• Proceedings of the KSRS Conference
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• v.2
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• pp.1011-1014
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• 2006

Coarse resolution (9 - 50 km pixels) Sea Surface Temperature satellite data are frequently considered adequate for open ocean research. However, coastal regions, including coral reef, estuarine and mesoscale upwelling regions require high-resolution (1-km pixel) SST data. The AVHRR SST data often suffer from navigation errors of several kilometres and still require manual navigation adjustments. The second serious problem is faulty and ineffective cloud-detection algorithms used operationally; many of these are based on radiance thresholds and moving window tests. With these methods, increasing sensitivity leads to masking of valid pixels. These errors lead to significant cold pixel biases and hamper image compositing, anomaly detection, and time-series analysis. Here, after manual navigation of over 40,000 AVHRR images, we implemented a new cloud filter that differs from other published methods. The filter first compares a pixel value with a climatological value built from the historical database, and then tests it against a time-based median value derived for that pixel from all satellite passes collected within ${\pm}3$ days. If the difference is larger than a predefined threshold, the pixel is flagged as cloud. We tested the method and compared to in situ SST from several shallow water buoys in the Florida Keys. Cloud statistics from all satellite sensors (AVHRR, MODIS) shows that a climatology filter with a $4^{\circ}C$ threshold and a median filter threshold of $2^{\circ}C$ are effective and accurate to filter clouds without masking good data. RMS difference between concurrent in situ and satellite SST data for the shallow waters (< 10 m bottom depth) is < $1^{\circ}C$, with only a small bias. The filter has been applied to the entire series of high-resolution SST data since1993 (including MODIS SST data since 2003), and a climatology is constructed to serve as the baseline to detect anomaly events.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1407-1419
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• 2020

Long time-series gridded data is crucial for the analyses of Earth environmental changes. Climate reanalysis and satellite images are now used as global-scale periodical and quantitative information for the atmosphere and land surface. This paper examines the feasibility of DCT-PLS (penalized least square regression based on discrete cosine transform) for the spatial gap filling of gridded data through the experiments for multiple variables. Because gap-free data is required for an objective comparison of original with gap-filled data, we used LDAPS (Local Data Assimilation and Prediction System) daily data and MODIS (Moderate Resolution Imaging Spectroradiometer) monthly products. In the experiments for relative humidity, wind speed, LST (land surface temperature), and NDVI (normalized difference vegetation index), we made sure that randomly generated gaps were retrieved very similar to the original data. The correlation coefficients were over 0.95 for the four variables. Because the DCT-PLS method does not require ancillary data and can refer to both spatial and temporal information with a fast computation, it can be applied to operative systems for satellite data processing.

• Korean Journal of Remote Sensing
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• v.32 no.4
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• pp.383-401
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• 2016

In this paper, we described the statistical modeling of crop yields using satellite images, climatic datasets, soil property maps, and fertilizer data for the Midwestern United States during 2001-2012. Satellite images were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), and climatic datasets were provided by the Parameter-elevation Regressions on Independent Slopes Model (PRISM) Climate Group. Soil property maps were derived from the Harmonized World Soil Database (HWSD). Our multivariate regression models produced quite good prediction accuracies, with differences of approximately 8-15% from the governmental statistics of corn and soybean yields. The unfavorable conditions of climate and vegetation in 2012 could have resulted in a decrease in yields according to the regression models, but the actual yields were greater than predicted. It can be interpreted that factors other than climate, vegetation, soil, and fertilizer may be involved in the negative biases. Also, we found that soybean yield was more affected by minimum temperature conditions while corn yield was more associated with photosynthetic activities. These two crops can have different potential impacts regarding climate change, and it is important to quantify the degree of the crop sensitivities to climatic variations to help adaptation by humans. Considering the yield decreases during the drought event, we can assume that climatic effect may be stronger than human adaptive capacity. Thus, further studies are demanded particularly by enhancing the data regarding human activities such as tillage, fertilization, irrigation, and comprehensive agricultural technologies.

