 0.15 | -3 |
AVHRR | pfsstpfin | Linear | 0.15 | -3 |
AVHRR | r1 | Linear | 0.1 | 0 |
AVHRR | r2 | Linear | 0.1 | 0 |
AVHRR | wsst | Linear | 0.15 | -3 |
AVHRR | wsstp | Linear | 0.15 | -3 |
BINARY | aatsr_sst | Linear | 0.15 | -2 |
BINARY | analysed_sst | Linear | 1.18 | 0 |
BINARY | CO2flux | Linear | 0.117 | -15 |
BINARY | Dpco2 | Linear | 1.176 | -150 |
BINARY | gascoefSW06 | Linear | 7.8e-05 | 0.0001 |
BINARY | kSW06 | Linear | 0.196 | 0 |
BINARY | mod_SST_t | Linear | 1 | 0 |
BINARY | pCO2_air | Linear | 1.176 | -150 |
BINARY | pCO2_sw | Linear | 1.176 | -150 |
BINARY | pressure | Linear | 0.39216 | 950 |
BINARY | salinity | Linear | 0.078 | 20 |
BINARY | sea_ice_coverage | Linear | 1 | 0 |
BINARY | solubility | Linear | 0.00031 | 0.01 |
BINARY | SST_t | Linear | 1 | 0 |
BINARY | u10wind | Linear | 1 | 0 |
BINARY | vCO2_air | Linear | 1 | 0 |
BINARY | wind_t | Linear | 1 | 0 |
MERIS | a_412_pml | Logarithmic | 0.01 | -1.852 |
MERIS | a_413_pml | Logarithmic | 0.01 | -1.852 |
MERIS | a_443 | Logarithmic | 0.01 | -1.852 |
MERIS | a_443_pml | Logarithmic | 0.01 | -1.852 |
MERIS | a_490_pml | Logarithmic | 0.01 | -1.852 |
MERIS | a_510_pml | Logarithmic | 0.01 | -1.852 |
MERIS | a_555_pml | Logarithmic | 0.01 | -1.852 |
MERIS | a_560_pml | Logarithmic | 0.01 | -1.852 |
MERIS | a_620_pml | Logarithmic | 0.01 | -1.852 |
MERIS | adg_413_pml | Logarithmic | 0.01 | -1.852 |
MERIS | adg_443_pml | Logarithmic | 0.01 | -1.852 |
MERIS | adg_490_pml | Logarithmic | 0.01 | -1.852 |
MERIS | adg_510_pml | Logarithmic | 0.01 | -1.852 |
MERIS | adg_560_pml | Logarithmic | 0.01 | -1.852 |
MERIS | adg_620_pml | Logarithmic | 0.01 | -1.852 |
MERIS | aero_opt_thick | Linear | 0.015 | 0 |
MERIS | algal_1 | Logarithmic | 0.015 | -2 |
MERIS | algal_1_land | Logarithmic | 0.015 | -2 |
MERIS | algal_1_mean | Logarithmic | 0.015 | -2 |
MERIS | algal_2 | Logarithmic | 0.015 | -2 |
MERIS | algal_2_land | Logarithmic | 0.015 | -2 |
MERIS | aot_865 | Linear | 0.002 | 0 |
MERIS | aph_412_pml | Logarithmic | 0.01 | -1.852 |
MERIS | aph_413_pml | Logarithmic | 0.01 | -1.852 |
MERIS | aph_443_pml | Logarithmic | 0.01 | -1.852 |
MERIS | aph_490_pml | Logarithmic | 0.01 | -1.852 |
MERIS | aph_510_pml | Logarithmic | 0.01 | -1.852 |
MERIS | aph_555_pml | Logarithmic | 0.01 | -1.852 |
MERIS | aph_560_pml | Logarithmic | 0.01 | -1.852 |
MERIS | aph_620_pml | Logarithmic | 0.01 | -1.852 |
MERIS | bb_412_pml | Logarithmic | 0.0118 | -3 |
MERIS | bb_413_pml | Logarithmic | 0.0118 | -3 |
MERIS | bb_443_pml | Logarithmic | 0.0118 | -3 |
MERIS | bb_490_pml | Logarithmic | 0.0118 | -3 |
MERIS | bb_510_pml | Logarithmic | 0.0118 | -3 |
MERIS | bb_555_pml | Logarithmic | 0.0118 | -3 |
MERIS | bb_560 | Logarithmic | 0.01 | -1.852 |
MERIS | bb_560_pml | Logarithmic | 0.0118 | -3 |
MERIS | bb_620_pml | Logarithmic | 0.0118 | -3 |
MERIS | bbp_413_pml | Logarithmic | 0.0118 | -3 |
MERIS | bbp_443_pml | Logarithmic | 0.0118 | -3 |
MERIS | bbp_490_pml | Logarithmic | 0.0118 | -3 |
MERIS | bbp_510_pml | Logarithmic | 0.0118 | -3 |
MERIS | bbp_560_pml | Logarithmic | 0.0118 | -3 |
