Global Lunar Christiansen Feature From LRO Diviner Radiometer Observation Data

The lunar surface Christiansen feature (CF), known as a prominent maximum emissivity centered near
$8~\mu \text{m}$
, has been widely used to identify silicate mineral types and estimate their compositions. The Diviner sensor provides global lunar surface observation at three 8-
$\mu \text{m}$
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 LST and emissivity from the Diviner’s three CF channels dataset, on the 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 article first 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 the 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 to
$0.372~\mu \text{m}$
with a bias of
$0.02~\mu \text{m}$
, and its value is generally larger than the previous study, indicating that the previous CF wavelength might be underestimated.