Tag Archives: Satellites

Big Ben Eruption Update 2017-02-27

Mawson Peak's summit crater glows orange in this false-color infrared image (bands 7-6-5) taken February 27, 2017.  Image credit: Bill Mitchell (CC-BY) using data from USGS LANDSAT 8 (public domain).
Mawson Peak’s summit crater glows orange in this false-color infrared image (bands 7-6-5) taken February 27, 2017. Image credit: Bill Mitchell (CC-BY) using data from USGS LANDSAT 8 (public domain).

It has been three weeks since I reported on an active eruption on Heard Island seen by Landsat 8. Since then, the presence of lava at or near the surface in the summit crater of Mawson Peak has continued, and a thermal anomaly is present both in the February 27 Landsat 8 image shown above and in February 20 imagery. It is difficult to discern in the true-color imagery from February 27 whether there are any new lava/debris flows present. The two MODIS instruments (one on Aqua, one on Terra) have not picked up any thermal anomalies since early February.

Unfortunately, one of the best tools I’ve had at my disposal for keeping an eye on Mawson Peak is no longer available: NASA/USGS satellite EO-1 was decomissioned last week. EO-1 provided 10 m/pixel true-color imagery, which is significantly higher resolution than 15 m/pixel of Landsat. Archival data for both satellites remains available, but no new EO-1 data will be taken. New data from Landsat 8 typically comes in a few times each month (every 7-16 days), and I’ll be keeping an eye on it.

Mawson Peak in true color, February 27, 2017.  Image processing: Bill Mitchell (CC-BY) using USGS Landsat 8 data (public domain).
Mawson Peak in true color, February 27, 2017. Image processing: Bill Mitchell (CC-BY) using USGS Landsat 8 data (public domain).

Big Ben Eruption, 2017-02-04

Lava and debris flows radiate away from Mawson Peak on Heard Island.   February 4, 2017.  Image credit: Bill Mitchell (CC-BY) using data from Landsat 8 OLI (NASA/USGS; public domain).
Lava and debris flows radiate away from Mawson Peak on Heard Island. February 4, 2017. Image credit: Bill Mitchell (CC-BY) using data from Landsat 8 OLI (NASA/USGS; public domain).

On February 4th, Landsat 8 captured a clear view of the summit of Big Ben volcano, at Heard Island. Heard Island is a very cloudy location, so clear views are uncommon (I don’t have numbers, but would estimate <20%). However, the February 4th images are even more spectacular: they capture an ongoing volcanic eruption.

Observations
In the sharpened true-color image (above), four or five different lava/rock/debris flows are visible emanating from the summit. Using a false-color infrared image (below), two hot regions are apparent (red/orange/yellow), and are separated by about 250 meters. The longest of the flows stretches nearly 2 km, and drops from an elevation of roughly 2740 m to 1480 m (using 2002 Radarsat elevation data with 20 m contours). All three of the large flows to the west or southwest of the summit drop below 2000 m elevation at the toe.

False-color infrared imagery of Mawson Peak, Heard Island.  Two vents are visible in red/orange/yellow, separated by 250 meters. Data source: Landsat 8 OLI/TIRS bands 7-6-5.  Image credit: Bill Mitchell (CC-BY), data from NASA/USGS (public domain).
False-color infrared imagery of Mawson Peak, Heard Island. Two vents are visible in red/orange/yellow, separated by 250 meters. Data source: Landsat 8 OLI/TIRS bands 7-6-5. Image credit: Bill Mitchell (CC-BY), data from NASA/USGS (public domain).

Interpretation
In the sharpened true-color imagery, I have identified five rock and debris flows originating at the summit, as well as one potential avalanche. Annotation of these observations is found on the pictures below.

Annotation of lava/rock/debris flows from Mawson Peak, Heard Island, February 4, 2017.  Image credit: Bill Mitchell (CC-BY).
Annotation of lava/rock/debris flows from Mawson Peak, Heard Island, February 4, 2017. Image credit: Bill Mitchell (CC-BY).

