• Journal of Korean Society for Atmospheric Environment
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• v.21 no.E2
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• pp.49-56
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• 2005

A long-path transmissometer is one of the optical instruments widely used to measure atmospheric light extinction coefficient without enclosing a light beam and perturbing aerosols. Over the past two decades, a number of measurements have been carried out using the long-path transmissometer manufactured by OPTEC, Inc. Calibration of the transmissometer should be performed when any component of the transmissometer system is interchanged or installation condition is changed. For a better calibration of the transmissometer, application of a modified calibration method for the existing neutral density (ND)-filter method was recommended for the computation of the atmospheric transmittance using model MODTRAN 4 in this study. It was revealed that the measured light extinction coefficient from the transmissometer which was calibrated using the existing ND-filter method could be overestimated due to the assumption of the atmospheric transmittance suggested by OPTEC, Inc. The uncertainty of the measured light extinction coefficient from the transmissometer calibrated based on the modified ND-filter method was calculated to be approximately $13Mm^{-1}$.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.310-313
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• 2007

In this study, a new technique for atmospheric transmittance estimated from ALOS PRISM data is developed. It is based on satellite's observing radiances of different view angles and assumes that the cause of difference in radiances is the different view angles. The ALOS PRISM has three independent optical systems for viewing forward and backward and producing a stereoscopic image along the satellite's track. This stereo pair data can be used to estimate the transmittance according to the radiative transfer theory. This derived transmittance will be validated by the AERONET data and compared with the MODTRAN4 simulation results. Results show that the higher the land cover albedo, the better the derived transmittance compared to the AERONET data. Besides, this technique also shows the transmittance retrieval will be underestimated for the low land cover albedo.

• Aerospace Engineering and Technology
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• v.5 no.2
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• pp.213-219
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• 2006

Korea Aerospace Research Institute(KARI) performed field campaigns for absolute radiometric calibration with overpassing of satellite Orbview-3 on Cal/ Val site in Goheung and Daejeon. The performed Cal/Val method is the reflectance-based of vicarious calibration methods. We collected ground-based and meteology data such as temperature, surface pressure and reflectance of targets, and radiosonde data only collected on Goheung. Data collected on each field served as input to radiative transfer codes to generate a top-of-atmosphere(TOA) radiance. Derived TOA is compared with DN of overpassing satellite Orbview-3 to calculate calibration coefficient of gain and offset. Also, This study proposed a proper method to prepare absolute radiometic calibration of KOMPSAT-2 by using experience of Field campaign.

• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.125-130
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• 2008

Mid-Infrared (MIR) 영상은 화산 활동 및 산불로 인한 재난관측, 지표물질 관측, 해수 온도 측정과 같은 분야들에서 사용되고 있다. 그러나 MIR영역은 지표의 복사율과 대기의 영향으로 인한 변화가 매우 심하고, 낮 영상의 경우 태양 복사량에 의한 영향도 고려해야 하는 어려움이 있다. 따라서 단일밴드인 MIR영상을 이용하여 표면온도를 얻기 위해서는 영상이 취득된 시간과 장소에서 관측된 태양 복사량 및 여러 가지 대기 변수가 필요하다. 본 연구의 목적은 기존의 MIR 영상 연구가 다중밴드를 기반으로 한 것과 달리 단일 밴드의 영상을 이용하여 표면온도를 측정하는데 있다. 이를 위하여 MODIS MIR 영상을 대상으로 MODTRAN을 사용하여 MIR 영역의 대기보정 기법을 적용 한 뒤 복사전달 모델을 이용하여 지표의 복사량을 측정하였다. 획득된 지표온도 영상의 정밀도를 측정하기 위해서 기존의 온도 알고리즘인 split-window algorithm에 의해 얻어진 해수온도 영상과의 비교를 통하여 오차 원인에 대해 분석을 실시하였다. 그 결과 낮 영상의 경우 -4.19${\pm}$1.19$^{\circ}C$ 정도의 온도 차가 났으며, 밤 영상의 경우 0.5$^{\pm}C$0.39$^{\circ}C$ 정도로 비교적 좋은 결과를 보였다. 이는 낮 영상의 경우 지표의 복사율에 대한 온도의 민감도가 매우 높기 때문에 높은 오차가 발생하지만, 밤 영상의 경우 태양빛에 의한 영향이 없으므로 좋은 결과를 나타내기 때문이다. 따라서 단일밴드 MIR영상을 이용한 지표온도 추정 시 대기에 의한 영향보다 지표 복사율에 의한 영향이 높다고 추정할 수 있다.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.151-162
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• 2019

