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My Favorites

July 31st, 2014 by Robert Simmon

After nearly 20 years, it’s time for me to leave NASA and do something radically different—help Planet Labs develop world-class satellite imagery. OK, not that radically different. In any case, now is as good a time as ever to point to some of my favorite visualizations. If you’d like like to get in touch with me, drop me a note on Twitter (@rsimmon) or contact the Earth Observatory team—they’ll know where I am.

Thanks to everyone on the Earth Observatory team that I’ve worked with over the years. Among them: David Herring, Kevin Ward, Mike Carlowicz, Paul Przyborski, Rebecca Lindsey, Holli Riebeek, Adam Voiland, Jesse Allen, Reto Stöckli, Goran Halusa, and John Weier.

Ten: The Blue Marble

Let me get this out of the way: this was the first NASA image of an entire hemisphere of the Earth made with full-color data since the Apollo 17 crew returned from the Moon. It’s certainly striking, but once you look carefully a number of flaws start to appear. Flaws that I may or may not have pointed out when I described my design process. (I’ll also reiterate Reto Stöckli’s invaluable work building the land and cloud textures, which were the hard parts.)

Nine: Global Net Primary Productivity

Although these two datasets show the exact same property (net primary productivity, a measure of the amount of carbon the biosphere draws out of the atmosphere) they’re measured in different ways, and deserve to be differentiated. By using palettes with different hues but an identical range of lightness and saturation, they are directly comparable but remain distinct from one another.

Eight: The Landsat Long Swath

Shortly after launch, Landsat 8 collected what was probably the single largest satellite image ever made. Roughly 12,000 pixels wide by 600,000 pixels tall the image combined 56 individual Landsat scenes into a single strip from Siberia to South Africa.

Seven: LIDAR

Back when I used to be competent at 3D, I made this illustration showing how a pulse of laser light can measure the structure of a forest canopy.

Six: Air Quality over 16 Megacities


It may just be that I’m enamored with Alberto Cairo, but I’m growing increasingly fond of slope graphs. They occasionally tell stories more clearly than more conventional graph types.

Five: an Erupting Volcano from the International Space Station

When Sarychev Volcano blasted a column of ash high above the Kuril Islands an astronaut captured not one, but a whole sequence of photographs of the plume. Make sure you look for the pyroclastic flows coursing down the side of the volcano. (The eruption did not blast a hole in the clouds by the way, that’s a result of interactions between wind, clouds, and island topography.)

Four: Faults in Xinjiang

I’ve probably worked on more than 1,000 Landsat images over the course of my career. This scene of offset folds in Xinjian China is the best.

Three: Amazonia

I think this map of vegetation in South America was the first original color palette I really got right.

Two: The Original Earth at Night

For some reason the color and contrast work better in this version (originally published in 2000 to accompany the story Bright Lights, Big City) than any of my attempted remakes. This includes the 2012 Black Marble, which was made with much better data.

One: Seeing Equinoxes and Solstices from Space

I’m generally skeptical of animation in data visualization, but for some things motion is the story. I think this applies to the apparent motion of the sun over the course of a year, alternately lighting the North and South Poles. (Apologies for the poor quality of the YouTube compression. Make sure you check out the HD version.)

Finally: Thanks to the NASA family, and to all of you who’ve expressed appreciation for my pictures over the years.

Adjusting the Range: How to Scale Data

July 29th, 2014 by Robert Simmon

This post was originally published by the Society for News Design for the Malofiej Infographic World Summit.

Palettes aren’t the only important decision when visualizing data with color: you also need to consider scaling. Not only is the choice of start and end points (the lowest and highest values) critical, but the way intermediate values are stretched between them.

Note: these tips apply to scaling of smoothly varying, continuous palettes. For discrete palettes divided into distinct areas (countries or election districts, for example, technically called a choropleth map), read John Nelson’s authoritative post, Telling the Truth.

