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

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:



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:


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.


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.