• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.171-183
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• 2016

Land Surface Temperature (LST) retrieved from Landsat 8 measured from 2013 to 2014 and it is corrected by surface temperature observed from ground. LST maps are retrieved from Landsat 8 calculate using the linear regression function between raw Landsat 8 LST and ground surface temperature. Seasonal and annual LST maps developed an average LST from season to annual, respectively. While the higher LSTs distribute on the industrial and commercial area in urban, lower LSTs locate in surrounding rural, sea, river and high altitude mountain area over Seoul and surrounding area. In order to correct the LST, linear regression function calculate between Landsat 8 LST and ground surface temperature observed 3 Korea Meteorological Administration (KMA) synoptic stations (Seoul(ID: 108), Incheon(ID: 112) and Suwon(ID: 119)) on the Seoul and surrounding area. The slopes of regression function are 0.78 with all data and 0.88 with clear sky except 5 cloudy pixel data. And the original Landsat 8 LST have a correlation coefficient with 0.88 and Root Mean Square Error (RMSE) with $5.33^{\circ}C$. After LST correction, the LST have correlation coefficient with 0.98 and RMSE with $2.34^{\circ}C$ and the slope of regression equation improve the 0.95. Seasonal and annual LST maps represent from urban to rural area and from commercial to industrial region clearly. As a result, the Landsat 8 LST is more similar to the real state when corrected by surface temperature observed ground.

• 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.

• Journal of Korean Society for Atmospheric Environment
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• v.16 no.4
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• pp.363-372
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• 2000

In order to overtake a quantitative analysis of effect of anthropogenic heat and different land-use on urban thermal environment numerical simulation of surface energy budget was carried out under typical summer synoptic condition. It is beneficial to understand surface temperature of complex urban surace. The different land-use types are classified of rice field farm fruit garden residential region forest water and swamp by using map scaled 1/25000 of Pusan metropolitan. The model predicts that maximum heat island intensity in the central part of Pusan is 7$^{\circ}C$ at 2000 LST in summertime. The surface temperature is propotional to the density of constructions. The effect of anthropogenic heat generation on surface temperature is the increase of 0.3$^{\circ}C$ at 1400LST in the central part of Pusan during summertime.

• 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.

• 한국방재학회:학술대회논문집
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• 2010.02a
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• pp.104-104
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• 2010

지난 2008년 가을부터 시작되어 2009년 봄까지 발생했던 전국적인 극한 가뭄을 계기로 가뭄모니터링의 필요성은 증대되었다. 본 연구는 우리나라에서 가뭄 모니터링을 위한 MODIS 위성영상 자료의 활용을 제안하였다. MODIS 영상은 임의의 지역의 시 공간적 특성을 관찰할 수 있는 해상도를 보유하고 있으며, MODIS에서 제공하는 MOD11(LST: Land Surface Temperature)은 가뭄 발생의 판별에는 유효하나 가뭄 심도와 지속기간을 판단하기 위해서는 기준이 되는 강우량 및 가뭄지수와의 비교가 필요하다고 알려져 있다. 본 연구에서는 MOD11(LST) 위성자료와 EDI(Effective Drought Index) 가뭄지수의 상관성을 고려하여 한반도 가뭄모니터링을 위한 MODIS 위성영상의 활용성을 평가하였다.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.197-214
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• 2019

In this study, the correlation analysis was conducted between observed air temperature (maximum, minimum, and mean air temperature) and the daytime and nighttime data of Terra/Aqua MODIS LST(Moderate Resolution Imaging Spectroradiometer Land Surface Temperature) for 86 weather stations. All the data of the recent 11 years from 2008 to 2018 were prepared with daily base. In particular, the characteristics of the cold and heat waves incidence period in 2018 were analyzed. The correlation analysis was performed using the Pearson correlation coefficient(R) and root mean square error(RMSE). As a result of time series analysis, the trend between observed air temperature and MODIS LST were similar, showing the correlation above 0.9 in maximum temperature, above 0.8 in mean and minimum temperature. Especially, the maximum temperature was found to have the highest accuracy with Terra MODIS LST daytime, and the minimum temperature had the highest correlation with Terra MODIS LST nighttime. During the cold wave period, both Terra and Aqua MODIS LST showed higher correlations with nighttime data than daytime data. For the heat wave period, the Aqua MODIS LST daytime data was good, but the overall R was below 0.5. Additional analysis is necessary for further study considering such as land cover and elevation characteristics.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.290-293
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• 2007

