• 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 Korea Water Resources Association Conference
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• 2019.05a
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• pp.140-140
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• 2019

본 연구에서는 Terra MODIS 위성자료와 Tensorflow를 활용해 1 km 공간 해상도의 토양수분을 산정하는 알고리즘을 개발하고, 국내 관측 자료를 활용해 검증하고자 한다. 토양수분 모의를 위한 입력 자료는 Terra MODIS NDVI(Normalized Difference Vegetation Index)와 LST(Land Surface Temperature), GPM(Global Precipitation Measurement) 강우 자료를 구축하고, 농촌진흥청에서 제공하는 1:25,000 정밀토양도를 기반으로 모의하였다. 여기서, LST와 GPM의 자료는 기상청의 종관기상관측지점의 LST, 강우 자료와 조건부합성(Conditional Merging, CM) 기법을 적용해 결측치를 보간하였고, 모든 위성 자료의 공간해상도를 1 km로 resampling하여 활용하였다. 토양수분 산정 기술은 인공 신경망(Artificial Neural Network) 모형의 딥 러닝(Deep Learning)을 적용, 기계 학습기반의 패턴학습을 사용하였다. 패턴학습에는 Python 라이브러리인 TensorFlow를 사용하였고 학습 자료로는 농촌진흥청 농업기상정보서비스에서 101개 지점의 토양수분 자료(2014 ~ 2016년)를 활용하고, 모의 결과는 2017 ~ 2018년까지의 자료로 검증하고자 한다.

• Proceedings of the KSRS Conference
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• v.2
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• pp.864-867
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• 2006

Thermal infrared images of Landsat-5 TM and Landsat-7 ETM+ sensors have been unrivalled sources of high resolution thermal remote sensing (60m for ETM+, 120m for TM) for more than two decades. Atmospheric effect that degrades the accuracy of Sea Surface Temperature (SST) measurement significantly, however, can not be corrected as the sensors have only one thermal channel. Recently, MODIS sensor onboard Terra satellite is equipped with dual-thermal channels (31 and 32) of which the difference of at-satellite brightness temperature can provide atmospheric correction with 1km resolution. In this study we corrected the atmospheric effect of Landsat SST by using MODIS data obtained almost simultaneously. As a case study, we produced the Landsat SST near the eastern and western coast of Korea. Then we have obtained Terra/MODIS image of the same area taken approximately 30 minutes later. Atmospheric correction term was calculated by the difference between the MODIS SST (Level 2) and the SST calculated from a single channel (31 of Level 1B). This term with 1km resolution was used for Landsat SST atmospheric correction. Comparison of in situ SST measurements and the corrected Landsat SSTs has shown a significant improvement in $R^2$ from 0.6229 to 0.7779. It is shown that the combination of the high resolution Landsat SST and the Terra/MODIS atmospheric correction can be a routine data production scheme for the thermal remote sensing of ocean.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.396-396
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• 2018