The streaky, varying lightness of the flow areas, presence of snow and ice, and steep terrain lead me to believe that what is showing up here are mixed snow/rock/lava debris flows, rather than pure lava flows. A mix of rocky debris and snow would not be out of line for a supraglacial eruption on a steep mountain. The longest flow drops nearly 1300 m along its 2000 m horizontal path according to the 2002 Radarsat elevations. I’ll be the first to admit that I am distrustful of the specifics of the Radarsat contours due to the rapidly changing landscape and an intervening 15 years, but I think that it manges to get the general picture right.

Southwestern Heard Island is a high-precipitation area, so rocks exposed on the surface of the glaciers are likely quite fresh. It probably won’t be long before most of the deposits are covered in snow again.

Speaking of snow, it looks as though there is a faint outline of an avalanche scarp/deposit on the northeast side of the summit, which I annotated below in green.

Annotation of avalanche scarp and deposit, Mawson Peak, Heard Island, February 4, 2017.  Image credit: Bill Mitchell (CC-BY)
Annotation of avalanche scarp and deposit, Mawson Peak, Heard Island, February 4, 2017. Image credit: Bill Mitchell (CC-BY)

The two hot spots provide an interesting challenge for interpretation. Two scenarios come to mind quickly: there are two vents from which lava is issuing, or there is a lava tunnel from a summit crater down to a flow front or breakout. Analyzing the Landsat 8 OLI/TIRS infrared imagery from January 26th (most recent previous high-resolution image), only one hot spot is present—in the same place as the eastern hot spot in the February 4th infrared image. For spatial correlation without doing the whole image processing and GIS thing, use the forked flow to the south-southeast of the hotspot as a reference.

False-color infrared image of Mawson Peak, January 26, 2017.  Landsat 8 OLI/TIRS bands 7-6-5.  Image credit: Bill Mitchell (CC-BY) using NASA/USGS data (public domain).
False-color infrared image of Mawson Peak, January 26, 2017. Landsat 8 OLI/TIRS bands 7-6-5. Image credit: Bill Mitchell (CC-BY) using NASA/USGS data (public domain).

Due to a different time of day for imaging, there are significant shadows in the January image on the southwest side of ridges. It’s tricky to figure out what is going on for the flows (even in visible imagery), but the hot spot from January 26th is right on top of the eastern hot spot from February 4th.

Another thing which becomes apparent in the January image is the topography at the summit. The clouds form a blanket at an atmospheric boundary (and roughly-constant elevation), which is conveniently just below the elevation of the summit. A roughly circular hole in the clouds is present, and a conical mountain summit pokes through with the hot spot right in the center. That suggests that the second hot spot seen in the February 4th image is at a lower elevation—a possible flow front or breakout.

Excitement in the Mundane
Finding this eruption was a bit of a surprise to me: the low-resolution preview image for the Landsat data on EarthExplorer was so coarse that there wasn’t anything striking or out of the ordinary visible at the summit. Clouds covered most of the rest of the island. However, when I opened up the full-resolution color images (30 m/pixel), it was immediately evident that this was a special day. Sharpening the true-color bands with the high-resolution panchromatic band using QGIS made it pop all the more!

Upon seeing both the lava/debris flows and the thermal anomaly, I checked the MODIS volcanism (MODVOLC) site to see if the Terra and Aqua MODIS instruments had picked up thermal anomalies as well over the preceding week. They had, as shown below. Both satellites had recorded thermal anomalies at Heard on February 2nd and 3rd.

MODIS thermal events at Heard Island, in the week preceding February 6, 2017.  Image credit: MODVOLC.
MODIS thermal events at Heard Island, in the week preceding February 6, 2017. Image credit: MODVOLC.

Update:: Follow-up from February 27, 2017.

Cloud-Free Heard Island

Composite, cloud-free satellite imagery of Heard Island, being produced in QGIS.  Image credit: Bill Mitchell (CC-BY), using USGS (Landsat 8, EO-1) data (public domain).
Composite, cloud-free satellite imagery of Heard Island, being produced in QGIS. Image credit: Bill Mitchell (CC-BY), using USGS (Landsat 8, EO-1) data (public domain).

