• Journal of The Korean Astronomical Society
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• v.50 no.1
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• pp.7-20
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• 2017

We carry out the study of $850{\mu}m$ sources in a part of the XMM-LSS field. The $850{\mu}m$ imaging data were obtained by the SCUBA-2 on the James Clerk Maxwell Telescope (JCMT) for three days in July 2015 with an integration time of 6.1 hours, covering a circular area with a radius of 15'. We choose the central area up to a radius of 9'.15 for the study, where the noise distribution is relatively uniform. The root mean square (rms) noise at the center is 2.7 mJy. We identify 17 sources with S/N > 3.5. Differential number count is estimated in flux range between 3.5 and 9.0 mJy after applying various corrections derived by imaging simulations, which is consistent with previous studies. For detailed study on the individual sources, we select three sources with more reliable measurements (S/N > 4.5), and construct their spectral energy distributions (SEDs) from optical to far-infrared band. Redshift distribution of the sources ranges from 0.36 to 3.28, and their physical parameters are extracted using MAGPHYS model, which yield infrared luminosity $L_{IR}=10^{11.3}-10^{13.4}L_{\odot}$, star formation rate $SFR=10^{1.3}-10^{3.2}M_{\odot}yr^{-1}$ and dust temperature $T_D=30-53K$. We investigate the correlation between $L_{IR}$ and $T_D$, which appears to be consistent with previous studies.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.61-73
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• 2014

This study reviewed an application of water quality estimation using an Airborne Hyperspectral Imagery (A-HSI) and tested a part of Han River water quality (especially suspended solid) estimation with available in-situ data. The estimation of water quality was processed two methods. One is using observation data as downwelling radiance to water surface and as scattering and reflectance into water body. Other is linear regression analysis with water quality in-situ measurement and upwelling data as at-sensor radiance (or reflectance). Both methods drive meaningful results of RS estimation. However it has more effects on the auxiliary dataset as water quality in-situ measurement and water body scattering measurement. The test processed a part of Han River located Paldang-dam downstream. We applied linear regression analysis with AISA eagle hyperspectral sensor data and water quality measurement in-situ data. The result of linear regression for a meaningful band combination shows $-24.847+0.013L_{560}$ as 560 nm in radiance (L) with 0.985 R-square. To comparison with Multispectral Imagery (MSI) case, we make simulated Landsat TM by spectral resampling. The regression using MSI shows -55.932 + 33.881 (TM1/TM3) as radiance with 0.968 R-square. Suspended Solid (SS) concentration was about 3.75 mg/l at in-situ data and estimated SS concentration by A-HIS was about 3.65 mg/l, and about 5.85mg/l with MSI with same location. It shows overestimation trends case of estimating using MSI. In order to upgrade value for practical use and to estimate more precisely, it needs that minimizing sun glint effect into whole image, constructing elaborate flight plan considering solar altitude angle, and making good pre-processing and calibration system. We found some limitations and restrictions such as precise atmospheric correction, sample count of water quality measurement, retrieve spectral bands into A-HSI, adequate linear regression model selection, and quantitative calibration/validation method through the literature review and test adopted general methods.