SLC to GRD Sentinel 1

I am working on the sentinel 1 data.
I have worked with:

  1. GRD data directly. I have calculated sigma0 and then I have geocoded it, so I have GRD sigma0 geocoded data.

  2. SLC data. I have tried to process SLC data to obtain GRD sigma0 geocoeddata and compare with the results obatined in 1). Because working wiht SLC data is time consuming and the computer crahses, I have proceeded as follows:
    a) I have split the data to work on SLC_IW3 only.
    b) I have applied the SLC to GRD algorithm from snap. Using this procedure the data are directly debursted and converted to GRD sigma 0, speckle filtered data.
    c) I have geocoded the data obtained in b).

When comparing the results obtained using the GRD data directly (procedure 1)) to the data obtained from the SLC data (process 2 a) b) c)) the results do not match. In fact using procedure 2) urban areas are
displayed with lower sigma0 than the surroundings while urban areas have a bigger sigma0 than the surroundings using procedure 1).
Have you ever used the SLC to GRD algorithm in snap? Am I doing something wrong?


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The SLC to GRD graph will produce something similar to the GRDs produces
by the IPF but not exactly the same. At the IPF they might have used
different multilook factors or different speckle filtering.

Just to clarify, the IPF does not apply any speckle filtering (just multi-looking in the case of GRD products).

Thanks for your quick replies.
My concern is that the differences observed in the GRD sigma0 geocoded data between the two procedures are too big and importat.
For example:

  1. sigma0 (VV) > sigma0 (VH) in the GRD geocoded product obtained after downloading a GRD product from the scihub and after geocoding it using SNAP.
  2. sigma(VH) > sigma0(VV) in the GRD geocoded product obtained after converting the SLC product into GRD product using the SLC to GRD procedure in SNAP.
  3. sigma0 values seem to be reversed in both cases: areas with larger sigma0 values using one procedure become in areas with the lowest sigma0 values using the other procedure.
    My question is: if I want to use a reliable GRD geocoded sigma0 products, how should I proceed:
  4. should I download a GRD product from the scihub and geocode it using SNAP?
  5. should I download a SLC product from the scihub, convert it into GRD and then geocode it using SNAP?

Thanks in advance,

It depends on what you want to do. The SLC to GRD graph is not intended to replace the GRDs. If you are working with SLCs you may want to also process the product as a detected image. This graph applies the noise removal, calibration and speckle filtering which are not done at the IPF.

Have you thought about deriving the sigma0 of your region of interest directly from the GRD product without any geocoding? This will avoid any radiometric issues related to the geocoding step (if there are any).

There is a good agreement between sigma0 derived for the same region from GRD and SLC products and so it shouldn’t matter which type of product you use. To derive sigma0 from an SLC product there shouldn’t be any need to convert to GRD first.