• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.747-763
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• 2022

Ocean salinity affects ocean circulation on a global scale and low salinity water around coastal areas often has an impact on aquaculture and fisheries. Microwave satellite sensors (e.g., Soil Moisture Active Passive [SMAP]) have provided sea surface salinity (SSS) based on the dielectric characteristics of water associated with SSS and sea surface temperature (SST). In this study, a Light Gradient Boosting Machine (LGBM)-based model for generating high resolution SSS from Geostationary Ocean Color Imager (GOCI) data was proposed, having machine learning-based improved SMAP SSS by Jang et al. (2022) as reference data (SMAP SSS (Jang)). Three schemes with different input variables were tested, and scheme 3 with all variables including Multi-scale Ultra-high Resolution SST yielded the best performance (coefficient of determination = 0.60, root mean square error = 0.91 psu). The proposed LGBM-based GOCI SSS had a similar spatiotemporal pattern with SMAP SSS (Jang), with much higher spatial resolution even in coastal areas, where SMAP SSS (Jang) was not available. In addition, when tested for the great flood occurred in Southern China in August 2020, GOCI SSS well simulated the spatial and temporal change of Changjiang Diluted Water. This research provided a potential that optical satellite data can be used to generate high resolution SSS associated with the improved microwave-based SSS especially in coastal areas.

• Ocean and Polar Research
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• v.35 no.3
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• pp.229-237
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• 2013

We measured two-dimensional (2-D) oxygen distribution in the surface sediment layer of intertidal sediment using a simple and inexpensive planar oxygen optode, which is based on a color ratiometric image approach. The recorded emission intensity of red color luminophore light significantly changed with oxygen concentration by $O_2$ quenching of platinum(II)octaethylporphyrin (PtOEP). The ratios between the intensity of red and green emissions with oxygen concentration variation demonstrated the Stern-Volmer relationship. The 2-D oxygen distribution image showed microtopographic structure, diffusivity boundary layer and burrow in surface sediment layer. The oxygen penetration depth (OPD) was about 2 mm and the one-dimensional vertical diffusive oxygen uptake (DOU) was 12.6 mmol $m^{-2}d^{-1}$ in the undisturbed surface sediment layer. However, those were enhanced near burrow by benthic fauna, and the OPD was two times deeper and DOU was increased by 34%. The simple and inexpensive oxygen planar optode has great application potential in the study of oxygen dynamics with high spatiotemporal resolution, in benthic boundary layers.

• Journal of Environmental Science International
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• v.24 no.1
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• pp.133-150
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• 2015

Wind profiler provides vertical profiles of three-dimensional wind vectors with high spatiotemporal resolution. The wind vectors is useful to analyze severe weather phenomena and to validate the various products from numerical weather prediction model. However, the wind measurements are not immune to ground clutter, bird, insect, and aircraft. Therefore, quality of wind vectors from wind profiler must be quantitatively evaluated prior to its application. In this study, wind vectors from UHF wind profiler at Ganwon Regional Meteorological Administration was quantitatively evaluated using 27 radiosonde measurements that were launched every two or three hours according to rainfall intensity during Intensive Observation Period (IOP) from June to July 2013. In comparison between two measurements, wind vectors from wind profiler was relatively underestimated. In addition, the accuracy and quality of wind vectors from wind profiler decrease with increasing beam height. The accuracy and quality of the wind vectors for rainy periods during IOP were higher than for the clear-air measurements. The moderate rainfall intensity lead to multi-peaks in Doppler spectrum. It results in overestimation of vertical air motion, whereas wind vectors from wind profilers shows good agreement with those from radiosonde measurements for light rainfall intensity.

• Atmosphere
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• v.20 no.4
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• pp.415-426
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• 2010