MERIS | bbp_619_pml | Logarithmic | 0.0118 | -3 |
MERIS | bbp_620_pml | Logarithmic | 0.0118 | -3 |
MERIS | boa_veg | Linear | 0.0168 | 0 |
MERIS | c02to2 | Logarithmic | 0.015 | -2 |
MERIS | c2 | Logarithmic | 0.015 | -2 |
MERIS | c20to200 | Logarithmic | 0.015 | -2 |
MERIS | c2to20 | Logarithmic | 0.015 | -2 |
MERIS | chl_ci | Logarithmic | 0.015 | -2 |
MERIS | chl_oc5 | Logarithmic | 0.015 | -2 |
MERIS | chl_oc5ci | Logarithmic | 0.015 | -2 |
MERIS | chlor_a | Logarithmic | 0.015 | -2 |
MERIS | chlor_a_2 | Logarithmic | 0.015 | -2 |
MERIS | clarge | Logarithmic | 0.015 | -2 |
MERIS | cloud_albedo | Linear | 0.004 | 0 |
MERIS | cloud_top_press | Linear | 3.92 | 0 |
MERIS | conc_chl_nn | Logarithmic | 0.015 | -2 |
MERIS | conc_chl_oc4 | Logarithmic | 0.015 | -2 |
MERIS | f02to2 | Linear | 0.003921569 | 0 |
MERIS | f2 | Linear | 0.003921569 | 0 |
MERIS | f20to200 | Linear | 0.003921569 | 0 |
MERIS | f2to20 | Linear | 0.003921569 | 0 |
MERIS | flarge | Linear | 0.003921569 | 0 |
MERIS | kd_490 | Logarithmic | 0.011176 | -2 |
MERIS | Kd_490 | Logarithmic | 0.011176 | -2 |
MERIS | l2_flags | Linear | 1 | 0 |
MERIS | nLw_412 | Linear | 0.02 | 0 |
MERIS | nLw_413 | Linear | 0.02 | 0 |
MERIS | nLw_443 | Linear | 0.02 | 0 |
MERIS | nLw_490 | Linear | 0.02 | 0 |
MERIS | nLw_510 | Linear | 0.02 | 0 |
MERIS | nLw_560 | Linear | 0.02 | 0 |
MERIS | nLw_619 | Linear | 0.02 | 0 |
MERIS | nLw_620 | Linear | 0.02 | 0 |
MERIS | nLw_665 | Linear | 0.02 | 0 |
MERIS | nLw_681 | Linear | 0.02 | 0 |
MERIS | nLw_709 | Linear | 0.02 | 0 |
MERIS | nLw_754 | Linear | 0.02 | 0 |
MERIS | photosyn_rad_mean | Linear | 7.843 | 0 |
MERIS | Rrs_412 | Linear | 0.0002 | 0 |
MERIS | Rrs_413 | Linear | 0.0002 | 0 |
MERIS | Rrs_443 | Linear | 0.0002 | 0 |
MERIS | Rrs_490 | Linear | 0.0002 | 0 |
MERIS | Rrs_510 | Linear | 0.0002 | 0 |
MERIS | Rrs_560 | Linear | 0.0002 | 0 |
MERIS | Rrs_619 | Linear | 0.0002 | 0 |
MERIS | Rrs_620 | Linear | 0.0002 | 0 |
MERIS | Rrs_665 | Linear | 0.0002 | 0 |
MERIS | Rrs_681 | Linear | 0.0002 | 0 |
MERIS | spmi | Linear | 0.1 | 0 |
MERIS | toa_veg | Linear | 0.004 | 0 |
MERIS | total_susp | Logarithmic | 0.013 | -2 |
MERIS | total_susp_land | Logarithmic | 0.01 | -2 |
MERIS | tsm_clark | Logarithmic | 0.01 | -2 |
MERIS | yellow_subs | Logarithmic | 0.00784314 | -3 |
MERIS | yellow_subs_land | Logarithmic | 0.00784314 | -3 |
Microwave+IR | front_step4_sst | Linear | 0.01 | -0.5 |
Microwave+IR | sst | Linear | 0.15 | -3 |
Microwave+IR | sst_flags | Linear | 1 | 0 |
MODIS | 469_EDGESWATH_B | Linear | 1 | 0 |
MODIS | 469_HIGLINT_B | Linear | 1 | 0 |
MODIS | 555_EDGESWATH_G | Linear | 0.62 | 0 |
MODIS | 555_HIGLINT_G | Linear | 0.62 | 0 |
MODIS | 645_EDGESWATH_R | Linear | 0.48 | 0 |
MODIS | 645_HIGLINT_R | Linear | 0.48 | 0 |
MODIS | a_412_pml | Logarithmic | 0.01 | -1.852 |
MODIS | a_443_pml | Logarithmic | 0.01 | -1.852 |
MODIS | a_469_pml | Logarithmic | 0.01 | -1.852 |
MODIS | a_488_pml | Logarithmic | 0.01 | -1.852 |
MODIS | a_531_pml | Logarithmic | 0.01 | -1.852 |
MODIS | a_547_pml | Logarithmic | 0.01 | -1.852 |
MODIS | adg_412_carder | Logarithmic | 0.01 | -1.852 |