For most data simple linear scaling is appropriate. Each step in the data is represented by an equal step in the color palette. Choice is limited to the endpoints: the maximum and minimum values to be displayed. It’s important to include as much contrast as possible, while preventing high and low values from saturating (also called clipping). There should be detail in the entire range of data, like a properly exposed photograph.




These maps of sea surface temperature (averaged from July 2002 through January 2014) demonstrate the importance of appropriately choosing the range of data in a map. The top image varies from -5˚ to 45˚ Celsius, a few degrees wider than the bounds of the data. Overall it lacks contrast, making it hard to see patterns. The lower image ranges from 0˚ to 28˚ Celsius, eliminating details in areas with very low or very high temperatures. (NASA/MODIS.)



Ocean chlorophyll (a measure of plant life in the oceans) ranges from hundredths of a milligram per cubic meter to tens of milligrams per cubic meter, more than 3 orders of magnitude. Both of these maps use the almost same endpoints from near 0 (it’s impossible to start a logarithmic scale at exactly 0) to 11. Plotted linearly, the data show a simple pattern: narrow bands of chlorophyll along coastlines, and none in mid-ocean. A logarithmic base-10 scale reveals complex structures throughout the oceans, in both coastal and deep water. (NASA/MODIS.)

Some visualization applications support logarithmic scaling. If not, you’ll need to apply a little math to the data (for example calculate the square root or base 10 logarithm) before plotting the transformed data.

Appropriate decisions while scaling data are a complement to good use of color: they will aid in interpretation and minimize misunderstanding. Choose a minimum and maximum that reveal as much detail as possible, without saturating high or low values. If the data varies over a very wide range, consider a logarithmic scale. This may help patterns remain visible over the entire range of data.

Malofiej 22 Wrap-up

April 4th, 2014 by Robert Simmon

Last week I had the privilege of helping judge the 22nd Malofiej Awards. Presented by the Spanish Chapter of the Society for News Design, Malofiej 22 recognized the best news infographics of 2013. (For the uninitiated, infographics aren’t limited to those posters illustrating tenuously related facts with a handful of pie charts. A good infographic is an information-rich visual explanation; often incorporating illustration and text, but sometimes they’re as simple as a line graph.)

My fellow jurors for the online competition were an eclectic and brilliant bunch: Anatoly Bondarenko, interactive visualization designer for Texty (a Ukrainian guerrilla news site); Scott Klein, news applications editor at ProPublica, Golan Levin, artist & professor at Carnegie Mellon University; and Sérgio Lüdtke, journalist and professor of digital journalism at the International Institute of Social Sciences. (Good thing I didn’t read these bios before heading to Spain: I would have been more intimidated than I already was.)

A traditional Spanish lunch for the Malofiej jurors and student volunteers.

For four days we evaluated, discussed, and sometimes argued about 400 or so online visualizations. We first narrowed the field down to about 100, then selected 8 gold, 24 silver, and 39 bronze medals (assuming I counted right). It was interesting to me that we largely liked (and disliked) the same entries, but often for different reasons. This led to several “ah ha” moments, when I suddenly appreciated a new perspective on a figure.

Instead of writing about the golds, which have already garnered their fair share of praise, here are some of my favorite silver and bronze winners.

China’s Memory Hole: The Images Erased From Sina Weibo (ProPublica)
The Images Erased From Sina Weibo
The beauty of this piece is that the images are the data: images censored from Weibo, a Chinese service similar to Twitter.

Electionland (Zeit Online)
I have a few quibbles about this map of the 2013 German Bundestag elections, but it’s a brilliant bit of data crunching and abstraction. Voting districts are positioned based on the similarity of their voting patterns, rather than geography.

The federal health-care exchange’s abysmal success rate (The Washington Post)
The federal health-care exchange’s abysmal success rate
It’s a slope graph, what more do I need to say?

Of course, if you’re on the Malofiej jury picking awards is the easy part. You’re then on the hook to give a talk to some of the best designers and data visualizers in the world.

