• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• Journal of Positioning, Navigation, and Timing
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• 제12권2호
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• pp.113-119
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• 2023

Satellite navigation systems, with the exception of the GLObal NAvigation Satellite System (GLONASS), adopt ionosphere models and provide ionospheric coefficients to single-frequency users via navigation messages to correct ionospheric delay, the main source of positioning errors. A Global Navigation Satellite System (GNSS) mostly has its own ionospheric models: the Klobuchar model for Global Positioning System (GPS), the NeQuick-G model for Galileo, and the BeiDou Global Ionospheric delay correction Model (BDGIM) for BeiDou satellite navigation System (BDS)-3. On the other hand, a Regional Navigation Satellite System (RNSS) such as the Quasi-Zenith Satellite System (QZSS) and BDS-2 uses the Klobuchar Model rather than developing a new model. QZSS provides its own coefficients that are customized for its service area while BDS-2 slightly modifies the Klobuchar model to improve accuracy in the Asia-Pacific region. In addition, BDS broadcasts multiple ionospheric parameters depending on the satellites, unlike other systems. In this paper, we analyzed the different ionospheric models of GPS, QZSS, and BDS in Korea. The ionospheric models of QZSS and BDS-2, which are based in Asia, reduced error by at least 25.6% compared to GPS. However, QZSS was less accurate than GPS during geomagnetic storms or at low latitude. The accuracy of the models according to the BDS satellite orbit was also analyzed. The BDS-2 ionospheric model showed an error reduction of more than 5.9% when using GEO coefficients, while in BDS-3, the difference between satellites was within 0.01 m.

• 대한원격탐사학회지
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• 제37권4호
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• pp.815-831
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• 2021

Drought forecasting is crucial to minimize the damage to food security and water resources caused by drought. Satellite-based drought research has been conducted since 1980s, which includes drought monitoring, assessment, and prediction. Unlike numerous studies on drought monitoring and assessment for the past few decades, satellite-based drought forecasting has gained popularity in recent years. For successful drought forecasting, it is necessary to carefully identify the relationships between drought factors and drought conditions by drought type and lead time. This paper aims to provide an overview of recent research trends and challenges for satellite-based drought forecasts focusing on lead times. Based on the recent literature survey during the past decade, the satellite-based drought forecasting studies were divided into three groups by lead time (i.e., short-term, sub-seasonal, and seasonal) and reviewed with the characteristics of the predictors (i.e., drought factors) and predictands (i.e., drought indices). Then, three major challenges-difficulty in model generalization, model resolution and feature selection, and saturation of forecasting skill improvement-were discussed, which led to provide several future research directions of satellite-based drought forecasting.

• 항공우주기술
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• 제7권2호
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• pp.187-195
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• 2008

고해상도의 인공위성 데이터로부터 지상좌표를 해석하는 센서모델링 기술은 위성영상자료의 활용 확대 및 신뢰성 확보에 가장 중요한 연구부분으로서 이에 대한 연구과 증가되고 있다. 본 연구는 이러한 요구조건을 기본을 하여, 고해상도 인공위성에서 기본적으로 탑재되어 있는 GPS, Star-tracker, Gyro 등의 센서로부터 측정된 위성의 위치, 속도, 자세 및 시간 정보를 이용하여 위성자료로부터 지상좌표를 해석하는 direct sensor model (DSM)과 위성의 궤도 정보를 얻을 수 없는 경우나 궤도에 대한 정보가 불확실하여 물리적 센서모델로는 지형보정을 수행할 수 없는 경우에 사용될 수 있는 rational function model (RFM)의 적용하여 지상좌표를 해석하는 방법에 대해 살펴보고자 한다.

• 대한원격탐사학회지
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• 제19권3호
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• pp.247-253
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• 2003

The mainly used technique to correct satellite images with geometric distortion is to develop a mathematical relationship between pixels on the image and corresponding points on the ground. Polynomial models with various transformations have been designed for defining the relationship between two coordinate systems. GCP based geometric correction has peformed overall plane to plane mapping. In the overall plane mapping, overall structure of a scene is considered, but local variation is discarded. The Region with highly variant height is rectified with distortion on overall plane mapping. To consider locally variable region in satellite image, TIN-based rectification on a satellite image is proposed in this paper. This paper describes the relationship between GCP distribution and rectification model through experimental result and analysis about each rectification model. We can choose a geometric correction model as the structural characteristic of a satellite image and the acquired GCP distribution.

