Getting oil spill bit mask in snap from SLC product

i made the histo plot , but i could not able to interpret it

Then i have constructed the profile plot with sigma0 intensity as suggested.

i could able to get some signatures. i.e the values of intensity (<=0.004). i am taking this as a threshold value.
May i assume the pixels, which have this value, as oil ? can i refine this further ?

you can either test if some filters improve the spectral plot in terms of a better separability or use the oil spill module directy on the image

could you please give me the index of sigma0 values for Heavy fuel oils, so that i can do better seperation from the look alikes.
An index will definitely support, this seperation.

maybe you find something helpful here, especially chapter 11

Thank you.

1.i have imported an ESRI shapefile into SNAP. By default, it is appearing as white. How i can change the colour of it ?

2. How i can change the colour of the drawn geometry?
3. How can i export the sigma0_vv in netcdf format? as i need to merge into an existing netcdf files .

using the mask manager

File > Export > lists several netcdf formats

in the mask manager, i could able to change the colour of the particles, but they look tiny. How can i increase the size of them?

In the layer manager, the particles look bigger, but i could not able to change the colour of them.

I need to add band math for getting sigma0 from amplitude, could you please suggest an equation ?
where can i get the digital number ?
how can i compute the amplitude ? however it is displayed in pixel info

can i square the amplitudes for geting intensity?
How our SNAP converts the amplitudes into Sigma0_vv intensity?
By the way, The intensities of bit mask show higher range (500 to 1000) but the sigma0_vv intensities are in lesser range (0.004 to 1.5) why so ?
generally sigma0 is a dimensionless number, then why it is measured in db?
Please suggest me on the above .

on DN, amplitude and calibration, please see here:

on the sense of data in db scale, please see here:

Thank you ABraun.
whether the calibration carried out in oil spill detection belongs to Distributed or point target ?

while doing the radiometric calibration of GRD products , will the radiometric calibration be done for distributed or point targets ?

while doing the oil spill detection, the sigma0 is computed from the intensity values for Sigma0_vv in intensity. But amplitude values are taken for computing Sigma0_vv in bil mask. Why so?
Hope intensity = amplitude^2

Yes, intensity = amplitude^2

what was the value of the window size you ve used? 61 the default, 512 like someone suggests or a different one? what is the logic regarding how to set a proper window size?

can’t remember, sorry. Probably the default value.

regarding the logic, please have a look at the documentation. The pixel size times the window size is the extent of your background window in meters. It should be large enough to cover single oil spills.

Adaptive Threshold Algorithm
The dark spots are detected using an adaptive thresholding method.

  1. First the local mean backscatter level is estimated using pixels in a large window.
  2. Then the detecting threshold is set k decibel below the estimated local mean backscatter level. Pixels within the window with values lower than the threshold are detected as dark spot. k is a user selected parameter (see parameter Threshold Shift below).
  3. Shift the window to next window position and repeat step 1 and 2.


  1. First the contiguous detected pixels are clustered into a single cluster.
  2. Then clusters with their sizes smaller than user selected Minimum Cluster Size are eliminated.

Visualize Detected Oil Spill
The oil spill detection bit mask is output as a separated band. To view the oil spill detection results, the following steps should be followed:

  1. Bring up the image.
  2. Go to Layer Manager and add the oil spill bit mask band as a layer.

Parameters Used
For dark spot detection, the following parameters are used (see figure 1):

  • Source Bands: All bands (real or virtual) of the source product. User can select one or more bands for producing multi-looked images. If no bands are selected, then by default all bands are selected.
  • Background Window Size: The window size in pixels for computing local mean backscatter level.
  • Threshold Shift (dB): The detecting threshold is lower than the local mean backscatter level by this amount.

Hello ABraun,

While calibrating the GRDH products of sentinel (S1A_IW_GRDH_1SDV_20170129T003132_20170129T003157_015039_01892E_6D04.SAFE) , the sigma0 is not saved in dB, rather it shows only the sigma0band. Why it is not saved in dB? whereas other envisat products are being saved in dB. COULD YOU PLEASE SUGGEST ?

I also noticed that the conversion to dB is not included in the Sentinel-1 calibration. I don’t know why this is the case but you can easily do this afterwards by right-clicking the Sigma0 > convert to dB.

Hello sjpsentinel1!
Thank you for raising this topic! I’m wondering if you know why the adaptive threshold algorithm is picking up square shaped areas around the vessels? I had a similar looking product when I ran my algorithm. Do you know how to interpret this?
Would appreciate your help
many thanks