MODIS | adg_412_giop | Logarithmic | 0.01 | -1.852 |
MODIS | adg_412_pml | Logarithmic | 0.01 | -1.852 |
MODIS | adg_412_qaa | Logarithmic | 0.01 | -1.852 |
MODIS | adg_443_carder | Logarithmic | 0.01 | -1.852 |
MODIS | adg_443_giop | Logarithmic | 0.01 | -1.852 |
MODIS | adg_443_pml | Logarithmic | 0.01 | -1.852 |
MODIS | adg_443_qaa | Logarithmic | 0.01 | -1.852 |
MODIS | adg_469_pml | Logarithmic | 0.01 | -1.852 |
MODIS | adg_488_pml | Logarithmic | 0.01 | -1.852 |
MODIS | adg_531_pml | Logarithmic | 0.01 | -1.852 |
MODIS | adg_547_pml | Logarithmic | 0.01 | -1.852 |
MODIS | ady_443_pml | Logarithmic | 0.01 | -1.852 |
MODIS | angstrom_531 | Linear | 0.0062 | -0.1 |
MODIS | aot_869 | Linear | 0.002 | 0 |
MODIS | aph_412_pml | Logarithmic | 0.01 | -1.852 |
MODIS | aph_443_pml | Logarithmic | 0.01 | -1.852 |
MODIS | aph_469_pml | Logarithmic | 0.01 | -1.852 |
MODIS | aph_488_pml | Logarithmic | 0.01 | -1.852 |
MODIS | aph_531_pml | Logarithmic | 0.01 | -1.852 |
MODIS | aph_547_pml | Logarithmic | 0.01 | -1.852 |
MODIS | bb_412_pml | Logarithmic | 0.0118 | -3 |
MODIS | bb_443_pml | Logarithmic | 0.0118 | -3 |
MODIS | bb_469_pml | Logarithmic | 0.0118 | -3 |
MODIS | bb_488_pml | Logarithmic | 0.0118 | -3 |
MODIS | bb_531_pml | Logarithmic | 0.0118 | -3 |
MODIS | bb_547_pml | Logarithmic | 0.0118 | -3 |
MODIS | bbp_412_pml | Logarithmic | 0.0118 | -3 |
MODIS | bbp_443_pml | Logarithmic | 0.0118 | -3 |
MODIS | bbp_469_pml | Logarithmic | 0.0118 | -3 |
MODIS | bbp_488_pml | Logarithmic | 0.0118 | -3 |
MODIS | bbp_510_pml | Logarithmic | 0.0118 | -3 |
MODIS | bbp_531_pml | Logarithmic | 0.0118 | -3 |
MODIS | bbp_547_pml | Logarithmic | 0.0118 | -3 |
MODIS | bricaud | Logarithmic | 0.015 | -2 |
MODIS | BT_3959 | Linear | 0.06 | 2 |
MODIS | BT_7325 | Linear | 0.06 | 2 |
MODIS | c02to2 | Logarithmic | 0.015 | -2 |
MODIS | c2 | Logarithmic | 0.015 | -2 |
MODIS | c20to200 | Logarithmic | 0.015 | -2 |
MODIS | c2to20 | Logarithmic | 0.015 | -2 |
MODIS | calcite | Logarithmic | 0.012192 | -4.3 |
MODIS | CDOM | Linear | 0.02767 | -0.02767 |
MODIS | chl-a | Logarithmic | 0.015 | -2 |
MODIS | chl_ci | Logarithmic | 0.015 | -2 |
MODIS | chl_medoc3 | Logarithmic | 0.015 | -2 |
MODIS | chl_oc2 | Logarithmic | 0.015 | -2 |
MODIS | chl_oc3 | Logarithmic | 0.015 | -2 |
MODIS | chl_oc488 | Logarithmic | 0.015 | -2 |
MODIS | chl_oc5 | Logarithmic | 0.015 | -2 |
MODIS | chl_oc5ci | Logarithmic | 0.015 | -2 |
MODIS | chl_oc5plusflags | Logarithmic | 0.015 | -2 |
MODIS | chl_ocx | Linear | 0.015 | -2 |
MODIS | chlor_a | Logarithmic | 0.015 | -2 |
MODIS | chlor_a_2 | Logarithmic | 0.015 | -2 |
MODIS | chlor_a_3 | Logarithmic | 0.015 | -2 |
MODIS | chlor_a_500m_pml | Logarithmic | 0.015 | -2 |
MODIS | chlor_acomp | Logarithmic | 0.015 | -2 |
MODIS | chlor_MODIS | Logarithmic | 0.015 | -2 |
MODIS | clarge | Logarithmic | 0.015 | -2 |
MODIS | cp_oc3 | Linear | 0.02 | 0.01 |
MODIS | ct_oc3 | Linear | 0.02 | 0.01 |
MODIS | c_to_chl_oc3 | Linear | 1 | 0 |
MODIS | eps_78 | Linear | 0.01 | 0 |
MODIS | ev_000 | Linear | 0.48 | 0 |
MODIS | ev_001 | Linear | 0.62 | 0 |