Heard Island is a pretty cloudy place most of the time. However, there are occasional times when the weather clears, particularly on the southeastern (leeward) side of the island. On rare occasions, the northwest and southwest sides of the island come out from the clouds as a satellite passes over.

For the past two years, I have been watching Heard Island using true-color imagery from four satellites: Terra, Aqua, Landsat 8, and EO-1. I have posted previously about satellite imagery from these instruments. Although every image of the Island I have seen has clouds in it covering a portion of the island, I was curious whether or not I had accumulated clear imagery of the entirety of Heard Island.

In part, this question was spurred by a follower on Twitter asking about eruptive activity at Heard. I had to admit I didn’t really know whether the activity was low-level and continuous (like Kilauea) or more intermittent. Given that our knowledge of its eruptive activity is primarily from satellite observations, do the satellite “thermal anomalies” correspond to short eruptive events, or simply a cloud-free view of the volcano?

For high-resolution imagery of Heard Island, the datasets of interest are from EO-1 ALI, and Landsat 8 OLI. The two MODIS instruments (one on Aqua, one on Terra) are moderate-resolution, and while 250-m resolution is sufficient for some purposes, this one needs more. Looking through the archives, I was able to find EO-1 ALI data primarily for Mawson Peak and points southeast, and Landsat 8 OLI covered much of the island, particularly the northwest.

Not only is having cloud-free, high-resolution data important for me, but I want the data to be recent. There has been a retreat of up to 5.5 km for some of the glaciers since 1947, and the Google Maps imagery of that area (Stephenson Lagoon) is horribly outdated. Fortunately, I found most of the island covered in large swaths with images from 2014 onward, and mostly 2016. There was even good imagery from when I was on Heard Island! Our ship, the Braveheart, is visible as a few white pixels in Atlas Roads (just north of Atlas Cove), slightly closer to the Azorella Peninsula than to the Laurens Peninsula. The tents and campsite are too small and darkly colored to be visible on this image.

Braveheart in Atlas Roads and the campsite (non-contrasting) at Atlas Cove, Heard Island.  Satellite image pixels are 15 m across, and the Azorella Peninsula isthmus (along Campsite label) is 1 km wide.  Image credit: Bill Mitchell (CC-BY), using USGS (Landsat 8 OLI) data (public domain).
Braveheart in Atlas Roads and the campsite (non-contrasting) at Atlas Cove, Heard Island. Satellite image pixels are 15 m across, and the Azorella Peninsula isthmus (along Campsite label) is 1 km wide. Image credit: Bill Mitchell (CC-BY), using USGS (Landsat 8 OLI) data (public domain).

A small portion of the island between Atlas Cove and Mawson Peak was the most difficult to find. With the topography of the island, the steady stream of wind, and the humid air, the 2.5 km by 2.5 km region was cloudy pretty much all the time. Eventually, using the EO-1 ALI instrument and going back to early 2010, I found a reasonably clear image of it.

Once I had the images (after combining true-color and panchromatic brightness data in QGIS), I needed to stitch them together. Thanks to the wonderful QGIS training manual, I was able to create vector (polygon) layers which corresponded to the clear region of each image (plus some surrounding ocean). At this point the troublesome mostly-cloudy spot became evident, and the search was on for imagery to fill the void.

Creating polygons for clipping the satellite imagery using QGIS.  Four polygons are shown here, including the small polygon of much cloudiness.  A fifth dataset was subsequently incorporated.
Creating polygons for clipping the satellite imagery using QGIS. Four polygons are shown here, including the small polygon of much cloudiness. A fifth dataset was subsequently incorporated.

