• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.625-636
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• 2019

Soil moisture is a representative factor that plays a key role in hydrological cycle. It is involved in the interaction between atmosphere and land surface, and is used in fields such as agriculture and water resources. Advanced Microwave Scanning Radiometer 2 (AMSR2), Advanced SCATterometer (ASCAT), and European Space Agency Climate Change Initiative (ESACCI) data were used to analyze the applicability and uncertainty of satellite soil moisture product in the Korean peninsula. Cumulative distribution function (CDF) matching and triple collocation (TC) analysis were carried out to investigate uncertainty and correction of satellite soil moisture data. Comparisons of pre-calibration satellite soil moisture data with the Automated Agriculture Observing System (AAOS) indicated that ESACCI and ASCAT data reflect the trend of AAOS well. On the other hand, AMSR2 satellite data showed overestimated values during the freezing period. Correction of satellite soil moisture data using CDF matching improved the error and correlation compared to those before correction. Finally, uncertainty analysis of soil moisture was carried out using TC method. Clearly, the uncertainty of the satellite soil moisture, corrected by CDF matching, was diminished in both freezing and thawing periods. Overall, it is expected that using ASCAT and ESACCI rather than AMSR2 soil moisture data will give more accurate soil moisture information when correction is performed on the Korean peninsula.

• Journal of Wetlands Research
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• v.18 no.2
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• pp.132-147
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• 2016

In this study the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) sensor onboard the Soil Moisture Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission-Water (GCOM-W1) based soil moisture retrievals were revised to obtain better accuracy of soil moisture and higher data acquisition rate over East Asia. These satellite-based soil moisture products are revised against a reference land model data set, called Global Land Data Assimilation System (GLDAS), using Cumulative Distribution Function (CDF) matching and regression approach. Since MIRAS sensor is perturbed by radio frequency interferences (RFI), the worst part of soil moisture retrieval, East Asia, constantly have been undergoing loss of data acquisition rate. To overcome this limitation, the threshold of RFI, DQX, and composite days were suggested to increase data acquisition rate while maintaining appropriate data quality through comparison of land surface model data set. The revised MIRAS and AMSR2 products were compared with in-situ soil moisture and land model data set. The results showed that the revising process increased correlation coefficient values of SMOS and AMSR2 averagely 27% 11% and decreased the root mean square deviation (RMSD) decreased 61% and 57% as compared to in-situ data set. In addition, when the revised products' correlation coefficient values are calculated with model data set, about 80% and 90% of pixels' correlation coefficients of SMOS and AMSR2 increased and all pixels' RMSD decreased. Through our CDF-based revising processes, we propose the way of mutual supplementation of MIRAS and AMSR2 soil moisture retrievals.

• KIISE Transactions on Computing Practices
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• v.20 no.12
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• pp.646-651
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• 2014

Histogram equalization generally has the disadvantage that if the distribution of the gray level of an image is concentrated in one place, then the range of the gray level in the output image is excessively expanded, which then produces a visually unnatural result. However, a gamma transformation can reduce such unnatural appearances since it operates under a nonlinear regime. Therefore, this paper proposes a new histogram equalization method that can improve image quality by using a gamma transformation. The proposed method 1) derives the proper form of the gamma transformation by using the average brightness of the input image, 2) linearly combines the earlier gamma transformation with a CDF (Cumulative Distribution Function) for the image in order to obtain a new CDF, and 3) to finally perform histogram equalization by using the new CDF. The experimental results show that relative to existing methods, the proposed method provides good performance in terms of quantitative measures, such as entropy, UIQ, SSIM, etc., and it also naturally enhances the image quality in visual perspective as well.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.233-233
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• 2016