A Land cover map over East Asian region (Kongju national university Land Cover map: KLC) is classified by using support vector machine (SVM) and evaluated with ground truth data. The basic input data are the recent three years (2006-2008) of MODIS (MODerate Imaging Spectriradiometer) NDVI (normalized difference vegetation index) data. The spatial resolution and temporal frequency of MODIS NDVI are 1km and 16 days, respectively. To minimize the number of cloud contaminated pixels in the MODIS NDVI data, the maximum value composite is applied to the 16 days data. And correction of cloud contaminated pixels based on the spatiotemporal continuity assumption are applied to the monthly NDVI data. To reduce the dataset and improve the classification quality, 9 phenological data, such as, NDVI maximum, amplitude, average, and others, derived from the corrected monthly NDVI data. The 3 types of land cover maps (International Geosphere Biosphere Programme: IGBP, University of Maryland: UMd, and MODIS) were used to build up a "quasi" ground truth data set, which were composed of pixels where the three land cover maps classified as the same land cover type. The classification results show that the fractions of broadleaf trees and grasslands are greater, but those of the croplands and needleleaf trees are smaller compared to those of the IGBP or UMd. The validation results using in-situ observation database show that the percentages of pixels in agreement with the observations are 80%, 77%, 63%, 57% in MODIS, KLC, IGBP, UMd land cover data, respectively. The significant differences in land cover types among the MODIS, IGBP, UMd and KLC are mainly occurred at the southern China and Manchuria, where most of pixels are contaminated by cloud and snow during summer and winter, respectively. It shows that the quality of raw data is one of the most important factors in land cover classification.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.172-183
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• 2012

In 2011, floods were at the worst stage of devastation in Chao Phraya river basin of Thailand. The purpose of this study is to trace the flood inundation area around Chao Phraya river basin by using Terra MODIS image because it has the ability of spatiotemporal dynamics. The MODIS indices, which included the enhanced vegetation index(EVI), land surface water index(LSWI), and the difference in the values of EVI and LSWI(DVEL), were extracted from MODIS product MOD09 8-day composite datasets with a spatial resolution of 500m from Jul. 29, 2011 to Jan. 09, 2012. We found that combined application of EVI, LSWI, and DVEL was suitable for monitoring flood inundation. For the extracted flood inundation area and water-related area. The result can be used to acquire the flood inundation data scattered and demonstrate the potential for the use of MODIS data for temporal and spatial detection of flood effects.

• Journal of Korea Water Resources Association
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• v.35 no.4 s.129
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• pp.411-423
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• 2002

Accurate and real time forecasting of runoff has a high priority in the drainage basins prone to short, high intensity rainfall events causing flash floods. To take into account the resolution of hydrological variables within a drainage basin, use of distributed system models is preferred. The Landsat Thematic Mapper(TM) observations enable detailed information on distribution of land cover and other related factors within a drainage basin and permit the use of distributed system models. This paper describes monitoring technique of rainfall excess by SCS curve number method. The time series maps of rainfall excess were generated for all the storm events to show the spatiotemporal distribution of rainfall excess within study basin. A combination of the time series maps of rainfall excess with a flow routing technique would simulate the flow hydrograph at the drainage basin outlet.

• Journal of Climate Change Research
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• v.9 no.1
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• pp.103-115
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• 2018

The uncertainty of climate scenarios, as initial information, is one of the significant factors among uncertainties of climate change impacts and vulnerability assessments. In this sense, the quantification of the uncertainty of climate scenarios is essential to understanding these assessments of impacts and vulnerability for adaptation to climate change. Here we quantified the precision of surface temperature of ensemble scenarios (high resolution (1km) RCP4.5 and 8.5) provided by Korea Meteorological Administration, with spatiotemporal variation of the standard deviation of them. From 2021 to 2050, the annual increase rate of RCP8.5 was higher than that of RCP4.5 while the annual variation of RCP8.5 was lower than that of RCP4.5. The standard deviations of ensemble scenarios are higher in summer and winter, particularly in July and January, when the extreme weather events could occur. In general, the uncertainty of ensemble scenarios in summer were lower than those in winter. In spatial distribution, the standard deviation of ensemble scenarios in Seoul Metropolitan Area is relatively higher than other provinces, while that of Yeongnam area is lower than other provinces. In winter, the standard deviations of ensemble scenarios of RCP4.5 and 8.5 in January are higher than those of December. Especially, the standard deviation of ensemble scenarios is higher in the central regions including Gyeonggi, and Gangwon, where the mean surface temperature is lower than southern regions along with Chungbuk. Such differences in precisions of climate ensemble scenarios imply that those uncertainty information should be taken into account for the implementation of national climate change policy.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.202-202
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• 2023

