• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.555-568
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• 2013

Atmospheric optical thickness during nighttime was estimated in this study using analysis on the images of the moon taken from commercial digital camera. Basically the Langely Regression method was applied to the observations of the moon for the cloudless and optically stable sky conditions. The spectral response functions for the red(R), green(G), and blue(B) channels were employed to derive effective wavelength centers of each channel for the observations of the moon, and the correspondent Rayleigh optical thickness were also calculated. Aerosol optical thickness (AOT) was calculated by subtracting Rayleigh optical thickness from the atmospheric optical thickness derived from the Langley regression method. As there are only handful of nighttime AOT observations, the AOT from the moon observations was compared with the AOT from sun-photometers and the MODIS satellite sensor, which was taken several hours before the moon observations of this study. As a result, the values of AOT from moon observations agree with those from sun-photometers and MODIS within 0.1 for the R, G, B channels of the digital camera. On the other hand, ${\AA}$ngstr$\ddot{o}$m Exponent seems to be subject to larger errors due to its sensitiveness to the spectral errors of AOT. Nevertheless, the results of this study indicate that the method reported in this study is promising as it can provide nighttime AOT relatively easily with a low cost instrument like digital camera. More observations and analyses are warranted to attain improved nighttime AOT observations in the future.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.381-384
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• 2005

We have applied the TOMS aerosols retrieval algorithm to GLI measurements. TOMS has utilized the aerosol index, which is a measure of the change in spectral contrast due to the wavelength-dependent effects of aerosols. We have retrieved the GLI aerosol index, which is made by the pair of 380/400nm, 380/412nm, 380/460nm, and 412/460nm. We have found that the biomass burning aerosols represent the absorbing aerosols. In addition, the pair of 380/460nm has shown the best signal for detecting aerosols in Principal Component Analysis(PCA) and comparison of aerosol optical thickness from AERONET data. The theoretical aerosol index is also shown the best signal in the pair of 380/460nm.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.491-492
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• 2003

The large amount of smoke produced near Lake Baikal was transported to Northeast Asia with high AOT (Aerosol Optical Thickness) as seen in satellite images. Aerosol retrieval using a separation technique was applied to MODIS (Moderate Imaging Spectroradiometer) and SeaWiFS (Sea-viewing Wide Field-of-view Sensor) data observed during 14-22 May 2003. Large AOT, 2.0~5.0 was observed on 20 May 2003 over Korea due to the influence of the long range transport of smoke aerosol plume from the Russian fires, resulting in high PM10 concentration was observed at the surface.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.378-380
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• 2005

Remote sensing of atmospheric aerosol using MODIS satellite data has been proven to be very useful in global/regional scale aerosol monitoring. Due to their large spatial resolution of $10km^2$ MODIS aerosol optical thickness (AOT) data have limitations for local/urban scale aerosol monitoring applications. Modified Bremen Aerosol Retrieval (BAER) algorithm developed by von Hoyningen-Huene et al. (2003) and Lee et al. (2005) has been applied in this study to retrieve AOT in fe resolutions of $500m^2$ over Korea. Look up tables (LUTs) were constructed from the aerosol properties based on sun-photometer observation and radiation transfer model calculations. It was found that relative error between the satellite products and the ground observations was within about $15\%$. Resulting AOT products were correlated with surface PMIO concentration data. There was good correlation between MODIS AOT and surface PM concentration under certain atmospheric conditions, which supports the feasibility of using the high-resolution MODIS AOT for local and urban scale air quality monitoring

• Korean Journal of Remote Sensing
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• v.14 no.4
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• pp.366-375
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• 1998

Optical characteristics of the yellow sand and their influences on the ocean color remote sensing has been studied using ocean color sensor SeaWiFS measurements. Two cases of April 18 and April 25, 1998, representing yellow sand and background aerosol, are selected for emphasizing the impact of high aerosol concentration on the ocean color remote sensing. It was shown that NASA's standard atmospheric correction algorithm treats yellow sand area as either too high radiance or cloud area, in which ocean color information is not generated. Optical thickness of yellow sand arrived over the East Asian sea waters in April 18 indicates that there are two groups loaded with relatively homogeneous yellow sand, i.e.: heavy yellow sand area with optical thickness peak around 0.8 and mild area with about 0.4, which are consistent with ground observations. The movement of the yellow sand area obtained from surface weather maps and backward trajectory analysis manifest the notion that the weak yellow sand area was originated from the outer region of the dust storm. It is also noted that high optical thickness associated with the yellow sand is significantly different from what we may observe from background aerosol, which is about 0.2. These characteristics allow us to determine the yellow sand area with an aid of atmospheric correction parameter. Results indicate that the yellow sand area can be determined by applying the features revealed in scattergrams of atmospheric correction parameter and optical thickness.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.127-139
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• 2018