현재 한국건설기술연구원에서 X-밴드 이중편파레이더를 운영하고 있으며, 이 결과 NetCDF 파일형식의 레이더 관측자료가 생성되고 있다. 레이더 자료포맷인 Netcdf 자료의 경우 레이더 관측과정에서 발생한 결과를 극좌표 형식으로 저장하고 있어 이를 분석이나 시스템에 적용하여 활용하기 위해서는 격자좌표로 변환하는 것이 필요하고, 또한 다양한 자료 변환 및 추출작업을 텍스트 기반으로 하기 위해 다양한 사전 작업이 필요하여 일반사용자가 사용하는데 어려운 상황이다. 그래서 이를 쉽게 수행할 수 있도록 JAVA를 이용하여 윈도우 기반으로 사용할 수 있는 프로그램(KICTRadar4WIN) 프로그램을 개발하였다. KICTRadar4WIN 프로그램의 경우 레이더 자료 품질관리, 레이더 자료 관리, 레이더 자료 추출, 레이더 자료 표출의 4가지 기능을 포함하고 있다. ${\bullet}$ 레이더 자료 품질관리 - 원시자료에 QC 기준을 입력하여 QC된 레이더자료를 생성 ${\bullet}$ 레이더 자료관리 - CAPPI 자료생성 : 관측된 PPI 및 RHI 자료를 이용하여, CAPPI 자료를 생성 - QPE 자료생성 : CAPPI 자료를 이용하여 QPE 자료를 생 - QPE 자료보정 : 지점우량을 이용한 G/R비를 산정하여 QPE 보정자료를 생성 ${\bullet}$ 레이더 자료 추출 - 격자자료 추출 : PPI, CAPPI, QPE 자료를 TEXT 자료로 변환하여 저장 - 지점자료 추출 : 입력된 지점좌표 중심으로 선택한 범위의 평균값을 TEXT 파일로 저장 - 면적자료 추출 : 입력된 면적자료의 평균값을 추출하여 TEXT파일로 저장 ${\bullet}$ 레이더 자료 표출 - 영상표출 : PPI, CAPPI, QPE 관측변수 자료를 그림파일 생성 - KMZ 자료생성 : PPI, CAPPI, QPE 자료를 KMZ 파일 생성

• Journal of the Korean earth science society
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• v.34 no.1
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• pp.69-80
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• 2013

Terrain data is one of important basic data in various areas of Earth science. Recently, finer DEM data is available, which necessary to develop a method that deals with such huge data efficiently. This study was conducted on the lossless compression of DEM data and efficient partial reconstruction of terrain information from compressed data. In this study, we compressed the wavelet coefficients of DEM, obtained from integer wavelet transform (IWT) by entropy encoding. CDF (Cohen-Daubechies-Feauveau) 3.5 wavelet showed the best compression ratio of about 45.4% and the optimum decomposition level was 3. Results also showed that a small region of terrain could be restored from the inverse wavelet transform with a part of the wavelet coefficients that are related to such region instead of whole reconstruction. We discussed the potential applications of the terrain data compression for precise gravity terrain correction.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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• pp.95-106
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• 2013

It is now generally known that dynamical climate modeling outputs include systematic biases in reproducing the properties of atmospheric variables such as, preciptation and temerature. There is thus, general consensus among the researchers about the need of bias-correction process prior to using climate model results especially for hydrologic applications. Among the number of bias-correction methods, distribution (e.g., cumulative distribution fuction, CDF) mapping based approach has been evaluated as one of the skillful techniques. This study investigates the uncertainty of using various CDF mapping-based methods for bias-correciton in assessing regional climate change Impacts. Two different dynamicailly-downscaled Global Circulation Model results (CCSM and GFDL under ARES4 A2 scenario) using Regional Spectial Model for retrospective peiod (1969-2000) and future period (2039-2069) were collected over the west central Florida. Total 12 possible methods (i.e., 3 for developing distribution by each of 4 for estimating biases in future projections) were examined and the variations among the results using different methods were evaluated in various ways. The results for daily temperature showed that while mean and standard deviation of Tmax and Tmin has relatively small variation among the bias-correction methods, monthly maximum values showed as significant variation (~2'C) as the mean differences between the retrospective simulations and future projections. The accuracy of raw preciptiation predictions was much worse than temerature and bias-corrected results appreared to be more significantly influenced by the methodologies. Furthermore the uncertainty of bias-correction was found to be relevant to the performance of climate model (i.e., CCSM results which showed relatively worse accuracy showed larger variation among the bias-correction methods). Concludingly bias-correction methodology is an important sourse of uncertainty among other processes that may be required for cliamte change impact assessment. This study underscores the need to carefully select a bias-correction method and that the approach for any given analysis should depend on the research question being asked.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.833-842
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• 2013

