Help / Frequently Asked Questions (FAQ)

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.

Who can access restricted products?

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.

Has my password stopped working?

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.

How can I access SeaWiFS data?

Only SeaWiFS Authorised Research Users are permitted to access SeaWiFS ocean colour data. To become an authorised user you must apply to NASA, which takes about two weeks to be processed. After that please inform us by e-mail, then we will reply within two days to confirm that you have been initially granted access to view SeaWiFS quick-look images. For access to any additional products you must demonstrate that you are in one of the categories listed above. If you are not in any of these categories but require free data, you may order standard products directly from the NASA SeaWiFS web site. All data will be subject to a 14-day embargo, unless specifically approved by NASA for near-real time research usage.

What datasets are available?

Which sensors are available?

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).

What images are available?

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.

What products are available from different satellite sensors?

AVHRR

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. Sea-surface temperature (SST) images represent temperatures in the range 5.0 to 30.5 °C. 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. SST values can be derived from the image using the formula:

SST = pixel value * 0.1 + 5.0

Weekly and monthly SST composite images are available for the following areas: Bay of Biscay, Iberian Peninsula, Celtic Sea, Irish Shelf, Galicia, and also a large area covering most of NW Europe. 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

SeaWiFS

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

We have an example data set to explain these products. 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.

MODIS

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)

MERIS

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:

Why are there large areas of black (no-data) in these images?

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).

Why are there missing data for a specific date (YYYY-MM-DD)?

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.

What are the image filename formats?

AVHRR and SeaWiFS filenames

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.

MODIS and MERIS filenames

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
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

What systems do you use to process satellite images?

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.

What datum and ellipsoid do you use to map the data?

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:

What is the composite browser directory structure?

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

What are the composite image filename formats?

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:

Which products are available in MultiView for each sensor?

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 (Log₁₀(g m^-3))
MERIS	TOA vegetation	Top of atmosphere vegetation indices (dimensionless).

Table 2: Available products for each sensor.

Which model products are available in MultiView?

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.

Java Image Viewer:

Why does nothing appear when I click on an image?

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)

How do I use the image viewer?

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.

What do I need to run the image viewer?

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.

How can I determine which version of Java is installed?

The following applet will give you information about your version of Java.

Issues with Microsoft 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.

Issues with Apple Java

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).

How do I get Java?

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.

Why can't I access TIFF images?

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?

How do I convert digital numbers to real-world values?

Real-world values (e.g. chlorophyll concentration) can be extracted from the imagery by downloading the black and white GIFS 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) + log₁₀(intercept)]}

DN is the Digital Number within the 8bit or GIF file, which will be in the range 0 to 255. We use 0 (black) for pixels with no data and 255 (white) for annotation. Please use the 'nasa_chlor_a' product (rather than 'chlor_a') for extracting chlorophyll values; an 8bit version is provided for this purpose.

How do I convert real-world values to digital numbers?

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.

Where can I find the slope and intercept values of an image?

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.