우리나라의 가뭄은 통계적으로 5~6년 주기로 발생해 왔으나 최근에는 가뭄의 발생 빈도가 점점 증가하고 주기 또한 짧아지는 경향을 보이고 있다. 가뭄의 패턴 또한 지속적이고 국지적으로 강하게 나타내는 경향이 있어 피해가 심각해지고 있다. 2017년도에는 모내기가 시작되어야 할 시기에 극심한 물 부족으로 이앙시기가 지연되고 밭작물이 마르는 피해를 겪었다. 국가가뭄정보센터의 2017년 가뭄예경보 자료에 따르면, 1~7월에는 안성, 서산, 홍성 지역을 중심으로, 7~9월에는 남해안지역을 중심으로, 10월~12월에는 울주, 경주, 밀양 지역을 중심으로 가뭄이 나타났음을 확인 할 수 있다. 가뭄 파악을 위한 방법 중 하나로 인공위성영상을 활용한 원격탐사 기법이 있으며, 국내에서는 관측주기가 짧고 관측폭이 넓은 Terra MODIS 영상을 활용하는 연구 사례를 다수 찾아볼 수 있다. 최근에는 드론에 NIR, 열화상, 초분광 카메라 등을 탑재하여 탐지범위가 국소적이지만 가뭄에 따른 작물의 상태를 보다 상세하게 파악하기 위한 연구가 시도되고 있다. 본 연구에서는 드론을 이용한 가뭄지역의 영상특성을 분석하는 기초자료를 구축하기 위하여 2017년 극심한 가뭄이 발생하였던 안성지역을 대상으로 Terra MODIS NDVI를 이용한 식생상태지수(VCI), 정규식생지수(SVI)를 분석하여 가뭄으로 추정되는 드론촬영 대상지역을 파악하였으며, 선정된 지역을 대상으로 R-G-NIR 카메라를 탑재한 드론 촬영을 실시하였다. 드론영상의 전처리를 통하여 고해상도 NDVI영상을 작성하고 지상의 작물 및 토지이용 상태에 따른 NDVI 분포특성과 Terra MODIS NDVI와의 차이점을 분석하였다.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.4
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• pp.145-157
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• 2015

In 2015, drought was at the worst stage of devastation in Soyanggang Dam watershed. The purpose of this study is to trace the drought area around Soyanggang dam watershed by using Terra MODIS image because it has the ability of spatio-temporal dynamics. The MODIS indices, which included the enhanced vegetation index (NDVI), were extracted from MODIS product MOD13 16-day composite datasets with a spatial resolution of 250m from 2010.01.01 to 2015.06.30. We found that application of Vegetation Condition Index (VCI) and Standardized Vegetation Index (SVI) was suitable for monitoring the drought area. The result can be used to acquire the drought data scattered and demonstrate the potential for the use of MODIS data for temporal and spatial detection of drought effects.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.569-572
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• 2002

Terra MODIS is one of the few space-borne sensors currently capable of acquiring radiometric data over the range of view angles. Institute of Industrial Science, University of Tokyo, has been receiving Terra MODIS data at Tokyo since May 2001 and Asian Institute of Technology at Bangkok since May 2001. They can cover whole East Asia and is expected to monitor environmental changes regularly such as deforestation, forest fires, floods and typhoon. Over eight hundred scenes have been archived in the storage system and they occupy 2 TB of disk space so far. In this study, MODIS data processing system on WWW is developed including following functions: spectral subset (250m, 500m, 1000m channels), radiometric correction to radiance, spatial subset of geocoded data as a rectangular area with latitude-longitude grid system in HDF format, generation of a quick look file in JPEG format. Users will be notified just after all the process have finished via e-mail. Using this system enables us to process MODIS data on WWW with a few input parameters and download the processed data by FTP access. An easy to use interface is expected to promote the use of MODIS data. This system is available via the Internet on the following URL from September 1 2002, "http : //webmodis.iis.u-tokyo.ac.jp/".

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.2
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• pp.457-467
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• 2014

This study is to evaluate the snowmelt impact on dam inflow for the Chungju Dam watershed $6,642.0km^2$ using Terra MODIS (Moderate-Resolution Imaging Spectroradiometer) and Soil and Water Assessment Tool (SWAT). To determine the SWAT snowmelt parameter; snow cover depletion curve (SCDC) the snow depth distribution (SDD) using Terra MODIS was used, the snow depth was spatially interpolated using snowfall data of ground meteorological stations. For 10 sets (2000-2010) data during snowmelt period (November-April), the sno50cov parameter, that is, the 50% coverage at a fraction of SCDC which determines the shape of snow depletion process, showed the values of 0.4 to 0.7. The SWAT model was calibrated with average $R^2$ of 0.54 using the sno50cov of each year. The 10 years average streamflow during snowmelt period was 104.3 mm which covers 12.0% of the annual streamflow.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.103-103
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• 2019