• 대한원격탐사학회지
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• 제35권1호
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• pp.57-81
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• 2019

Satellite images can be integrated into a crop model to strengthen the advantages of each technique for crop monitoring and to compensate for weaknesses of each other, which can be systematically applied for monitoring inaccessible croplands. The objective of this study was to outline the productivity of paddy rice based on simulation of the yield of all paddy fields in North Korea, using a grid crop model combined with optical satellite imagery. The grid GRAMI-rice model was used to simulate paddy rice yields for inaccessible North Korea based on the bidirectional reflectance distribution function-adjusted vegetation indices (VIs) and the solar insolation. VIs and solar insolation for the model simulation were obtained from the Geostationary Ocean Color Imager (GOCI) and the Meteorological Imager (MI) sensors of the Communication Ocean and Meteorological Satellite (COMS). Reanalysis data of air temperature were achieved from the Korea Local Analysis and Prediction System (KLAPS). Study results showed that the yields of paddy rice were reproduced with a statistically significant range of accuracy. The regional characteristics of crops for all of the sites in North Korea were successfully defined into four clusters through a spatial analysis using the K-means clustering approach. The current study has demonstrated the potential effectiveness of characterization of crop productivity based on incorporation of a crop model with satellite images, which is a proven consistent technique for monitoring of crop productivity in inaccessible regions.

• 한국항해항만학회지
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• 제27권6호
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• pp.645-652
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• 2003

A virtual vessel traffic control system is introduced to contribute to prevent a marine accident such as collision and stranding from happening. Existing VTS has its limit. The virtual vessel traffic control system consists of both data acquisition by satellite remote sensing and a simulation of traffic environment stress based on the satellite data, remotely sensed data And it could be used to provide timely and detailed information about the marine safety, including the location, speed and direction of ships, and help us operate vessels safely and efficiently. If environmental stress values are simulated for the ship information derived from satellite data, proper actions can be taken to prevent accidents. Since optical sensor has a high spatial resolution, JERS satellite data are used to track ships and extract their information. We present an algorithm of automatic identification of ship size and velocity. It lastly is shown that based on ship information extracted from JERS data, a qualitative evaluation method of environmental stress is introduced.

• 한국위성정보통신학회논문지
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• 제6권2호
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• pp.46-50
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• 2011

인공위성에 대한 구조해석을 수행하기 위해서는 인공위성의 모든 기계적 특성이 반영된 유한요소모델을 필요로 한다. 인공위성의 개발특성상 여러 부품들은 서로 다른 회사에서 제작이 되며, 해당 유한요소모델의 제공이 불가능한 경우가 종종 발생하게 된다. 이러한 경우에 초요소 기법을 이용하면 쉽게 해결할 수 있다. 현재 개발중인 위성의 경우, 안테나 업체에서 안테나 기동시 발생하는 미소진동의 양을 계산하고자 인공위성 전체의 유한요소모델을 필요로 하였다. 이 때, 초요소 기법을 이용하여 축약된 모델을 제공할 수 있었다. 초요소기법은 Craig-Bampton 방법을 이용하여 유한요소모델을 동적축약하는 기법이며 본 논문에서는 인공위성 유한요소모델에 이를 적용하였다. 동적축약이 잘 이루어졌는지를 확인하기 위하여 전체 유한요소모델과, 축약모델의 모드해석 결과와 주파수 응답해석 결과를 비교하였으며, 두 결과가 매우 잘 맞는다는 것을 확인할 수 있었다. 이로부터 본 해석 방법의 유용성을 확인할 수 있었다.

• 대한원격탐사학회지
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• 제39권4호
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• pp.395-407
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• 2023

Super-resolution (SR) has great significance in image processing because it enables downstream vision tasks with high spatial resolution. Recently, SR studies have adopted deep learning networks and achieved remarkable SR performance compared to conventional example-based methods. Deep-learning-based SR models generally require low-resolution (LR) images and the corresponding high-resolution (HR) images as training dataset. Due to the difficulties in obtaining real-world LR-HR datasets, most SR models have used only HR images and generated LR images with predefined degradation such as bicubic downsampling. However, SR models trained on simple image degradation do not reflect the properties of the images and often result in deteriorated SR qualities when applied to real-world images. In this study, we propose an image degradation model for HR satellite images based on the modulation transfer function (MTF) of an imaging sensor. Because the proposed method determines the image degradation based on the sensor properties, it is more suitable for training SR models on remote sensing images. Experimental results on HR satellite image datasets demonstrated the effectiveness of applying MTF-based filters to construct a more realistic LR-HR training dataset.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.155-158
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• 2008

The needs for digital models of real environment such as 3D terrain or cyber city model are increasing. Most of applications related with modeling and simulation require virtual environment constructed from geospatial information of real world in order to guarantee reliability and accuracy of the simulation. The most fundamental data for building virtual environment, terrain elevation and orthogonal imagery is acquired from optical sensor of satellite or airplane. Providing interoperable and reusable digital model is important to promote practical application of high-resolution satellite imagery. This paper presents the new research regarding representation of geospatial information, especially for 3D shape and appearance of virtual terrain, and describe framework for constructing real-time 3D model of large terrain based on high-resolution satellite imagery. It provides infrastructure of 3D simulation with geographical context. Details of standard-based approach for providing infrastructure of real-time 3D simulation using high-resolution satellite imagery are also presented. This work would facilitate interchange and interoperability across diverse systems and be usable by governments, industry scientists and general public.