• Journal of the Korean earth science society
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• v.40 no.3
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• pp.212-226
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• 2019

This study derived spectral Lambertian Equivalent Reflectance (LER) over East Asia from the observations of Ozone Monitoring Instrument (OMI) onboard polar-orbit satellite Aura. The climatological (October 2004-September 2007) LER values were compared with the surface reflectance products of OMI or MODerate resolution Imaging Spectroradiometer (MODIS) in terms of the atmosphere-environment variables as follows: wavelength (UV, visible), surface properties (land, ocean), and cloud filtering. Four kinds of LER outputs in the UV and visible region (328-500 nm) were retrieved based on the averages of lowest (1, 5, and 10%) surface reflectance values as well as the minimum reflectance. The average of the lowest 10% among them was in best agreement with the OMI product: correlation coefficient (0.88), RMSE (1.0%) and mean bias (-0.3%). The 10% average and OMI LER values over ocean were 2% larger in UV than in visible, while the values over land were 1% smaller. The LER variability on the wavelength and surface property was highest (~3%) in the condition of both land and visible, particularly in the ice-cap and desert regions. The minimum reflectance values over the oceanic and inland sample areas overestimated the MODIS product by 1.4%. This high-resolution MODIS observations were effective in removing cloud contamination. The relative errors of the 10% average to MODIS were smaller (-0.6%) over ocean but larger (1.5%) over land than those of the OMI product to MODIS. The reduced relative error in the OMI product over land may result from additional cloud filtering using the Landsat data. This study will be useful when retrieveing the surface reflectance from geostationary-orbit environmental satellite (e.g., Geostationary Environment Monitoring Spectrometer; GEMS).

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.278-281
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• 2007

The purpose of this study is to develop snow and sea ice detection algorithm in Communication, Ocean and Meteorological Satellite (COMS) meteorological data processing system. Since COMS has only five channels, it is not affordable to use microwave or shortwave infrared data which are effective and generally used for snow detection. In order to estimate snow and sea ice coverage, combinations between available channel data(mostly visible and 3.7 ${\mu}m$) are applied to the algorithm based on threshold method. As a result, the COMS snow and sea ice detection algorithm shows reliable performance compared to MODIS products with channel limitation. Specifically, there is partial underestimation over the complicated vegetation area and overestimation over the area of high level clouds such as cirrus. Some corrections are performed by using water vapor and infrared channels to remove cloud contamination and by applying NDVI to detect more snow pixels for the underestimated area.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.609-626
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• 2020

Satellite-based land surface temperature (LST) has been used as one of the major parameters in various climate and environmental models. Especially, Moderate Resolution Imaging Spectroradiometer (MODIS) LST is the most widely used satellite-based LST product due to its spatiotemporal coverage (1 km spatial and sub-daily temporal resolutions) and longevity (> 20 years). However, there is an increasing demand for LST products with finer spatial resolution (e.g., 10-250 m) over regions such as urban areas. Therefore, various methods have been proposed to produce high-resolution MODIS-like LST less than 250 m (e.g., 100 m). The purpose of this review is to provide a comprehensive overview of recent research trends and challenges for the downscaling of MODIS LST. Based on the recent literature survey for the past decade, the downscaling techniques classified into three groups-kernel-driven, fusion-based, and the combination of kernel-driven and fusion-based methods-were reviewed with their pros and cons. Then, five open issues and challenges were discussed: uncertainty in LST retrievals, low thermal contrast, the nonlinearity of LST temporal change, cloud contamination, and model generalization. Future research directions of LST downscaling were finally provided.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.2
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• pp.67.2-67.2
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• 2010

We present results of searching for the Very Low Luminosity Objects (VeLLOs; internal luminosity : $L_{int}$ < $0.1(d/140pc)^2\;L\odot$) in the Gould's Belt clouds using observations from 3.6 to 70 micron by the Spitzer Space Telescope. The clouds are California, Chamaeleon I, III, Musca, Lupus V, VI, Scorpius, Serpens, Corona Australis, Cepheus, and IC 5146 having the properties of active low-mass star-forming such as the Taurus cloud. The observing sensitivity of the Spitzer data is estimated to be about $L_{int}\;\geq\;5\times10^{-3}(d/140pc)^2\;L\odot$, a factor of 20 better than that of the Infrared Astronomical Satellite (IRAS). The observing data were processed by the c2d Legacy pipeline. As the criteria to select the VeLLOs, we slightly modified previous ones by Dunham et al. The most important criterion is a flux density at 70 micron that is directly converted to the internal luminosity. Also, we used additional criteria to remove the contamination of evolved stars and extragalaxies which have colors or SEDs very similar to YSOs. We identified a total of 64 new embedded VeLLO candidates with $L_{int}$ < $0.1(d/140pc)^2\;L\odot$, consisting of 8 in California, 15 in Chamaeleon-Musca, 13 in Scorpius-Lupus, 20 in Serpens, 3 in Corona Australis, 3 in Cepheus, and 2 in IC 5146. The classification of the spectral index (${\alpha}$) fitted to the shape of the Ks-24 micron SEDs shows most of VeLLO candidates (89%) are in types of Class I and Flat spectrum. We plot various diagrams based on their 2MASS-Spitzer bands colors and magnitudes to discuss properties of the VeLLOs. This search will lead us new adventure toward a future systematic study of the VeLLOs.

