• 대한원격탐사학회지
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• 제35권4호
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• pp.609-616
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• 2019

Land surface temperature ($T_s$) is a critical variable for understanding the surface energy exchange between land and atmosphere. Using the data measured from micrometeorological flux towers, three types of $T_s$, obtained using a thermal-infrared radiometer (IRT), a net radiometer, and an equation for sensible heat flux, were compared. The $T_s$ estimated using the net radiometer was highly correlated with the $T_s$ obtained from the IRT. Both values acceptably fit the $T_s$ from the Terra/MODIS (Moderate Resolution Imaging Spectroradiometer)satellite. These results will enhance the measurement of land surface temperatures at various scales. Further, they are useful for understanding land surface energy partitioning to evaluate and develop land surface models and algorithms for satellite remote sensing products associated with surface thermal conditions.

• 환경영향평가
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• 제18권4호
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• pp.219-227
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• 2009

Because of urban development and changed land cover types, It is very important to acquire pixel unit of land surface temperature(LST) information when the heat island effect(HIE) of regional area are investigated. The brightness temperature observed by satellite is very useful for assessing the pixel unit of LST distributions for the analysis of thermal environment problems of urban areas. Also, satellite land cover data are very useful to our understanding of surface conditions of study areas. In this study, brightness temperature information of Landsat TM thermal channel was analyzed and compared with land cover information of Jeon-ju city. The atmospheric correction of TM thermal channel carried out to explain for compared LST long term monitoring errors. However, simple estimation and evaluation methods to find a physical relationship between LST from satellite images and in-situ data are compared with reference channel emissivity.

• 농업과학연구
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• 제39권1호
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• pp.87-95
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• 2012

Land surface temperature (LST) is an important factor in human health, thermal environment, heat balance, global change studies, and as control for climate change. The objective of this study was to assess the influence of Urban Heat Island (UHI) Effects on the LST and NDVI in Cheongju, Korea. The aim was to evaluate the effect of urban thermal environment for LST comparison of satellite-derived and in situ measured temperature. In this study, LANDSAT TM and KOMPSAT scene were used. The results indicated that the minimum LST is observed over dense forest as about $21{\sim}25^{\circ}C$ and maximum LST is observed over industrial area of about $28{\sim}32^{\circ}C$. The estimated LST showed that industrial area, bare soils and built-up areas exhibit higher surface temperatures, while forest, water bodies, agricultural croplands, and dense vegetations have lower surface temperatures during the summer daytime. Result corroborates the fact that LST over land use/land cover (LULC) types are greatly influenced by the amount of vegetation and water bodies present. The LST of industrial area and urban center is higher than that of suburban area, so it is clearly proved that there are obvious UHIE in Cheongju.

• 대한원격탐사학회지
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• 제20권1호
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• pp.1-11
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• 2004

Based on surface energy balance climatology, surface temperatures should respond to drying conditions well before plant response. To test this hypothesis, land surface temperatures (LST) derived from MODIS data were analyzed to determine how the data were correlated with climatic water balance variables and NDVI anomalies during a growing season in Western and Central Kansas. Daily MODIS data were integrated into weekly composites so that each composite data set included the maximum temperature recorded at each pixel during each composite period. Time-integrated, or cumulative values of the LST deviation standardized with mean air temperatures had significantly high correlation coefficients with SM, AE/PE, and MD/PE, ranging from 0.65 to 0.89. The Standardized Thermal Index (STI) is proposed in this study to accomplish the objective. The STI, based on surface temperatures standardized with observed mean air temperatures, had significant temporal relationships with the hydroclimatological factors. STI classes in all the composite periods also had a strong correlation with NDVI declines during a drought episode. Results showed that, based on LST, air temperature observations, and water budget analysis, NDVI declines below normal could be predicted as early as 8 weeks in advance in this study area.

• 한국환경과학회지
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• 제18권4호
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• pp.355-367
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• 2009

Comparison study on the land surface temperatures, which are calculated from four different algorithms for MODIS data, was carried out and the characteristics of each algorithm on land surface temperature estimation were also analysed in this study. Algorithms, which are well used for various satellite data analysis, in the comparisons are proposed by Price, Becker and Li, Ulivieri et al., and Wan. Verification of estimated land surface temperature from each algorithm is also performed using observation based regression data. The coefficient of determination ($R^2$) for daytime land surface temperature estimated from Wan's algorithm is higher than that of another algorithms at all seasons and the value of $R^2$ reach on 0.92 at spring. Although $R^2$ for Ulivieri's algorithm is slightly lower than that for Wan's algorithm, the variation pattern of land surface temperature for two algorithms are similar. However, the difference of estimated values among four algorithms become small at the region of high land surface temperature.

