Tag Archives: SAR

Suspected Sep. 2017 Oil Spill in Clifton, Karachi: A Follow-up Analysis with SAR Images

On the third day of Eid-ul-Azha, September 4, 2017, beachgoers in Karachi reported oil or oil-like substance washing ashore on the Clifton Beach. Geo News reported the incident here.

As a researcher in the field of radar remote sensing, it got me thinking whether we can spot it on satellite images, if incidentally acquired by a space-borne Synthetic Aperture Radar (SAR) sensor. Interestingly, I found some acquisitions acquired by the European Space Agency (ESA)’s Sentinel-1A sensor. Unfortunately there was no acquisition on the 4th of September. The closest acquisition before the suspected spill is on 01.09.2017 @ 01:26, and the latest is on 10.09.2017 @ 13:35. The good news is that the latest image shows no sign of ‘low brightness’ characteristic of oil slicks. However, in the image on 01.09, we do see some dark areas which are somewhat troubling.


Referring to the figure below, the dark areas immediately below the Clifton area made me nervous — if that is oil spill traveling towards the shoreline, it’s huge! But it’s probably not, since it’s just too huge to have gotten ignored! It’s likely a ‘look-alike’ [1], which may appear in the radar image indicating local calmness of the water. However, I’m no expert in oceanography, so I don’t make any claim about it. Nonetheless, it does cause to raise an eyebrow.


Sentinel-1 C-Band SAR images, projected in map coordinates, and overlaid in Google Earth. No clear evidence of oil slick close to Clifton Beach. Two patches of probable oil slick detected on 01.09.2017, 15-30 km southwards of the beach.

At the same time, there are two instances (marked in red) which do seem to be oil spills, perhaps in the wake of the very same vessels passing nearby. In each case, it extends more than 6 km. Since the image is now 12 days old, and we don’t observe the suspected spill in the latest image — it may have dispersed by now — the main lesson is that the “authorities should keep a closer look” in future!


A close-up of the suspected oil spill marked in red in the figure above.

I am open to feedback/comments from other fellow scientists/experts in the field of SAR/Remote-Sensing/Oceanography, especially if they fear I may have missed something.

Disclaimer: This is an analysis performed from “remote” sensing images. Authorities must confirm or reject the suspicions on the basis of local forensic evaluation.

About this post: This is a guest post by M. Adnan Siddique.


Aperture Synthesis and Azimuth Resolution in Synthetic Aperture Radar – Lecture Notes

Teaching the fundamentals of Synthetic Aperture Radar (SAR) system design and imaging mechanism to remote sensing students / professionals is always a difficult task. Remote sensing students / professionals generally do not have an in-depth background of signal processing and radar system design, and as an instructor, I always have to think over how much I need to tell them about SAR system design, without diving into the detailed mathematics of signal processing and imaging mechanism. Normally, I go in-depth towards the imaging geometry and an understanding of the Doppler history curve, and briefly go over the signal-processing heavy concepts like pulse compression and matched filtering. A good fundamental understanding of the SAR system design, imaging geometry, and image formation is essential for remote sensing students / professionals to have a background context knowledge when they select SAR data and process / analyze it for different remote sensing applications.

For the past few years, I have been teaching a graduate course in Radar Remote Sensing and also run an annual Summer School on Earth Remote Sensing with SAR at our research group GREL. One of the core issues in understanding the aperture synthesis process is the requirement for enhancement of the azimuth / along-track resolution. It is always interesting to discuss in class how in normal imaging radar the azimuth resolution depends inversely on the antenna along-track length, while in fully-focussed SAR the azimuth resolution becomes half of the antenna along-track length. This is a significant reversal: In normal imaging radar, we need a bigger antenna in along-track dimension to get better azimuth resolution, while in SAR, the smaller the antenna in the along-track dimension, the better the azimuth resolution.


To explain how aperture synthesis changes the azimuth resolution to half of the along-track antenna length, I have made some detailed notes for my ongoing graduate class on Radar Remote Sensing. These notes require just basic knowledge of geometry, algebra, and sum series in mathematics. I would like to share them with the wider scientific audience, please access the PDF notes here: Aperture Synthesis and Azimuth Resolution.


The synthetic aperture length is defined in the figure above. The azimuth resolution in fully-focussed SAR becomes half of the antenna along-track dimension.

