• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.51-60
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• 2010

To determine subglacial topography and internal features of the Fourcade Glacier on King George Island in Antarctica, helicopter-borne and ground-towed ground-penetrating radar (GPR) data were recorded along four profiles in November 2006. Signature deconvolution, f-k migration velocity analysis, and finite-difference depth migration applied to the mixed-phase, single-channel, ground-towed data, were effective in increasing vertical resolution, obtaining the velocity function, and yielding clear depth images, respectively. For the helicopter-borne GPR, migration velocities were obtained as root-mean-squared velocities in a two-layer model of air and ice. The radar sections show rugged subglacial topography, englacial sliding surfaces, and localised scattering noise. The maximum depth to the basement is over 79m in the subglacial valley adjacent to the south-eastern slope of the divide ridge between Fourcade and Moczydlowski Glaciers. In the ground-towed profile, we interpret a complicated conduit above possible basal water and other isolated cavities, which are a few metres wide. Near the terminus, the GPR profiles image sliding surfaces, fractures, and faults that will contribute to the tidewater calving mechanism forming icebergs in Potter Cove.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.321-335
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• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.