In this second part of the Sentinel-1 tutorial we will go through some simple steps to pre-process, georeference and export the data we downloaded using the procedures described in the previous post. To this end, we will use SNAP (Sentinels Application Platform), a free software package that you can download here.

We begin by downloading and installing the SNAP software package from the above mentioned webpage. You will have to enter your name and e-mail address, and will then receive a message in your mailbox with a link to download the executable. Choose the one that is right for your specific platform. Installation is straightforward, at least in Linux, the platform I use. Important: at the beginning of the installation check the boxes that tell the program to also install the toolbox for Sentinel-1.

Start SNAP. It is fairly rich in options, but the graphical user interface is intuitive and will be familiar to anyone who has worked with other GIS or remote sensing tools before:

SNAP (Sentinels Application Platform) menus.
SNAP (Sentinels Application Platform) menus.

In our example, we got two TIFF images of the same region. The difference between them is that they were acquired with different combinations of polarization in the transmitted and received signals (see the Sentinel-1 SAR user guide and file naming conventions). We access the data by targeting the entire ZIP file in SNAP. Once accessed, the datasets are shown in the panel in the upper left, but the images are not yet displayed. With a double click in any field we can access the metadata:

sentinel1a_012

and then, by double-clicking in the label image, we get the image displayed in the main panel (images are big files and this can be a slow process, depending on your computer):

Displaying the raw image and its ground control points.
Displaying the raw image and its ground control points.

Now we see that this image is inverted. It is displayed according to the order of data acquisition, which is not necessarily according to a cartographic representation. To bring this image to something resembling a map we can georeference it using the ground control points embedded in the metadata.

Before we can reproject, we have to correct the image with either “terrain correction” or “ellipsoid correction”. We choose this in the menu Radar -> Geometric and then choose the preferred method:

Selection of ellipsoid correction.
Selection of ellipsoid correction.

This can take a long time to complete, and will produce a .dim file. We can then proceed to do reproject using the menu: Raster and then Reprojection, after which the following menu opens:

Reprojection dialogue.
Reprojection dialogue.

In this case we will reproject this image into its corresponding UTM zone (33 North). SNAP has the interesting feature that it can automatically find the zone for you. Before continuing, check the menu I/O Parameters and select output format you desire (I chose GeoTIFF), possible the pixel size, the name for the resulting file and the folder where it will be located. We then click Run and wait (long process again, sometimes over 40 minutes in my case!).

Our new file is the added to the file panel, and looks likes this:

Reprojected image of the Bay of Naples.
Reprojected image of the Bay of Naples.

We then proceed to cut the file, to fit the same area as in our Landsat tutorial. I prefer to do this with GDAL tools from the command line:

gdal_translate -projwin 431000 4539000 471000 4489000 Sentinel-1A_IW_GRD_HR_L1_reprojected.tif vesubius_sentinel.tif
Input file size is 25855, 22363
Computed -srcwin 20623 13330 4125 5156 from projected window.
0...10...20...30...40...50...60...70...80...90...100 - done.

We can then do some kind of simple magic and add this subset with as a channel to our visible Landsat image of Vesuvius and surroundings:

gdal_merge.py -o vesuvius_landsat_sentinel_merged.tif -separate vesuvius.tif vesubius_sentinel.tif

the results look wonderful, although one has to be very careful because of the topographic distortion in the mountain slopes in the radar image:

False color composite of the Bay of Naples. Sentinel-1A (red), Landsat 8 band 4 (green) and Landsat 8 band 3 (blue).
False color composite of the Bay of Naples. Sentinel-1A (red), Landsat 8 band 4 (green) and Landsat 8 band 3 (blue).

Merging SAR and VNIR images is nevertheless done best using image fusion, an interesting and potentially useful digital image processing technique. For a simple way of doing it have a look at this other post on fusion of multisensor data using image multiplication.

SNAP includes the possibility of doing topographic correction, if a DEM is available. Something we can think for a future post. On the other hand, on relatively flat areas the details are neat and potentially very useful:

sentinel1a_017

Note however, that the Landsat and Sentinel images combined here are from slightly different times, and therefore there are some obvious changes (highlighted in red), that are particularly noticed in the harbor area.

Of course, all these processes are meant as a very simple guideline on how to use data from the new satellite Sentinel-1A. Radar remote sensing is serious business and fairly unintuitive. Users considering using these data should dedicate serious effort to learn the gory details of this technology.

Happy working with Sentinel-1A data!

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