One Moon, Many Measurements 3: Spectral reflectance
Introduction
Recent successes of lunar exploration missions further our understanding of the Moon by analyzing the spectral reflectance of the lunar surface measured by a variety of instruments (Haruyama et al., 2008a, Ohtake et al., 2009, Matsunaga et al., 2008, Pieters et al., 2009a, Mall et al., 2009). We can derive more information by combining data obtained by different missions because each instrument on the Selenological and Engineering Explorer (SELENE) and Chandrayaan-1 spacecraft has different advantages (e.g., spatial and spectral resolution, and spectral coverage). Because reflectance data are widely used for compositional analyses, comparing data obtained by different instruments is important for evaluating and understanding variations among instrument datasets. However, combining data obtained by different instruments with confidence is not simple because each dataset was obtained under different observation conditions (e.g., incidence (i), emission (e), or phase angle (g)) and with different spatial (and spectral) resolutions. Differences in calibration procedures and the quality of each instrument are additional factors. This paper summarizes the basic characteristics of reflectance data derived by each instrument and briefly describes the data-processing conversion from radiance to reflectance. Several examples of combined data demonstrate what can be achieved by integrating data obtained by different instruments on different missions. This information will be useful to all in the science community who use data acquired by different instruments for their research.
Section snippets
Derivation of reflectance spectra
Table 1 summarizes the data-processing procedure used for each instrument’s data to derive final reflectance values, which the instrument team then provides to the community. As described in Table 1, each instrument requires different data-correction steps according to the characteristics of that instrument. The following sections describe the procedures for each instrument. A companion paper by Pieters et al. (2013) described the hardware design of each instrument and the data processing from
Spectra derived in each data-processing step
Spectra derived for different data-processing steps described in Section 2 are compared for each instrument in Fig. 1. Spectra that result from each data-processing procedure (Table 1) are labeled as M3_step1, M3_step2, and so on. All data in Fig. 1 are spectra of the Apollo 16 standard site, which is used by the Clementine UVVIS camera (Eliason et al., 2003) and Earth-based telescopic observations (Pieters, 1999) as an optical standard site. It is 2 × 5 km in size and is located 10 km west of the
Representative laboratory-measured spectra
Laboratory-measured reflectance spectra of representative lunar material from the RELAB collection are presented in Fig. 3a to illustrate some of the diagnostic spectral properties of the near-infrared bands. The presented materials are mature highland soil (62231, which was also used for ground-truth calibration for Clementine UVVIS data as reported in Pieters (1999), immature highland soil (67461), immature mare soil (12030), mature mare soil (12070), and a particulate form of mare basalt
Spectral reflectance properties of the Moon
The absolute reflectance of the Moon at 1500 nm ranges from 0.08 to 0.3 in most locations, as demonstrated in Fig. 12 of a companion paper by Besse et al. (2013b). The average reflectance of highland regions is around 0.2 while that of the mare regions is around 0.1. The current dataset for the Moon, observed by several instruments, enables us to constrain and understand the lunar surface composition, compaction state, thermal condition, and space weathering. However, it is important to realize
Combined reflectance data for the Bullialdus area
This section presents the results of combined (and in some cases compared) analyses using all datasets (M3/SP/MI/SIR-2/TC data) for Bullialdus crater (−20.7°N, 337.8°E; 60 km in diameter) to demonstrate the efficiency of such analyses. Footprint locations of the available datasets for each instrument are indicated in Fig. 5a, overlaid on an MI base map. Red dots denote the SP footprints, blue hatches represent the M3 imaged areas, and light blue dots represent the SIR-2 footprints. TC
Acknowledgments
We are grateful for the dedication and accomplishments of the SELENE and Chandrayaan-1 flight teams who successfully provided extensive new lunar data to the science community. Participation by several authors in this analysis was supported through the NASA Lunar Science Institute (NNA09DB34A). This is SOEST publication number 8925 and HIGP publication number 2012.
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