Hello @bvs,

Just to make sure I understand correctly: you are querying Sentinel-1 data over an area of interest using EoLearn (area is split into patches). For each patch, you retrieve multiple Sentinel-1 scenes over the time-period you have chosen. Looking at your code, you would like to average the acquisitions so that you end up with a single image for the time-period in each patch.

If my understanding is correct, then your script is heading in the right direction. The only part you should change is the following:

mean = MapFeatureTask((FeatureType.DATA, 'IW_VH'),  # input features
                      (FeatureType.DATA_TIMELESS, 'm1'),  # output feature
                      np.mean,                    # a function to apply to each feature
                      axis=0)  

Using DATA_TIMELESS enables you to create a single band. Then you can export the band you created by changing the task slightly:

export_tiff = ExportToTiff(feature=(FeatureType.DATA_TIMELESS, 'm1'),
                     #folder="img_tiff",
                     crs=CRS.UTM_35N)

Here you save the band you created: m1 (not the input DATA bands).

Does this help?

Maxim

PS: There are a couple of typos in your example (inconsistent naming of variables): i.e. s1_vv_des & s1_vh_des or maximum & mean. They might just be in your example in the forum post, but I thought I would mention it because these are things that can cause problems in scripts (speaking out of experience here).

PPS: Is there a reason to limit the data search to orbit="descending" only? I found a lot more images with orbit="both".

Hello @maxim.lamare

You understood correctly. I noticed that downloading sentinel-1 data using Bash process API could be problematic since each tile can have different acquisition, and it is not easy to merge the images, so I decided to use eo-learn Python package. However, it was required to find a way to aggregate the acquisition in each patch. Is there a better way to deal with it using Bash process API?

What you suggested can solve my problem. Thanks for it 🙂

I copied the code from first draft so it is why you can see some typos. Regarding your question, since we use orbit orientation separately, so I only used the specific one in this example.

As I mentioned, if you think there could be better strategy please let me know!

All the best,
Behzad

Glad that helped!

Your approach with eo-learn looks fine to me, I think you would only start considering batch if you wanted to process huge areas.