Monthly Archives: November 2016

How to Export SAR Images with Geocoding in ESA SNAP

I’ve been using ESA Sentinel-1 Toolbox (S1TBX) and SNAP (Sentinel Applications Platform) since a long time for SAR processing, since back when it was known as NEST. One issue that I’ve faced is that when any SAR intensity data is exported from SNAP into some other format, e.g. ENVI or GeoTIFF format, the coordinates are not exported. I found the solution on ESA Sentinel Toolbox Exploitation Platform (STEP) Forum, and want to share it with others too.

What’s actually going on is that S1TBX, being a focused toolset for SAR, automatically interprets the geocoding information of the SAR metadata, while the data itself is mostly not projected. However, SNAP does not carry forward this geocoding interpretation to the export function. Therefore, we need to tell SNAP to attach / imbed the geocoding information before exporting.

I’ve worked with Envisat ASAR and ALOS-1 / 2 PALSAR intensity data, and here are the solutions for both (thanks to ESA STEP forum):

  • For Envisat ASAR intensity Image Mode IMG / IMP .N1 data, the simple way to make sure coordinates are exported is to apply the Radar > Ellipsoid Correction > Gelocation-Grid function. Export the output in ENVI or GeoTiff format, and opening it in any external software will now show the data with coordinates.
  • For ALOS-1 / 2 PALSAR Level 1.5 CEOS data, things are a bit more complicated. For some reason, S1TBX / SNAP do not give full support for this data. The Geolocation-Grid function does not work, as it says “Source product should not be map projected.” Attempts to use the Radar > Geometric > Update Geo Reference function instead, which requires DEM to be provided, works in terms of generating the exported output, but the coordinates are not carried over. The Raster > Geometric Operations > Reproduction tool, but that doesn’t work either. The “trick” to solve this issue was discovered in an ESA STEP forum post: Use Radar > Geometric > SAR-Mosaic with only 1 image as input; the output will be geolocated correctly.

I think the geolocation issue for intensity images from other SAR sensors like TerraSAR-X and Radarsat would be quite the same. Once again, thanks to the ESA STEP Forum and its members for providing the solution.

For details and background discussion, see these ESA STEP Forum posts:

http://forum.step.esa.int/t/coordinates-disappear-when-exporting-sar-data/3788

http://forum.step.esa.int/t/alos-1-l1-5-terrain-correction/2927

Using Synthetic Aperture Radar (SAR) Imagery to Look Beneath Dry Soil Surfaces

One of the unique characteristics of Synthetic Aperture Radar (SAR) satellite remote sensing is that at smaller frequencies, the SAR signal can penetrate sand under dry conditions. The electromagnetic (EM) wave penetration in soil depends upon a parameter called the “relative permittivity”, which is actually a “complex” quantity, with real and imaginary parts. The real part is called “dielectric constant” and the imaginary part is called “loss factor.” The study of penetration of EM waves in materials is based on some mathematics and physics, which we will not discuss here (relax!). These theoretical foundations are mostly covered in any undergrad / grad course or relevant book on EM waves.

Anyway, to cut a long story short, scientists define the “penetration depth” of EM wave in any material as (beware, scientific jargon coming up): “the distance at which the power density of the electromagnetic wave drops to 1/e of its value at the immediate sub-surface.” Here, e is the base of the natural logarithm, with a value of 2.72, and therefore 1/e has a value of 0.37. So, in more layman terms, we can think of penetration depth as follows: If the incoming EM wave has a power density of 1 units at the surface, then the depth at which it is reduced to 0.37 units, is the penetration depth.

Under certain approximations, such as uniform material properties with depth, the penetration depth d can be defined mathematically as:

penetrationdeptheqn

This equation is very interesting; a quick analysis shows us the following:

  • Larger wavelength (smaller frequency) means more EM wave penetration
  • EM wave penetration increases as dielectric constant dielectric constant increases
  • EM wave penetration decreases as loss factor increases

So, to penetrate any material, the frequency should be small, the dielectric constant should be large, and the loss factor should be small. As moisture content in an object increases, the loss factor generally increases. Therefore, penetration depth decreases with increase in moisture content: More water molecules cause more EM wave observation at the microwave frequencies. Incidentally, this is the same principle on which the microwave oven works.

Summarizing the above passage in the context of soil surfaces, we can now state: Low-frequency SAR signals can penetrate in dry soil. In the case of very dry and arid regions, e.g. Sahara desert, low-frequency SAR signals can penetrate sand down to a depth of a few meters. In the figure below is a simulation of penetration depth with respect to volumetric moisture content in sand, at L-band frequencies.

lbandsarpenetration_richards2005

Simulation for SAR penetration depth in sand as a function of moisture content, at L-band wavelength of 23.5 cm. Taken from Richards (2009) – Remote Sensing with Imaging Radar.

I hope this blog post serves as a good introduction to the material penetration properties of SAR, which works for not only soil, but also for forests and snow / ice studies, among others.

In my next post, I will describe a research study we have conducted to detect a paleochannel in the Cholistan Desert in Pakistan using both SAR and optical remote sensing data.