five

Characterization of Titan’s Northern Polar Terrains from Inversion of Cassini RADAR Data

收藏
DataCite Commons2025-10-01 更新2026-05-03 收录
下载链接:
http://dataverse.jpl.nasa.gov/citation?persistentId=doi:10.48577/jpl.GLQEDY
下载链接
链接失效反馈
官方服务:
资源简介:
We investigated the surface geophysical properties of Titan through a multi-angular backscattering analysis of Cassini Ku-band radar data acquired in altimetry, SAR, scatterometry and radiometry modes. We test quasi-specular scattering models in combination with a volume scattering formulation to model the backscatter coefficient as a function of incidence angle over specific areas identified from topographic and radar imaging data. From data inversion we found that the exponential model provides the better fit to the observed data. We present our results in terms of effective permittivity, surface roughness and volume scattering albedo across different backscattering uniform regions at Titan’s northern hemisphere, where both bright and dark terrains have been observed from radar imagery acquired during the mission. Our results indicate that these bright regions belong to a large low- lying plain made of material with moderate roughness, a relatively high dielectric constant and high-volume scattering albedo. In contrast, dark regions appear to consist of accumulations of solid hydrocarbons, with thicknesses reaching up to hundreds of meters, overlaying this plain. Dark regions exhibit large-scale surface roughness on the order of tens of meters, a lower dielectric constant, and lower volume albedo when compared to the bright regions. Finally, we propose some most-probable scenarios based on a list of candidate materials that may be present at the surface of Titan. Although our analysis focuses on a specific, albeit extensive, region near the North Pole, our findings may offer broader insights into the global distribution of complex organics on Titan surface, which are likely superimposed on top of a mixture of ice and organic material.
提供机构:
Root
创建时间:
2025-10-01
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作