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GUEST POST: A Very Brief History of Optical High Resolution Satellite Imaging

The history of the optical high resolution satellite images starts from classified military satellite systems of the United States of America that captured earth’s surface from 1960 to 1972. All these images were declassified by Executive Order 12951 in 1995 and made publically available (Now freely available through the USGS EarthExplorer data platform under the category of declassified data). From 1999 onward, commercial multispectral and panchromatic datasets have been available for public. Launch of Keyhole Earthviewer in 2001, later renamed as Google Earth in 2005, opened a new avenue for the layman to visualize earth features through optical high resolution satellite images.

A comparison of declassified Corona (1974) vs. GeoEye-1 (2014) image. Image credits: EarthExplorer (Corona) and Google Earth (GeoEye-1).

In the current era, most high resolution satellite images are commercially available, and are being used as a substitute to aerial photographs. The launch of SPOT, IKONOS, QuickBird, OrbView, GeoEye, WorldView, KOMPSAT etc. offer data at fine resolutions in digital format to produce maps in much simpler, cost effective and efficient manner in terms of mathematical modeling. A number of meaningful products are being derived from high resolution datasets, e.g., extraction of high resolution Digital Elevation Models (DEMs) with 3D building models, detailed change assessments of land cover and land use, habitat suitability, biophysical parameters of trees, detailed assessments of pre and post-disaster conditions, among others.

Both aerial photographs and high resolution images are subject to weather conditions but satellites offer the advantage of repeatedly capturing same areas on a reliable basis by considering the user demand without being restricted by considering borders and logistics, as compared to aerial survey.

Pansharpening / resolution merge provides improved visualization and is also used for detecting certain features in a better manner. Pansharpening / resolution merge is a fusion process of co-georegistered panchromatic (high resolution) and multispectral (comparatively lower resolution) satellite data to produce high-resolution color multispectral image. In high resolution satellite data, the spectral resolution is being increased and more such sensors with enhanced spectral sensitivity are being planned in the future.

List of the Spaceborne Sensors with <5 m Spatial Resolution

Sensors Agency/Country Launch Date Platform altitude (km) GSD Pan/MSS (m) Pointing capability (o) Swath width at nadir (km)
IKONOS-2 GeoEye Inc./USA 1999 681 0.82/3.2 Free View 11.3
EROS A1 ImageSat Int./Cyprus (Israel) 2000 480 1.8 Free View 12.6
QuickBird DigitalGlobe/USA 2001 450 0.61/2.44 Pan and MSS alternative Free View 16.5
HRS SPOT Image/France 2002 830 5X10 Forward/left +20/-20 120
HRG SPOT Image/France 2002 830 5(2.5)x10 sideways up to ±27 60
OrbViw-3 GeoEye Inc./USA 2003 470 1/4 Free View 8
FORMOSAT 2 NSPO/China, Taiwan 2004 890 2/8 Free View 24
PAN (Cartosat-1) ISRO/India 2005 613 2.5 Forward/aft 26/5 Free view to side up to 23 27
TopSat Telescope BNSC/UK 2005 686 2.8/5.6 Free View 15/10
PRISM JAXA/Japan 2005 699 2.5 Forward/Nadir/aft -24/0/+24 Free view to side 70 35 (Triplet stereo observations
PAN(BJ-1) NRSCC (CAST)/China 2005 686 4/32 Free View 24/640
EROS B ImageSat Int./Cyprus (Israel) 2006 508 0.7/- Free View 7
Geoton-L1Resurs-DK1 Roscosmos/Russia 2006 330-585 1/3 for h = 330km Free View 30 for h = 330km
KOMPSAT-2 KARI/South Korea 2006 685 1/4 sideways up to ±30 15 km
CBERS-2B CNSA/INPE China/Brazil 2007 778 2.4/20 Free View 27/113
WorldView-1 DigitalGlobe/USA 2007 494 0.45/- Free View 17.6
THEOS GISTDA/Thailand 2008 822 2/15 Free View 22/90
AlSat-2 Algeria 2008 680 2.5 up to 30 cross track Free view 17.5
GeoEye-1 GeoEye Inc./USA 2008 681 0.41/1.65 Free View 15.2
WorldView-2 DigitalGlobe/USA 2009 770 0.45/1.8 Free View 16.4
PAN (Cartosat-2, 2A, 2B) ISRO/India Cartosat 2-2007 Cartosat 2A-2008 Cartosat   2B-2010 631 0.82/- Free View 9.6
KOMPSAT-3 KARI/South Korea 2012 685 0.7/2.8 ±45º into any direction (cross-track or along-track) 15
WorldView-3 DigitalGlobe/USA 2014 617 0.3/1.24/3.7/30 13.1

 Conflicts of Interest: The findings reported stand as scientific study and observations of the author and do not necessarily reflect as the views of author’s organizations.

 About this post: This is a guest post by Hammad Gilani. Learn more about this blog’s authors here

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:

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…

View original post 668 more words

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.

alos-h1_5_ua-orbit__alpsrp084270410_chipview1

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: