• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.61-64
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• 2008

Seasonal distribution of the oceanic water intrusion was investigated using satellite SST (sea surface temperature) and chl-a (chlorophyll-a) images taken by the MODIS Aqua sensor. The warm water mass emanating periodically from the meandering Kuroshio Current brings the oceanic water intrusion, known as the 'Kyucho' phenomenon, into Kagoshima bay during the winter. Satellite SST images and buoy robot data show that this warm water intrusion has the characteristics of a semigeostrophic gravity current influenced by the Coriolis effect. However, it is difficult to find the oceanic water intrusion during the summer season considering that it is accompanied by thermal stratification, and SST shows almost the same temperature between the inner side of the bay and the ocean. In this research, the satellite chl-a images taken by MODIS Aqua were employed instead of SST images to reveal the oceanic water intrusion in each season. The enclosed bay has the tendency to undergo eutrophication caused by organic materials from land and differences in chl-a concentration of the bay water and the oceanic water. As a result, distribution of low concentration chl-a with oceanic water intrusion in summer season shows almost the same pattern in winter season. On the other hand, in spring season, both SST and chl-a images are available to differentiate the oceanic water intrusion. Therefore, applying the suitable satellite sensor images for each season is effective in the monitoring of oceanic water intrusion. Moreover, in this area, SST and chl-a distribution reveal not only the oceanic water intrusion into Kagoshima bay but also the intrusion at Fukiage seashore facing East China Sea.

• Journal of Ecology and Environment
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• v.31 no.2
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• pp.153-159
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• 2008

The timing of the canopy phenology onset (CPO hereafter) indicates the initiation of the growing season, with rapid increases in exchange rates of carbon dioxide and water vapor between vegetation and atmosphere. The CPO is regarded as a potential indicator of ecosystem responses to global warming, but the CPO shows considerable spatial variation depending on the species composition and local temperature regime. at a given geographic location. In this study, we evaluated the utility of satellite observation data for detection of the timing of the CPO. Leaf area indices (LAI) obtained from the Moderate Resolution Imaging Spectrora-diometer (MODIS) were utilized to detect and map the onset dates from 2001 to 2006. The reliability of MODIS-based onset dates was evaluated with ground measured cherry blossom flowering data from national weather stations. The MODIS onset dates preceded the observed flowering dates by 8 days and were linearly related with a correlation coefficient of 0.58 (p < 0.05). In spite of the coarse spatial (1 km) and temporal (8 days) resolutions of MODIS LAI, the MODIS-based onset dates showed reasonable ability to predict flowering dates.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.213-218
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• 2009

에너지 전달 과정과 밀접한 관계가 있는 증발산(Evapotranspiration)은 기후 변화나 육상 생태계 생산성에서 매우 중요한 요소이며, 수문학적 순환과 지역적 물 관리 측면에서 매우 중요하다. 최근 인공위성을 이용하여 증발산을 추정하기 위한 노력이 많이 진행되고 있으며, 특히 MODIS (Moderate Resolution Imaging Spectroradiometer)는 증발산을 추정하기 위한 좋은 정보를 제공하고 있다 하지만, 구름 등에 의한 증발산 입력 자료 결측은 전체 자료의 획득률을 낮추고, 연속적인 증발산 모니터 링을 제한한다. 따라서 본 연구에서는 MODIS 기반의 증발산 입력 자료의 개선하여 서로 다른 식생과 지형 구조를 갖는 플럭스 연구지에 대한 증발산의 추정 및 평가하고, 남한에 대한 MODIS 기반의 증발산 지도 작성하였다. 또한 구름에 의해 결측된 날에 대해서는 MODIS-MM5 4차원 자료동화 기법을 이용한 증발산의 연속적인 모니터링 기법을 개발하였다. MODIS 기반의 증발산을 추정하기 위해 Revised RS-PM 알고리즘을 사용하였다. 증발산을 평가하기 위해 4 곳의 플럭스 연구지(광릉, 해남 이상 대한민국, 타카야마, 토마코아미 이상 일본) 자료와 비교하였고, 매우 신뢰성 있는 결과를 얻을 수 있었다. MODIS 입력 자료의 개선으로 획득률은 2배 가량 증가하였다. 남한에 대한 연간 증발산은 평균적으로 약 35%의 획득률 (365일 중 약 120일)과 함께 산출되었고, 시 공간적인 분포를 잘 나타내었다. 구름 낀 날에 대한 MODIS-MM5 자료 동화 기법의 적용은 증발산의 연속적인 모니터링을 가능하게 하였다.

• Journal of the Korean earth science society
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• v.40 no.3
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• pp.212-226
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• 2019

This study derived spectral Lambertian Equivalent Reflectance (LER) over East Asia from the observations of Ozone Monitoring Instrument (OMI) onboard polar-orbit satellite Aura. The climatological (October 2004-September 2007) LER values were compared with the surface reflectance products of OMI or MODerate resolution Imaging Spectroradiometer (MODIS) in terms of the atmosphere-environment variables as follows: wavelength (UV, visible), surface properties (land, ocean), and cloud filtering. Four kinds of LER outputs in the UV and visible region (328-500 nm) were retrieved based on the averages of lowest (1, 5, and 10%) surface reflectance values as well as the minimum reflectance. The average of the lowest 10% among them was in best agreement with the OMI product: correlation coefficient (0.88), RMSE (1.0%) and mean bias (-0.3%). The 10% average and OMI LER values over ocean were 2% larger in UV than in visible, while the values over land were 1% smaller. The LER variability on the wavelength and surface property was highest (~3%) in the condition of both land and visible, particularly in the ice-cap and desert regions. The minimum reflectance values over the oceanic and inland sample areas overestimated the MODIS product by 1.4%. This high-resolution MODIS observations were effective in removing cloud contamination. The relative errors of the 10% average to MODIS were smaller (-0.6%) over ocean but larger (1.5%) over land than those of the OMI product to MODIS. The reduced relative error in the OMI product over land may result from additional cloud filtering using the Landsat data. This study will be useful when retrieveing the surface reflectance from geostationary-orbit environmental satellite (e.g., Geostationary Environment Monitoring Spectrometer; GEMS).

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1035-1046
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• 2022

This study evaluated the applicability of multi-resolution data fusion for land-cover classification. In the applicability evaluation, a spatial time-series geostatistical deconvolution/fusion model (STGDFM) was applied as a multi-resolution data fusion model. The study area was selected as some agricultural lands in Iowa State, United States. As input data for multi-resolution data fusion, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellite images were used considering the landscape of study area. Based on this, synthetic Landsat images were generated at the missing date of Landsat images by applying STGDFM. Then, land-cover classification was performed using both the acquired Landsat images and the STGDFM fusion results as input data. In particular, to evaluate the applicability of multi-resolution data fusion, two classification results using only Landsat images and using both Landsat images and fusion results were compared and evaluated. As a result, in the classification result using only Landsat images, the mixed patterns were prominent in the corn and soybean cultivation areas, which are the main land-cover type in study area. In addition, the mixed patterns between land-cover types of vegetation such as hay and grain areas and grass areas were presented to be large. On the other hand, in the classification result using both Landsat images and fusion results, these mixed patterns between land-cover types of vegetation as well as corn and soybean were greatly alleviated. Due to this, the classification accuracy was improved by about 20%p in the classification result using both Landsat images and fusion results. It was considered that the missing of the Landsat images could be compensated for by reflecting the time-series spectral information of the MODIS images in the fusion results through STGDFM. This study confirmed that multi-resolution data fusion can be effectively applied to land-cover classification.

• Journal of Korea Water Resources Association
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• v.45 no.8
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• pp.755-765
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• 2012

The Land Surface Temperature (LST) is one of the significant factors to understand the water and energy cycles between the land surface and atmosphere. However, few previous studies for spatio-temporal variations of LST has been investigated. In this study, we conducted comparative analyses between the Communication, Ocean and Meteorological Satellite (COMS) and MOderate-Resolution Imaging Spectroradiometer (MODIS) LST data. We compared COMS data with observations to identify the accuracy and found relative underestimated patterns of the COMS data as compared to observations. We also found that COMS LST were underestimated in compare to MODIS LST. The Terra LST was verified to have more similar trends with the COMS LST rather than Aqua LST. While we identified the applicability of COMS based on the results of similar tendencies of two comparisons, more intensive validation research at a variety of field conditions should be conducted to gurantee current COMS LST.

• Journal of Korea Water Resources Association
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• v.47 no.10
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• pp.935-943
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• 2014

Normalized Difference Vegetation Index (NDVI) used as input data for various hydrologic models plays a key role in understanding the variation of Hydrometeological parameters and Interaction between surface and atmosphere. Many studies have been conducted to estimate accurate remotely-sensed NDVI using spectral characteristics of vegetation. In this study, we conducted comparative analysis between Communication, Ocean and Meteorological Satellite and MOderate-Resolution Imaging Spectroradiometer (MODIS) NDVI. For comparison, Maximum Value Composite (MVC) was used to estimate 8-day and 16-day composite COMS NDVI. Both 8-day and 16-day COMS NDVI showed high statistical results compared with MODIS NDVI. Based on the results in this study, it can be concluded that COMS can be widely applicable for further ecological and hydrological studies.

• The Korean Journal of Ecology
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• v.28 no.4
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• pp.215-222
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• 2005

MODIS (Moderate Resolution Image Spectrometer) is a core satellite sensor boarded on Terra and Aqua satellite of NASA Earth Observing System since 1999 and 2001, respectively. MODIS LAI, FPAR, and GPP provide useful means to monitor plant phonology and material cycles in terrestrial ecosystems. In this study, LAI, FPAR, and GPP in Korea were evaluated and errors associated with cloud contamination on MODIS pixels were eliminated for years $2001\sim2003$. Three-year means of cloud-corrected annual GPP were 1836, 1369, and 1460g C $m^{-2}y^{-1}$ for evergreen needleleaf forest, deciduous broadleaf forest, and mixed forest, respectively. The cloud-originated errors were 8.5%, 13.1%, and 8.4% for FPAR, LAI, and GPP, respectively. Summertime errors from June to September explained by 78% of the annual accumulative errors in GPP. This study indicates that cloud-originated errors should be mitigated for practical use of MODIS vegetation products to monitor seasonal and annual changes in plant phonology and vegetation production in Korea.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.543-557
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• 2021

Because aerosols have different spectral characteristics according to the size and composition of the particle and to the satellite sensors, a comparative analysis of aerosol products from various satellite sensors is required. In South Korea, however, a comprehensive study for the comparison of various official satellite AOD (Aerosol Optical Depth) products for a long period is not easily found. In this paper, we aimed to assess the performance of the AOD products from MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite), Himawari-8, and Sentinel-3 by referring to the AERONET (Aerosol Robotic Network) sun photometer observations for the period between January 2015 and December 2019. Seasonal and geographical characteristics of the accuracy of satellite AOD were also analyzed. The MODIS products, which were accumulated for a long time and optimized by the new MAIAC (Multiangle Implementation of Atmospheric Correction) algorithm, showed the best accuracy (CC=0.836) and were followed by the products from VIIRS and Himawari-8. On the other hand, Sentinel-3 AOD did not appear to have a good quality because it was recently launched and not sufficiently optimized yet, according to ESA (European Space Agency). The AOD of MODIS, VIIRS, and Himawari-8 did not show a significant difference in accuracy according to season and to urban vs. non-urban regions, but the mixed pixel problem was partly found in a few coastal regions. Because AOD is an essential component for atmospheric correction, the result of this study can be a reference to the future work for the atmospheric correction for the Korean CAS (Compact Advanced Satellite) series.

• Asian Journal of Atmospheric Environment
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• v.12 no.3
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• pp.193-203
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• 2018

The operational aerosol retrieval algorithm for the Moderate Resolution Imaging Spectroradiometer (MODIS) measurements was recently updated and named collection 6 (C6). The C6 MODIS aerosol algorithm, a substantially improved version of the collection 5 (C5) algorithm, uses an enhanced aerosol optical thickness(AOT) retrieval process consisting of new surface reflection and aerosol models. This study reports on the estimation and validation of the two latest versions, the C5 and C6 MODIS aerosol products over the East Asian region covering $20^{\circ}N$ to $56^{\circ}N$ and $80^{\circ}E$ to $150^{\circ}E$. This study also presents a comparative validation of the two versions(C5 and C6) of algorithms with different methods(Dark Target(DT) and Deep Blue (DB) retrieval methods) from the Terra and Aqua platforms to make use of the Aerosol Robotic Network (AERONET) sites for the years 2000-2016. Over the study region, the spatially averaged annual mean AOT retrieved from C6 AOT is about 0.035 (5%) less than the C5 counterparts. The linear correlations between MODIS and AERONET AOT are R = 0.89 (slope = 0.86) for C5 and R = 0.95 (slope = 1.00) for C6. Moreover, the magnitude of the mean error in C6 AOT-the difference between MODIS AOT and AERONET AOT-is 40% less than that in C5 AOT.