• Proceedings of the Korean Society of Crop Science Conference
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• 2005.08a
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• pp.75-87
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• 2005

Remote sensing(RS)- and geographic information system(GIS)-based information management to measure and assess agri-environment schemes, and to quantify and map environment indicators for nature and land use, climate change, air, water and energy balance, waste and material flow is in high demand because it is very helpful in assisting decision making activities of farmers, government, researchers, and consumers. The versatility and ability of RS and GIS containing huge soil database to assess agricultural environment spatially and temporally at various spatial scales were investigated. Spectral and microwave observations were carried out to characterize crop variables and soil properties. Multiple sources RS data from ground sensors, airborne sensors, and also satellite sensors were collected and analyzed to extract features and land cover/use for soils, crops, and vegetation for support precision agriculture, soil/land suitability, soil property estimation, crop growth estimation, runoff potential estimation, irrigated and the estimation of flooded areas in paddy rice fields. RS and GIS play essential roles in a management and monitoring information system. Biosphere-atmosphere interection should also be further studied to improve synergistic modeling for environment and sustainability in agri-environment schemes.

• Journal of Power System Engineering
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• v.4 no.4
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• pp.78-83
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• 2000

It is often necessary to observe the working environment of a robot to control it efficiently in the remote location. The remote sensing data and control commands are transmitted via various media such as radio, microwave, and computer network. The World Wide Web can be used as the infrastructure for teleoperation of mobile robots. In this paper we propose an advanced technique of the remote control of mobile robots on the web. For the real-time control, the image separation algorithm is proposed to transmit the current positions of mobile robots instead of transmitting the full frame image. It is shown by experiments that the proposed algorithm is effective.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.2184-2189
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• 2009

China is one of the countries in which flood occurs most frequently in the world and with the current economic growth; flood disaster causes more and more economic losses. Chinese government pays more attention to flood monitoring and assessment by space technology. Since1983, NOAA(AVHRR), Landsat-TM, LANDSAT-ETM+, JERS-1, SPOT, ERS-2, Radarsat-1, CBERS-1, Envisat have been used for flood monitoring and assessment. Due to the bad weather conditions during flood, microwave remote sensing is the major tools for flood monitoring. Envisat is one of the best satellite with powerful SAR. Its application for flood monitoring has been studied and its near real time(NRT) application can be realized on the basis of real-time delivery of image. During the 2005, 2006 and 2007 flood seasons, over the 31 NRT flood monitoring based on Envisat, had been carried out in Yangtze, Songua, Huaihe, pearl river basin. The result shows that Envisat SAR is very useful data source for flood disaster monitoring and assessment.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.14-17
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• 2005

Global latent heat flux data sets are crucial for many studies such as those related to air-sea interaction and climate variation. Currently, various global latent heat flux data sets are constructed using satellite data. Japanese Ocean Flux data sets with Use of Remote sensing Observations (J-OFURO) includes one of the satellite-derived global latent heat flux data (Kubota et aI., 2000). In this study, we review future development of J-OFURO global latent heat flux data set. In particular, we investigate usage of multi-satellite data for estimating accurate global latent heat flux. Accurate estimation of surface wind speeds over the global ocean is one of key factors for the improved estimation of global latent heat flux. First, we demonstrate improvement of daily wind speed estimation using multi-satellites data from microwave radiometers and scatterometers such as DMSP/SSMI, ERS/AMI, QuikSCAT/SeaWinds, AqualAMSR-E, ADEOS2/AMSR etc. Next, we demonstrate improvement of global latent heat flux estimation using the wind speed data derived from multi-satellite data.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.178-181
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• 2004

The Tropical Rainfall Measuring Mission (TRMM) is an US-Japan joint space mission to observe tropical and subtropical rainfall. This satellite is equipped with the world's first precipitation radar that operates at 13.8 GHz. We introduce the TRMM precipitation radar (PR) system, along with the PR data processing and analysis algorithms, and some observation results obtained by the TRMM PR. It is concluded that the TRMM PR can give quite useful rainfall data for the understanding of global climate changes, meteorology, climatology, atmospheric science, and also for the studies of satellite communication.

