• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.507-515
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• 2009

Land surface temperature (LST) is a key environmental variable in a wide range of applications, such as weather, climate, hydrology, and ecology. However, LST is one of the most difficult surface variables to observe regularly due to the strong spatio-temporal variations. So, we have developed the LST retrieval algorithm from COMS (Communication, Ocean and Meteorological Satellite) data through the radiative transfer simulations under various atmospheric profiles (TIGR data), satellite zenith angle (SZA), spectral emissivity, and surface lapse rate conditions using MODTRAN 4. However, the LST retrieval algorithm has a tendency to overestimate and underestimate the LST for surface inversion and superadiabatic conditions, respectively. To minimize the overestimation and underestimation of LST, we also developed day/night LST algorithms separately based on the surface lapse rate (local time) and recalculated the final LST by using the weighted sum of day/night LST. The analysis results showed that the quality of weighted LST of day/night algorithms is greatly improved compared to that of LST estimated by original algorithm regardless of the surface lapse rate, spectral emissivity difference (${\Delta}{\varepsilon}$) SZA, and atmospheric conditions. In general, the improvements are greatest when the surface lapse rate and ${\Delta}{\varepsilon}$ are negatively large (strong inversion conditions and less vegetated surface).

• The Korean Journal of Quaternary Research
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• v.24 no.2
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• pp.1-11
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• 2010

Energy Balance Model (EBM) was used to experiment the distribution of surface equilibrium temperature which responds to external forcing associated with the surface characteristics. Surface equilibrium temperature is calculated as sum of incoming solar radiation and latitudinal transport is balanced with outgoing infrared radiation. To treat incoming solar radiation, the source of the earth energy, significantly for energy balance, the experiment for surface equilibrium temperature distribution was performed considering the energy balance with the latitudinal albedo change as well as land and sea distribution. In addition, linear albedo change experiment, arctic albedo 5%, 10%, 15% change experiments and the opposite albedo change experiments between arctic and mid-latitudes were performed using incoming solar radiation as an external forcing. Moreover, with and without ice-albedo feedback experiments were performed. Increasing of arctic albedo is blocked out the incoming solar radiation so that it induces decreasing of latitudinal heat transport. It is strengthened energy transport from low latitudes by keeping arctic low energy states. Therefore the temperature change in the mid-latitudes exhibits larger response than that of arctic due to the difference of transport. The land which has lower heat capacity than sea can be reach to equilibrium temperature shortly. Also land is more sensitive to temperature change with respects to albedo. Thus it induces the thermal difference between land and sea. As a result, the equilibrium temperature exhibits differently as the difference of albedo and heat capacity which are the one of surface characteristics. Surface equilibrium temperature decreases as albedo increase and the ratio of temperature change is large as heat capacity is small. The decreasing of surface equilibrium temperature with respects to increasing of linear albedo is accelerated by ice-albedo feedback. However local change of surface equilibrium temperature decreases non-linearly.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.2
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• pp.123-130
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• 2020

The impact of urbanization on LST (Land Surface Temperature) and TN (Tropical Night) was observed with the analyses of land cover change and LST by associating with the frequency of TN during the period of 1996 to 2016. The analyses of land cover and LST was based on the images of Landast 5 and 8 for September in 1996, 2006, and 2016 at a 10 year interval. The hourly-collected atmospheric temperatures for the months of July and August during the period were collected from AWSs (Automatic Weather Stations) in Seoul for the frequency analysis of TN. The study area was categorized into five land cover classes: urban or built-up area, forest, mixed vegetation, bare soil and water. It was found that vegetation (-7.71%) and bare soil (-9.04%) decreased during the period while built-up (17.29%) area was expanded throughout the whole period (1996-2016), indicating gradual urbanization. The changes came along with the LST rise in the urban area of built-up and bare soil in Seoul. In addition, the frequency of TN has increased in 4.108% and 7.03% for July and August respectively between the two periods of the 10 year interval, 1996-2006 and 2006-2016. By comparing the increasing trends of land cover, LST, and TN, we found a high probability that the frequency of TN had a relationship with land cover changes by the urbanization process in the study area.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.197-207
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• 2013