The Malofiej audience.

Jonathan Corum (Two Little Ducks) and Anatoly Bondarenko (Texty on Malofiej-22) gave two of the standouts.

Phew. What a long, exhausting, tremendously rewarding week.

Finally, I’d like to personally thank the student volunteers that functioned as tour guides, travel agents, translators, and personal assistants. They also made sure the online jury got our fair share of chocolate croissants, showed us the best pintxos bars in Pamplona, and even provided tips on surviving a run with the bulls [train, wear white, and run sober (which should’t be that hard, considering it starts at 8:00 a.m.!)]: Carmen Guitián, Alicia Arza, Ann Radjassamy, Beatriz Ciordia, Carmen Arroyo, Carmen Camey, Gabriela Suescum, Kristina Votrubová, Lucía Pérez, Andrés Juárez, Leire Emparanza, María Elena Quiñonero, Mariu Tena, and Alicia Alamillos.


Subtleties of Color (Part 2 of 6)

August 6th, 2013 by Robert Simmon

The “Perfect” Palette

Despite the near-ubiquity of the rainbow palette—which distorts patterns in the underlying data—the basics of using color to represent numerical data are well-established.

1823 map depicting temperature zones in color. This 1823 map by W. C. Woodbridge is an early example of the use of colors to represent numbers—in this case more qualitative than quantitative. The rainbow palette is effective for this map because colors in the spectrum are perceived as “cool” and “warm,” and the colors clearly segment the climate zones. Map from the Historic Maps Collection, Princeton University Library.

By the mid-1960s cartographers had already established guidelines for the appropriate use of color in map-making. Jacques Bertin pointed out shortcomings of the rainbow palette in Sémiologie Graphique (The Semiology of Graphics), and Eduard Imhof was crafting harmonious color gradients for use in topographic maps [published in Kartographische Geländedarsellung (Cartographic Relief Presentation)].

Topographic and bathymetric map of Crater Lake, Oregon. The subtle colors in this bathymetric map of Crater Lake are a direct descendent of the palettes created by Eduard Imhof. Map courtesy National Park Service Harper’s Ferry Center.

In the 1980s and 1990s researchers in perception and visualization were investigating the efficacy of palettes, based on the ways our brains and eyes physically respond to light. These color scales were crafted to achieve the principal goals of spatial displays: to show patterns and relationships in data, and to allow a viewer to accurately read individual values. [Colin Ware (1988) Color Sequences for Univariate Maps: Theory, Experiments, and Principles; Brewer (1994) Color Use Guidelines for Mapping and Visualization; Rogowitz and Treinish (1995) How NOT to Lie with Visualization; Tufte (1997) Visual Explanations; Spence et al. (1999) Using Color to Code Quantity in Spatial Displays.]

According to much of this research, a color scale should vary consistently across the entire range of values, so that each step is equivalent, regardless of its position on the scale. In other words, the difference between 1 and 2 should be perceived the same as the difference between 11 and 12, or 101 and 102, preserving patterns and relationships in the data. (For data with a wide range that is better displayed logarithmically, relative proportions should be maintained: the perceived difference between 1 and 10 should be the same as 1,000 and 10,000.) Consistent relationships between numbers—like in a grayscale palette—preserves the form of the data. Palettes with abrupt or uneven shifts can exaggerate contrast in some areas, and hide it others.

Rainbow and grayscale palettes. Compared to a monochromatic or grayscale palette the rainbow palette (IDL number 35) tends to accentuate contrast in the bright cyan and yellow regions, but blends together through a wide range of greens.

A palette should also minimize errors from the color shifts introduced by nearby areas of differing color or lightness, a phenomenon known as simultaneous contrast.

Demonstration of simultaneous contrast. Simultaneous contrast (a visual phenomenon that helps us interpret shapes through variations in brightness) shifts the appearance of colors and shades based on their surroundings. (After Ware (1988).)