Finally, I tried to put them together. This turned out to be more trouble than it was worth for my purposes, having only five images. Several of the images had differing resolutions (10 m/pixel for EO-1 ALI, 15 m/pixel for Landsat 8 OLI). Additionally, since I was handling these in their raw format, color balances/exposures were not consistent across images. I decided it best, then, to leave them separate, and sent them around to the Heard Island Expedition team.

Soon I had an email from the expedition leader: he was very interested in the imagery, but it wasn’t opening in Google Earth. Some searching later, I found that Google Earth works best with a certain map projection (EPSG:4326), and when exporting the GeoTIFF, I needed to select “rendered image” rather than “raw data”. I re-exported the images, zipped them up, and tested it out on another computer: success! This Google Earth friendly imagery is now available here (17 MB zip).

One continuation of this project would be to keep looking through the documentation on GeoTiffs to find out how to make the rendered images use a transparent, not white, border where there is no data. That would likely let me create a virtual raster catalog to load all of them in one go, rather than having to load them separately.

Show Me the Data: Satellite Observations

Heard Island, March 27, 2013.  Elephant Spit imagery (at right) is from March 3, 2013.  Image has been adjusted to increase bring out detail in exposed land.  Image credit: processed by Bill Mitchell (CC-BY) using data from NASA/EO-1 (public domain).
Heard Island, March 27, 2013. Elephant Spit imagery (at right) is from March 3, 2013. Image has been adjusted to increase bring out detail in exposed land. Image credit: processed by Bill Mitchell (CC-BY) using data from NASA/USGS/EO-1 ALI (public domain).

With Heard Island being remote and uninhabited, studying it can be a bit difficult. However, as readers of this blog (and my Twitter followers) are aware, one of the ways I have been preparing for the expedition is by keeping an eye on it using various satellites and their remote sensing capabilities. Sure, there is often cloudcover at Heard, but some days it’s clearer and on a few of those days, the satellites pass over.

Most of my information comes from NASA’s MODIS instruments, aboard the Terra and Aqua satellites. These have at least every-other-day coverage of everywhere on Earth, although with a moderate resolution of 250 m/pixel. In the morning when I’m catching up on email and comics, I’ll check the near-real-time MODIS image page to see whether there are clear images of Heard Island from either instrument. Finding Heard Island can be difficult: I still usually find the Kerguelen Islands first, then look to the south-southeast. Many times there are indications such as vortices or gravity waves (not gravitational waves, those are different).

A related page is MODVOLC, which uses MODIS for volcano monitoring. In addition to visible light, MODIS can detect several wavelengths of infrared, and the signature from those wavelengths can be used to determine whether there is a likely volcanic eruption occurring at a given place.

MODIS is a great instrument in that it has daily or every-other-day coverage. However, the 250 m/pixel resolution can be quite limiting. For higher-resolution imagery, I look to the ALI instrument on NASA’s EO-1 satellite. These images are available (free registration required) from EarthExplorer, a data search portal from the USGS. ALI has a 30 m/pixel resolution on its color imagery, and 10 m/pixel resolution on the panchromatic image (total light intensity). These can be combined using QGIS into 10 m/pixel color images. By exploring the EO-1 page I found that members of the public can make requests for image targets! Imaging requests are subject to a bunch of conditions (availability of satellite, >30-day lead time, recommended >3 month window for imaging), but the request and any data generated from fulfillment of the request are free.

How did I come to know about these great resources? It takes time, searching, and some attention to detail. MODIS I learned about as a graduate student, from friends who used data products (not the true-color imagery) in their doctoral research on atmospheric chemistry. I came across EO-1 ALI from searching for images of Heard Island: I found some higher-than-MODIS resolution images from NASA which were good about indicating the source satellite/instrument. Citing image sources is incredibly useful, and I’m always disappointed when images (at least, non-screenshot images) are given without any sort of source information.

MODVOLC I learned about from the Smithsonian’s Global Volcanism Program, which cites the sources of their eruption reports. Information about the source plus a little searching yielded an interesting and useful data source.

