Archive for November, 2017

Ground to Space: Iguazú Falls

November 29th, 2017 by Kathryn Hansen

In 2016, we published space-based imagery of Iguazú Falls—South America’s famous system of waterfalls, which is near a bend in the Iguazú River between Argentina and Brazil. Spray from the falls reaches so high that it is visible from space. A crew member aboard the International Space Station captured the photograph above on May 24, 2016.

The view from the ground is also quite compelling, attracting more than a million visitors per year. The images below show ground-based views of the falls, photographed photographed by NASA’s Alexey Chibisov from the Argentine side of the river on November 28, 2017. Chibisov took the photos while on vacation after weeks in the field with the Operation IceBridge mission.

Photo by Alexey Chibisov.

Lush, subtropical rainforest surrounds the falls. The vegetation here is part of a remaining fragment of the Atlantic Forest, which stretches from the east coast of South America inland toward the Amazon. The forest is habitat for tens of thousands of plant species and thousands of animal species.

Photo by Alexey Chibisov.

Sediment carried by the fast-moving river can impart a red-brown color to the water, especially after periods of heavy rain.

Photo by Alexey Chibisov.

The mist is the result of water that plunges as much as 260 feet (80 meters) over layers of basalt cliffs.

November 2017 Puzzler

November 28th, 2017 by Mike Carlowicz

Every month on Earth Matters, we offer a puzzling satellite image. The November 2017 puzzler is above. Your challenge is to use the comments section to tell us what we are looking at, when the image was acquired, and why the scene is interesting.

How to answer. You can use a few words or several paragraphs. You might simply tell us the location. Or you can dig deeper and explain what satellite and instrument produced the image, what spectral bands were used to create it, or what is compelling about some obscure feature in the image. If you think something is interesting or noteworthy, tell us about it.

The prize. We can’t offer prize money or a trip to Mars, but we can promise you credit and glory. Well, maybe just credit. Roughly one week after a puzzler image appears on this blog, we will post an annotated and captioned version as our Image of the Day. After we post the answer, we will acknowledge the first person to correctly identify the image at the bottom of this blog post. We also may recognize readers who offer the most interesting tidbits of information about the geological, meteorological, or human processes that have shaped the landscape. Please include your preferred name or alias with your comment. If you work for or attend an institution that you would like to recognize, please mention that as well.

Recent winners. If you’ve won the puzzler in the past few months or if you work in geospatial imaging, please hold your answer for at least a day to give less experienced readers a chance to play.

Releasing Comments. Savvy readers have solved some puzzlers after a few minutes. To give more people a chance to play, we may wait between 24 to 48 hours before posting comments.

Good luck!

What A Wonderful World: Pinacate Peaks

November 10th, 2017 by Jesse Allen

In this satellite image, the prominent Pinacate Peaks stick out above the sand dune landscape of the Gran Desierto de Altar in Mexico’s Sonoran Province. The peaks are located just south of the Mexico-United States border. The Gran Desierto de Altar is one section of the broader Soronan Desert which covers much of northwestern Mexico and reaches into Arizona and California.

Steady, consistent winds in the area have shifted low-lying sand into dune fields in intriguing regular patterns. These same patterns of sand dune fields appear around the world in desert areas.

The volcanic peaks and cinder cones are believed to have formed from volcanic activity that first started roughly 4 million years ago — most likely due to the plate tectonics that also formed the Gulf of California around the same time. The most recent activity was perhaps 11,000 years ago. During the late 1960s, NASA trained astronauts in field geology at a number of sites around the world, including Pinacate Peaks, as preparation for the lunar landings.

The natural color image here is from the Landsat 8 satellite using its Operational Line Imager (OLI) instrument. The image was acquired on October 3, 2017. The volcanic cinder cone field stains the landscape of bright sand and tall dunes in the El Pinacate y Gran Desierto de Altar Biosphere Reserve.


NOTE: In a previous version of this post, I featured the EO-1 ALI image below, and an astute reader pointed out that these peaks, while in the Biosphere Reserve, are not Pinacate Peaks, but rather the Sierra de Rosario range nearby. I am geographically and tectonically embarassed…

The natural color image here is from the now-defunct Earth Observing 1 (EO-1) satellite using its Advanced Land Imager (ALI). The image was acquired on December 16, 2012. This late-year scene was just days before the solstice (the farthest south the Sun appears in the sky), so the tallest sand dunes and the volcanic peaks cast unusually long shadows across the ground.

EO-1 was launched in November 2000 as an engineering testbed for new sensor technology; in particular, the ALI instrument was a predecessor for the Landsat 8 Operational Land Imager. The EO-1 mission was so successful that it was extended past its original 18-month mission, and was only recently retired after 17 years of operation.

This post is republished from the Landsat science team page

In the giddy, early days following the flawless launch of Landsat 8, as the satellite commissioning was taking place, the calibration team noticed something strange. Light and dark stripes were showing up in images acquired by the satellite’s Thermal Infrared Sensor (TIRS).

Comparing coincident data collected by Landsat 8 and Landsat 7 — acquired as Landsat 8 flew under Landsat 7, on its way to its final orbit — showed that thermal data collected by Landsat 8 was off by several degrees.

This was a big deal. The TIRS sensor had been added to the Landsat 8 payload specifically because it had been deemed essential to a number of applications, especially water management in the U.S’s arid western states.

