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- W4383750084 abstract "Lunar exploration has put further demands on high spatial resolution of the lunar surface temperature (LuST) data. However, available LuST data with coarse resolution provided by current remote sensing platforms (e.g., LRO Diviner) are unable to meet the requirement. Therefore, it is necessary to develop new methods to obtain LuST data with high spatial resolution. Inspired by the study of land surface temperature (LST) downscaling, here, we perform the exploratory study of the LuST downscaling. The core of the exploration work consists of two parts: 1) the 16 pixels per degree (ppd) Diviner Gridded Data Records (GDRs) images are downscaled to 128 ppd using a downscaling model based on geographically weighted regression (GWR), in order to explore the feasibility of downscaling LuST; and 2) two sets of experiments with scales of 4 and 8 are conducted and the growth characteristics of the errors are observed to determine whether it is feasible to obtain higher-spatial-resolution data. The evaluation metrics for the experiments in two specific craters with scale of 8 are 4.03/5.42 K for mean absolute error, 5.89/8.05 K for root mean square error and 0.037/0.048 for normalized root mean square error, respectively. The LuST downscaling study is feasible, and it provides an effective way for obtaining high-spatial-resolution LuST data, which has positive implications for lunar exploration." @default.
- W4383750084 created "2023-07-11" @default.
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- W4383750084 date "2023-01-01" @default.
- W4383750084 modified "2023-09-25" @default.
- W4383750084 title "Spatial Downscaling of Lunar Surface Temperature Based on Geographically Weighted Regression" @default.
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- W4383750084 doi "https://doi.org/10.1109/lgrs.2023.3293831" @default.
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