Satellite Image Processing Revisited

Heard Island on Nov. 20, 2015, with image processing underway in QGIS.  Image credit: Bill Mitchell (CC-BY) with satellite imagery from USGS (EO-1 satellite, ALI instrument).
Heard Island on Nov. 20, 2015, with image processing underway in QGIS. Image credit: Bill Mitchell (CC-BY) with satellite imagery from USGS (EO-1 satellite, ALI instrument).

Following up on my earlier post about satellite image processing, I am happy to report that I have made progress in being able to process images myself! Through a fortunate combination of search terms, timing, and luck, I managed to come across two key pieces of information that I needed.

First, I found out how to make RGB images from raster data layers, such as different spectral bands on a satellite, fairly easily with QGIS. That was a big step forward from how I had been doing it previously, which was inelegant, inefficient, and only mostly worked. Stacking three layers (one each for red, green, and blue) into a virtual raster catalog was just a few clicks away (Raster | Miscellaneous | Build Virtual Raster (Catalog)).

Encouraged by the success with that project, I continued clicking around and stumbled across some mention of pan-sharpening (also pan sharpening), where a panchromatic (all-color) detector at high resolution is used to enhance the resolution of a colored image (sharpen). Alternately, you can think of it in the complementary way, where lower-resolution color data is added to a high-resolution greyscale image. So thanks to this blog post, I was able to find out what I needed to do to make that happen in QGIS (and Orfeo Toolbox).

Of course, it would be too easy for that to work. I didn’t have the Orfeo Toolbox installed which that needed, and ended up having to compile that from source code.* When the compiler finished and the program was installed, I went to tell QGIS where it was—but a bug in QGIS prevented me from entering the folder location. First, having just installed and compiled stuff, I attempted to get the latest version of QGIS and many of the tools on which QGIS relies. Being unsuccessful in making all of those and some of the compiler configuration software play nicely with each other, I eventually remembered I could get updated packages through apt-get, which gets pre-compiled binary files put out by the maintainers of Debian Linux. That solution worked, I added the folder location, and now I can have my pan-sharpened images.

Here for your viewing pleasure is my first properly pan-sharpened image: Heard Island on Nov. 20, 2015, seen in “true color” by the Advanced Land Imager (ALI) on the EO-1 satellite.** I’m not convinced it’s right, and I think the contrast needs to be brought down a bit, but I think it’s close.

Heard Island in true color on Nov. 20, 2015.  Image processing: Bill Mitchell (CC-BY) using data from USGS/EO-1.
Heard Island in true color on Nov. 20, 2015. Image processing: Bill Mitchell (CC-BY) using data from USGS (EO-1/ALI).

* Knowing how to compile software from source code is a rather handy skill.
** Emily Lakdawalla has written a great explanation of what “true color” means.

Geoscientist’s Toolkit: Terra MODIS

Terra satellite being prepared for placement in the payload fairing.  Image credit: NASA (public domain).
Terra satellite being prepared for placement in the payload fairing. Image credit: NASA (public domain).

In my previous post on satellite communications, I discussed two types of satellites: geostationary and low-Earth-orbit. One of NASA’s low-Earth-orbit satellites, orbiting at an altitude of 705 km (438 mi), is Terra.

Launched in December of 1999, Terra is in a polar orbit, and is sun-synchronous—it makes its north-to-south pass on the daylight side of Earth, crossing the equator around 10:30 AM in the local time zone. As 10:30 AM moves around the Earth, so too does Terra, with each orbit taking 99 minutes.

Aboard Terra is one of my favorite instruments: the MODerate resolution Imaging Spectroradiometer, or MODIS. Unpacking the name, we find that MODIS has moderate resolution: its best resolution is about 250 m/pixel. It is an imaging instrument (i.e. it sends back pretty pictures), and it is a spectroradiometer, meaning that it measures the amount of light (radiometer) across a spectrum of wavelengths (visible and infrared, in this case). Most of my use of the instrument is for its true-color imagery, or “Bands 1-4-3” (corresponding to red, green and blue). An example image is shown below.

