S3TBX FAQs

What medium resolution missions are supported?

The following medium resolution missions are supported: 

Is a SMILE correction available for OLCI?

No, this is not available. A smile correction as implemented in the OLCI ground segment processor. However, for the OLCI bands 13, 14 and 15 such a standard smile correction is not possible.
But you can have a look at the O2A Harmonisation. You can find at Optical / Preprocessing / OLCI O2A Harmonisation. It tries to harmonise the O2 bands by modifying the effective transmittance in the O2A bands.

Is an atmospheric correction available for OLCI?

The level-2 data provided by ESA/Copernicus/Eumetsat is already atmospherically corrected. If you want to do the AC on your own, you currently have three options:

1. iCOR: https://remotesensing.vito.be/case/icor
   Beside Sentinel-2, Landsat 8 and Proba-V it also supports Sentinel-3 OLCI

2. C2RCC: https://www.brockmann-consult.de/portfolio/water-quality-from-space/
   The main purpose of the C2RCC processor is the retrieval of Inherent Optical Properties (IOP) but it also performs an atmospheric correction over water. If you are only interested in water pixels, then you can give it a try. It is included in the Sentinel-3 Toolbox.

3. Rayleigh Correction:
   This simplified atmospheric correction procedure is a first attempt to normalise the TOA signal for gaseous and Rayleigh effects, whilst ignoring the more complicated and variable effects of absorbing aerosols. It is available in the menu at Optical / Preprocessing / Rayleigh Correction.

What’s the difference between tie-point-based and pixel-based geo-coding for Sentinel-3 data?

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With Sentinel-3 data geo-location information is provided in two different way. As tie-points and for each pixel. For the tie-points the geo-location is only provided for every n-th (16 for RR and 64 for FR data) pixel for each line. Between the tie-points the geo-location information is interpolated. Beside this difference the geo-location information per pixel is also orthorectified. The benefit of the tie-points is that processing where the geo-location is important (eg. reprojection) is faster.

In SNAP you can switch between both in the options dialog.

Is there a cloud masking including cloud shadow for OLCI?

You can run Idepix on OLCI. Idepix is a pixel classification. available for multiple sensors. For OLCI a cloud shadow is included. You can install this additional Plugin via the ‘Plugins Window’.
After installation, you can find it in the menu at Optical / Preprocessing / Masking / Idepix.

Are C2X neural nets available for OLCI in the C2RCC processor? If not, are there plans to implement them?

The C2X nets are not available for OLCI. At the time of the C2X project, the training range for the “normal” neural nets was rather restricted to moderately turbid and absorbing coastal waters. With C2X really extreme situations were included, at the costs of good performance in clearer waters. After this exercise, the ranges used for training neural nets for C2RCC, in general, has been extended – not as extreme as in C2X but sufficiently to cover many situations in coastal and inland waters, without scrutinizing clear waters.
Thus, currently, we don’t work on a version of the C2X nets for OLCI but could consider this if there is demand in the community.

Which water-related algorithms are available and for which sensors?

How to apply system vicarious calibration gains before C2RCC computation?

In the by EUMETSAT provided and regularly updated Product Notice for OLCI Level-2 Ocean Colour data, gains for the system vicarious calibration (SVC) are published. These are actually not calculated for the C2RCC processor but can be applied. Actually new gains need to be computed specifically for the C2RCC. For more information see the EUMETSAT Website about the Ocean Colour System Vicarious Calibration Tool.
Assuming that applying these gains is better than applying no gains, the following graphs show how they can be applied with GPT. For general information about processing with GPT, please take a loo at the Processing page in this wiki.

The following graph xml files and the properties files can be used to apply gains to the data. This can either be the gains from the Product Notice or gains you have calculated yourself.

The olci_vicarious.xml graph only applies the gains and the olci_vicarious_c2rcc.xml combines the SVC and C2RCC in one processing chain.

For each band an expression is defined like the following

This expression first checks for no-data-value (feq(Oa01_radiance, 913.9812317630276, 1.0e-4)) and sets NaN to the resulting band, if so. If the source value is valid the gain is applied. The variable ${Oa01_vic} is read from the properties file.

The two following property files contain the latest (16. April 2021) gains for OLCI S3A and S3B from the Product Notice.

Each of the graph xml files contains an example how to call gpt with the XML file and the property file.

One is shown here too:

How to read Landsat L1 data as TOA reflectance?

By default, the S3TBX provides the TOA radiance of Landsat L1 data, but you can switch to TOA reflectance by specifying a property.
To do this lookup the .snap folder in your user directory. Inside this folder create a folder named etc if not already existing. And in this folder create a file named s3tbx.properties.
To this file add the following line:
s3tbx.landsat.readAs=reflectance
Afterwards the values of Landsat will be provided as TOA reflectance.

Can I use C2RCC in the Graph Builder?

The user Interface of C2RCC is not compatible with the Graph Builder. Therefor it can’t be used.
If you want to use C2RCC for batch processing you need to write your own graph XML file and use it from the command line. Please see the following guides for help.

Creating a GPF Graph Bulk Processing with GPT

You can also call gpt from the command line.

This will print the help for the OLCI C2RCC operator. At the end you will find a XML template. You can combine it with other templates from other operators. This is shown in the guides mentioned above.

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