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	cmed	Linear	0.1	-3
AVHRR	csstd	Linear	0.1	-10
AVHRR	csstp	Linear	0.1	-3
AVHRR	csstpfin	Linear	0.1	-3
AVHRR	med	Linear	0.1	5
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	sstp	Linear	0.1	5
AVHRR	sstpfin	Linear	0.1	5
AVHRR	sstpmsk	Linear	0.1	5
BINARY	Dpco2	Linear	1.176	-150
BINARY	salinity	Linear	0.078	20
MERIS	a_412_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	aero_opt_thick	Linear	0.015	0
MERIS	algal_1	Logarithmic	0.015	-2
MERIS	algal_1_mean	Logarithmic	0.015	-2
MERIS	algal_2	Logarithmic	0.015	-2
MERIS	aot_865	Linear	0.002	0
MERIS	aph_412_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	bb_412_pml	Logarithmic	0.01	-1.852
MERIS	bb_443_pml	Logarithmic	0.01	-1.852
MERIS	bb_490_pml	Logarithmic	0.01	-1.852
MERIS	bb_510_pml	Logarithmic	0.01	-1.852
MERIS	bb_555_pml	Logarithmic	0.01	-1.852
MERIS	bb_560	Logarithmic	0.01	-1.852
MERIS	boa_veg	Linear	0.0168	0
MERIS	chlor_a	Logarithmic	0.015	-2
MERIS	chlor_a_2	Logarithmic	0.015	-2
MERIS	cloud_albedo	Linear	0.004	0
MERIS	cloud_top_press	Linear	3.92	0
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_RGB	Linear	0.02	0
MERIS	photosyn_rad_mean	Linear	7.843	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_620	Linear	0.0002	0
MERIS	Rrs_665	Linear	0.0002	0
MERIS	Rrs_681	Linear	0.0002	0
MERIS	toa_veg	Linear	0.004	0
MERIS	total_susp	Logarithmic	0.01	-2
MERIS	yellow_subs	Logarithmic	0.00784314	-3
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_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	a_551_pml	Logarithmic	0.01	-1.852
MODIS	adg_443_pml	Logarithmic	0.01	-1.852
MODIS	adg_547_pml	Logarithmic	0.01	-1.852
MODIS	adg_551_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_443_pml	Logarithmic	0.01	-1.852
MODIS	aph_547_pml	Logarithmic	0.01	-1.852
MODIS	aph_551_pml	Logarithmic	0.01	-1.852
MODIS	bb_547_pml	Logarithmic	0.01	-2.85
MODIS	bb_551_pml	Logarithmic	0.01	-2.85
MODIS	bricaud	Logarithmic	0.015	-2
MODIS	calcite	Logarithmic	0.012192	-4.3
MODIS	chl-a	Logarithmic	0.015	-2
MODIS	chl_oc2	Logarithmic	0.015	-2
MODIS	chl_oc3	Logarithmic	0.015	-2
MODIS	chl_oc5	Logarithmic	0.015	-2
MODIS	chl_oc5plusflags	Logarithmic	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	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	front_step2_sst	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	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_RGB	Linear	0.02	0
MODIS	oc_l2_flags	Linear	1	0
MODIS	p90_oc5	Logarithmic	0.015	-2
MODIS	p90_oc5_comp	Linear	1	0
MODIS	par	Linear	0.3048	0
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	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	vvis531	Linear	0.2	0
NETCDF	aatsr_sst	Linear	0.15	-3
NETCDF	C_mean_ss	Linear	0.000392	0
NETCDF	Dpco2	Linear	1.176	-150
NETCDF	flux_N00	Linear	0.0235	-2
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	salinity	Linear	0.078	20
NETCDF	sig_wv_ht	Linear	0.039	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	chl_oc5	Logarithmic	0.015	-2
SeaWiFS	chl_oc5plusflags	Logarithmic	0.015	-2
SeaWiFS	chlor_a	Logarithmic	0.015	-2
SeaWiFS	eps_78	Linear	0.01	0
SeaWiFS	K_490	Logarithmic	0.02	-2
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_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_F	Linear	0.02	0
SeaWiFS	nLw_555	Linear	0.02	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	tau_865	Linear	0.002	0

How do I convert latitude/longitude to image coordinates?

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.

How do I convert image coordinates to latitude/longitude?

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.

Specific algorithms:

MODIS NDVI/EVI

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:

MERIS TOA/BOA vegetation index

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:

MODIS chlorophyll OC5

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.

MODIS 90th percentile of chlorophyll OC5

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.

MERIS Inherent Optical Properties

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:

How to acknowledge the use of our data

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 (info -at- neodaas.ac.uk) with the details. The service relies on users' publications as one measure of success.

What is your privacy policy?

Our privacy policy is detailed here.

Who holds the copyright on NEODAAS data?

Our copyright status is covered in the terms of use here.

References

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.