Simultaneous contrast is most pronounced in monochromatic palettes, while sharp variations in hue minimize the effect. As a result variations of the rainbow palette are good for preserving exact quantities.

How to take advantage of the strengths of both the grayscale palette (preservation of form) and rainbow palette (preservation of quantity), while minimizing their weaknesses? Combine a linear, proportional change in lightness with a simultaneous change in hue and saturation. Colin Ware describes this type of palette as “a kind of spiral in color space that cycles through a variety of hues while continuously increasing in lightness” (Information Visualization: Perception for Design, Second Edition). The continuous, smooth increase in lightness preserves patterns, the shift in hue aids reading of exact quantities, and the change in saturation enhances contrast.

Three sequential palettes. A color palette that combines a continuous increase in lightness with a shift in hue is a good compromise that preserves both form and quantity. These three palettes show the smooth, even gradations that result from color scales calculated in perceptual color spaces. Color scales with varied hues and contrast are suitable for representing different datasets. (After Spence et al. (1999), chroma.js, and Color Brewer.)

Of the three components of color—hue, saturation, and lightness—lightness is the strongest. As a result, accurate, one-way changes in lightness are more important than those in hue or saturation. For example, a color scale that goes from black to color to white can still be read accurately, even though the saturation is lower at both ends of the scale than in the middle. This allows a bit of flexibility in designing palettes, especially for datasets that benefit from high-contrast color ramps. You also don’t need to worry too much about color scales that drift a little bit out of gamut (the complete range of colors displayed on a particular device) for a portion of the ramp. Just make sure lightness is still changing smoothly.

A variation on the perfect palette that goes from 100 percent black  to 100 percent white. This palette differs from the ideal with saturation that increases from low-to-mid values, and decreases from mid-to-high values. It’s still readable because lightness, the component of color perceived most strongly, changes continuously. (Derived with the NASA Ames color tool).

All of these palettes are appropriate for sequential data. Data that varies continuously from a high to low value; such as temperature, elevation, or income. Different palettes are suited to other types of data, such as divergent and qualitative, which I’ll discuss next week.

Subtleties of Color
Part 1: Introduction
Part 3: Different Data, Different Colors
Part 4: Connecting Color to Meaning
Part 5: Tools & Techniques
Part 6: References & Resources for Visualization Professionals

(This series on the use of color in data visualization is being cross-posted on Thanks to Drew Skau at for the invitation.)

Subtleties of Color (Part 1 of 6)

August 5th, 2013 by Robert Simmon

The use of color to display data is a solved problem, right? Just pick a palette from a drop-down menu (probably either a grayscale ramp or a rainbow), set start and end points, press “apply,” and you’re done. Although we all know it’s not that simple, that’s often how colors are chosen in the real world. As a result, many visualizations fail to represent the underlying data as well as they could.

The purpose of data visualization—any data visualization—is to illuminate data. To show patterns and relationships that are otherwise hidden in an impenetrable mass of numbers.

Hand-colored image of Mars from Mariner 4.
Encoding quantitative data with color is (sometimes literally) a simple matter of paint-by-numbers. In 1964 Richard Grumm and his team of engineers at NASA’s Jet Propulsion Laboratory hand-colored the first image of Mars taken from an interplanetary probe as they waited for computers to process the data.

In spatial datasets [datasets with at least two dimensions specifying position, and at least one additional dimension of quantity (a category that includes not only maps, but everything else ranging from individual atoms to cosmic background radiation)] color is probably the most effective means of accurately conveying quantity, and certainly the most widespread. Careful use of color enhances clarity, aids storytelling, and draws a viewer into your dataset. Poor use of color can obscure data, or even mislead.

Scanning Tunneling Electron Microscope and Cosmic Background Radiation images.
Color can be used to encode data from the atomic scale (left), to the universal (right). (Scanning tunneling microscope image originally created by IBM Corporation (left), cosmic background radiation image courtesy ESA and the Planck Collaboration (right)).