Minneapolis area seen by NASA's Terra satellite Sept. 30, 2015.  The Minneapolis and St. Paul airport is the concrete-colored smudge just left of center; St. Cloud is in the upper left, Winona toward the bottom right, and at furthest bottom right is La Crosse, WI.  Image credit: NASA (public domain).
Minneapolis area seen by NASA’s Terra satellite Sept. 30, 2015. The Minneapolis and St. Paul airport is the concrete-colored smudge just left of center; St. Cloud is in the upper left, Winona toward the bottom right, and at furthest bottom right is La Crosse, WI. Image credit: excerpt from NASA imagery (public domain).

MODIS is a push-broom type imager. It takes one very wide “picture” (2,330 km East-West), and splits that into 36 spectral bands. As the spacecraft flies (North-to-South), those wide “pictures” are put together along the track of the satellite to create a swath image. The instrument’s resolution is highest at the center of the image.

One great thing about MODIS is that it has pretty good spatial coverage (that’s the advantage of the moderate resolution). In 24 hours, it will get images of most of the Earth, but with a few gaps between swaths at the equator. Orbits are offset day-to-day (with a 16-day cycle), so it takes two days to get full global coverage. Global maps are produced daily (give or take) by NASA Earth Observations, and tend to have a day or two of lag behind real-time.

Terra MODIS image of Earth, Oct. 7, 2015.  The tan-grey streaks in the center of the swath over some equatorial regions is caused by glare from the sun reflecting off the ocean surface.  Image credit: NASA Earth Observations (public domain).
Terra MODIS image of Earth, Oct. 7, 2015. The tan-grey streaks in the center of the swath over some equatorial regions is caused by glare from the sun reflecting off the ocean surface. Image credit: NASA Earth Observations (public domain).

You may notice that in the picture of the whole world, Antarctica is nicely lit up, but the data for the North pole is missing? What’s up with that? Is NASA taking part in a conspiracy with Santa to hide his gift-production and distribution facilities?

In a word: no.

In more words, having recently passed the September equinox (autumnal equinox to folks in the northern hemisphere), the North pole is now in darkness at 10:30 AM “local time”. It doesn’t really matter what you choose as local time, because it’s dark regardless. With it being dark, the instrument is off.

Beyond pretty pictures, Terra MODIS is used for scientific purposes. Its images can detect wildfires,[1] be used to estimate area burned by fires, monitor drought severity and snow cover, study aerosols and atmospheric pollutants, and even chlorophyll (phytoplankton) concentrations in the ocean.

Using the images to understand the productivity of plants can in turn influence the estimates for how much carbon is being removed from the atmosphere, and can serve as a gauge of ecosystem health in remote areas. Volcanic eruptions, major wildfire events, and even thick pollution from human sources can be seen in these images. By analyzing MODIS data, scientists can gauge how much of various types of atmospheric gases are being emitted by wildfires.[2, 3]

***
[1] Near-real-time swath data are available from the Rapid Response website.

[2] Mebust, A. K., Russell, A. R., Hudman, R. C., Valin, L. C., and Cohen, R. C.: Characterization of wildfire NOx emissions using MODIS fire radiative power and OMI tropospheric NO2 columns, Atmos. Chem. Phys., 11, 5839-5851, doi:10.5194/acp-11-5839-2011, 2011. [Open access]

[3] Mebust, A. K. and Cohen, R. C.: Space-based observations of fire NOx emission coefficients: a global biome-scale comparison, Atmos. Chem. Phys., 14, 2509-2524, doi:10.5194/acp-14-2509-2014, 2014. [Open access]

Satellite Communications

Geostationary orbits, used by some communications satellites.  Image credit: Lookang (CC-BY-SA).
Geostationary orbits, used by some communications satellites. Image credit: Lookang (CC-BY-SA).

One important aspect of field work in remote places is keeping lines of communication open. At a minimum, the ability to call for help is needed. Sending status updates, checking email, talking with loved ones, and a number of other uses are good to have. Even in this day and age, though, not every remote place has good cell phone coverage. These places are where satellite phone systems are extremely useful.