The quantile mapping is utilized to reproduce reliable GCM(Global Climate Model) data by correct systematic biases included in the original data set. This scheme, in general, projects the Cumulative Distribution Function (CDF) of the underlying data set into the target CDF assuming that parameters of target distribution function is stationary. Therefore, the application of stationary quantile mapping for nonstationary long-term time series data of future precipitation scenario computed by GCM can show biased projection. In this research the Nonstationary Quantile Mapping (NSQM) scheme was suggested for bias correction of nonstationary long-term time series data. The proposed scheme uses the statistical parameters with nonstationary long-term trends. The Gamma distribution was assumed for the object and target probability distribution. As the climate change scenario, the 20C3M(baseline scenario) and SRES A2 scenario (projection scenario) of CGCM3.1/T63 model from CCCma (Canadian Centre for Climate modeling and analysis) were utilized. The precipitation data were collected from 10 rain gauge stations in the Han-river basin. In order to consider seasonal characteristics, the study was performed separately for the flood (June~October) and nonflood (November~May) seasons. The periods for baseline and projection scenario were set as 1973~2000 and 2011~2100, respectively. This study evaluated the performance of NSQM by experimenting various ways of setting parameters of target distribution. The projection scenarios were shown for 3 different periods of FF scenario (Foreseeable Future Scenario, 2011~2040 yr), MF scenario (Mid-term Future Scenario, 2041~2070 yr), LF scenario (Long-term Future Scenario, 2071~2100 yr). The trend test for the annual precipitation projection using NSQM shows 330.1 mm (25.2%), 564.5 mm (43.1%), and 634.3 mm (48.5%) increase for FF, MF, and LF scenarios, respectively. The application of stationary scheme shows overestimated projection for FF scenario and underestimated projection for LF scenario. This problem could be improved by applying nonstationary quantile mapping.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.1
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• pp.22-29
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• 2008

In this paper, we propose an adaptive color enhancement algorithm. It is used for the flat panel displays (FPDs) such as LCD, PDP, and so on. The proposed algorithm consists of an adaptive linear approximation CDF(Cumulative Density Function) algorithm and an adaptive saturation enhancement algorithm. The one is for contrast enhancement which prevents an image from the distortion by luminance transient of an input image. The other is the algorithm which improves the saturation without the contour artifact and over-saturation, whose problems are generated during the enhancing saturation. In addition, it allows to achieve the high quality image using the saturation enhancement method for a preferred color of original image. Visual test and standard deviation of their histograms have been applied to evaluate the resultant output images of the proposed algorithm.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.143-154
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• 2012

As demand of water resources and attentions to changes in climate (e.g., due to ENSO) increase, long/short term prediction of precipitation is getting necessary in water planning. This research evaluated the ability of MM5 to predict precipitation in the Tampa Bay region over 23 year period from 1986 to 2008. Additionally MM5 results were statistically bias-corrected using observation data at 33 stations over the study area using CDF-mapping approach and evaluated comparing to raw results for each ENSO phase (i.e., El Ni$\tilde{n}$o and La Ni$\tilde{n}$a). The bias-corrected model results accurately reproduced the monthly mean point precipitation values. Areal average daily/monthly precipitation predictions estimated using block-kriging algorithm showed fairly high accuracy with mean error of daily precipitation, 0.8 mm and mean error of monthly precipitation, 7.1 mm. The results evaluated according to ENSO phase showed that the accuracy in model output varies with the seasons and ENSO phases. Reasons for low predictions skills and alternatives for simulation improvement are discussed. A comprehensive evaluation including sensitivity to physics schemes, boundary conditions reanalysis products and updating land use maps is suggested to enhance model performance. We believe that the outcome of this research guides to a better implementation of regional climate modeling tools in water management at regional/seasonal scale.