MODIS | ev_002 | Linear | 1 | 0 |
MODIS | EVI | Linear | 0.004 | 0 |
MODIS | f02to2 | Linear | 0.003921569 | 0 |
MODIS | f2 | Linear | 0.003921569 | 0 |
MODIS | f20to200 | Linear | 0.003921569 | 0 |
MODIS | f2to20 | Linear | 0.003921569 | 0 |
MODIS | flarge | Linear | 0.003921569 | 0 |
MODIS | front_step2_chlor_a | Linear | 1 | 0 |
MODIS | front_step2_sst | Linear | 1 | 0 |
MODIS | front_step4_chlor_a | Linear | 1 | 0 |
MODIS | front_step4_sst | Linear | 1 | 0 |
MODIS | hab_karenia | Linear | 1 | 0 |
MODIS | hab_karenia_screen | Linear | 1 | 0 |
MODIS | hvis531 | Linear | 0.2 | 0 |
MODIS | ipar | Linear | 1.5e-05 | 0 |
MODIS | K_490 | Logarithmic | 0.011176 | -2 |
MODIS | Kd_490 | Logarithmic | 0.011176 | -2 |
MODIS | l2_flags | Linear | 1 | 0 |
MODIS | lda_karenia_harmful | Linear | 0.003952569 | -0.003952569 |
MODIS | lda_karenia_harmless | Linear | 0.003952569 | -0.003952569 |
MODIS | lda_karenia_nobloom | Linear | 0.003952569 | -0.003952569 |
MODIS | lda_karenia_regr_nob_hab | Linear | 0.005025126 | -0.005025126 |
MODIS | lda_karenia_rgb | Linear | 1 | 0 |
MODIS | lda_karenia_rgb_chl | Linear | 1 | 0 |
MODIS | lda_karenia_unknown | Linear | 0.003952569 | -0.003952569 |
MODIS | lda_phaeo_harmful | Linear | 0.003952569 | -0.003952569 |
MODIS | lda_phaeo_harmless | Linear | 0.003952569 | -0.003952569 |
MODIS | lda_phaeo_nobloom | Linear | 0.003952569 | -0.003952569 |
MODIS | lda_phaeo_regr_nob_hab | Linear | 0.005025126 | -0.005025126 |
MODIS | lda_phaeo_rgb | Linear | 1 | 0 |
MODIS | lda_phaeo_rgb_chl | Linear | 1 | 0 |
MODIS | lda_phaeo_unknown | Linear | 0.003952569 | -0.003952569 |
MODIS | Lt_1240 | Linear | 0.06 | 2 |
MODIS | Lt_1640 | Linear | 0.06 | 2 |
MODIS | Lt_2130 | Linear | 0.06 | 2 |
MODIS | Lt_469 | Linear | 0.06 | 6 |
MODIS | Lt_555 | Linear | 0.06 | 2 |
MODIS | Lt_645 | Linear | 0.06 | 2 |
MODIS | Lt_859 | Linear | 0.06 | 2 |
MODIS | NDDI | Linear | 0.00627 | 0 |
MODIS | NDDI_c | Linear | 0.00627 | 0 |
MODIS | NDVI | Linear | 0.004 | 0 |
MODIS | NDVI_c | Linear | 0.004 | 0 |
MODIS | nLw | Linear | 0.02 | 0 |
MODIS | nLw_412 | Linear | 0.02 | 0 |
MODIS | nLw_443 | Linear | 0.02 | 0 |
MODIS | nLw_469 | Linear | 0.02 | 0 |
MODIS | nLw_469_pml | Linear | 0.02 | 0 |
MODIS | nLw_488 | Linear | 0.02 | 0 |
MODIS | nLw_488_500m_pml | Linear | 0.02 | 0 |
MODIS | nLw_490 | Linear | 0.02 | 0 |
MODIS | nLw_510 | Linear | 0.02 | 0 |
MODIS | nLw_531 | Linear | 0.02 | 0 |
MODIS | nLw_547 | Linear | 0.02 | 0 |
MODIS | nLw_551 | Linear | 0.02 | 0 |
MODIS | nLw_555 | Linear | 0.02 | 0 |
MODIS | nLw_555_pml | Linear | 0.02 | 0 |
MODIS | nLw_667 | Linear | 0.02 | 0 |
MODIS | nLw_678 | Linear | 0.02 | 0 |
MODIS | nLw_748 | Linear | 0.02 | 0 |
MODIS | nLw_869 | Linear | 0.02 | 0 |
MODIS | oc_l2_flags | Linear | 1 | 0 |
MODIS | opp_befa | Logarithmic | 0.0079 | 2 |
MODIS | opp_eppley | Logarithmic | 0.0079 | 2 |
MODIS | p90_oc5 | Logarithmic | 0.015 | -2 |
MODIS | p90_oc5_comp | Linear | 1 | 0 |
MODIS | par | Linear | 0.3048 | 0 |
MODIS | pic | Logarithmic | 0.012192 | -4.3 |
MODIS | pp | Logarithmic | 0.0075 | 2 |
