Because of its thick and opaque atmosphere, Titan had to be mapped in radar and infrared during a series of close flybys by the Cassini spacecraft. One artifact of this process: the resolution, lighting and atmospheric conditions were not consistent, so mosaic images and maps of Titan’s surface showed visible seams. That’s been corrected in these infrared images of Titan’s surface, released last week. The false-colour images remap infrared wavelengths to the visible spectrum, using a band-ratio technique that minimizes seams. “With the seams now gone, this new collection of images is by far the best representation of how the globe of Titan might appear to the casual observer if it weren’t for the moon’s hazy atmosphere, and it likely will not be superseded for some time to come.”
Because of its thick and opaque atmosphere, Saturn’s largest moon, Titan, has to be mapped piece by piece during close fly-bys by the Cassini spacecraft, using radar, infrared and visual data. The above image is one of two montages that “shows four synthetic views of Titan created using data acquired by the visual and infrared mapping spectrometer (VIMS) on board NASA’s Cassini spacecraft between 2004 and 2015. These views demonstrate some of the progress researchers have made in creating smooth-looking maps of Titan from the multitude of different VIMS observations made under a wide variety of lighting and viewing conditions.” More on VIMS here.
There are several online versions of the Carte générale de France, the first comprehensive map of France produced by the Cassini family in the 18th century. Some, like those hosted by the EHESS and the David Rumsey Map Collection, georectify and stitch together the individual maps together to make a more-or-less seamless whole. On Gallica, the Bibliothèque nationale de France’s digital library, it’s presented as individual sheets; the Library of Congress does the same with its copy—the better to appreciate the originals, I suppose. [via]
Lorenz’s team used a mathematical process called splining—effectively using smooth, curved surfaces to “join” the areas between grids of existing data. “You can take a spot where there is no data, look how close it is to the nearest data, and use various approaches of averaging and estimating to calculate your best guess,” he said. “If you pick a point, and all the nearby points are high altitude, you’d need a special reason for thinking that point would be lower. We’re mathematically papering over the gaps in our coverage.”