I have taken the help of two excellent resources on SAR remote sensing in developing these notes:

For more in-depth understanding and analysis of how SAR is used for remote sensing, you can consider attending the next Summer School on Earth Remote Sensing with SAR, which I will be offering this summer. The summer school is coming up in July, 2018, and it will be open for international participants; formal dates will be announced soon. Keep watching the GREL website for updates.

Summary: ISNET / NARSS Workshop on SAR Remote Sensing, 27th Nov. – 1st Dec., 2016

The Inter-Islamic Network on Space Sciences & Technology (ISNET), in collaboration with National Authority for Remote Sensing & Space Sciences (NARSS), held a 5-day Workshop on “Earth Remote Sensing with Synthetic Aperture Radar (SAR)” from 27 November – 1st Dec 2016 at NARSS premises, Cairo, Egypt. This workshop was supported by the OIC Ministerial Standing Committee for Scientific and Technological Cooperation (COMSTECH) and the Islamic Development Bank (IDB).

Teaching complex numbers NARSS SAR workshop

Reviewing complex numbers, which form the basis of SAR imaging.

The initial part of the workshop comprised of seminar and research presentations on SAR remote sensing applications. This was followed by 2.5 days of extensive tutorial modules on SAR fundamentals, and hands-on training workshop sessions on different softwares and tools that are required for SAR remote sensing applications. The tutorial and workshop sessions were led by me, and I was honoured to be invited by ISNET and NARSS to conduct these sessions.

Group picture NARSS SAR workshop

Participants of the hands-on training workshop sessions.

The hands-on workshop modules were conducted with actual SAR remote sensing imagery to give experience to participants on processing and analysis of SAR data. Open-source software tools specifically made for SAR data processing, such as ESA Sentinel Applications Platform (SNAP) and ASF MapReady, were utilized for this workshop to ensure large no. of participants and to make the hands-on workshop modules accessible to all participants. The hands-on modules covered topics like identifying errors in SAR imagery (topographic, radiometric, geometric), data pre-processing, SAR sub-surface imaging and SAR-optical data fusion, interpreting SAR data over the ocean, understanding complex SAR data, and basics of interferometry.

Overall, more than 60 participants took part in the training workshop. Although I teach a graduate course on Radar Remote Sensing and also conduct a SAR Remote Sensing summer school since the last 2 years at our research group, yet this was a first experience for me to conduct a international SAR workshop. I got great feedback, and more motivation to continue forward on my SAR journey.


Ocean Eddies & Slicks in SAR Imagery

In a recent post, I talked about observing an eddy in the Arabian Sea in L-band ALOS PALSAR SAR imagery. In this post, I want to talk briefly about the physical interaction between SAR signals and eddies.

Spiral eddies are often convergence zones and act as accumulators of surface slicks. These surface slicks (could be biogenic / natural oil seeps / mineral oil etc.) make a surface layer over the ocean and actually dampen the surface waves of the ocean through a phenomenon called Marangoni Damping (see this seminal paper by Alpers and Hühnerfuss).

However, sometimes it is also possible that an eddy may appear brighter in SAR imagery than the surrounding ocean, due to wave-current and shear interactions.

In my paper on ocean currents from sequential SAR imagery, I talk about this phenomenon in the introduction, and you can also find some good references therein.

For further interest, here are a few other seminal papers on the science of ocean wave damping by surface slicks:

Discovering Submesoscale Eddies in the Arabian Sea through SAR Images

I am working on a research study to analyze physical oceanography features in the Arabian Sea using Synthetic Aperture Radar (SAR) remote sensing imagery. For this study, we are using L-band ALOS PALSAR imagery. In the first phase, we have been looking at summer monsoon upwelling and related biogenic slicks. After processing some SAR imagery, and just going through some of the images visually, we discovered a really nice sub-mesoscale eddy in one image.

For the uninitiated, submesoscale eddies are fleeting and shy creatures of physical oceanography, and have not proven easy to find, due to their short temporal and spatial scales. Submesoscale eddies and dynamics are subjects of current research in physical oceanography, both in terms of modeling and observations (see,, Few ships with wakes are also clearly visible in the SAR image.


Submesoscale eddy in the Arabian Sea. Image from JAXA. Data processed and analysed by Waqas Qazi and Aaqib Javad.