MODIS | psc_oc3 | Linear | 1 | 0 |
MODIS | qual_sst | Linear | 1 | 0 |
MODIS | Rrs_412 | Linear | 0.0002 | 0 |
MODIS | Rrs_443 | Linear | 0.0002 | 0 |
MODIS | Rrs_469 | Linear | 0.0002 | 0 |
MODIS | Rrs_488 | Linear | 0.0002 | 0 |
MODIS | Rrs_531 | Linear | 0.0002 | 0 |
MODIS | Rrs_547 | Linear | 0.0002 | 0 |
MODIS | Rrs_555 | Linear | 0.0002 | 0 |
MODIS | Rrs_645 | Linear | 0.0002 | 0 |
MODIS | Rrs_667 | Linear | 0.0002 | 0 |
MODIS | Rrs_678 | Linear | 0.0002 | 0 |
MODIS | Rrs_859 | Linear | 0.0002 | 0 |
MODIS | spmi | Linear | 0.1 | 0 |
MODIS | sst | Linear | 0.15 | -3 |
MODIS | sst4 | Linear | 0.15 | -3 |
MODIS | sst_l2_flags | Linear | 1 | 0 |
MODIS | tau_869 | Linear | 0.002 | 0 |
MODIS | Tau_869 | Linear | 0.002 | 0 |
MODIS | TOALR_469 | Linear | 0.0031 | 0 |
MODIS | TOALR_555 | Linear | 0.0036 | 0 |
MODIS | TOALR_645 | Linear | 0.0035 | 0 |
MODIS | tsm_clark | Logarithmic | 0.01 | -2 |
MODIS | vvis531 | Linear | 0.2 | 0 |
MODIS | zeu_mb | Linear | 0.8 | 0 |
NETCDF | aatsr_meris_pp | Logarithmic | 0.007 | 2 |
NETCDF | C_mean_ss | Linear | 0.000392 | 0 |
NETCDF | Dpco2 | Linear | 1.176 | -150 |
NETCDF | flux_diff | Linear | 0.157 | -20 |
NETCDF | flux_H06 | Linear | 0.00235 | -0.3 |
NETCDF | flux_LM86 | Linear | 0.00235 | -0.3 |
NETCDF | flux_M01 | Linear | 0.00235 | -0.3 |
NETCDF | flux_N00 | Linear | 0.00235 | -0.3 |
NETCDF | flux_W92 | Linear | 0.00235 | -0.3 |
NETCDF | flux_WM99 | Linear | 0.00235 | -0.3 |
NETCDF | flux_Woolf | Linear | 0.00235 | -0.3 |
NETCDF | fr_vel | Linear | 0.0392 | 0 |
NETCDF | kb | Linear | 0.0078 | 0 |
NETCDF | kd | Linear | 0.0078 | 0 |
NETCDF | kt | Linear | 0.0078 | 0 |
NETCDF | ku_mean_ss | Linear | 0.000392 | 0 |
NETCDF | ku_sigma0 | Linear | 0.196 | 0 |
NETCDF | ku_wind_sp | Linear | 0.08 | 0 |
NETCDF | OF | Linear | 0.00235 | -0.3 |
NETCDF | OFA1 | Linear | 0.00392 | 0 |
NETCDF | OFA4 | Linear | 0.01176 | 0 |
NETCDF | OK3 | Linear | 0.0078 | 0 |
NETCDF | salinity | Linear | 0.078 | 20 |
NETCDF | sea_ice_coverage | Linear | 0.0039 | 0 |
NETCDF | sig_wv_ht | Linear | 0.039 | 0 |
OLCI | ADG443_NN | Linear | 0.011176 | -2 |
OLCI | chl | Logarithmic | 0.015 | -2 |
OLCI | CHL_NN | Logarithmic | 0.015 | -2 |
OLCI | CHL_OC4ME | Logarithmic | 0.015 | -2 |
OLCI | chl_oc5 | Logarithmic | 0.015 | -2 |
OLCI | chl_oc5ci | Logarithmic | 0.015 | -2 |
OLCI | chl_ocx | Linear | 0.015 | -2 |
OLCI | chlor_a | Logarithmic | 0.015 | -2 |
OLCI | conc_chl | Logarithmic | 0.015 | -2 |
OLCI | Kd_490 | Logarithmic | 0.011176 | -2 |
OLCI | KD490_M07 | Linear | 0.011176 | -2 |
OLCI | l2_flags | Linear | 1 | 0 |
OLCI | Oa01_reflectance | Linear | 0.015 | -2 |
OLCI | Oa02_reflectance | Linear | 0.015 | -2 |
OLCI | Oa03_reflectance | Linear | 0.015 | -2 |
OLCI | Oa04_reflectance | Linear | 0.015 | -2 |
OLCI | Oa05_reflectance | Linear | 0.015 | -2 |
OLCI | Oa06_reflectance | Linear | 0.015 | -2 |
OLCI | Oa07_reflectance | Linear | 0.015 | -2 |
OLCI | Oa08_reflectance | Linear | 0.015 | -2 |
OLCI | Oa09_reflectance | Linear | 0.015 | -2 |
OLCI | Oa10_reflectance | Linear | 0.015 | -2 |
OLCI | Oa11_reflectance | Linear | 0.015 | -2 |