전 세계적으로 지구 온난화로 인해 발생한 가뭄은 사회적, 경제적, 환경적으로 막대한 피해를 야기하고 있다. 국내의 경우, 2022년부터 현재까지 지속되고 있는 가뭄 상황은 강수의 지역적 편차로 인해 남부 지역 중심으로 극심한 피해가 발생하였다. 남부 지역의 주요 용수공급원인 영산강, 섬진강권역의 용수 공급율은 예년의 57%(3.8억 톤)에 불과하며, 일부 도서·산간 지역은 용수공급이 제한되는 현상까지 발생하였다. 이러한 가뭄 피해를 대비하기 위해 초기에 모니터링을 통한 선제적 대응 방안을 구축해야 한다. 가뭄 모니터링의 경우 미계측 지역에 대한 모니터링 방법으로 주기적이고 균질한 자료를 제공 받을 수 있는 위성영상을 활용한 연구가 수행되고 있다. 가뭄을 정량적으로 분석하고 판단하기 위해 가뭄지수를 활용하고 있으며, 대표적인 가뭄지수는 지상 관측강수량자료를 활용한 확률분포 기반의 표준강수지수 (Standardized Precipitation Index, SPI)와 강수 및 기온의 변동성이 포함된 표준강수증발산지수 (Standardized Precipitation Evapotranspiration Index, SPEI)가 있으며, 위성영상 자료를 활용한 가뭄지수인 증발스트레스지수(Evaporative Stress Index, ESI) 등이 있다. 본 연구에서는 강수와 기온을 고려한 가뭄지수인SPEI와 위성영상 기반의 가뭄지수인 ESI를 활용하여 2022년 남부 지역의 가뭄 사상을 중심으로 지표별 시공간적 변화를 분석하고자 한다. SPEI의 경우 기상관측소 지점자료의 기온과 강수량을 활용하였으며, Terra 위성의 MODIS (Moderate Resolution Imaging Spectroradiometer) 센서에서 제공되는 위성영상자료를 활용한 ESI는 미계측 지역에 대한 가뭄 판단을 위해 시·군별로 세분화하여 산정하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.34-34
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• 2023

The Chah-Nimeh reservoirs, which are a sort of natural lakes located in the border of Iran and Afghanistan, are the main drinking and agricultural water resources of Sistan arid region. Considering the occurrence of intense seasonal wind, locally known as levar wind, this study aims to explore the possibility to provide a TSM (Total Suspended Matter) monitoring model of Chah-Nimeh reservoirs using multi-temporal satellite images and in-situ wind speed data. The results show that a strong correlation between TSM concentration and wind speed are present. The developed empirical model indicated high performance in retrieving spatiotemporal distribution of the TSM concentration with R²=0.98 and RMSE=0.92g/m³. Following this observation, we also consider a machine learning-based model to predicts the average TSM using only wind speed. We connect our in-situ wind speed data to the TSM data generated from the inversion of multi-temporal satellite imagery to train a neural network based mode l(Wind2TSM-Net). Examining Wind2TSM-Net model indicates this model can retrieve the TSM accurately utilizing only wind speed (R²=0.88 and RMSE=1.97g/m³). Moreover, this results of this study show tha the TSM concentration can be estimated using only in situ wind speed data independent of the satellite images. Specifically, such model can supply a temporally persistent means of monitoring TSM that is not limited by the temporal resolution of imagery or the cloud cover problem in the optical remote sensing.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.136-136
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• 2021

In this study, a deep convolutional neural network (DCNN) model is proposed for short-term precipitation forecasting using weather radar-based images. The DCNN model is a combination of convolutional neural networks, autoencoder neural networks, and U-net architecture. The weather radar-based image data used here are retrieved from competition for rainfall forecasting in Korea (AI Contest for Rainfall Prediction of Hydroelectric Dam Using Public Data), organized by Dacon under the sponsorship of the Korean Water Resources Association in October 2020. This data is collected from rainy events during the rainy season (April - October) from 2010 to 2017. These images have undergone a preprocessing step to convert from weather radar data to grayscale image data before they are exploited for the competition. Accordingly, each of these gray images covers a spatial dimension of 120×120 pixels and has a corresponding temporal resolution of 10 minutes. Here, each pixel corresponds to a grid of size 4km×4km. The DCNN model is designed in this study to provide 10-minute predictive images in advance. Then, precipitation information can be obtained from these forecast images through empirical conversion formulas. Model performance is assessed by comparing the Score index, which is defined based on the ratio of MAE (mean absolute error) to CSI (critical success index) values. The competition results have demonstrated the impressive performance of the DCNN model, where the Score value is 0.530 compared to the best value from the competition of 0.500, ranking 16^th out of 463 participating teams. This study's findings exhibit the potential of applying the DCNN model to short-term rainfall prediction using weather radar-based images. As a result, this model can be applied to other areas with different spatiotemporal resolutions.