Trying to change the NESZ of Sentinel-1 Data

Hello Everyone,

I would like to change/ deteriorate the NESZ of Sentinel-1 data to perform PSI processing on the modified (noisy) data using the SNAP/ StaMPS workflow.


I have written some Python code (using snappy) to read S1 IW-products in SAFE format, calibrate them, add noise, and save the products in BEAM-DIMAP format. However, when I load the products into SNAP to look at the Interferometric Stack Overview, I get this error message:

The error message pops up for every product in the data set.

My workflow

I have written a python script which iterates over all S1 products in a dataset, and does the following:

  1. unzip product

  2. perform calibration and save calibrated product in BEAM-DIMAP (outputImageInComplex=True)

  3. create a new product with the same bands as the calibrated product. The i- and q-bands are “real” bands, whereas the intensity band is a virtual band with the following expression:

    expression = "i_IW1_VV * i_IW1_VV + q_IW1_VV * q_IW1_VV"

    The number varies from subswath to subswath, of course.

  4. load calibrated product and add noise to all i- and q-bands.

  5. Save new product in BEAM-DIMAP.

Below is the code I use for steps 3.-5.

# Parse command line
 args = _parse_args()

 # Read source product
 source_product = su.read_product(args.source_product_path)
 source_bands = list(source_product.getBandNames())
 w = []
 h = []
 for band in source_bands:
     sb = source_product.getBand(band)

 # Create new product
 width = max(w)
 height = max(h)
 target_product = snappy.Product(

 # Copy metadata from source to target product
 snappy.ProductUtils.copyMetadata(source_product, target_product)
 snappy.ProductUtils.copyGeoCoding(source_product, target_product)

 # Set product writer
 target_dir = args.target_product_path


 # Add and configure target bands
 nodata_value = np.nan
 for band in source_bands:
     print(f"Processing band {band}...", file=sys.stdout)
     if "Intensity" not in band:
         target_band = target_product.addBand(
             band, snappy.ProductData.TYPE_FLOAT32


         source_band = source_product.getBand(band)
         w = source_band.getRasterWidth()

         var = 10 ** (args.NESZ / 10) / 2
         std = np.sqrt(var)

         print("Setting pixel values...", file=sys.stdout)
         for y in tqdm(range(height), file=sys.stdout):
             data_source = np.empty(w, dtype=np.float32)
             data_target = np.empty(width, dtype=np.float32)

                 source_band.readPixels(0, y, w, 1, data_source)

                 noise = np.random.normal(0, std, data_source.shape)
                 noise[data_source == 0] = nodata_value
                 data_target[:w] = data_source + noise
             except RuntimeError:

             target_band.setPixels(0, y, width, 1, data_target)
         iq_names = [
             f"{comp}_{band[band.find('IW'):]}" for comp in ["i", "q"]
         expression = f"{iq_names[0]} * {iq_names[0]} + {iq_names[1]} * {iq_names[1]}"  # noqa
         target_band = target_product.addBand(
             band, expression, snappy.ProductData.TYPE_FLOAT32

 print("Writing data to file...", file=sys.stdout)
 pm = su.createProgressMonitor()
 snappy.ProductIO.writeProduct(target_product, target_dir, WRITE_FORMAT, pm)

Note that I set the width and height of the target product to the corresponding values of the largest subswath in the original product. I read the snappy guide and the snappy examples, and I thought I had to set the product width and height to

width = source_product.getSceneRasterWidth() # often the target product dimensions are the same as the source product dimensions
height = source_product.getSceneRasterHeight()

If I do that, however, each subswath has the dimensions of the whole product, and the target product will be 9-10 times larger (in terms of storage) than the original product.

Also, I had to restructure the individual steps into subprocesses, as otherwise I would run into memory issues. Shoutout to @Ciaran_Evans for his solution. I changed to a bigger machine now (128GB RAM, 24CPUs instead of 32GB, 8CPUs), but I haven’t checked whether the procedure would work in a single process now. Anyway, this is a side note and I don’t think the end result would depend on whether I use subprocesses or not.

When I run the steps explained above, I end up with a dataset that can actually be opened in snap, and the NESZ has changed to the desired value. I can also add the products to the Interferometric Stack Overview menu by pressing “Add Opened”. But when I click “Overview” I get the error message shown in the Summary above.

I thought the error could be caused by the BEAM-DIMAP format (because until now I have only fed SAFE data to the SNAP/ StaMPS workflow). Thus, I tried to get the Interferometric Stack Overview of the calibrated data set, which is also saved in BEAM-DIMAP, to see if I get the same error: I don’t. Instead, the Interferometric Stack Overview menu does not even appear under Radar -> Interferometric, where it is usually located.


In essence, I would like to have a Sentinel-1 data set, which is completely identical to the one I downloaded from Copernicus, except for the NESZ/ noise level. I want to be able to use the modified dataset in exactly the same way as the original one.

Does anyone have any idea if and how I could do that? Is my current approach close, or should I do something completely different? I was thinking to not use snappy at all, and work directly on the geotiffs in the SAFE directory. I wasn’t sure how to implement the calibration though, so I thought using snappy would make things easier for me.

Also, this is my first post in the step forum, so I hope my post conforms with the guidelines and requirements. If not, I’d appreciate feedback :-).


  • SNAP Version 8.0

  • Debian machine with 128GB RAM