• Proceedings of the KSRS Conference
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• v.1
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• pp.90-93
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• 2006

The first Geostationary Ocean Color Imager (GOCI) onboard its Communication Ocean and Meteorological Satellite (COMS) is scheduled for launch in 2008. GOCI includes the eight visible-to-near-infrared (NIR) bands, 0.5km pixel resolution, and a coverage region of 2500 ${\times}$ 2500km centered at 36N and 130E. GOCI has had the scope of its objectives broadened to understand the role of the oceans and ocean productivity in the climate system, biogeochemical variables, geological and biological response to physical dynamics and to detect and monitor toxic algal blooms of notable extension through observations of ocean color. The special feature with GOCI is that like MODIS, MERIS and GLI, it will include the band triplets 660-680-745 for the measurements of sun-induced chlorophyll-a fluorescence signal from the ocean. The GOCI will provide SeaWiFS quality observations with frequencies of image acquisition 8 times during daytime and 2 times during nighttime. With all the above features, GOCI is considered to be a remote sensing tool with great potential to contribute to better understanding of coastal oceanic ecosystem dynamics and processes by addressing environmental features in a multidisciplinary way. To achieve the objectives of the GOCI mission, we develop the GOCI Data Processing System (GDPS) which integrates all necessary basic and advanced techniques to process the GOCI data and deliver the desired biological and geophysical products to its user community. Several useful ocean parameters estimated by in-water and other optical algorithms included in the GDPS will be used for monitoring the ocean environment of Korea and neighbouring countries and input into the models for climate change prediction.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.42-55
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• 2017

Since the phases and patterns of the climate adaptability of vegetation can greatly differ from region to region, an intensive pixel scale approach is required. In this study, Partial Least Squares (PLS) regression on satellite image-based vegetation index is conducted for to assess the effect of climate factors on vegetation productivity and to predict future productivity of forests vegetation in South Korea. The results indicate that the mean temperature of wettest quarter (Bio8), mean temperature of driest quarter (Bio9), and precipitation of driest month (Bio14) showed higher influence on vegetation productivity. The predicted 2050 EVI in future climate change scenario have declined on average, especially in high elevation zone. The results of this study can be used in productivity monitoring of climate-sensitive vegetation and estimation of changes in forest carbon storage under climate change.

• Atmosphere
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• v.18 no.2
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• pp.97-109
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• 2008

The aerosol characteristics in terms of 4 different cases (Asian dust, fog, clear sky and cloud) which had happened at Anmyeon Island in April 2006 were studied using various measurements such as the Micro Pulse Lidar (MPL), sunphotometer, $\beta$-ray $PM_{10}$ Analyzer, anemoscope and anemometer. In addition, synoptic charts, back trajectory analyses and satellite images were also used to help characterize the aerosol events. The aerosol optical properties were featured by the Aerosol Optical Depth (AOD) and ${\AA}ngstr\ddot{o}m$ exponent which were estimated by the sunphotometer. When Anmyeon Island was dominated by the Asian dust, the AOD was sharply increased as seven times as a yearly average of it (0.35). As compared with a yearly average of the ${\AA}ngstr\ddot{o}m$ exponent of 0.97, the ${\AA}ngstr\ddot{o}m$ exponent of a dust day was significantly low (0.099). In addition, $PM_{10}$ mass concentration showed an extremely high record. The maximum concentration reached $1790.5{\mu}gm^{-3}$ on 8 April 2006. The maximum mass concentration was shown with delay when the wind speed of $0ms^{-1}$ was observed. It was also found that a satellite image of the MODIS-RGB had a good agreement with the results of those measurements. It was shown that the MPL was able to describe effectively the vertical distribution of aerosol for all the cases. In particular, the MPL evidently captured the aerosol layer before the cloud observation. The aerosol layer was similarly described by the AOD. On a clear sky day, the AOD had not only a very low value (0.054) but also a feature of homogeneity.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.605-605
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• 2016