Fortunately, the principles behind the effective use of color to represent data are straightforward. They were developed over the course of more than a century of work by cartographers, and refined by researchers in perception, design, and visualization from the 1960s on.

Although the basics are straightforward, a number of issue complicate color choices in visualization. Among them:
The relationship between the light we see and the colors we perceive is extremely complicated.
There are multiple types of data, each suited to a different color scheme.
A significant number of people (mostly men), are color blind.
Arbitrary color choices can be confusing for viewers unfamiliar with a data set.
Light colors on a dark field are perceived differently than dark colors on a bright field, which can complicate some visualization tasks, such as target detection.

(Very) Basic Color Theory
Although our eyes see color through retinal cells that detect red, green, and blue light, we don’t think in RGB. Rather, we think about color in terms of lightness (black to white), hue (red, orange, yellow, green, blue, indigo, violet), and saturation (dull to brilliant). These three variables (originally defined by Albert H. Munsell) are the foundation of any color system based on human perception. Printers and painters use other color systems to describe the mixing of ink and pigment.

Lighntess, hue, and saturation color ramps.
Lightness, hue, and saturation (sometimes called chroma) are the building blocks of color.

Computers (and computer programmers) on the other hand, do process colors in terms of red, green, and blue. Just not the same red, green, and blue that our eyes detect. Computer screens display colors that are a combination of very narrow frequency bands, while each type of cone in our eyes detect a relatively broad spectrum. Complicating things further, computers calculate light linearly, while humans perceive exponentially (we are more sensitive to changes at low light levels than high light levels), and we’re more sensitive to green light than red light, and even less sensitive to blue light.

Red, green, blue color ramps (and their grayscale equivalents).
Computers calculate color using three primary colors—red, green, and blue. Unfortunately, we see green as brighter than red, which itself is brighter than blue, so colors specified in terms a computer understands (RGB intensities from 0-255) don’t always translate well to how we see.

The combined result of these nonlinearities in our vision is color perception that’s, well, lumpy. For example, the range of saturation we’re capable of seeing for a single hue is highly dependent on its lightness. In other words, there’s no such thing as a dark yellow. Near the center of the lightness range, blue and red shades can be very saturated, but green tones cannot. Very light and very dark colors are always dull.

An illustration of CIE L*C*h color space.
The range of colors perceived by humans is uneven. (Equiluminant colors from the NASA Ames Color Tool)

CIE Color Spaces
The unevenness of color perception was mapped by the International Commission on Illumination (Commission Internationale de l´Eclairage in French, hence “CIE”) in the 1930s. The CIE specified (and continues to refine) a series of color spaces that allow scientists, artists, and printers—anyone who works with light—to describe colors consistently, and accurately translate color between mediums. CIE L*a*b, for example, is used internally by Adobe Photoshop to interpolate color gradients and convert images from RGB (screen) to CMYK (print).

Another of these specifications: CIE L*C*h [lightness, chroma (saturation), hue] is my preferred tool for crafting color palettes for use in visualization. Because the three components of CIE L*C*h are straightforward, it’s simple to use. Because it’s based on studies of perception, color scales developed with L*C*h help accurately represent the underlying data. I say “help” because perfect accuracy is impossible—there are too many variables in play between the data and our brains. [Another option (used in Color Brewer) is the Munsell Color System, which is accurate in lightness and hue, but not in saturation.]

Comparison of linear and nonlinear color ramps.
Choosing and interpolating colors in a perceptual space—CIE L*c*h—helps ensure consistent change across the entire palette. In this example, which varies from pale yellow to blue, the range of green shades is expanded, and blues are compressed in the nonlinear palette relative to the linear palette. Palettes generated via Gregor Aisch’s L*C*h color gradient picker and chroma.js

In short, people aren’t computers. Computer colors are linear and symmetrical, human color perception is non-linear and uneven. Yet many of the tools commonly used to create color schemes are designed more for computers than people. These include tools that calculate or specify colors in the red, green, blue (RGB) or hue, saturation, value (HSV) color spaces. A constant increase in brightness is not perceived as linear, and this response is different for red, green, and blue. Look for tools and color palettes that describe colors in a perceptual color space, like CIE L*C*h or Munsell.

