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:

A comparison of Landsat 7 ETM+, Landsat 8 OLI, and Sentinel 2A MSI over the visible and near-infrared parts of the spectrum

Scientia Plus Conscientia

How do different sensors perform across the electromagnetic spectrum? This question bears practical importance when we want to combine data acquired by different sensors. I therefore thought it would be interesting and fun to do a simulation of how different common sensors see the same feature.

We could in principle do this using subsets of images of the same region captured by different sensors, but it is actually easier to compare them using a given spectral signature, the reflectance (or emittance) of a certain material as a function of wavelength.

I therefore went to the Aster spectral library and downloaded several datasets corresponding to different spectral signatures. In the following example, we use that of common lawn grass:

Spectral signature of lawn grass. Spectral signature of lawn grass. Source: ASTER spectral library.

How do Landsat 7 ETM+, landsat 8 OLI and Sentinel 2A MSI “see” this grass? To answer this question…

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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