Stacking S1 and S2 data

[MCG]

Hi,

I would like to bring back a topic that has been addressed a while ago in this forum but I find that no clear answer is available yet. I have a dataset of S1 and S2 images:

• For S2, I am re-sampling and subsetting to my ROI
• For S1, I am pre-processing the IW GRD products in SNAP using the following chain: apply orbit file, calibration, speckle filter, terrain correction (including UTM projection) and subset (to the same ROI as before).

My doubt is about stacking both datasets after that. Based on some previous discussion on this forum, I have seen that some people have tried the corregistration tool available for SAR.

Image Fusion Using Sentinel 1 and Sentinel 2

hello, i want to combine sentinel 1 and sentinel 2 image for land cover with some transformation of image fusion methods. I found a problem when i want to continue my research, I use Sentinel 1 GRD product, with IW sensor and have done pre-processing steps like calibration, multilook, speckle filtering, and terrain correction, then I combine the results of pre prosessing data sentinel 1 with sentinel 2 which has done semi-automatic clasification process using qgis , I combine these two data u…

But based on those replies:

Image Fusion Using Sentinel 1 and Sentinel 2

IMO one should terrain correct the SAR images and reproject the optical images into the map projection of choice, and then perform stacking. Co-registration between SAR and optical cannot work very well since the geometry of the images is not the same (even though it is similar if the the area of interest is relatively flat).

Image Fusion Using Sentinel 1 and Sentinel 2

This is the initial offset selection in the CreateStack. There isn’t orbit information in the Sentinel-2 product so you’ll need to use Geolocation. Follow the advice above by @mengdahl and terrain correct the SAR and use the same map projection with the optical then just use CreateStack with Geolocation for the initial offset and bilinear resampling.

DEM assisted Coregistration S1 + S2

Terrain-correct the SAR image into the same map-geometry as the optical image and see how well it lines up with the SAR image. In theory you should get sub-pixel accuracy even without co-registration.

However, based on this post

DEM assisted Coregistration S1 + S2

Hi guys, I want to perform a coregistration between S1 and S2 using the DEM assisted coregistration in the new SNAP version (coregister S1 to S2). As an experiment I want to run a coregistration between one S2 image (just RGB channels, 10m) and one S1 image (VV). My S2 image is in geotiff format my S1 image is GRD, precise orbits applied, no terrain correction. Once I want to run the process I get an error message: [image] Do you have an idea for a solution/work around? My first thought is…

It seems in that case there was an offset of around 40 m when doing the terrain correction to S1 images and then stacking with Sentinel-2

So, what should be the procedure or at least the one leading to a higher accuracy? How can we check the accuracy (visualizing the overlapping images, adding some control points with known coordinates, etc.)

Once S1 images are terrain corrected, re-projected in UTM (as S2) and stacked together with S2, I guess the geo-location accuracy of overlapping S1/S2 pixels in the stack is defined by the orthorectification algorithm.

Which level/order of accuracy can we expect in terms of pixel alignment? (sub-pixel?)

Thanks for leaving your comment!

M

[mengdahl]

Done correctly the accuracy is sub-pixel. Use collocate after the S-1 image has been terrain corrected.

[MCG]

@mengdahl, what do you mean by done correctly?

[mengdahl]

@MCG

@mengdahl, what do you mean by done correctly?

Terrain-correct the SAR image into the same map-geometry and pixel size as the optical image , then collocate.

[MCG]

Which component of the terrain correction/map projection is leading to such high accuracy without co-registering both images?

[ABraun]

accuracy of terrain correction depends on the quality of the DEM and probably also the the quality of the SAR image’s metadata.

[mengdahl]

SAR has inherently very high geometric accuracy (centimeter-level) provided that the auxiliary data is of high quality (like it is for S-1 for example).

[eyeinsky]

Achieving centimeter accuracy presumes that one perform additional corrections for finer perturbations like atmospheric path delay, frame shift, and solid Earth tides. AFAIK, these are not included in the SNAP s1tbx processing.

[ABraun]

The GeFolki Coregistration is now availavle in SNAP and allows coregistration of data of various sources (optical, radar, lidar):

GeFolki coregistration

I wanted to test the GeFolki coregistration and had a closer look at it. According to the homepage it is able to coregister two SAR products but also optical data with SAR data. Homepage Manual GitHub Two references: SAR/SAR coregistration (interferometry, change detection,…): Plyer, A., et al. (2015). A New Coregistration Algorithm for Recent Applications on Urban SAR Images. Geoscience and Remote Sensing Letters, IEEE, 12(11), 2198-2202. Other cases (optics/SAR, optics/hyperspectral…

[hriston_bg]

Dear ABraun, did you succeed with GeFolki? Could you share experience?

Thank you.

[ABraun]

I did not pursue this further yet, sorry.

[mengdahl]

Co-location of S2 and terrain corrected S1 should be straightforward - there should be no need to use GeFolki in the standard case.

[FedericoF]

An alternative way to snap Sentinel-1 GRDH pixel grid to Sentinel-2 pixel grid is to set the ‘standardGridOrigin’ of the output Sentinel-1 product when applying the Range Doppler Terrain Correction.
This could be done by setting <standardGridOriginX> and <standardGridOriginY> parameters in the Range Doppler Terrain Correction operator.

I believe this solution could reduce as less as possible the introduction of artifacts. Later, a stacking operator should be used in order to stack together (multitemporal) Sentinel-1 and Sentinel-2 data.

I have shared a GPF graph that is preprocessing Sentinel-1 GRDH data taking into account this solution to snap output images to Sentinel-2 grids:

Sentinel-1 GRD preprocessing graph

Standard workflow for the preprocessing of Sentinel-1 GRDH data. The GPF graph executes a Sentinel-1 GRDH preprocessing workflow that consists of seven processing steps, applying a series of standard corrections: Apply Orbit File Thermal Noise Removal Border Noise Removal Calibration Speckle filtering (optional) Range Doppler Terrain Correction Conversion to dB Sentinel-1 GRD products can be spatially coregistered to Sentinel-2 MSI data grids, in order to promote the use of satellite virtual …

Federico

[shima.gh]

I have problem with stacking GRD data. I get the error : "please add source product ", I include the graph and the error pictures, do you have nay suggestions? I already processed and train-corrected the GRD data.

[ABraun]

Is there a reason why you do this in a graph? In your screenshot there is just Read-Stack-Write, so this could be easier done (and probably avoiding the error) with the stack operator alone.

[shima.gh]

I want to automatize the process later in a bash file thats the reson I want to do it in a garph. I am not sure if it is possible in SNAP

[ABraun]

ah, okay. I don’t know that actually but it should be something like this: How to Use gpt for the step CreatStack

About the error: Does it go away if you switch to the second or third tab and then back? I had this every now and then (basically the graph just had to update all parameters).