The TIRS error source was a mystery. The prelaunch TIRS testing in the lab had shown highly accurate data (to within 1 degree K); and on-orbit internal calibration measurements (measurements taken of an onboard light source with a known temperature) were just as good as they had been in the lab. But when TIRS radiance measurements were compared to ground-based measurements, errors were undeniably present. Everywhere TIRS was reporting temperatures that were warmer than they should have been, with the error at its worst in regions with extreme temperatures like Antarctica.

After a year-long investigation, the TIRS team found the problem. Stray light from outside the TIRS field-of-view was contaminating the image. The stray light was adding signal to the TIRS images that should not have been there—a “ghost signal” had been found.

The Ghostly Culprit

Scans of the Moon, together with ray tracing models created with a spare telescope by the TIRS instrument team, identified the stray light culprit. A metal alloy retaining ring mounted just above the third lens of the four-lens refractive TIRS telescope was bouncing out-of-field reflections onto the TIRS focal plane. The ghost-maker had been found.

Getting the Ghost Out—Landsat Exorcists in Action

With the source of the TIRS ghosts discovered, Matthew Montanaro and Aaron Gerace, two thermal imaging experts from the Rochester Institute of Technology, were tasked with getting rid of them.

Montanaro and Gerace had to first figure out how much energy or “noise” the ghost signals were adding to the TIRS measurements. To do this, a stray light optical model was created using reverse ray traces for each TIRS detector. This essentially gave Montanaro and Gerace a “map” of ghost signals. Because TIRS has 1,920 detectors, each in a slightly different position, it wasn’t just one ghost signal they had to deal with— it was a gaggle of ghost signals.

To calculate the ghost signal contamination for each detector, they compared TIRS radiance data to a known “correct” top-of-atmosphere radiance value (specifically, MODIS radiance measurements made during the Landsat 8 / Terra underflight period in March 2013).

Comparing the MODIS and TIRS measurements showed how much energy the ghost signal was adding to the TIRS radiance measurements. These actual ghost signal values were then compared to the model-based ghost signal values that Montanaro and Gerace had calculated using their stray light maps and out-of-field radiance values from TIRS interval data (data collected just above and below a given scene along the Landsat 8 orbital track).

Using the relationships established by these comparisons, Montanaro and Gerace came up with generic equations that could be used to calculate the ghost signal for each TIRS detector.

Once the ghost signal value is calculated for each pixel, that value can be subtracted from the measured radiance to get a stray-light corrected radiance, i.e. an accurate radiance. This algorithm has become known as the “TIRS-on-TIRS” correction. After performing this correction, the absolute error can be reduced from roughly 9 K to 1 K and the image banding, that visible vestige of the ghost signal, largely disappears.

“The stray light issue is very complex and it took years of investigation to determine a suitable solution,” Montanaro said.

This work paid off. Their correction—hailed as “innovative” by the Landsat 8 Project Scientist, Jim Irons—has withstood the scrutiny of the Landsat Science Team. And Montanaro and Gerace’s “exorcism” has now placed the Landsat 8 thermal bands in-line with the accuracy of the previous (ghost-free) Landsat thermal instruments.

USGS EROS has now implemented the software fix developed by these “Landsat Ghostbusters” as part of the Landsat Collection 1 data product. Savvy programmers at USGS, led by Tim Beckmann, made it possible to turn the complex de-ghosting calculations into a computationally reasonable fix that can be done for the 700+ scenes collected by Landsat 8 each day.

“EROS was able to streamline the process so that although there are many calculations, the overall additional processing time is negligible for each Landsat scene,” Montanaro explained.

A Ghost-Free Future

Gerace is now determining if an atmospheric correction based on measurements made by the two TIRS bands, a technique known as a split window atmospheric correction, can be developed with the corrected TIRS data.

Meanwhile, Montanaro has been asked to support the instrument team building the Thermal Infrared Sensor 2 that will fly on Landsat 9. A hardware fix for TIRS-2 is planned. Baffles will be placed within the telescope to block the stray light that haunted the Landsat 8 TIRS.

The Landsat future is looking ghost-free.

Related Reading:
+ RIT University News
TIRS Stray Light Correction Implemented in Collection 1 Processing, USGS Landsat Headline
+ Landsat Level-1 Collection 1 Processing, USGS Landsat Update Vol. 11 Issue 1 2017
+ Landsat Data Users Handbook, Appendix A – Known Issues

Montanaro, M., Gerace, A., Lunsford, A., & Reuter, D. (2014). Stray light artifacts in imagery from the Landsat 8 Thermal Infrared Sensor. Remote Sensing, 6(11), 10435-10456. doi:10.3390/rs61110435

Montanaro, M., Gerace, A., & Rohrbach, S. (2015). Toward an operational stray light correction for the Landsat 8 Thermal Infrared Sensor. Applied Optics, 54(13), 3963-3978. doi: 10.1364/AO.54.003963 (

Barsi JA, Schott JR, Hook SJ, Raqueno NG, Markham BL, Radocinski RG. (2014) Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration. Remote Sensing, 6(11), 11607-11626.

Montanaro, M., Levy, R., & Markham, B. (2014). On-orbit radiometric performance of the Landsat 8 Thermal Infrared Sensor. Remote Sensing, 6(12), 11753-11769. doi: 10.3390/rs61211753

Gerace, A., & Montanaro, M. (2017). Derivation and validation of the stray light correction algorithm for the Thermal Infrared Sensor onboard Landsat 8. Remote Sensing of Environment, 191, 246-257. doi: 10.1016/j.rse.2017.01.029

Gerace, A. D., Montanaro, M., Connal, R. (2017). Leveraging intercalibration techniques to support stray-light removal from Landsat 8 Thermal Infrared Sensor data. Journal of Applied Remote Sensing, Accepted for Publication.