OLCI | Oa12_reflectance | Linear | 0.015 | -2 |
OLCI | OCIME-RG | Logarithmic | 0.015 | -2 |
OLCI | OCIME-RG_mask | Linear | 1 | 0 |
OLCI | PAR | Linear | 1.5e-05 | 0 |
OLCI | pixel_classif_flags | Linear | 1 | 0 |
OLCI | polymer_idepix_flags | Linear | 1 | 0 |
OLCI | quality_flags | Linear | 1 | 0 |
OLCI | Rrs_1020 | Linear | 0.0002 | 0 |
OLCI | Rrs_400 | Linear | 0.0002 | 0 |
OLCI | RRS400 | Linear | 0.0002 | 0 |
OLCI | Rrs_412 | Linear | 0.0002 | 0 |
OLCI | RRS412 | Linear | 0.0002 | 0 |
OLCI | Rrs_442 | Linear | 0.0002 | 0 |
OLCI | Rrs_443 | Linear | 0.0002 | 0 |
OLCI | RRS443 | Linear | 0.0002 | 0 |
OLCI | Rrs_490 | Linear | 0.0002 | 0 |
OLCI | RRS490 | Linear | 0.0002 | 0 |
OLCI | Rrs_510 | Linear | 0.0002 | 0 |
OLCI | RRS510 | Linear | 0.0002 | 0 |
OLCI | Rrs_560 | Linear | 0.0002 | 0 |
OLCI | RRS560 | Linear | 0.0002 | 0 |
OLCI | Rrs_620 | Linear | 0.0002 | 0 |
OLCI | RRS620 | Linear | 0.0002 | 0 |
OLCI | Rrs_665 | Linear | 0.0002 | 0 |
OLCI | RRS665 | Linear | 0.0002 | 0 |
OLCI | Rrs_674 | Linear | 0.0002 | 0 |
OLCI | RRS674 | Linear | 0.0002 | 0 |
OLCI | Rrs_681 | Linear | 0.0002 | 0 |
OLCI | RRS681 | Linear | 0.0002 | 0 |
OLCI | Rrs_709 | Linear | 0.0002 | 0 |
OLCI | RRS709 | Linear | 0.0002 | 0 |
OLCI | Rrs_754 | Linear | 0.0002 | 0 |
OLCI | Rrs_779 | Linear | 0.0002 | 0 |
OLCI | Rrs_865 | Linear | 0.0002 | 0 |
OLCI | Rrs_885 | Linear | 0.0002 | 0 |
OLCI | spmi | Linear | 0.1 | 0 |
OLCI | TSM_NN | Linear | 0.01 | -2 |
OLCI | WQSF | Linear | 1 | 0 |
OLCI | WQSF_supplement | Linear | 1 | 0 |
RA2 | ku_mod_wind_sp_u | Linear | 0.118 | 0 |
RA2 | ku_mod_wind_sp_v | Linear | 0.118 | 0 |
RA2 | ku_sigma0 | Linear | 0.196 | -25 |
RA2 | ku_sig_wv_ht | Linear | 0.118 | 0 |
RA2 | ku_wind_sp | Linear | 0.118 | 0 |
SeaWiFS | a_412_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | a_443_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | a_490_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | a_510_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | a_555_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | a_670_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | adg_412_gsm | Logarithmic | 0.01 | -1.852 |
SeaWiFS | adg_412_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | adg_443_gsm | Logarithmic | 0.01 | -1.852 |
SeaWiFS | adg_443_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | adg_490_gsm | Logarithmic | 0.01 | -1.852 |
SeaWiFS | adg_490_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | adg_510_gsm | Logarithmic | 0.01 | -1.852 |
SeaWiFS | adg_510_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | adg_555_gsm | Logarithmic | 0.01 | -1.852 |
SeaWiFS | adg_555_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | adg_670_gsm | Logarithmic | 0.01 | -1.852 |
SeaWiFS | adg_670_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | aph_412_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | aph_443_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | aph_490_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | aph_510_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | aph_555_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | aph_670_pml | Logarithmic | 0.01 | -1.852 |