• Proceedings of the KSRS Conference
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• v.2
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• pp.997-1000
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• 2006

We estimate the net heat flux in the tropical and subtropical Atlantic Ocean using satellite data. These fluxes are related to changes in sea surface temperature (SST). This variable influences atmospheric circulations and is indicative of surface and subsurface oceanic circulations. We employ data from the geostationary METEOSAT-7 and 8 satellites and from the Special Sensor Microwave/Imager (SSM/I) for the shortwave and long-wave radiative fluxes, and for estimates of SST. For turbulent flux calculations, we use the bulk aerodynamic method with satellite estimates for wind speed and atmospheric humidity and temperature.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.375-386
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• 2022

The city of Khartoum, the capital of Sudan, was heavily damaged by the flood of the Nile in 2020. Classification using satellite images can define the damaged area and help emergency response. As Synthetic Aperture Radar (SAR) uses microwave that can penetrate cloud, it is suitable to use in the flood study. In this study, Random Forest classifier, one of the supervised classification algorithms, was applied to the flood event in Khartoum with various sizes of the training dataset and number of images using Sentinel-1 SAR. To create a training dataset, we used unsupervised classification and visual inspection. Firstly, Random Forest was performed by reducing the size of each class of the training dataset, but no notable difference was found. Next, we performed Random Forest with various number of images. Accuracy became better as the number of images in creased, but converged to a maximum value when the dataset covers the duration from flood to the completion of drainage.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.257-268
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• 2023

Sea ice plays an important role in Earth's climate by regulating the amount of solar energy absorbed and controlling the exchange of heat and material across the air-sea interface. Its growth, drift, and melting are monitored on a regular basis by satellite observations. However, low-resolution products with passive microwave radiometer have reduced accuracy during summer to autumn when the ice surface changes rapidly. Synthetic aperture radar (SAR) observations are emerging as a powerful complementary, but previous researches have mainly focused on winter ice. In this study, sea ice drift tracking was evaluated and analyzed using SAR images and tracker with global positioning system (GPS) during late summer-early autumn period when ice surface condition changes a lot. The results showed that observational uncertainty increases compared to winter period, however, the correlation coefficient with GPS measurements was excellent at 0.98, and the performance of the ice tracking algorithm was proportional to the sea ice concentration with a correlation coefficient of 0.59 for ice concentrations above 50%.

• Korean Journal of Remote Sensing
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• v.16 no.1
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• pp.47-54
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• 2000

We compared the satellite observed temperature with the radiosonde observed temperature in the Korean Peninsula. The radiosonde observed data were obtained from four upper air observation stations in the Korean Peninsula from 1981 to 1998, and were compared with the satellite observed data of the channel-2 and channel-4 of microwave sounding unit(MSU) on board NOAA series of polar-orbiting satellites. The radiosonde data were reconstructed from radiosonde T$_b$ using MSU weighting function. The monthly climatology shows radiosonde T$_{b2}$ is higher than MSU T$_{b2}$ in summer. The correlation between MSU T$_{b2}$ and radiosonde T$_{b2}$ is 0.72-0.76 and 0.73-0.81 between MSU T$_{b4}$ and radiosonde T$_{b4}$. The T$_{b2}$ show a positive trend and the T$_{b4}$ has a negative trend during the 18 years.

• Korean Journal of Remote Sensing
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• v.13 no.2
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• pp.151-163
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• 1997

In this paper, an algorithm for retrieving soil moisture from measurement of microwave reflection at the end of a coaxial canble is presented. Because the wave reflection from the boundary between air and soil layers depends on the dielectric constant of the soil layer, the dielectric constant can be obtained from measured reflection coefficient. At first, an equivalent circuit for the coaxial probe contaced on the soil surface was chosen with two unknown circuit elements. Then, the unknown circuit elements are obtained experrmentally by measuring the reflection constants of 20 soil samples, and consequently, an empirical formula for computing the dielectric constant from the reflection coefficient is obtained. The dielectric constant is mainly influenced by the soil moisture, and the soil moisture can be computed from the dielecfic constant using an existing empirical formula. HP Network Analyzer 8510C was used to measure the magnitude and the phase of the reflection coeffcient at 4.65 GHz, and the measured data set were used to obtain an empirical formula for computing the dielectric constant. The empirical formula obtained in this study was proven by other soil samples.