• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.1
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• pp.106-118
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• 2009

Satellite imagery with high resolution of about one meter is used widely in commerce and government applications ranging from earth observation and monitoring to national digital mapping. Due to the expensiveness of IKONOS Pro and Precision products, it is attractive to use the low-cost IKONOS Geo product with vendor-provided rational polynomial coefficients (RPCs), to produce highly accurate mapping products. The imaging geometry of IKONOS high-resolution imagery is described by RFs instead of rigorous sensor models. This paper presents four different polynomial models, that are the offset model, the scale and offset model, the Affine model, and the 2nd-order polynomial model, defined respectively in object space and image space to improve the accuracies of the RF-derived ground coordinates. Not only the algorithm for RF-based ground coordinates but also the algorithm for accuracy improvement of RF-based ground coordinates are developed which is based on the four models, The experiment also evaluates the effect of different cartographic parameters such as the number, configuration, and accuracy of ground control points on the accuracy of geopositioning. As the result of a experimental application, the root mean square errors of three dimensional ground coordinates which are first derived by vendor-provided Rational Function models were averagely 8.035m in X, 10.020m in Y and 13.318m in Z direction. After applying polynomial correction algorithm, those errors were dramatically decreased to averagely 2.791m in X, 2.520m in Y and 1.441m in Z. That is, accuracy was greatly improved by 65% in planmetry and 89% in vertical direction.

• Atmosphere
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• v.28 no.1
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• pp.99-112
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• 2018

In this study, the accuracy of ocean analysis data, which are produced from the Korea Meteorological Administration (KMA) Nucleus for European Modelling of the Ocean/Variational Data Assimilation (NEMO/NEMOVAR, hereafter NEMO) system and the HYbrid Coordinate Ocean Model/Navy Coupled Ocean Data Assimilation (HYCOM/NCODA, hereafter HYCOM) system, was evaluated using various oceanic observation data from March 2015 to February 2016. The evaluation was made for oceanic thermal environments in the tropical Pacific, the western North Pacific, and the Korean peninsula. NEMO generally outperformed HYCOM in the three regions. Particularly, in the tropical Pacific, the RMSEs (Root Mean Square Errors) of NEMO for both the sea surface temperature and vertical water temperature profile were about 50% smaller than those of HYCOM. In the western North Pacific, in which the observational data were not used for data assimilation, the RMSE of NEMO profiles up to 1000 m ($0.49^{\circ}C$) was much lower than that of HYCOM ($0.73^{\circ}C$). Around the Korean peninsula, the difference in RMSE between the two models was small (NEMO, $0.61^{\circ}C$; HYCOM, $0.72^{\circ}C$), in which their errors show relatively big in the winter and small in the summer. The differences reported here in the accuracy between NEMO and HYCOM for the thermal environments may be attributed to horizontal and vertical resolutions of the models, vertical coordinate and mixing scheme, data quality control system, data used for data assimilation, and atmosphere forcing. The present results can be used as a basic data to evaluate the accuracy of NEMO, before it becomes the operational model of the KMA providing real-time ocean analysis and prediction data.

• Korean Journal of Remote Sensing
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• v.27 no.5
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• pp.507-520
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• 2011

Conventional radar altimeter system measured directly the distance between the satellite and the ocean surface and frequently used by aircraft for approach and landing. The radar altimeter is good at flat surface like sea whereas it is difficult to determine precise three dimensional ground coordinates because the ground surface, unlike ocean, is very indented. To overcome this drawback of the radar altimeter, we have developed and validated the interferometric radar altimeter signal processing which is combined with new synthetic aperture and interferometric signal processing algorithm to extract precise three-dimensional ground coordinates. The proposed algorithm can accurately measure the three dimensional ground coordinates using three antennas. In a set of 70 simulations, the averages of errors in x, y and z directions were approximately -0.40 m, -0.02 m and 4.22 m, respectively and the RMSEs were about 3.40 m, 0.30 m and 6.20 m, respectively. The overall results represent that the proposed algorithm is effective for accurate three dimensional ground positioning.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1373-1389
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• 2019

