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of Titan might appear to the casual observer if it weren’t for the moon’s hazy atmosphere, and it likely will not be superseded for some time to come.”
The Moon and Mars were relatively early additions to Google Earth; that application may have been migrated to the web, but the planets and moons keep coming. Yesterday Google announced the addition of a dozen other worlds in our solar system; the space layer of Google Maps now includes planets Mercury, Venus and Mars; dwarf planets Ceres and Pluto;1 Jupiter’s moons Io, Europa and Ganymede; and Saturn’s moons Dione, Enceladus, Iapetus, Mimas, Rhea and Titan. Large moons Callisto and Triton aren’t included, and Iapetus is projected onto a sphere rather than appearing as the bizarre space walnut it is.
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.
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.”