SeaWiFS | bb_412_pml | Logarithmic | 0.0118 | -3 |
SeaWiFS | bb_443_pml | Logarithmic | 0.0118 | -3 |
SeaWiFS | bb_490_pml | Logarithmic | 0.0118 | -3 |
SeaWiFS | bb_510_pml | Logarithmic | 0.0118 | -3 |
SeaWiFS | bb_555_pml | Logarithmic | 0.0118 | -3 |
SeaWiFS | bb_670_pml | Logarithmic | 0.0118 | -3 |
SeaWiFS | bbp_412_pml | Logarithmic | 0.0118 | -3 |
SeaWiFS | bbp_443_pml | Logarithmic | 0.0118 | -3 |
SeaWiFS | bbp_490_pml | Logarithmic | 0.0118 | -3 |
SeaWiFS | bbp_510_pml | Logarithmic | 0.0118 | -3 |
SeaWiFS | bbp_555_pml | Logarithmic | 0.0118 | -3 |
SeaWiFS | bbp_670_pml | Logarithmic | 0.0118 | -3 |
SeaWiFS | c02to2 | Logarithmic | 0.015 | -2 |
SeaWiFS | c2 | Logarithmic | 0.015 | -2 |
SeaWiFS | c20to200 | Logarithmic | 0.015 | -2 |
SeaWiFS | c2to20 | Logarithmic | 0.015 | -2 |
SeaWiFS | chl_ci | Logarithmic | 0.015 | -2 |
SeaWiFS | chl_oc488 | Logarithmic | 0.015 | -2 |
SeaWiFS | chl_oc5 | Logarithmic | 0.015 | -2 |
SeaWiFS | chl_oc5ci | Logarithmic | 0.015 | -2 |
SeaWiFS | chl_oc5plusflags | Logarithmic | 0.015 | -2 |
SeaWiFS | chlor_a | Logarithmic | 0.015 | -2 |
SeaWiFS | clarge | Logarithmic | 0.015 | -2 |
SeaWiFS | cocco_anom_BY | Linear | 1 | 0 |
SeaWiFS | eps_78 | Linear | 0.01 | 0 |
SeaWiFS | f02to2 | Linear | 0.003921569 | 0 |
SeaWiFS | f2 | Linear | 0.003921569 | 0 |
SeaWiFS | f20to200 | Linear | 0.003921569 | 0 |
SeaWiFS | f2to20 | Linear | 0.003921569 | 0 |
SeaWiFS | flarge | Linear | 0.003921569 | 0 |
SeaWiFS | K_490 | Logarithmic | 0.02 | -2 |
SeaWiFS | l2_flags | Linear | 1 | 0 |
SeaWiFS | nasa_chlor_a | Logarithmic | 0.015 | -2 |
SeaWiFS | nLw_412 | Linear | 0.02 | 0 |
SeaWiFS | nLw_412_F | Linear | 0.02 | 0 |
SeaWiFS | nLw_443 | Linear | 0.02 | 0 |
SeaWiFS | nLw_443_anom | Linear | 1 | 0 |
SeaWiFS | nLw_443_F | Linear | 0.02 | 0 |
SeaWiFS | nLw_490 | Linear | 0.02 | 0 |
SeaWiFS | nLw_490_F | Linear | 0.02 | 0 |
SeaWiFS | nLw_510 | Linear | 0.02 | 0 |
SeaWiFS | nLw_510_anom | Linear | 1 | 0 |
SeaWiFS | nLw_510_F | Linear | 0.02 | 0 |
SeaWiFS | nLw_555 | Linear | 0.02 | 0 |
SeaWiFS | nLw_555_anom | Linear | 1 | 0 |
SeaWiFS | nLw_555_F | Linear | 0.02 | 0 |
SeaWiFS | nLw_670 | Linear | 0.02 | 0 |
SeaWiFS | nLw_670_F | Linear | 0.02 | 0 |
SeaWiFS | Rrs_412 | Linear | 0.0002 | 0 |
SeaWiFS | Rrs_443 | Linear | 0.0002 | 0 |
SeaWiFS | Rrs_490 | Linear | 0.0002 | 0 |
SeaWiFS | Rrs_510 | Linear | 0.0002 | 0 |
SeaWiFS | Rrs_555 | Linear | 0.0002 | 0 |
SeaWiFS | Rrs_670 | Linear | 0.0002 | 0 |
SeaWiFS | tau_865 | Linear | 0.002 | 0 |
VIIRS | a_410_pml | Logarithmic | 0.01 | -1.852 |
VIIRS | a_443_pml | Logarithmic | 0.01 | -1.852 |
VIIRS | a_486_pml | Logarithmic | 0.01 | -1.852 |
VIIRS | a_551_pml | Logarithmic | 0.01 | -1.852 |
VIIRS | a_671_pml | Logarithmic | 0.01 | -1.852 |
VIIRS | adg_410_pml | Logarithmic | 0.01 | -1.852 |
VIIRS | adg_443_pml | Logarithmic | 0.01 | -1.852 |
VIIRS | adg_486_pml | Logarithmic | 0.01 | -1.852 |
VIIRS | adg_551_pml | Logarithmic | 0.01 | -1.852 |
VIIRS | adg_671_pml | Logarithmic | 0.01 | -1.852 |