• 대한원격탐사학회지
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• 제39권1호
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• pp.99-110
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• 2023

Land surface temperature (LST) is a critical environmental indicator affected by land cover (LC) changes. Currently, the most convenient and fastest way to retrieve LST is to use remote sensing images due to their continuous monitoring of the Earth's surface. The work intended to investigate land cover change and temperature response inAn-Najaf province. Landsat multispectral imageries acquired inAugust 1989, 2004, and 2021 were employed to estimate land cover change and LST responses. The findings exhibited an increase in water bodies, built-up areas, plantations, and croplands by 7.78%, 7.27%, 6.98%, 3.24%, and 7.78%, respectively, while bare soil decreased by 25.27% for the period (1989-2021). This indicates a transition from barren lands to different land cover types. The contribution index (CI) was employed to depict how changes in land cover categories altered mean region surface temperatures. The highest LSTs recorded were in bare lands (42.2℃, 44.25℃, and 46.9℃), followed by built-up zones (41.6℃, 43.96℃, and 44.89℃), cropland (30.9℃, 32.96℃, and 34.76℃), plantations (35.4℃, 36.97℃, and 38.92℃), and water bodies (27.3℃, 29.35℃, and 29.68℃) respectively, in 1989, 2004, and 2021. Consequently, these changes resulted in significant variances in LST between different LC types.

• 지역연구
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• 제31권3호
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• pp.99-112
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• 2015

기상관측망은 충분히 고밀도로 구축되어 있기 않기 때문에 산업지역의 전체 지표온도를 얻기는 쉽지 않다. 넓은 지역에 걸쳐 취득하고 짧은 시간에 처리할 수 있는 다기능 원격탐사 정보를 통하여 특정지역에서 현재의 변화하는 지표온도를 정확하고 연속적으로 관측할 수 있다. 전라남도는 광양제철소, 여수산업단지, 율촌산업단지, 대불산업단지와 같은 많은 산업단지와 함께 빠르게 산업화되고 있다. 산업단지 내 지표온도 특성을 살펴보기 위하여 본 연구는 Landsat 7 ETM+의 열적외 파장영상을 이용하여 4개의 산업단지의 지표온도를 비교하였다. 이로부터 환경적 자연적 자원관리를 위한 산업단지의 기초자료를 얻을 수 있었다. 연구 결과는 산업단지계획가에서 환경적 문제와 관련된 개발방식에 도움을 줄 수 있다.

• 한국측량학회지
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• 제35권4호
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• pp.313-318
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• 2017

Land surface temperature is essential for monitoring abnormal climate phenomena such as UHI (Urban Heat Islands), and for modeling weather patterns. However, the quality of surface temperature obtained from the optical space imagery is affected by many factors such as, revisit period of the satellite, instance of capture, spatial resolution, and cloud coverage. Landsat 8 imagery, often used to obtain surface temperatures, has a high resolution of 30 meters (100 meters rearranged to 30 meters) and a revisit frequency of 16 days. On the contrary, MODIS imagery can be acquired daily with a spatial resolution of about 1 kilometer. Many past attempts have been made using both Landsat and MODIS imagery to complement each other to produce an imagery of improved temporal and spatial resolution. This paper applied machine learning methods and performed downscaling which can obtain daily based land surface temperature imagery of 30 meters.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.282-285
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• 2007

Satellite observed brightness temperature simulation using a radiative transfer model (here after, RTM) is useful for various fields, for example sensor design and channel selection by using theoretically calculated radiance data, development of satellite data processing algorithm and algorithm parameter determination before launch. This study is focused on elaborating the simulation procedure, and analyzing of difference between observed and modelled clear sky brightness temperatures. For the CMDPS (COMS Meteorological Data Processing System) development, the simulated clear sky brightness temperatures are used to determine whether the corresponding pixels are cloud-contaminated in cloud mask algorithm as a reference data. Also it provides important information for calibrating satellite observed radiances. Meanwhile, simulated brightness temperatures of COMS channels plan to be used for assessing the CMDPS performance test. For these applications, the RTM requires fast calculation and high accuracy. The simulated clear sky brightness temperatures are compared with those of MTSAT-1R observation to assess the model performance and the quality of the observation. The results show that there is good agreement in the ocean mostly, while in the land disagreement is partially found due to surface characteristics such as land surface temperature, surface vegetation, terrain effect, and so on.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.574-577
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• 2006

In this paper, we developed a forest fire detection algorithm which uses a regression function between NDVI and land surface temperature. Previous detection algorithms use the land surface temperature as a main factor to discriminate fire pixels from non-fire pixels. These algorithms assume that the surface temperatures of non-fire pixels are intrinsically analogous and obey Gaussian normal distribution, regardless of land surface types and conditions. And the temperature thresholds for detecting fire pixels are derived from the statistical distribution of non-fire pixels’ temperature using heuristic methods. This assumption makes the temperature distribution of non-fire pixels very diverse and sometimes slightly overlapped with that of fire pixel. So, sometimes there occur omission errors in the cases of small fires. To ease such problem somewhat, we separated non-fire pixels into each land cover type by clustering algorithm and calculated the residuals between the temperature of a pixel under examination whether fire pixel or not and estimated temperature of the pixel using the linear regression between surface temperature and NDVI. As a result, this algorithm could modify the temperature threshold considering land types and conditions and showed improved detection accuracy.