Interference Imaging Spectrometer Data Research Articles

Quantitative Inversion of Lunar Surface Chemistry Based on Hyperspectral Feature Bands and Extremely Randomized Trees Algorithm

In situ resource utilization (ISRU) is required for the operation of both medium and long-term exploration missions to provide metallic materials for the construction of lunar base infrastructure and H2O and O2 for life support. The study of the distribution of the lunar surface elements (Fe, Ti, Al, and Si) is the basis for the in situ utilization of mineral resources. With the arrival of the era of big data, the application of big data concepts and technical methods to lunar surface chemistry inversion has become an inevitable trend. This paper is guided by big data theory, and the Apollo 17 region and the area near the Copernicus crater are selected for analysis. The dimensionality of the first-order differential spectral features of lunar soil samples is reduced based on Pearson correlation analysis and the successive projections algorithm (SPA), and the extremely randomized trees (Extra-Trees) algorithm is applied to Chang’E-1 Interference Imaging Spectrometer (IIM) data to establish a prediction model for the lunar surface chemistry and generate FeO, TiO2, Al2O3, and SiO2 distribution maps. The results show that the optimum number of variables for FeO, TiO2, Al2O3, and SiO2 is 17, 5, 8, and 30, respectively. The accuracy of the Extra-Trees model using the best variables was improved over that of the original band model, with determination coefficients (R2) of 0.962, 0.944, 0.964, and 0.860 for FeO, TiO2, Al2O3, and SiO2, and root mean square errors (RMSEs) of 1.028, 0.672, 0.942, and 0.897, respectively. The modeling feature variables and model preference methods in this study can improve the inversion accuracy of chemical abundance to some extent, demonstrating the potential of IIM data in predicting chemical abundance and providing a good data basis for lunar geological evolution studies and ISRU.

Modeling the Reflectance of the Lunar Regolith by a New Method Combining Monte Carlo Ray Tracing and Hapke’s Model with Application to Chang’E-1 IIM Data

In this paper, we model the reflectance of the lunar regolith by a new method combining Monte Carlo ray tracing and Hapke's model. The existing modeling methods exploit either a radiative transfer model or a geometric optical model. However, the measured data from an Interference Imaging spectrometer (IIM) on an orbiter were affected not only by the composition of minerals but also by the environmental factors. These factors cannot be well addressed by a single model alone. Our method implemented Monte Carlo ray tracing for simulating the large-scale effects such as the reflection of topography of the lunar soil and Hapke's model for calculating the reflection intensity of the internal scattering effects of particles of the lunar soil. Therefore, both the large-scale and microscale effects are considered in our method, providing a more accurate modeling of the reflectance of the lunar regolith. Simulation results using the Lunar Soil Characterization Consortium (LSCC) data and Chang'E-1 elevation map show that our method is effective and useful. We have also applied our method to Chang'E-1 IIM data for removing the influence of lunar topography to the reflectance of the lunar soil and to generate more realistic visualizations of the lunar surface.

The improvement of cross-calibration of IIM data and band selection for FeO inversion

Chang’E-1 (CE-1) Interference Imaging Spectrometer (IIM) dataset suffers from the weak response in the near infrared (NIR) bands, which are the important wavelength for retrieving the minerals and elements of the Moon. In this paper, the cross-calibration was implemented to the IIM hyperspectral data for improving the weak response in NIR bands. The results show that the cross-calibrated IIM spectra were consistent to the Earth-based telescopic spectra, which suggests that the cross-calibration yields acceptable results. For further validating the influence of the cross-calibration on the FeO inversion and searching the optimal bands to retrieve lunar FeO contents, four band selection schemes were designed to retrieve FeO using the original and cross-calibrated IIM spectra. By comparing the distribution patterns and histograms of the IIM derived FeO contents with the Clementine derived FeO, the IIM 891 nm band after cross-calibration showed a higher accuracy in the FeO inversion, hence most useful for lunar FeO inversion.