Surface temperature has been derived from the MODIS/ASTER airborne simulator (MASTER) mid-wave infrared single channel data using the MODerate resolution atmospheric TRANsmission (MODTRAN) radiative transfer model with input data including the University of Wisconsin (UW) emissivity, the National Centers for Environmental Prediction (NCEP) atmospheric profiles, and solar and line-of-sight geometry. We have selected the study area that covers some surface types such as water, sand, agricultural (vegetated) land, and clouds. Results of the current study show the reasonable geographical distribution of surface temperature over land and water similar to the pattern of the MASTER L2 surface temperature. The thorough quantitative validation of surface temperature retrieved from this study is somehow limited due to the lack of in-situ measurements. One point comparison at the Salton Sea buoy shows that the present estimate is 1.8 K higher than the field data. Further comparison with the MASTER L2 surface temperature over the study area reveals statistically good agreement with mean differences of 4.6 K between two estimates. We further analyze the surface temperature differences between two estimates and find primary factors to be emissivity and atmospheric correction.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.653-662
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• 2011

Land surface temperature (LST) is a one of the key variables of land surface which can be estimated from geostationary meteorological satellite. In this study, we have developed the three sets of LST retrieval algorithm from MTSAT-2 data through the radiative transfer simulations under various atmospheric profiles (TIGR data), satellite zenith angle, spectral emissivity, and surface lapse rate conditions using MODTRAN 4. The three LST algorithms are daytime, nighttime and total LST algorithms. The weighting method based on the solar zenith angle is developed for the consistent retrieval of LST at the early morning and evening time. The spectral emissivity of two thermal infrared channels is estimated by using vegetation coverage method with land cover map and 15-day normalized vegetation index data. In general, the three LST algorithms well estimated the LST without regard to the satellite zenith angle, water vapour amount, and surface lapse rate. However, the daytime LST algorithm shows a large bias especially for the warm LST (> 300 K) at day time conditions. The night LST algorithm shows a relatively large error for the LST (260 ~ 280K) at the night time conditions. The sensitivity analysis showed that the performance of weighting method is clearly improved regardless of the impacting conditions although the improvements of the weighted LST compared to the total LST are quite different according to the atmospheric and surface lapse rate conditions. The validation results of daytime (nighttime) LST with MODIS LST showed that the correlation coefficients, bias and RMSE are about 0.62~0.93 (0.44~0.83), -1.47~1.53 (-1.80~0.17), and 2.25~4.77 (2.15~4.27), respectively. However, the performance of daytime/nighttime LST algorithms is slightly degraded compared to that of the total LST algorithm.

• Korean Journal of Remote Sensing
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• v.32 no.5
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• pp.435-452
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• 2016

Land Surface Temperature (LST) has been operationally retrieved from the Communication, Ocean, and Meteorological Satellite (COMS) data by the spilt-window method (CSW_v2.0) developed by Cho et al. (2015). Although the CSW_v2.0 retrieved the LST with a reasonable quality compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) LST data, it showed a relatively poor performance for the strong inversion and lapse rate conditions. To solve this problem, the LST retrieval algorithm (CSW_v2.0) was updated using the simulation results of radiative transfer model (MODTRAN 4.0) by considering the diurnal variations of air temperature. In general, the upgraded version, CSW_v3.0 showed a similar correlation coefficient between the prescribed LSTs and retrieved LSTs (0.99), the relatively smaller bias (from -0.03 K to-0.012 K) and the Root Mean Square Error (RMSE) (from 1.39 K to 1.138 K). Particularly, CSW_v3.0 improved the systematic problems of CSW_v2.0 that were encountered when temperature differences between LST and air temperature are very large and/or small (inversion layers and superadiabatic lapse rates), and when the brightness temperature differences and surface emissivity differences were large. The bias and RMSE of CSW_v2.0 were reduced by 10-30% in CSW_v3.0. The indirect validation results using the MODIS LST data showed that CSW_3.0 improved the retrieval accuracy of LST in terms of bias (from -0.629 K to -0.049 K) and RMSE (from 2.537 K to 2.502 K) compared to the CSW_v2.0.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.203-215
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• 2019

As hyperspectral sensors that can be mounted on drones are developed, it is possible to acquire hyperspectral imagery with high spatial and spectral resolution. Although the importance of atmospheric correction has been reduced since imagery of drones were acquired at a low altitude,studies on the conversion process from raw data to spectral reflectance should be done for studies such as estimating the concentration of surface materials using hyperspectral imagery. In this study, a vicarious radiometric calibration and an atmospheric correction algorithm based on atmospheric radiation transfer model were applied to hyperspectral data of drone and the results were compared and analyzed. The vicarious calibration method was applied to an empirical line calibration using the spectral reflectance of a tarp made of uniform material. The atmospheric correction algorithm used ATCOR-4 based Modran-5 that was widely used for the atmospheric correction of aerial hyperspectral imagery. As a result of analyzing the RMSE of the difference between the reference reflectance and the correction, the vicarious calibration using the tarp in a single period of hyperspectral image was the most accurate, but the atmospheric correction was possible according to the application purpose of using hyperspectral imagery. If the correction process of normalized spectral reflectance is carried out through the additional vicarious calibration for imagery from multiple periods in the future, accurate analysis using hyperspectral drone imagery will be possible.