The image is a processed SAR image from ALOS PALSAR. Processing steps include calibration, speckle filtering, geocoding, and resampling through automated processing workflows. I am working with a graduate student to analyze ALOS PALSAR-1 and PALSAR-2 images spread over 4 years to analyze physical oceanography features in the Arabian Sea. This research is supported by the International Foundation for ScienceJAXA Research Announcement 4 (RA-4), and the Institute of Space Technology. We have also published some basic work on identifying a temperature front in SAR imagery in the Arabian Sea (more on that in an upcoming blog post).

I had previously found a submesoscale eddy in the California Current System when deriving ocean currents from sequential SAR imagery, see the paper here. Also, Marmorino et al. (2010) found submesoscale eddies in SAR imagery in the Southern California Bight.

To learn more about the state-of-the-art in submesoscale ocean dynamics, see:

Looking Back in Time with SAR Satellite Imagery: Tracing the Path of a Dead River

In a recent blog post, I had explained how the low-frequency SAR signal can penetrate dry soil and give us sub-surface imaging capability. Building on that, I want to highlight our recently published paper on an application of the penetration property of SAR images, through which we detected a buried paleochannel in the Cholistan desert area in Eastern Pakistan. A “palaeochannel” is a dried up old river bed or stream bed that has been either filled or buried by younger sediment. Paleochannels either change their courses due to past seismic or flooding activities or cease to exist due to various climatological factors. The Hakra paleochannel in the Cholistan desert is well-renowned in the region, especially with its connection to the old Indian Saraswati river.


The Cholistan desert and main network of irrigation canals in Punjab, Pakistan. Figure from Islam et al. (2016).

In our paper published in the SPIE Journal of Applied Remote Sensing, we used both optical and SAR remote sensing imagery to identify and delineate the Hakra river paleochannel. The dried river channel is buried under sand and not visible from the surface in optical / IR wavelengths, but SAR signals can penetrate dry sand (see earlier blog post)! The detailed methodology is given in the paper. To summarise the methodology, we utilized a 3-band false color combination of bands 3, 5, and 7 from Landsat 8 reflectance data and merged it with pre-processed Envisat ASAR imagery through data fusion to generate one image product for analysis. Data fusion was done through the Principal Component (PC) fusion method, in which the 3-band false color composite is transformed into principal components, the first component is replaced with the SAR data, and the resulting new merged 3-band composite in the PC feature space is transformed back into regular feature space.


3-band multisensor fused image generated from Principal Component image fusion of Landsat 8 reflectance data false color composite (bands 357) and Envisat ASAR calibrated sigma-nought image. The Hakra palaeochannel signature is visible as linear green segments extending toward southwestren direction from the visible portion of Hakra. Figure and more details in Islam et al. (2016).

Ideally, we would have liked to use L-band SAR data for this study, as it penetrates more into dry sand, however ALOS PALSAR L-band data was not available for this study. We settled therefore for the next best frequency, i.e. C-band, and utilised data from Envisat ASAR satellite. Sentinel-1 data is also C-band, however we needed a long-term time series to choose the best data for analysis, and Sentinel-1 being a recently launched satellite, does not provide that advantage. Furthermore, the Envisat ASAR datasets selected for this study were acquired in the hottest / driest part of the seasons, so as to capture maximum subsurface signal.

The remote sensing results were validated with in-situ geophysical surveys for groundwater, i.e. electrical resistivity and conductivity. The presence of high apparent electrical resistivity with corresponding low soil water conductivity values intersects well with the paleochannels identified from the remote sensing data. We also utilized ancillary data and historical evidences like locations of old wells and forts for validation.


Point locations of old forts and water sources (at which water conductivity readings were taken) in the regions overlaid on the detected Hakra palaeochannel from the Landsat 8 and Envisat ASAR fused imagery. Figure and more details in Islam et al. (2016).

I had presented the initial results of this work during my TEDxIslamabad 2014 talk. This paper is the result of collaborative research between research groups at GREL-IST and IGIS-NUST. We also thank officials from Pakistan Council for Research in Water Resources (PCRWR) for guidance and support during this research.

See the paper here:

Islam Z., Iqbal J., Khan J., Qazi W. A. (2016). Paleochannel delineation using Landsat 8 OLI and Envisat ASAR image fusion techniques in Cholistan desert, Pakistan. J. Appl. Remote Sens. 0001;10(4):046001.  doi:10.1117/1.JRS.10.046001

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: