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

• Korean Journal of Remote Sensing
• /
• v.37 no.3
• /
• pp.463-473
• /
• 2021

Recently, United States Geological Survey (USGS) distributed Landsat 8 Collection 2 Level 2 Science Product (L2SP). This paper aims to derive land surface temperature from L2SP and to validate it. Validation is made by comparing the land surface temperature with the one calculated from Landsat 8 Collection 1 Level 1 Terrain Precision (L1TP) and the one from Automated Synoptic Observing System (ASOS). L2SP is calculated from Landsat 8 Collection 2 Level 1 data and it provides land surface temperature to users without processing surface reflectance data. Landsat 8 data from 2018 to 2020 is collected and ground sensor data from eight sites of ASOS are used to evaluate L2SP land surface temperature data. To compare ground sensor data with remotely sensed data, 3×3 grid area data near ASOS station is used. As a result of analysis with ASOS data, L2SP and L1TP land surface temperature shows Pearson correlation coefficient of 0.971 and 0.964, respectively. RMSE (Root Mean Square Error) of two results with ASOS data is 4.029℃, 5.247℃ respectively. This result suggests that L2SP data is more adequate to acquire land surface temperature than L1TP. If seasonal difference and geometric features such as slope are considered, the result would improve.

• Proceedings of the Korean Environmental Sciences Society Conference
• /
• 2007.05a
• /
• pp.165-168
• /
• 2007

본 연구에서는 보다 정확한 지표온도 산출을 위해 세 가지 알고리즘을 이용하여 지표면 온도를 산정하여 NASA MODIS에서 제공하는 지표면 온도 자료와 상관분석을 수행하였다. 지표면 온도 산출 결과, 2005년 11월 16일 오전 11경에는 대도시를 중심으로 온도가 $9{\sim}16^{\circ}C$의 분포는 나타냈으며, 상관분석결과 Becker and Li 알고리즘이 가장 높게 나타났으며, Ulivieri et al, Price 알고리즘 순으로 나타났다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2012.05a
• /
• pp.284-288
• /
• 2012

토양온도는 기후변화, 지역기상모형, 수생태 영향과 밀접한 상관성을 가지고 있으며 이에 대한 연구들이 활발하게 진행 중에 있다. 특히 기후는 토양의 분포와 성장, 그리고 소멸에 영향을 미치고, 식생은 증산과정에 의해 대기로 수분을 내보내는 과정을 통해 기후에 영향을 미치고 있다. 이 때, 지표면의 토양온도는 토양수분 및 식생의 성장에 영향을 미치게 된다. 이에 본 연구에서는 격자기반 일 지표면 토양온도 모형이 제안되며, 이를 이용하여 한반도 남동쪽에 위치한 낙동강 유역 내 안동댐 상류지역에 대한 지표면 토양온도가 모의된다. 제안된 모형의 구동을 위해 필요한 입력 자료는 일평균기온 및 관측 NDVI 자료이다. 전국 60개 지점에서 관측된 일 평균기온은 고도가 고려된 Krignig기법을 이용하여 격자별로 구축되며, NDVI 및 지표면 토양온도를 위한 위성자료는 적절한 전처리 과정을 거쳐 자료를 구축한다. 전반적으로 모의된 일 지표면 토양온도는 관측 자료를 잘 재현하고 있는 것으로 분석된다. 추가적으로 감쇠율을 적용하여 토양온도를 토양깊이에 따라 예측하는 방법이 제안되며, 토양깊이에 따라 토양온도가 감소하는 경향을 살펴볼 수 있을 것이다. 이상과 같이 본 연구에서 제안된 모형은 추후 하천수온예측 및 격자기반의 수문모형의 구성을 위한 기초자료를 제공할 것으로 기대된다. 더 나아가 본 연구는 기후-토양-식생의 관계를 바탕으로 미래기후에 대한 물 환경 영향을 평가하는데 있어서 기여할 수 있을 것으로 판단된다.

• Proceedings of the Korean Environmental Sciences Society Conference
• /
• 2007.05a
• /
• pp.49-51
• /
• 2007

본 연구에서는 NASA에서 제공하는 지표면 온도자료인 MODIS MOD11 위성영상을 이용하여, 대기역학모형을 이용하여 1963년, 2002년의 대구지방 토지이용에 따른 지표면 열환경의 변화를 살펴보았다. 수치실험의 경우 고도에 따른 온도벼화가 나타났으며 특히 실제 대구도시지 내의 온도가 높게 나타났다. 그리고 1963년과 2002년의 도시화 정도에 따른 온도변화도 나타났다. 위성에서 관측하 지표면 온도는 한반도를 지나는 시간이 오전 11임에도 불구하고 대구 중심으로 높게 나타나는 것으로 조사되었으며, 팔공산과 앞산으로는 낮은 지표면 온도가 산출되었다. 모형수치는 위성영상과 비교하여 다소 낮게 산출되었으나 전체적인 분포는 잘 일치하고 있다. 그러므로 위성자료에서 측정한 기온은 지표면 토지이용도 분석과 그에 따른 지표면 열변화를 예측 분석하는데 주요한 지표가 될 수 있다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.4
• /
• pp.263-270
• /
• 2018

Land surface temperature is known to be an important factor in understanding the interactions of the ground-atmosphere. However, because of the large spatio-temporal variability, regular observation is rarely made. The existing land surface temperature is observed using satellite images, but due to the nature of satellite, it has the limit of long revisit period and low accuracy. In this study, in order to confirm the possibility of replacing land surface temperature observation using satellite imagery, images acquired by TIR (Thermal Infrared) sensor mounted on UAV (Unmanned Aerial Vehicle) are used. The acquired images were transformed from JPEG (Joint Photographic Experts Group) to TIFF (Tagged Image File Format) format and orthophoto was then generated. The DN (Digital Number) value of orthophoto was used to calculate the actual land surface temperature. In order to evaluate the accuracy of the calculated land surface temperature, the land surface temperature was compared with the land surface temperature directly observed with an infrared thermometer at the same time. When comparing the observed land surface temperatures in two ways, the accuracy of all the land covers was below the measure accuracy of the TIR sensor. Therefore, the possibility of replacing the satellite image, which is a conventional land surface temperature observation method, is confirmed by using the TIR sensor mounted on UAV.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2012.05a
• /
• pp.99-102
• /
• 2012

지표면 온도는 토지피복의 상태, 식생의 분포 상태, 토양수분, 증발산 등의 영향으로 많은 차이를 가지게 되며, 지면-대기의 상호순환의 중요한 인자로써 기후모델 및 농업 등의 기본적인 데이터로 사용되고 있다. 이러한 지표면의 온도를 정확하게 파악하는 것은 수문학적 관점 및 기상적인 관점에서 매우 중요하다. 기존에 LST (Land Surface Temperature, 지표면온도), ET (EvapoTranspiration, 증발산), NDVI (Normalized Difference Vegetation Index, 정규식생지수) 등의 검증이 많이 이루어진 MODIS위성의 Terra/Aqua센서는 한반도를 스캔하고 지나갈 때의 순간적인 데이터를 산출된다. 공간적인 면에서는 많은 이점이 있으나 시간적인 면에서는 시간에 따른 인자들의 변동성을 파악 하는데는 많은 문제가 있다. 그렇기 때문에 시 공간적으로 변화양상을 측정 할 수 있는 정지궤도위성의 중요성이 대두되고 있다. 본 연구에서는 국내에서 2010년 6월 27일 발사된 정지궤도위성인 천리안의 데이터를 활용하였다. 천리안 위성은 기상 센서와 해양관측 센서 그리고 통신센서를 가진 위성이다. 천리안 위성의 기상 센서는 MTSAT-1 위성과 같은 적외선 센서를 탑재하고 있으며, 평시에는 15분 단위의 데이터를 산출하게 된다. 천리안에서 제공되는 많은 Product(강우강도, 해수면온도, 가강수량, 지구방출복사 등)는 수자원 및 기상에 관련된 데이터가 제공된다. 하지만 아직 검증이 많이 이루어지지 못하였다. 그래서 천리안 위성 데이터인 지표면 온도자료를 이용하여 천리안 위성의 효율성에 대해서 알아보고자 하며, 기존의 검증이 많이 이루어진 MODIS의 데이터와의 상관성을 분석하고 지상과의 관계를 검증 및 비교하여 천리안 위성의 활용성에 대해서 알아보려고 한다.

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

• Korean Journal of Remote Sensing
• /
• v.28 no.2
• /
• pp.203-214
• /
• 2012

The physical environment of urban areas covered mostly by concrete and asphalt is the main cause of the urban heat island effect, primarily becoming apparent through increased land surface temperature. This study examined the effect of different urban land cover types on the land surface temperature using MODIS, Landsat ETM+ and RapidEye satellite data. As a result, the remote sensing based land surface temperature showed a marked difference according to the land use pattern in the case study of Ilsan new city. The high-rise apartment residential districts with less building-to-land ratio and higher green area ratio revealed lower land surface temperature than the low-story single-family housing districts characterized by relatively high building-to-land ratio and low green area ratio. From the view of climate zone and land cover types, there is a strong linear correlation between the impervious land cover ratio and the land surface temperature; the land surface temperature increases as the impervious built-up areas expand. In contrast, vegetation;water and shadow areas affect the decrease of land surface temperature. There is also a negative (-) correlation between NDVI and land surface temperature but the seasonal variation of NDVI can be hardly corrected.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1423-1444
• /
• 2022

In this study, the sensitivity of the mid-infrared radiance to atmospheric and surface factors was analyzed using the radiative transfer model, MODerate resolution atmospheric TRANsmission (MODTRAN6)'s simulation data. The possibility of retrieving the land surface temperature (LST) using only the mid-infrared bands at night was evaluated. Based on the sensitivity results, the LST retrieval algorithm that reflects various factors for night was developed, and the level of the LST retrieval algorithm was evaluated using reference LST and observed LST. Sensitivity experiments were conducted on the atmospheric profiles, carbon dioxide, ozone, diurnal variation of LST, land surface emissivity (LSE), and satellite viewing zenith angle (VZA), which mainly affect satellite remote sensing. To evaluate the possibility of using split-window method, the mid-infrared wavelength was divided into two bands based on the transmissivity. Regardless of the band, the top of atmosphere (TOA) temperature is most affected by atmospheric profile, and is affected in order of LSE, diurnal variation of LST, and satellite VZA. In all experiments, band 1, which corresponds to the atmospheric window, has lower sensitivity, whereas band 2, which includes ozone and water vapor absorption, has higher sensitivity. The evaluation results for the LST retrieval algorithm using prescribed LST showed that the correlation coefficient (CC), the bias and the root mean squared error (RMSE) is 0.999, 0.023K and 0.437K, respectively. Also, the validation with 26 in-situ observation data in 2021 showed that the CC, bias and RMSE is 0.993, 1.875K and 2.079K, respectively. The results of this study suggest that the LST can be retrieved using different characteristics of the two bands of mid-infrared to the atmospheric and surface conditions at night. Therefore, it is necessary to retrieve the LST using satellite data equipped with sensors in the mid-infrared bands.