Difficulties of emissivity determination and atmospheric correction degrade the estimation accuracy of land surface temperature (LST). That is, since the emissivity determination of land surface material and the correction of atmospheric effect are not perfect, it is very difficult to estimate the precise LST from a thermal infrared image such as Landsat TM and ETM+, ASTER, etc. In this study, we propose an efficient method to estimate land surface temperature difference (LSTD) rather than LST from Landsat thermal band images. This method is based on the assumptions that 1) atmospheric effects are same over a image and 2) the emissivity of vegetation region is 0.99. To validate the performance of the proposed method, error sensitive analysis according to error variations of reference land surface temperature and the water vapor is performed. The results show that the estimated LSTD have respectively the errors of ${\pm}0.06K$, ${\pm}0.15K$ and ${\pm}0.30K$ when the water vapor error of ${\pm}0.302g/cm^2$ and the radiance differences of 0.2, 0.5 and $1.0Wm^{-2}sr^{-1}{\mu}m$ are considered. And also the errors of the LSTD estimation are respectively ${\pm}0.037K$, ${\pm}0.089K$, ${\pm}0.168K$ in the reference land surface temperature error of ${\pm}2.41K$. Therefore, the proposed method enables to estimate the LSTD with the accuracy of less than 0.5K.

• Atmosphere
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• v.25 no.2
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• pp.353-366
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• 2015

The impact of land and ocean initial condition on coupled general circulation model seasonal predictability is assessed in this study. The CGCM used here is Pusan National University Couple General Circulation Model (PNU CGCM). The seasonal predictability of the surface air temperature and ocean potential temperature for boreal winter are evaluated with 4 different experiments which are combinations of 2 types of land initial conditions (AMI and CMI) and 2 types of ocean initial conditions (DA and noDA). EXP1 is the experiment using climatological land initial condition and ocean initial condition to which the data assimilation technique is not applied. EXP2 is same with EXP1 but used ocean data assimilation applied ocean initial condition. EXP3 is same with EXP1 but AMIP-type land initial condition is used for this experiment. EXP4 is the experiment using the AMIP-type land initial condition and data assimilated ocean initial condition. By comparing these 4 experiments, it is revealed that the impact of data assimilated ocean initial is dominant compared to AMIP-type land initial condition for seasonal predictability of CGCM. The spatial and temporal patterns of EXP2 and EXP4 to which the data assimilation technique is applied were improved compared to the others (EXP1 and EXP3) in boreal winter 2m temperature and sea surface temperature prediction.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1457-1459
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• 2003

This study reports the surface temperature pattern of the Tokyo Metropolitan area using the ASTER surface temperature product. The product is an image processed by applying temperature-emissivity separation to atmospheric corrected infrared thermal radiance of the land surface, then converted to surface temperature by using Planck's function. Daytime and nighttime observation in a cold season and a warm season were used in this study. As a result, 1) contrast between urban and suburban, 2) extraction of heating area in urban, 3) measurement of cooling effect of green space were achieved.

• Spatial Information Research
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• v.22 no.1
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• pp.55-63
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• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.319-322
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• 1999

Land surface hydrological conditions have been considered to play an important role in the global and regional climate variability. Especially, snow, soil moisture, surface temperature, vegetation and rain are the key parameters which should be observed in the global scale. In this paper, new algorithms for these land surface hydrological parameters have been developed by introducing frequency and polarization dependencies of these parameters in the microwave radiative-transfer equations. The algorithms were applied to the TRMM Microwave Radiometer. (TMI) and validated by using the ground data obtained in the Tibetan Plateau. The estimated snow, soil moisture, surface temperature, water content of vegetation and rain patterns corresponded reasonably to the observed ones.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.146-148
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• 2005

This study investigates to detect sea surface temperature (SST) and land cover change after tide embankment construction using Landsat Thematic Mapper (TM) thermal infrared (TIR) band data at Shihwa Lake and surrounding area. SST measurement is important for studies of both the structure of the ocean and as the thermal boundary between the ocean and the atmosphere. The TIR band of TM images can be used to detect SST change whose shoreline is complicated and narrow like the study site. The purpose of this study is to estimate SST and land cover change at Shihwa Lake and surrounding area.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.203-210
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• 2004

The average temperatures by the land cover class, by the elevation extent, by the slope and by the aspect have been calculated using multitemporal Landsat TM band 6 and DEM. For this, the TM band 6 data from October 21, 1985, June 2, 1992, September 1, 1996, May 7, 2000 and the 28.5m x 28.5m grid elevation data of Seoul have been used. From the varying curve of the average land surface temperature by the elevation extent, the presence of the atmospheric inversion phenomenon and the scope of the inversion layer can be inferred. Especially, the average land surface temperature by the aspect can be effective for deciding a road line. For these reasons, it is expected that temperature estimation using remote sensing data shall be effective for the survey of heat damage, environmental temperature monitoring, and urban and environmental Planning usage.