VIIRS | aot_862 | Linear | 0.002 | 0 |
VIIRS | aph_410_pml | Unknown | 0.01 | -1.852 |
VIIRS | aph_443_pml | Logarithmic | 0.01 | -1.852 |
VIIRS | aph_486_pml | Logarithmic | 0.01 | -1.852 |
VIIRS | aph_551_pml | Logarithmic | 0.01 | -1.852 |
VIIRS | aph_671_pml | Logarithmic | 0.01 | -1.852 |
VIIRS | bb_410_pml | Linear | 0.0118 | -3 |
VIIRS | bb_443_pml | Logarithmic | 0.0118 | -3 |
VIIRS | bb_486_pml | Logarithmic | 0.0118 | -3 |
VIIRS | bb_551_pml | Logarithmic | 0.0118 | -3 |
VIIRS | bb_671_pml | Logarithmic | 0.0118 | -3 |
VIIRS | bbp_410_pml | Logarithmic | 0.0118 | -3 |
VIIRS | bbp_443_pml | Logarithmic | 0.0118 | -3 |
VIIRS | bbp_486_pml | Logarithmic | 0.0118 | -3 |
VIIRS | bbp_551_pml | Logarithmic | 0.0118 | -3 |
VIIRS | bbp_671_pml | Unknown | 0.0118 | -3 |
VIIRS | c02to2 | Logarithmic | 0.015 | -2 |
VIIRS | c2 | Logarithmic | 0.015 | -2 |
VIIRS | c20to200 | Logarithmic | 0.015 | -2 |
VIIRS | c2to20 | Logarithmic | 0.015 | -2 |
VIIRS | chl_ci | Logarithmic | 0.015 | -2 |
VIIRS | chl_oc488 | Logarithmic | 0.015 | -2 |
VIIRS | chl_oc5 | Logarithmic | 0.015 | -2 |
VIIRS | chl_oc5ci | Logarithmic | 0.015 | -2 |
VIIRS | chl_ocx | Logarithmic | 0.015 | -2 |
VIIRS | chlor_a | Logarithmic | 0.015 | -2 |
VIIRS | clarge | Logarithmic | 0.015 | -2 |
VIIRS | f02to2 | Linear | 0.003921569 | 0 |
VIIRS | f2 | Linear | 0.003921569 | 0 |
VIIRS | f20to200 | Linear | 0.003921569 | 0 |
VIIRS | f2to20 | Linear | 0.003921569 | 0 |
VIIRS | flarge | Linear | 0.003921569 | 0 |
VIIRS | front_step4_chlor_a | Linear | 0.001 | 0 |
VIIRS | Kd_490 | Logarithmic | 0.011176 | -2 |
VIIRS | l2_flags | Linear | 1 | 0 |
VIIRS | opp_cbpm2 | Logarithmic | 0.0079 | 2 |
VIIRS | par | Linear | 0.3048 | 0 |
VIIRS | pic | Logarithmic | 0.012192 | -4.3 |
VIIRS | pp | Logarithmic | 0.0075 | 2 |
VIIRS | Rrs_410 | Linear | 0.0002 | 0 |
VIIRS | Rrs_443 | Linear | 0.0002 | 0 |
VIIRS | Rrs_486 | Linear | 0.0002 | 0 |
VIIRS | Rrs_551 | Linear | 0.0002 | 0 |
VIIRS | Rrs_671 | Linear | 0.0002 | 0 |
VIIRS | spmi | Linear | 0.1 | 0 |
VIIRS | sst | Linear | 0.15 | -3 |
VIIRS | zeu_mb | Linear | 0.8 | 0 |
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.
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.
For converting from x/y pixel co-ordinates on a Mercator projection image to latitude/longitude (lat/lon) positions use:
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 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:
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.
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.
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:
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.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:
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:
This is a published case 2 waters chlorophyll-a estimation algorithm.
Reference:
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)
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.
Our privacy policy is detailed here.
Our copyright status is covered in the terms of use here.
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.