• Journal of Information Processing Systems
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• v.11 no.4
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• pp.556-572
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• 2015

This paper presents the applications of spatial interpolation and assimilation methods for satellite and ground meteorological data, including temperature, relative humidity, and precipitation in regions of Vietnam. In this work, Universal Kriging is used for spatially interpolating ground data and its interpolated results are assimilated with corresponding satellite data to anticipate better gridded data. The input meteorological data was collected from 98 ground weather stations located all over Vietnam; whereas, the satellite data consists of the MODIS Atmospheric Profiles product (MOD07), the ASTER Global Digital Elevation Map (ASTER DEM), and the Tropical Rainfall Measuring Mission (TRMM) in six years. The outputs are gridded fields of temperature, relative humidity, and precipitation. The empirical results were evaluated by using the Root mean square error (RMSE) and the mean percent error (MPE), which illustrate that Universal Kriging interpolation obtains higher accuracy than other forms of Kriging; whereas, the assimilation for precipitation gradually reduces RMSE and significantly MPE. It also reveals that the accuracy of temperature and humidity when employing assimilation that is not significantly improved because of low MODIS retrieval due to cloud contamination.

• Korean Journal of Remote Sensing
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• v.16 no.4
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• pp.305-313
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• 2000

A vicarious calibration method was developed for the OSMI sensor calibration. Employing measured aerosol optical thickness by a sunphotometer and a sky radiometer and water leaving radiance by ship measurements as inputs, TOA (top of the atmosphere) radiance at each OSMI band was simulated in conjunction with a radiative transfer model (Rstar5b) by Nakajima and Tanaka (1988). As a case of examining the accuracy of this method, we simulated TOA radiance based on water leaving radiance measured at NASA/MOBY site and aerosol optical thickness estimated nearby at Lanai, and compared simulated results with SeaWiFS-estimated TOA radiances. The difference falls within about $\pm$5%, suggesting that OMSI sensor can be calibrated with the suggested accuracy. In order to apply this method for the OSMI sensor calibration, ground-based sun photometry and ship measurements were carried out off the east coast of Korean peninsula on May 31, 2000. Simulations of TOA radiance by using these measured data as input to the radiative transfer model show that there are substantial differences between simulated and OSMI-estimated radiances. Such a discrepancy appears to be mainly due to the cloud contamination because satellite image indicates optically thin clouds over the experimental area. Nevertheless results suggest that sensor calibration can be achieved within 5% uncertainty range if there are ground-based measurements of aerosol optical thickness, and water leaving radiances under clear-sky and optically thin atmospheric conditions.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.65.2-66
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• 2021

Observational studies of supernova (SN) feedback are limited. In our galaxy, most supernova remnants (SNRs) are located in the Galactic plane, so there is contamination from foreground/background sources. SNRs located in other galaxies are too far, so we cannot study them in detail. The Large Magellanic Cloud (LMC) is a unique place to study the SN feedback due to their proximity, which makes possible to study the structure of individual SNRs in some detail together with their environment. Recently, we carried out a systematic study of 13 LMC SNRs using [P II] (1.189 ㎛) and [Fe II] (1.257 ㎛) narrowband imaging with SIRIUS/IRSF, four SNRs (SN 1987A, N158A, N157B and N206), show [P II]/[Fe II] ratio much higher than the cosmic abundance. While the high ratio of SN 1987A could be due to enhanced abundance in SN ejecta, we do not have a clear explanation for the other cases. We investigate the [P II] knots found in SNRs N206, N157B and N158A, using optical spectra obtained last November with GMOS-S mounted on Gemini-South telescope. We detected several emission lines (e.g., H I, [O I], He I, [O III], [N II] and [S II]) that are present in all three SNRs, among other lines that are only found in some of them (e.g., [Ne III], [Fe III] and [Fe II]). Various line ratios are measured from the three SNRs, which indicate that the ratios of N157B tend to differ from those of other two SNRs. We will use the abundances of He and N (from the detection of [N II] and He I emission lines), together with velocity measurements to tell whether the origin of the [P II] knots are SN ejecta or CSM/ISM. For this purpose we have built a family of radiative shock with self-consistent pre-ionization using MAPPINGS 5.1.18, with shock velocities in the range of 100 to 475 km/s. We will compare the observed and modeled line fluxes for different depletion factors.