There are two main types of satellite systems: geostationary satellite systems, and low-Earth-orbit satellite systems.

Geostationary satellite systems have satellites over fixed locations above Earth’s equator, at an altitude of roughly 36,000 km (22,000 mi). Geostationary satellites are nice in that they are always in the same spot relative to a location on Earth, so there are no signal hand-offs where calls may drop, nor do the stations on the ground need to have any kind of tracking mechanism to keep the antenna pointed at the satellite. Unfortunately, because the geostationary satellites are located over the equator, they do not work well pole-ward of 70° latitude, because they are too close to the horizon for reliable, interference-free signals. Geostationary satellites also have a noticeable delay, because the round-trip light time is a minimum of ~0.25 seconds, and the time to receive a response back doubles that.

Low-Earth-orbit satellite systems require many more satellites, but the satellites are much closer to Earth, generally only 650–1100 km above the surface. Many of these satellites are in a polar or near-polar orbit, which gives them good coverage near the poles. Each satellite is only over any given area for 4–15 min, so hand-offs are necessary (and are not always reliable). One advantage of low-Earth-orbit systems is that the transmitter and antenna on the ground do not need to be especially powerful or carefully aimed. Low-Earth orbit systems have substantially less data throughput than the geostationary systems (9600 kbps for LEO vs. 60–512 kbps for geostationary). For reference, the LEO throughput is much less than dial-up modems, and geostationary throughput is up to 10x higher than dial-up, though still far short of broadband internet access (4 Mbps down, 1 Mbps up).

I mentioned that the antennas (and power) for a geostationary satellite setup need to be better than ones for low-Earth orbit satellites. This is because of the inverse-square law, where the as the distance is increased, the power which reaches the receiver drops by the square of that increase. Think of standing outside at night with a friend (representing the ground station and satellite), and each of you has a flashlight (representing the radio transmitters) and eyes (the radio receivers). When you are close, the light is very bright, and you probably have to look away. As you move away from each other, the lights appear dimmer and dimmer. Each time you double the distance between you, the brightness of the light dims by a factor of four. If you need a certain level of brightness at the receiver (your eye, or the satellite antenna), then there has to be either a sufficiently bright light shining (power level), or it needs to be focused enough—and harvested enough by a sufficiently large receiver—to achieve that level of signal.

Inverse-square law in action; as the distance increases (e.g. from r to 2r), the area the energy is directed over increases as the square of the distance (e.g. from 1 to 4 units).  Image credit: Borb (CC-BY-SA).
Inverse-square law in action; as the distance increases (e.g. from r to 2r), the area the energy is directed over increases as the square of the distance (e.g. from 1 to 4 units). Image credit: Borb (CC-BY-SA).

With a difference in altitude of ~40x between low-Earth orbit and geostationary orbit, there is a difference of 1600x in the signal level, all else being equal. For that reason, satellite phones for low-Earth-orbit satellites can get away with less powerful radios and smaller antennas that are less sensitive to proper positioning. It’s handy to not need exact positioning for the low-Earth-orbit satellites, because their quick movement across the sky can be difficult to track without a motorized, computer-driven antenna. Mobile or ship-based satellite communication systems tend to rely more on the low-Earth-orbit satellites precisely because the aim of the antenna is much less important. Nobody wants to try to hold an antenna pointing in a certain direction while pitching about on a ship in 4 m seas in the wind and the cold.

As an amateur radio operator, one thing I enjoy doing is going outside when the International Space Station is flying over, and listening to the radio signals it sends down. During the morning or evening passes on clear days where the space station is visible, it is quite easy to point in the right direction. Spot the station, then point your hand-held antenna toward it. During the day, in the depths of night, or when it’s cloudy, tracking the station can be more difficult (at least without computer assistance). Still, it’s pretty neat to hear astronauts answering questions from the local middle school students, all the while knowing that the signal coming from the space station is coming directly to your radio, no internet or commercial broadcast station required.