Computer Science – Performance
Scientific paper
Dec 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010agufm.p51c1450l&link_type=abstract
American Geophysical Union, Fall Meeting 2010, abstract #P51C-1450
Computer Science
Performance
[5410] Planetary Sciences: Solid Surface Planets / Composition, [5464] Planetary Sciences: Solid Surface Planets / Remote Sensing
Scientific paper
This work is part of our efforts for quantifying lunar surface minerals (agglutinate, clinopyroxene, orthopyroxene, plagioclase, olivine, ilmenite, and volcanic glass) from the lunar soil characterization consortium (LSCC) dataset with Hapke's radiative transfer model. We have implemented Hapke's radiative transfer model in the inverse mode in which instead of commonly used look-up table (LUT) Newton's theory was used to solve nonlinear questions for derivation of mineral absorption coefficients and estimation of mineral abundances. While the effects of temperature and surface roughness are incorporated into the implementation to improve the model performance for application of lunar spacecraft data, these effects are not considered in the current work because of the use of lab measured reflectance data. We first tested the inverse model with all samples of the LSCC dataset, the model showed poor performance, which is primarily degraded by samples with a high amount of SMFe. The model was then tested with relatively fresh samples (Is/FeO <= 50, totally 20 samples), and the results were compared with those resulting from genetic algorithm - partial least square models (GA-PLS). This comparison indicates radiative transfer modeling resulted in higher squared correlations and lower root mean square correlations than those from GA-PLS for all minerals (Figure 1). It is concluded that the inverse RTM is preferred over GA-PLS for deriving mineral information of lunar fresh samples. To apply this approach to lunar spacecraft data for mineral abundance estimation, the model needs to be improved for handling more mature lunar soil samples. Figure 1. Comparison of relative RMSE and r-squares of GA-PLS and inversion RTM results.
Li Lexin
Li Shiheng
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