Photometric correction and in-flight calibration of Chang’ E-1 Interference Imaging Spectrometer (IIM) data

The main objective of this study is to develop a new photometric correction that is suitable for global Chang’ E-1 (CE-1) Interference Imaging Spectrometer (IIM) data. We considered two improvements in the accuracy of the photometric correction: (1) classifying the whole Moon’s surface into four classes (very bright rays, mature highlands, low FeO basalts, and high FeO basalts) based on the FeO contents; (2) decoupling the combined effects of the solar photometry and the detector responsivity along the cross-track. The results showed that our decoupling method could correct the non-uniformity of detector response but retain the cross-track photometry. Based on these in-flight calibrated data, spectrally continuous photometric functions of the four classes were obtained. By eliminating the contamination from highlands ejecta and low FeO basalts, the opposition surge of the high FeO maria was evident for the first time. The amplitude and width of the opposition surge presents wavelength dependence, which suggests that shadow hiding is the mechanism responsible for the opposition surge. A global lunar reflectance map whose boundaries between adjacent orbits are invisible was generated using the new four-type photometric functions. The quality of the global mosaic and the consistency of the reflectance spectra of the same area obtained in different orbits indicate the effectiveness of our photometric correction method. Although our method was specifically applied to the IIM data, the photometric correction method and the parameter values derived in this study is expected to be suitable for other optical instruments.

Refinement of lunar TiO2 analysis with multispectral features of Chang’E-1 IIM data

We develop a method based on the samples from Apollo and Luna landing sites to determine lunar TiO2 content with Chang’E-1 interference imaging spectrometer (IIM) imagery. By analyzing the nonlinear relationship between the optical and compositional parameters of lunar soil samples, the method employs two Support Vector Machines (SVMs) to estimate the titanium abundance of the lunar surface. Developed with the soil compositions of the Apollo and Luna sample-return stations, the RMS (root mean square) error of our method is 0.24 wt% TiO2, and the correlation coefficient of the TiO2 values and our predicted ones is 99.72 %. Compared with the other 3 models, the method proposed in this paper exhibits a good performance for determining the chemical composition of the lunar surface. TiO2 maps of Sinus Iridum, part of the Marius Hills plateau, and part of Mare Smythii are produced using our method, which could be useful for future lunar missions.

Major elements and Mg# of the Moon: Results from Chang’E-1 Interference Imaging Spectrometer (IIM) data

Major elements such as Fe, Ti, Mg, Al, Ca, and Si play very important roles in understanding the origin and evolution of the Moon. Up to now there are still no high resolution maps for all the six major elements and Mg# showing their abundances and distributions. The objective of this paper is to introduce the techniques and results to create high resolution maps of the six major elements with Chang’E-1 Interference Imaging Spectrometer (IIM) data. The elemental contents of returned soils and feldspathic lunar meteorites are used as ground truth and correlated with the reflectance of the landing sites and northern farside highlands extracted from Chang’E-1 IIM data. The relationships linking geochemical information and optical data are established with empirical methods. The final maps of the abundances of the six major oxides are produced with the modified partial least squares regression (PLSR) model at a spatial resolution of 200m/pixel at the equator. Finally some implications (for example, producing the high resolution map of Mg#; identifying the crustal heterogeneity of lunar highlands; helping to search for HA basalts; assessing the average elemental abundances of each terrane and the whole Moon) of these new maps are shown.

Mapping Lunar global chemical composition from Chang'E-1 IIM data

The global distribution of the chemical composition of the lunar surface is an important factor helping us to understand the formation and evolution of the Moon. In this paper, formulas were established for deriving FeO, TiO2, Al2O3 and MnO abundances from Chang'E-1 (CE-1) Interference Imaging Spectrometer (IIM) data on the basis of the method “color ratio of UV/VIS and NIR/VIS versus VIS reflectance diagram” which was put forward by Lucey and Blewett. Global high-resolution maps (200m/pixel) of FeO, TiO2, Al2O3 and MnO were produced, and then compared qualitatively with results from Clementine UVVIS, Lunar Prospector (LP) Gamma-Ray Spectrometer (GRS) and Neutron Prospector (NS) data. The abundance ranges of the above four elements are 0–21.0wt%, 0–9.5wt%, 5.4–32.1wt%, and 0.015–0.28wt% respectively. The abundance range of FeO is consistent with the results from LP-GRS data reported by Gillis et al. (2004), and the abundance range of TiO2 is consistent with the results from LP-NS data reported by Elphic et al. (2002). Relative abundance distributions of FeO and TiO2 from Clementine and IIM data are slightly different from those from LP-GRS and LP-NS data. In map from the LP-GRS data, FeO abundances are the highest at Oceanus Procellarum and Mare Imbrium. However, in the map from CE-1 IIM data they are the highest at Oceanus Procellarum and Mare Tranquillitatis. Although the spatial resolution of these maps is high, caution must be taken when the maps in this paper are used at the crater scale because they suffer from errors owing to topographically induced shading. In future work, a high-accuracy DEM from Lunar Reconnaissance Orbiter Mission Laser Altimeter (LOLA) data coupled with a photometric model can probably be used to resolve this problem.