In the rest of this series, I’ll outline the principles behind the “perfect” color palette, describe different types of data that require unique types of palettes, give some suggestions for mitigating color blindness, and illustrate some tricks enabled by careful use of colors.

Subtleties of Color
Part 2: The “Perfect” Palette
Part 3: Different Data, Different Colors
Part 4: Connecting Color to Meaning
Part 5: Tools & Techniques
Part 6: References & Resources for Visualization Professionals

(This series on the use of color in data visualization is being cross-posted on Thanks to Drew Skau at for the invitation.)

From Tweet Density to Satellite Remote Sensing

June 28th, 2013 by Robert Simmon

Somehow, a recent conversation on Twitter about tweet density led to a mention of the installation of variable highway lighting in the Netherlands, by way of the 2012 NASA/NOAA city lights map. Which made me wonder—would we be able to see the effects of the new lighting from space? After all, the day night band on the VIIRS instrument is sensitive enough to see by starlight.

According to Philips, streetlights along the A7 from Purmerend (just north of Amsterdam) to Wognum are dimmed 50% after 9 pm. The system was switched on in early 2013, so any change should be visible in 2012 vs. 2013 data:



It’s subtle, but the difference is clear.

A wider view of the area reveals more interesting features:


The brightest lights in the Netherlands are from clusters of greenhouses—perhaps growing hydroponic vegetables 24 hours a day. The Netherlands and Belgium are more densely populated and brightly lit than neighboring France and Germany, including their highways. Unsurprisingly water bodies are largely dark, but there is a scattering of boats in the North Sea.

A few notes on the image processing. These data are from the NOAA CLASS system, which is the primary archive for VIIRS data. Unfortunately they’re not in a convenient format: each file is an 80-second chunk of the satellite’s orbit (called a granule), with no preview images. The file format is HDF, which many scientists like but can be (extremely) difficult to read. (This Beginner’s Guide from Colorado State University may be helpful.)

The product is labeled “VIIRS Day Night Band SDR.” SDR means “science data record” which is a calibrated measurement, in this case Watts per square meter per steradian. It turns out this is a very low number, so to visualize the data we multiply everything by 1,500,000,000,000,000 (1.5E15) to make a usable 16-bit grayscale image, which looks like this:


There’s little detail aside from the very brightest lights, so we take the square root of the data to accentuate low values and compress high values—the results of which are in the top images. I usually apply additional contrast adjustments on published imagery, but here I wanted to be more conservative.

Thanks to Gregor Aisch (@driven_by_data), Moritz Stefaner (‏@moritz_stefaner), Eric Fischer (@enf), and Jan Willem Tulp (@JanWillemTulp) for piquing my interest. My Twitter handle is @rsimmon if you’re interested in data visualization, Earth science, and NASA (with the occasional digression into hockey and dogs). Additional thanks to my colleague Jesse Allen for converting the HDF into something useful.


Dan W. Williams made a GIF with the 2012 and 2013 images which makes the changes easier to see. Thanks!


Update 2:

By request, here are the full files as GeoTIFFs. You’ll need a TIFF reader that supports floating point (Photoshop does not) to read the “Raw” and “Scaled” data. Photoshop (and even Safari on a Mac) will read the two “Square Root” files.

March 27, 2012
Raw Floating Point
Scaled Floating Point
Square Root 16 bit Integer

March 4, 2013
Raw Floating Point
Scaled Floating Point
Square Root 16 bit Integer

A Quick Guide to Earth Explorer for Landsat 8

May 31st, 2013 by Robert Simmon

The Landsat Data Continuity Mission is now Landsat 8, and that means images are now public (woohoo!). NASA handed control of the satellite to the USGS yesterday (May 30, 2013), and calibrated imagery is available through the Earth Explorer. Unfortunately, the Earth Explorer interface is a bit of a pain, so I’ve put together a guide to make it easier.