본 연구에서는 Terra MODIS 위성영상과 Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) 모형을 이용하여 2012년부터 2017년까지 한반도 전국의 증발산량을 산정하고 플럭스 타워 실측 증발산량과 비교하였다. METRIC은 전 세계에 널리 적용된 바 있는 에너지 수지 기반의 Surface Energy Balance Algorithm for Land (SEBAL) 모형의 개념과 기술을 기반으로 현열(Sensible Heat Flux) 추정 모듈을 개선한 모형이다. 본 연구에서 METRIC 모형은 기존 C#으로 개발되어 있던 SEBAL 코드에서 현열 추정 모듈을 수정하였고 연산 속도 개선을 위해 Python으로 재작성하였다. METRIC 모형의 위성 자료로 Terra MODIS 위성의 MOD13A2(16day, 1km) NDVI, MOD11A1(Daily, 1km) Land Surface Temperature (LST) 및 MCD43A3(Daily, 500m) Albedo를 구축하였으며 500m 공간해상도의 Albedo는 1000m 해상도로 resample하여 활용하였다. 기상자료는 기상청 기상관측소의 풍속, 풍속측정높이, 습도, 10분 간격 이슬점 온도, 일사량 자료를 위성 자료와 같은 공간해상도로 내삽(Interpolation)하여 구축하였다. 모형결과 검증을 위해 국내 플럭스 타워 (설마천, 청미천, 덕유산) 증발산량 관측 자료와의 결정계수(Coefficient of determination, $R^2$), RMSE(Root mean square error) relative RMSE (RMSE%), Nash-Sutcliffe efficiency (NSE) 및 IOA(Index of Agreement)를 산정하고, 기존 SEBAL 모형 결과와의 비교를 통해 본 모형의 개선점을 보이고자 한다.

• Atmosphere
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• v.17 no.3
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• pp.281-289
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• 2007

Micrometeorlogical and upper air observation have been conducted in order to determine the atmospheric boundary layer depth based on data from satellite and automatic weather systems. Terra/MODIS temperature profiles and sensible heat fluxes from the gradient method were used to estimate the mixed layer height over a coastal region. Results of the integral model were in good agreement with the mixed layer height observed using GPS radiosonde at Wolsung ($35.72^{\circ}N$, $129.48^{\circ}E$). Since the variation of the mixed layer height depends on the surface sensible heat flux, the integral model estimated properly the mixed layer height in the daytime. The buoyant heat flux, which is more important than the sensible heat flux in the coastal region, must be taken into consideration to improve the integral model. The vertical structure of atmospheric boundary layer can be analyzed only with the routine data and the satellite data.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.101-115
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• 2021

The purpose of this study is to evaluate the compatibility of the vegetation index between the two satellites and the applicability of agricultural monitoring by comparing and verifying NDVI (Normalized Difference Vegetation Index) based on Sentinel-2 and Terra MODIS (Moderate Resolution Imaging Spectroradiometer). Terra MODIS NDVI utilized 16-day MOD13Q1 data with 250 m spatial resolution, and Sentinel-2 NDVI utilized 10-day Level-2A BOA (Bottom Of Atmosphere) data with 10 m spatial resolution. To compare both NDVI, Sentinel-2 NDVIs were reproduced at 16-day intervals using the MVC (Maximum Value Composite) technique. As a result of time series NDVIs based on two satellites for 2019 and compare by land cover, the average R² (Coefficient of determination) and RMSE (Root Mean Square Error) of the entire land cover were 0.86 and 0.11, which indicates that Sentinel-2 NDVI and MODIS NDVI had a high correlation. MODIS NDVI is overestimated than Sentinel-2 NDVI for all land cover due to coarse spatial resolution. The high-resolution Sentinel-2 NDVI was found to reflect the characteristics of each land cover better than the MODIS NDVI because it has a higher discrimination ability for subdivided land cover and land cover with a small area range.