KOMPSAT-5 is the first satellite in Korea equipped with X-band Synthetic Aperture Radar (SAR) instrument and has been operated since August 2013. KOMPSAT-5 is used to monitor the global environment according to its observation purpose and the availability of KOMPSAT-5 is also highlighted as the need of high resolution wind data for investigating the coastal region. However, the previous study for the validation of wind derived from KOMPSAT-5 showed that the accuracy is lower than that of other SAR satellites. Therefore, in this study, we developed the correction equation of normalized radar cross section (NRCS or backscattering coefficient) for improvement of wind from the KOMPSAT-5 and validated the effect of the equation using the in-situ measurement of ocean buoys. Theoretical estimated NRCS and observed NRCS from KOMPSAT-5 showed linear relationship with incidence angle. Before applying the correction equation, the accuracy of the estimated wind speed showed the relatively high root-mean-square errors (RMSE) of 2.89 m s^-1 and bias of -0.55 m s^-1. Such high errors were significantly reduced to the RMSE of 1.60 m s^-1 and bias of -0.38 m s^-1 after applying the correction equation. The improvement effect of the correction equation showed dependency relying on the range of incidence angle.

• Journal of the Korean earth science society
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• v.29 no.3
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• pp.280-289
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• 2008

An extensive set of both in-situ and satellite data regarding oceanic sea surface temperatures in Northeast Asian seas, collected over a 10-year period, was collocated and surveyed to assess the accuracy of satellite-observed sea surface temperatures (SST) and investigate the characteristics of satellite measured SST errors. This was done by subtracting insitu SST measurements from multi-channel SST (MCSST) measurements. 845 pieces of collocated data revealed that MCSST measurements had a root-mean-square error of about 0.89$^{\circ}C$ and a bias error of about 0.18$^{\circ}C$. The SST errors revealed a large latitudinal dependency with a range of $\pm3^{\circ}C$ around 40$^{\circ}N$, which was related to high spatial and temporal variability from smaller eddies, oceanic currents, and thermal fronts at higher latitudes. The MCSST measurements tended to be underestimated in winter and overestimated in summer when compared to in-situ measurements. This seasonal dependency was discovered from shipboard and moored buoy measurements, not satellite-tracked surface drifters, and revealed the existence of a strong vertical temperature gradient within a few meters of the upper ocean. This study emphasizes the need for an effort to consider and correct the significant skin-bulk SST difference which arises when calculating SST from satellite data.

• Korean Journal of Remote Sensing
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• v.15 no.1
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• pp.1-8
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• 1999

The estimation technique of surface water temperature by satellite remote sensing has been applied to ocean and large lakes using AVHRR. However, the spatial resolution AVHBR is not abquate for coastal region and small lakes. Landsat 5 TM has 120 m spatial resolution, which suits better. We carried out analysis of surface water temperature in Lake Sihwa and near coastal area using Landsat 5 TM. To relate digital number to the brightness temperature, we applied Empirical, NASA, RESTEC, Quadratic methods. Comparing calculated and observed value, we obtained as follows; NASA method, $R^2=0.9343$, RMSE(Root Mean Square Error)=3.5876$^{\circ}C$; RESTEC method, $R^2=0.8937$, RMSE=3.76$^{\circ}C$; Quadratic method, $R^2=0.8967$, RMSE=2.949$^{\circ}C$. Because Landsat TM has only one band for extracting surface temperature, it was difficult to correct for the atmospheric errors. For improving the accuracy of surface temperature detection using Landsat TM, there is a need for a method to decrease the effect of atmospheric contents.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1383-1398
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• 2018

Sea surface wind is one of the most fundamental variables for understanding diverse marine phenomena. Although scatterometers have produced global wind field data since the early 1990's, the data has been used limitedly in oceanic applications due to it slow spatial resolution, especially at coastal regions. Synthetic Aperture Radar (SAR) is capable to produce high resolution wind field data. KOMPSAT-5 is the first Korean satellite equipped with X-band SAR instrument and is able to retrieve the sea surface wind. This study presents the validation results of sea surface wind derived from the KOMPSAT-5 backscattering coefficient data for the first time. We collected 18 KOMPSAT-5 ES mode data to produce a matchup database collocated with buoy stations. In order to calculate the accurate wind speed, we preprocessed the SAR data, including land masking, speckle noise reduction, and ship detection, and converted the in-situ wind to 10-m neutral wind as reference wind data using Liu-Katsaros-Businger (LKB) model. The sea surface winds based on XMOD2 show root-mean-square errors of about $2.41-2.74m\;s^{-1}$ depending on backscattering coefficient conversion equations. In-depth analyses on the wind speed errors derived from KOMPSAT-5 backscattering coefficient data reveal the existence of diverse potential error factors such as image quality related to range ambiguity, discrete and discontinuous distribution of incidence angle, change in marine atmospheric environment, impacts on atmospheric gravity waves, ocean wave spectrum, and internal wave.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.42-54
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• 2019