First, go to the Earth Explorer site:

You can search, but not order data, without logging in—so register if you don’t have an account (don’t worry, it’s instant and free), or log in if you do.

First step, register.

The simplest way to select a location is to simply pick a single point on the map. You can define a box or even a polygon, but that makes it more likely you’ll get images with only partial coverage. Navigate to the location you’re interested in, and click to enter the coordinates. You can choose a data range, but right now there are only 3 or 4 scenes for a given spot, so skip it and just click “Data Sets”.

Pick a spot, then click "Data Sets"

On the data sets page, you can search everything from Aerial Imagery to Vegetation Monitoring. Click the “+” symbol next to Landsat Archive, then the first check box that appears: “L8 OLI/TIRS” (which stands for Landsat 8 Operational Land Imager/Thermal Infrared Sensor (creative, no?)). Click “Results” to start a search.

Select Landsat 8, then Results.

After a short wait, you’ll get a list of available images. The thumbnails aren’t big enough to show much, so click on one to see a slightly larger image. Close that window, and click the download icon: a green arrow pointing down towards a hard drive …


… which doesn’t actually download the data, just provides a list of download options. “LandsatLook” are full-resolution JPEGs, and are a quick way to check image quality (I’d prefer full-resolution browse images without a separate download, but I digress). The Level 1 Product is terrain-corrected, geolocated, calibrated data—a bundle of 16 bit, single-channel GeoTIFFs. Select the “Level 1 Product” radio button, then click “Select Download Option”.


Done! Oh, wait. Not done. You need to click one more button: “Download”.


Now you’re done. The data should arrive in your browser’s designated download folder.

Drop a note in the comments section if I’ve skipped a step, or if you have any other questions. Next week I’ll explain what to do with the data once you’ve got it.

Skylab 2 above the Earth's terminator.

This photo of Skylab was taken by the astronauts of Skylab-2 as they left the space station and departed for Earth on June 22, 1973. More photos from all three Skylab missions are archived on NASA’s Gateway to Astronaut Photography of Earth.

To look through the rest of the Skylab collection, select Find Photos > Search > Mission-Roll-Frame from the menu in the upper-left hand corner of the Gateway to Astronaut Photography of Earth home page. Under Missions pick one or more of SL2, SL3, and SL4, then delete the “E” in the Roll field. Finally, hit Run Query at the bottom of the page. On the Database Search Results page, enable the Show thumbnails if they are available checkbox. Click the number in the Frame column to view a screen-sized image. High-res images are downloadable from each Display Record, just click the View link for the image size you want.


Annular Solar Eclipse: May 10, 2013

May 10th, 2013 by Robert Simmon

Japan’s MTSAT-2 (also known as Himawari-7) collected these images of today’s annular solar eclipse from geostationary orbit. The satellite (similar to the United State’s GOES satellites), observed the moon’s shadow as it passed over Australia & the Pacific Ocean. The image sequence begins at 21:32 UTC, with an additional image each hour until 02:32 UTC. The eclipse itself lasted from 22:33 UTC until 02:20 UTC.


Visible-light & infrared MTSAT images dating back to October 2006 are available from the Earthquake Research Institute & Institute of Industrial Science, University of Tokyo.

Earth Day and Night

April 22nd, 2013 by Robert Simmon



Earth Day (8,192 by 8,192 pixels, 9.1 MB JPEG)
Earth Night (8,192 by 8,192 pixels, 4.2 MB JPEG)

Credit: NASA Earth Observatory images by Robert Simmon, using Suomi NPP VIIRS data from Chris Elvidge (NOAA National Geophysical Data Center). Suomi NPP is the result of a partnership between NASA, NOAA, and the Department of Defense.