Global estimates of lunar iron and titanium contents from the Chang' E‐1 IIM data

Until recently, global high spatial resolution maps of FeO and TiO2 of the Moon were only derived from Clementine data. In this study, we show global maps of FeO and TiO2 using Chang'E‐1 Interference Imaging Spectrometer (IIM) at a spatial resolution of 200 m/pixel. With a newly developed calibration presented here, spectra obtained by IIM compare well with telescopic spectra. Spectral parameters previously shown to be sensitive to iron and titanium, derived from the calibrated IIM data are highly correlated with the measured elemental concentration with R2 = 0.96 for FeO and 0.95 for TiO2. The maps were developed using this calibration. Histograms of basalt FeO estimates have a negatively skewed distribution, while TiO2 distributions are unimodal. They also revealed that the lunar highland crust is relatively uniform on the quadrant scale (several hundred to thousand kilometers scale) but inhomogenous on the global scale. The area of highest elevation of the Moon has very low FeO and TiO2 raising the question about South Pole‐Aitken (SPA) (whether its ejecta deposits covered the highest elevation and when it was formed). Although the average FeO and TiO2 abundances for basalts are highly correlated, local areas of elevated iron can be associated with both high and low titanium.

Lunar titanium abundance characterization using Chang’E-1 IIM data

Lunar titanium characterization is an important goal of the China Lunar Exploration Program. We suggest a method to determine the lunar titanium abundance using Chang’E-1 IIM (Interference Imaging Spectrometer) imagery. Using samples from Apollo and Luna landing sites, the method firstly establishes the spectral parameters that possess good non-linear correlations with lunar titanium abundance. Secondly, the method estimates lunar titanium abundance using a DT-SVM (Decision Tree Method C5.0-Support Vector Machine) method. Namely, according to the established spectral parameters, it uses the C5.0 algorithm to classify the titanium abundance into the 4 classes of very low, low, intermediate and high. Then, in terms of the spectral parameters and the corresponding classes, it employs the SVM to estimate the titanium abundance. The method makes good use of hyperspectral information, analyzes the nonlinear correlations between spectral characteristics of lunar soils and the composition parameter, and well determines the titanium abundance. Validated by the Apollo and Luna station samples, the RMSE (root mean square error) is 0.72wt% TiO2 and the correlation coefficient of the measured and predicted values is 97.29%. So, the method proposed in this paper has a good predictive capability for TiO2 abundance on the lunar surface. The maps of TiO2 content in the partial region of Sinus Iridium, the Apollo 17 landing site and the Apollo 16 landing site are constructed by our method. This paper demonstrates the potential of IIM data for the investigation of lunar surface chemistry and mineralogy.

A preliminary experience in the use of Chang’E-1 IIM data

The Interference Imaging Spectrometer (IIM) onboard the first lunar satellite of China, Chang’E-1, has acquired 84% of the area between south and north latitude 70°. To contribute to its usability, this paper presents our preliminary experience in the use of IIM data. Firstly, we provide one practicable method for the on-orbit correction of the inhomogeneity of sensor response. Secondly, aiming at the problem that the spectral range of IIM does not cover the absorption peak of the mafic mineral completely, we explore a method to approximate the absorption band center for IIM data. A strong correlation between the absorption band center and the wavelength at which the first derivative equals to 0 (i.e., stagnation point) was revealed. Based on the corrected data and the correlation, the absorption band center of several large craters was mapped. The distribution of rocks and minerals shown in the map of absorption band center for Aristarchus and Copernicus is in agreement with previous studies but with much finer structure. Horizontal and vertical lithologic diversity was detected in Zucchius crater. This paper demonstrates the potential of IIM data for the identification of lunar rocks due to its high spatial and spectral resolution. In a future study we will produce a global map of the absorption band center with greater accuracy and it is expected that this global map will provide complementary information for other hyperspectral data such as SP on KAGUYA or M 3 on Chandrayaan-1.