The land surface temperature (LST) derived from the meteorological satellite can be used to investigate the urban heat island (UHI) and its temporal variations. In this study, we developed LST retrieval algorithm from MTSAT-1R by means of a statistical regression analysis from radiative transfer simulations using MODTRAN 4 for a wide range of atmospheric, satellite viewing angle (SVA) and lapse rate conditions. 535 sets of thermodynamic initial guess retrieval (TIGR) were used for the radiative transfer simulations. Sensitivity and intercomparison results showed that the algorithm, developed in this study, estimated the LST with a similar bias and root mean square errors to that of other algorithms. The magnitude, spatial extent, and seasonal and diurnal variations of the UBI of Korean peninsula were well demonstrated by the LST derived from MTSAT-1R data. In general, the temporal variations of UHI clearly depend on the weather conditions and geographic environment of urban.

• Journal of The Korean Society of Agricultural Engineers
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• v.48 no.5
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• pp.39-48
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• 2006

The objective of this research was to find an indirect method to estimate land surface temperature (LST) efficiently, using Landsat images. Agricultural fields including paddy fields have long been known to have multi-functions beneficial to the environment and ecology of the urban surrounding areas. Among these functions, the ambient temperature cooling (ATC) effect is widely acknowledged. However, quantitative and regional assessment of such effect has not been performed. Thermal remote sensing has been used over urban areas to assess the ATC effect, Thermal Island Effect(TIE), and as input for models of urban surface atmosphere exchange. Here, we review the use of thermal remote sensing in the study of paddy fields and urban climates, focusing primarily on the ATC effect. Landsat satellite images were used to determine the surface temperatures of different land cover types of a $44km^{2}$ study area in Cheongiu, Korea. The results show that the ATC is a function of paddy area percentage in Landsat pixels. Landsat pixels with higher paddy area percentage have much more cooling effect. The use of satellite data may contribute to a globally consistent method for analysis of ATC effect.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.315-329
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• 2014

In order to analyze the Land Surface Temperature (LST) in metropolitan area including Seoul, Landsat and MODIS land surface temperature, Automatic Weather Station (AWS) temperature, digital elevation model and landuse are used. Analysis method among the Landsat and MODIS LST and AWS temperature is basic statistics using by correlation coefficient, root-mean-square error and linear regression etc. Statistics of Landsat and MODIS LST are a correlation coefficient of 0.32 and Root Mean Squared Error (RMSE) of 4.61 K, respectively. And statistics of Landsat and MODIS LST and AWS temperature have the correlations of 0.83 and 0.96 and the RMSE of 3.28 K and 2.25 K, respectively. Landsat and MODIS LST have relatively high correlation with AWS temperature, and the slope of the linear regression function have 0.45 (Landsat) and 1.02 (MODIS), respectively. Especially, Landsat 5 has lower correlation about 0.5 or less in entire station, but Landsat 8 have a higher correlation of 0.5 or more despite of lower match point than other satellites. Landsat 7 have highly correlation of more than 0.8 in the center of Seoul. Correlation between satellite LSTs and AWS temperature with landuse (urban and rural) have 0.8 or higher. Landsat LST have correlation of 0.84 and RMSE of more than 3.1 K, while MODIS LST have correlation of more than 0.96 and RMSE of 2.6 K. Consequently, the difference between the LSTs by two satellites have due to the difference in the optical observation and detection the radiation generated by the difference in the area resolution.

• Journal of Korean Society on Water Environment
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• v.29 no.2
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• pp.247-258
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• 2013

A grid-based configuration of Land Surface Models (LSMs) coupled with a climate model can be advantageous in impact assessment of climate change for a large scale area. We assessed the applicability of Common Land Model (CoLM) to runoff and land surface temperature (LST) simulations at the domain that encompasses the Nakdong river basin. To establish a high resolution model configuration of a $1km{\times}1km$ grid size, both surface boundary condition and atmospheric inputs from the observed weather data in 2009 were adjusted to the same resolution. The Leaf Area Index (LAI) was collected from MODerate esolution Imaging Spectroradiometer (MODIS) and the downward short wave flux was produced by a nonstationary multi-site weather state model. Compared with the observed runoffs at the stations on Nakdong river, simulated runoffs properly responded to rainfall. The spatial features and the seasonal variations of the domain fairly well were captured in the simulated LSTs as well. The monthly and seasonal trend of LST were described well compared to the observations, however, the monthly averaged simulated LST exceeded the observed up to $2^{\circ}C$ at the 24 stations. From the results of our study, it is shown that high resolution LSMs can be used to evaluate not only quantity but also quality of water resources as it can capture the geographical features of the area of interest and its rainfall-runoff response.