The GOCI atmospheric correction overland surfaces is essential for the time-series analysis of terrestrial environments with the very high temporal resolution. We develop an operational GOCI atmospheric correction method over land surfaces, which is rather different from the one developed for ocean surface. The GOCI atmospheric correction method basically reduces gases absorption and Rayleigh and aerosol scatterings and to derive surface reflectance from at-sensor radiance. We use the 6S radiative transfer model that requires several input parameters to calculate surface reflectance. In the sensitivity analysis, aerosol optical thickness was the most influential element among other input parameters including atmospheric model, terrain elevation, and aerosol type. To account for the highly variable nature of aerosol within the GOCI target area in northeast Asia, we generate the spatio-temporal aerosol maps using AERONET data for the aerosol correction. For a fast processing, the GOCI atmospheric correction method uses the pre-calculated look up table that directly converts at-sensor radiance to surface reflectance. The atmospheric correction method was validated by comparing with in-situ spectral measurements and MODIS reflectance products. The GOCI surface reflectance showed very similar magnitude and temporal patterns with the in-situ measurements and the MODIS reflectance. The GOCI surface reflectance was slightly higher than the in-situ measurement and MODIS reflectance by 0.01 to 0.06, which might be due to the different viewing angles. Anisotropic effect in the GOCI hourly reflectance needs to be further normalized during the following cloud-free compositing.

• Proceedings of KOSOMES biannual meeting
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• 2009.06a
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• pp.53-54
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• 2009

The influence of atmospheric aerosol on satellite ocean color data were evaluated using SeaWiFS monthly standard mapped image products. The atmospheric optical thickness (AOT) was increased in spring and summer, and it showed the strong positive correlation with remote sensing reflectance, normalized waterleaving radiance /solar irradiance, at 555 nm (Rrs555) which is a component of the satellite chlorophyll estimation. Such the high AOT and high Rrs555 pixels showed overestimation of satellite chlorophyll in spring, especially in the area which showed large phytoplankton absorption which 1s expressed by low remote sensing reflectance at 443, 490 and 510 nm (Rrs 443, Rrs490 and Rrs510).

• Proceedings of the KSRS Conference
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• v.1
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• pp.305-307
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• 2006

A multi-spectral photometer was set up as an NASA/AERONET site at Kinki University campus in Higashi-Osaka in 2002 for measuring urban aerosols. In addition, the SPM-613D (Kimoto Electric) commenced measurement of suspended particles matter (SPM) as $PM_{10}$ and $PM_{2.5}$ on March 15, 2004 at the same AERONET site. The obtained results revealed that the poor air quality of the Higashi-Osaka site is due not only to anthropogenic particles from local emissions, such as diesel vehicles and chemical industries, but also to dust particles brought from continental desert areas by large scale climatic conditions. To understand the characteristics of background atmosphere over Higashi-Osaka, we examined the relationship between $PM_{2.5}$ concentration and aerosol optical thickness (AOT) at a wavelength of 0.87 μm based on AERONET data for background atmosphere (AOT<0.2). We obtained a linear regression line between AOT and $PM_{2.5}$ concentration. Using the linear relationships between AOT and $PM_{2.5}$, we show ground-level concentrations of $PM_{2.5}$ of background atmosphere from Terra/MODIS satellite measurements.

• Korean Journal of Remote Sensing
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• v.23 no.1
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• pp.1-5
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• 2007

Relatively simplified method for determination of surface reflectance has been used by using the ratio between SWIR and VIS band reflectance over land surface. The surface reflectance ratios (SWIR/VIS) were estimated over land in Korea from Terra Moderate Resolution Imaging Spectre-radiometer (MODIS) L1 data. The ratios by using the minimum reflectance technique were lower than those by MODIS operational aerosol retrieval algorithm. Although the comparison between MODIS and sunphotometer Aerosol Optical Thickness (AOT) has a good correlation coefficient (R=0.84), slightly overestimated MODIS AOTs were shown with a slope of linear regression line of 0.89. The comparison between the ratio and AOT dearly exhibit that the error of MODIS AOT could be originated from the underestimated surface reflectances by MODIS operational algorithm.

• Ocean Science Journal
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• v.41 no.4
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• pp.235-244
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• 2006

Determining 'photosynthetically active radiation' (PAR) is a key part of calculating phytoplankton productivity in a biogeochemical model. We explore the daily and seasonal variability in the ratio of PAR irradiance to total irradiance that occurred at Ieodo Ocean Research Station (IORS) in the East China Sea under clear-sky conditions in 2004 using a simple radiative transfer model (RTM). Meteorological data observed at IORS and aerosol optical properties derived from Aerosol Robotic Network observations at Gosan are used for the RTM. Preliminary results suggest that the use of simple PAR irradiance-ratio values is appropriate in calculating phytoplankton productivity as follows: an average of $0.44\;({\pm}0.01)$ in January to an average of $0.48\;({\pm}0.01)$ in July, with average daily variabilities over these periods of about $0.016\;({\pm}0.008)$ and $0.025\;({\pm}0.008)$, respectively. The model experiments demonstrate that variations in the major controlling input parameters (i.e. solar zenith angle, precipitable water vapor and aerosol optical thickness) cause PAR irradiance ratio variation at daily and seasonal timescales. Further, increases (>0.012) in the PAR irradiance ratio just below the sea-surface are positively correlated with high solar zenith angles and strong wind stresses relative to those just above the sea-surface.