Global Lunar Christiansen Feature From LRO Diviner Radiometer Observation Data

发布时间： 2024-02-14 点击次数：

• 影响因子：8.4
• DOI码：10.1109/TGRS.2024.3357528
• 发表刊物：IEEE Transactions on Geoscience and Remote Sensing
• 关键字：Christiansen Feature; Lunar surface temperature and emissivity; Diviner; Thermal infrared; TES algorithm
• 摘要：The lunar surface Christiansen feature (CF), known as a prominent maximum emissivity centered near 8 μm, has been widely used to identify silicate mineral types and estimate their compositions. The Diviner sensor provides global lunar surface observation at three 8-μm CF channels and thus its data have promoted the extraction and mapping of global lunar surface CF. However, the previous studies used the empirical regression (ER) algorithm to extract the CF wavelength (or called CF position) after estimating lunar surface temperature (LST) and emissivity using a three-point parabola approximation, which inevitably leads to uncertainty. The physical temperature-emissivity separation (TES) algorithm was proposed recently by the authors to retrieve lunar surface temperature and emissivity from the Diviner’s three CF channels dataset, on basis of the surface’s physical radiative transfer equation, and therefore, this algorithm provides a promising way to revise the accuracy in the extraction of CF wavelength. From this point of view, this paper firstly illustrates the difference of LST, emissivity and CF wavelength between the TES and ER algorithms, and finds that the TES algorithm got higher accuracy in extracting CF wavelength. Consequently, the global lunar CF wavelengths are extracted from Diviner dataset. Compared to the result from the ER algorithm, the CF wavelength from the TES algorithm is found to be revised in a range of −0.051 μm to 0.372 μm with a bias of 0.02 μm, and its value is generally larger than previous study, indicating that the previous CF wavelength might be underestimated.
• 论文类型：期刊论文
• 论文编号：5001511
• 学科门类：理学
• 一级学科：地理学
• 文献类型：J
• 卷号：62
• 期号：5001511
• 页面范围：1-11
• 是否译文：否
• 收录刊物：EI、SCI
• 发布期刊链接：https://ieeexplore.ieee.org/document/10412187
• 第一作者：Zian Wang
• 通讯作者：Huazhong Ren
• 发表时间：2024-01-23