• Korean Journal of Remote Sensing
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• v.28 no.2
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• pp.215-226
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• 2012

NASA MODIS GPP provides a useful tool to monitor global terrestrial vegetation productivity. Two major problems of NASA GPP in regional applications are coarse spatial resolution ($1.25^{\circ}{\times}1^{\circ}$) of DAO meteorological data and cloud contamination of MODIS FPAR product. In this study, we improved the NASA GPP by using enhanced input data of high spatial resolution (3 km${\times}$3 km) WRF meteorological data and cloud-corrected FPAR over the North Korea. The improved GPP was utilized to investigate characteristics of GPP interannual variation and spatial patterns from 2000 to 2008. The GPP varied from 645 to 863 $gC\;m^{-2}\;y^{-1}$ in 2000 and 2008, respectively. Mixed forest showed the highest GPP (1,076 $gC\;m^{-2}\;y^{-1}$). Compared to NASA GPP (790 $gC\;m^{-2}\;y^{-1}$);FPAR enhancement increased GPP (861) but utilization of WRF data decreased GPP (710). Enhancements of both FPAR and meteorological input resulted in GPP increase (809) and the improvement was the greatest for mixed forest regions (+10.2%). The improved GPP showed better spatial heterogeneity reflecting local topography due to high resolution WRF data. It is remarkable that the improved and NASA GPPs showed distinctly different interannual variations with each other. Our study indicates improvement of NASA GPP by enhancing input variables is necessary to monitor region-scale terrestrial vegetation productivity.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.283-292
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• 2017

In this study, the empirical models were established to estimate the concentrations of surface-level $PM_{2.5}$ over Seoul, Korea from 1 January 2012 to 31 December 2013. We used six different multiple linear regression models with aerosol optical thickness (AOT), ${\AA}ngstr{\ddot{o}}m$ exponents (AE) data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua satellites, meteorological data, and planetary boundary layer depth (PBLD) data. The results showed that $M_6$ was the best empirical model and AOT, AE, relative humidity (RH), wind speed, wind direction, PBLD, and air temperature data were used as input data. Statistical analysis showed that the result between the observed $PM_{2.5}$ and the estimated $PM_{2.5}$ concentrations using $M_6$ model were correlations (R=0.62) and root square mean error ($RMSE=10.70{\mu}gm^{-3}$). In addition, our study show that the relation strongly depends on the seasons due to seasonal observation characteristics of AOT, with a relatively better correlation in spring (R=0.66) and autumntime (R=0.75) than summer and wintertime (R was about 0.38 and 0.56). These results were due to cloud contamination of summertime and the influence of snow/ice surface of wintertime, compared with those of other seasons. Therefore, the empirical multiple linear regression model used in this study showed that the AOT data retrieved from the satellite was important a dominant variable and we will need to use additional weather variables to improve the results of $PM_{2.5}$. Also, the result calculated for $PM_{2.5}$ using empirical multi linear regression model will be useful as a method to enable monitoring of atmospheric environment from satellite and ground meteorological data.

• Journal of the Korean earth science society
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• v.39 no.2
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• pp.119-130
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• 2018

Aerosol Optical Properties (AOPs) are retrieved using the geostationary satellite instruments such as Geostationary Ocean Color Imager (GOCI), Meteorological Imager (MI), and Advanced Himawari Imager (AHI) through Yonsei AErosol Retrieval algorithm (YAER). In this study, the retrieved aerosol optical depths (AOD)s from each instrument were intercompared and validated with the ground-based sunphotometer AErosol Robotic NETwork (AERONET) data. As a result, the four AOD products derived from different instruments showed consistent results over land and ocean. However, AODs from MI and GOCI tend to be overestimated due to cloud contamination. According to the comparison results with AERONET, the percentage within expected errors (EE) are 36.3, 48.4, 56.6, and 68.2% for MI, GOCI, AHI-minimum reflectivity method (MRM), and AHI-estimated surface reflectance from shortwave Infrared (ESR) product, respectively. Since MI AOD is retrieved from a single visible channel, and adopts only one aerosol type by season, EE is relatively lower than other products. On the other hand, the AHI ESR is more accurate than the minimum reflectance method as used by GOCI, MI, and AHI MRM method in May and June when the vegetation is relatively abundant. These results are explained by the RMSE and the EE for each AERONET site. The ESR method result show to be better than the other satellite product in terms of EE for 15 out of 22 sites used for validation, and they are better than the other product for 13 sites in terms of RMSE. In addition, the error in observation time in each product is found by using characteristics of geostationary satellites. The absolute median biases at 00 to 06 Universal Time Coordinated (UTC) are 0.05, 0.09, 0.18, 0.18, 0.14, 0.09, and 0.10. The absolute median bias by observation time has appeared in MI and the only 00 UTC appeared in GOCI.