최근 기후변화로 인한 국지적 또는 대규모 극한 가뭄과 홍수가 빈발함에 따라 수자원 관리 여건은 점점 더 어려워지고 있다. 이런 물 관련 재해에 보다 효과적으로 대응하기 위해서는 수자원인자에 대한 시공간적 모니터링이 필수적인데, 이러한 관점에서 시공간적 광역관측이 가능한 위성자료의 활용가치는 매우 높게 평가되고 있으며, 최근에는 국내외적으로 위성자료를 이용하여 수문 인자 산출, 가뭄 홍수 등의 모니터링 기술 등에 대한 연구가 활발히 진행되고 있다. K-water는 위성정보 활용기술력 축적을 통한 보다 효율적인 수자원 관리를 하기 위하여 수자원 분야에 활용 가능한 해외의 주요 위성자료를 실시간 직수신 처리하여 표출하는 K-water 위성영상관리시스템(K-SIMS, K-water Satellite Image Management System)을 2015년에 구축하였다. 현재 K-SIMS를 통해 관리되는 위성은 AQUA, TERRA, NPP, GCOM-W, GPM로서 총 5개이다. AQUA, TERRA, NPP 위성은 각 궤도운영 스케쥴에 따라 한반도 상공을 통과하는 시각에 안테나가 위성의 궤도를 따라가며 수신하고, GCOM-W, GPM 위성자료는 FTP 접속를 통해 준실시간으로 수신하고 있다. 산출물은 AQUA, TERRA, NPP가 각각 23종, GCOM-W 9종, GPM 2종 등 총 80여종으로 위성원시자료 수신즉시 처리 표출까지 실시간 자동 수행되고 있으나 식생지수, 강수, 구름, 대기온도, 수증기 등 대부분 수문기상학적 변수들로 구성되어 있어 수자원 관리 현업 업무에는 직접 사용하기에는 다소 한계가 있다. 따라서, 위성자료의 활용성을 높이기 위하여 수문해석에 중요한 변수인 토양수분에 대해서 AQUA, TERRA의 MODIS LST(Land Surface Temperature)와 식생지수(Vegetation Index)를 이용하여 SMI(Soil Moisture Index)를 산출하고 이를 K-SIMS에 표출하는 체계를 추가로 구축하여 현업 활용도가 높은 자료를 생산하고 있으며, 향후 위성자료를 활용한 가뭄지수를 추가로 산출하여 표출할 계획이다. 이와 함께 K-water는 차세대 중형위성 개발 사업에 따른 수자원 위성 확보에 대비해 수자원 분야 위성활용 중장기 계획을 마련하였다. 향후에 광학위성, SAR위성 등 다양한 위성자료의 융복합적 활용을 통하여 위성산출물 알고리즘을 지속적으로 개발함으로써 홍수, 가뭄, 수질 등 물 재해 대응 및 수자원 관리 전 분야에 위성자료의 활용을 확대해 나갈 계획이다.

• Journal of the Korean earth science society
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• v.31 no.6
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• pp.600-607
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• 2010

In 2008, multiple episodes of large-scale transport of natural airborne particles and anthropogenically affected particles from different sources in the East Asian continent were identified in the National Oceanic and Atmospheric Administration (NOAA) satellite RGB-composite images and the mass concentrations of ground level particulate matters. To analyze the aerosol size distribution during the large-scale transport of atmospheric aerosols, both aerosol optical depth (AOD; proportional to the aerosol total loading in the vertical column) and fine aerosol weighting (FW; fractional contribution of fine aerosol to the total AOD) of Moderate resolution Imaging Spectroradiometer (MODIS) aerosol products were used over the East Asian region. The six episodes of massive natural airborne particles were observed at Cheongwon, originating from sandstorms in northern China, Mongolia and the loess plateau of China. The $PM_{10}$ and $PM_{2.5}$ stood at 70% and 16% of the total mass concentration of TSP, respectively. However, the mass concentration of $PM_{2.5}$ among TSP increased as high as 23% in the episode in which they were flowing in by way f the industrial area in east China. In the other five episodes of anthropogenically affected particles that flowed into the Korean Peninsula from east China, the mass concentrations of $PM_{10}$ and $PM_{2.5}$ among TSP reached 82% and 65%, respectively. The average AOD for the large-scale transport of anthropogenically affected particle episodes in the East Asian region was measured at $0.42{\pm}0.17$ compared with AOD ($0.36{\pm}0.13$) for the natural airborne particle episodes. Particularly, the regions covering east China, the Yellow Sea, the Korean Peninsula, and the east Korean sea were characterized by high levels of AOD. The average FW values observed during the event of anthropogenically affected aerosols ($0.63{\pm}0.16$) were moderately higher than those of natural airborne particles ($0.52{\pm}0.13$). This observation suggests that anthropogenically affected particles contribute greatly to the atmospheric aerosols in East Asia.

• Korean Journal of Ecology and Environment
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• v.51 no.1
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• pp.105-123
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• 2018

Currently, satellite images act as essential and important data in water resources, environment, and ecology as well as information of geographic information system. In this paper, we will investigate basic characteristics of satellite images, especially application examples in water resources. In recent years, researches on spatial and temporal characteristics of large-scale regions utilizing the advantages of satellite imagery have been actively conducted for fundamental hydrological components such as evapotranspiration, soil moisture and natural disasters such as drought, flood, and heavy snow. Furthermore, it is possible to analyze temporal and spatial characteristics such as vegetation characteristics, plant production, net primary production, turbidity of water bodies, chlorophyll concentration, and water quality by using various image information utilizing various sensor information of satellites. Korea is planning to launch a satellite for water resources and environment in the near future, so various researches are expected to be activated on this field.