Crop models depend on a large number of input parameters including the cultivar parameters that represent the genetic characteristics of a given cultivar. The cultivar parameters have been estimated using high quality data for crop growth, which require considerable costs and efforts. The objective of this study was to examine the feasibility of using low quality data for the parameter estimation. In the present study, the cultivar parameters for cv. Shindongjin were estimated using the data obtained from the report of new cultivars development and research from 2005 to 2016. The root mean square errors (RMSE) of the heading dates were less than 3 days when the parameters associated with phenology were estimated. In contrast, the coefficient of determination for yield tended to be less than 0.1. The large errors incurred by the fact that no growth data collected over a season was used for parameter estimation. This suggests that detailed observation data needs to be prepared for parameter calibration, which would be aided by remote sensing approaches. The occurrence of natural disasters during a growing season has to be considered because crop models cannot take into account the effects of those events. Still, our results provide a reasonable range for the parameters, which could be used to set the boundary of a given parameter for cultivars similar to cv. Shindongjin in further studies.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.485-493
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• 2021

Groundwater is an important water resource that can be used along with surface water. In particular, in the case of island regions, research on groundwater level variability is essential for stable groundwater use because the ratio of groundwater use is relatively high. Researches using artificial intelligence models (AIs) for the prediction and analysis of groundwater level variability are continuously increasing. However, there are insufficient studies presenting evaluation criteria to judge the appropriateness of groundwater level prediction. This study comprehensively analyzed the research results that predicted the groundwater level using AIs for various regions around the world over the past 20 years to present the range of allowable groundwater level prediction errors. As a result, the groundwater level prediction error increased as the observed groundwater level variability increased. Therefore, the criteria for evaluating the adequacy of the groundwater level prediction by an AI is presented as follows: less than or equal to the root mean square error or maximum error calculated using the linear regression equations presented in this study, or NSE ≥ 0.849 or R² ≥ 0.880. This allowable prediction error range can be used as a reference for determining the appropriateness of the groundwater level prediction using an AI.

• Journal of Astronomy and Space Sciences
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• v.34 no.4
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• pp.271-280
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• 2017

This study presents the application of satellite laser ranging (SLR) to orbit determination (OD) of high-Earth-orbit (HEO) satellites. Two HEO satellites are considered: the Quasi-Zenith Satellite-1 (QZS-1), a Japanese elliptical-inclinedgeosynchronous-orbit (EIGSO) satellite, and the Compass-G1, a Chinese geostationary-orbit (GEO) satellite. One week of normal point (NP) data were collected for each satellite to perform the OD based on the batch least-square process. Five SLR tracking stations successfully obtained 374 NPs for QZS-1 in eight days, whereas only two ground tracking stations could track Compass-G1, yielding 68 NPs in ten days. Two types of station bias estimation and a station data weighting strategy were utilized for the OD of QZS-1. The post-fit root-mean-square (RMS) residuals of the two week-long arcs were 11.98 cm and 10.77 cm when estimating the biases once in an arc (MBIAS). These residuals were decreased significantly to 2.40 cm and 3.60 cm by estimating the biases every pass (PBIAS). Then, the resultant OD precision was evaluated by the orbit overlap method, yielding three-dimensional errors of 55.013 m with MBIAS and 1.962 m with PBIAS for the overlap period of six days. For the OD of Compass-G1, no station weighting strategy was applied, and only MBIAS was utilized due to the lack of NPs. The post-fit RMS residuals of OD were 8.81 cm and 12.00 cm with 49 NPs and 47 NPs, respectively, and the corresponding threedimensional orbit overlap error for four days was 160.564 m. These results indicate that the amount of SLR tracking data is critical for obtaining precise OD of HEO satellites using SLR because additional parameters, such as station bias, are available for estimation with sufficient tracking data. Furthermore, the stand-alone SLR-based orbit solution is consistently attainable for HEO satellites if a target satellite is continuously trackable for a specific period.