Speckle Filtering (Single Product) Error

I’m newbie. I use SNAP 7.0. I have just tried to run Speckle Filtering (Single Product) on a subset image, got Error: The specified region, if not null, must intersect with the image`s bounds.

Need your help, thank you.

Please describe what kind of product you use (sensor, acquisition mode, product level) and how you imported it into SNAP.

Hi ABroun,

The data product that I used was a Sentinel-1A, GRD, IW, Decending image. I used Graph to pre-process that image. The steps order were Input > Apply-Orbit-File > ThermalNoiseRemovel > Callibration > Terrain-Correction > Subset (I interested only on VH polarization and a certain region/smaller area) > Speckle-Filtering > Write.

Actually I followed ‘RUS Webinar: Rice detection with Sentinel-1 using SNAP GPT - LAND10’ that posted on Youtube. This webinar used time-series data. In order to better reducing speckles, they used ‘Multi-Temporal-Speckle-Filter’. Since I only used one data, I used ‘Speckle-Filter’ instead. I run the graph, but ended up with ‘error’ that I mentioned before.

Probably I did wrong process or something left. Need your Idea and help, thank you

did you have a look at the image after it was processed by the graph? Does it contain data and does it look alright?
Maybe you can share a screenshot of the image in here (copy and paste)

Dear ABraun,

I’m sorry for belated replay. I did not see the image after the graph was executed, but I got the result in windows explorer, the size capacity looks too small. I attached both screenshot graph and result here.

Executed graph and its notification

Results in windows explorer

Anyway, I’ve tried another way following a recent paper ‘Sentinel-1 GRD Preprocessing Workflow’ (https://doi.org/10.3390/ECRS-3-06201), it worked fine. My question then, why RUS proposed the steps in their weibinar (I mentioned before) that I face the problem above?.

Thank you

I can’t tell, sorry. At best you ask this here: https://rus-copernicus.eu/forum/

Good to hear that you found a solution.

Thank you ABraun