• Journal of Korea Water Resources Association
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• v.47 no.4
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• pp.371-384
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• 2014

This study developed a new algorithm of extreme rainfall extraction based on the Communication, Ocean and Meteorological Satellite (COMS) and the Tropical Rainfall Measurement Mission (TRMM) Satellite image data and evaluated its applicability for the heavy rainfall event in July-2011 in Seoul, South Korea. The power-series-regression-based Z-R relationship was employed for taking into account for empirical relationships between TRMM/PR, TRMM/VIRS, COMS, and Automatic Weather System(AWS) at each elevation. The estimated Z-R relationship ($Z=303R^{0.72}$) agreed well with observation from AWS (correlation coefficient=0.57). The estimated 10-minute rainfall intensities from the COMS satellite using the Z-R relationship generated underestimated rainfall intensities. For a small rainfall event the Z-R relationship tended to overestimated rainfall intensities. However, the overall patterns of estimated rainfall were very comparable with the observed data. The correlation coefficients and the Root Mean Square Error (RMSE) of 10-minute rainfall series from COMS and AWS gave 0.517, and 3.146, respectively. In addition, the averaged error value of the spatial correlation matrix ranged from -0.530 to -0.228, indicating negative correlation. To reduce the error by extreme rainfall estimation using satellite datasets it is required to take into more extreme factors and improve the algorithm through further study. This study showed the potential utility of multi-geostationary satellite data for building up sub-daily rainfall and establishing the real-time flood alert system in ungauged watersheds.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.497-508
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• 2023

Agricultural reservoirs are crucial structures for water resources monitoring especially in Korea where the resources are seasonally unevenly distributed. Optical and Synthetic Aperture Radar (SAR) satellites, being utilized as tools for monitoring the reservoirs, have unique limitations in that optical sensors are sensitive to weather conditions and SAR sensors are sensitive to noises and multiple scattering over dense vegetations. In this study, we tried to improve water body detection accuracy through optical-SAR data fusion, and quantitatively analyze the complementary effects. We first detected water bodies at Edong, Cheontae reservoir using the Compact Advanced Satellite 500(CAS500), Kompsat-3/3A, and Sentinel-2 derived Normalized Difference Water Index (NDWI), and SAR backscattering coefficient from Sentinel-1 by K-means clustering technique. After that, the improvements in accuracies were analyzed by applying K-means clustering to the 2-D grid space consists of NDWI and SAR. Kompsat-3/3A was found to have the best accuracy (0.98 at both reservoirs), followed by Sentinel-2(0.83 at Edong, 0.97 at Cheontae), Sentinel-1(both 0.93), and CAS500(0.69, 0.78). By applying K-means clustering to the 2-D space at Cheontae reservoir, accuracy of CAS500 was improved around 22%(resulting accuracy: 0.95) with improve in precision (85%) and degradation in recall (14%). Precision of Kompsat-3A (Sentinel-2) was improved 3%(5%), and recall was degraded 4%(7%). More precise water resources monitoring is expected to be possible with developments of high-resolution SAR satellites including CAS500-5, developments of image fusion and water body detection techniques.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.457-470
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• 2019

Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) has been attracted attention in the fields of surveillance, reconnaissance, and national security due to its advantage of all-weather and day-and-night imaging capabilities. However, there have been some difficulties in automatically identifying targets in real situation due to various observational and environmental conditions. In this paper, ATR problems in Extended Operating Conditions (EOC) were investigated. In particular, we considered partial occlusions of the target (10% to 50%) and differences in the depression angle between training ($17^{\circ}$) and test data ($30^{\circ}$ and $45^{\circ}$). To simulate various occlusion conditions, SARBake algorithm was applied to Moving and Stationary Target Acquisition and Recognition (MSTAR) images. The ATR accuracies were evaluated by using the template matching and Adaboost algorithms. Experimental results on the depression angle showed that the target identification rate of the two algorithms decreased by more than 30% from the depression angle of $45^{\circ}$ to $30^{\circ}$. The accuracy of template matching was about 75.88% while Adaboost showed better results with an accuracy of about 86.80%. In the case of partial occlusion, the accuracy of template matching decreased significantly even in the slight occlusion (from 95.77% under no occlusion to 52.69% under 10% occlusion). The Adaboost algorithm showed better performance with an accuracy of 85.16% in no occlusion condition and 68.48% in 10% occlusion condition. Even in the 50% occlusion condition, the Adaboost provided an accuracy of 52.48%, which was much higher than the template matching (less than 30% under 50% occlusion).

• Atmosphere
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• v.24 no.3
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• pp.283-301
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• 2014

In 2010, only 14 tropical cyclones (TCs) were generated over the western North Pacific (WNP), which was the smallest since 1951. This study summarizes characteristics of TCs generated in 2010 over the WNP and investigates the causes of the record-breaking TC genesis. A long-term variation of TC activity in the WNP and verification of official track forecast in 2010 are also examined. Monthly tropical sea surface temperature (SST) anomaly data reveal that El Ni$\tilde{n}$o/Southern Oscillation (ENSO) event in 2010 was shifted from El Ni$\tilde{n}$o to La Ni$\tilde{n}$a in June and the La Ni$\tilde{n}$a event was strong and continued to the end of the year. We found that these tropical environments leaded to unfavorable conditions for TC formation at main TC development area prior to May and at tropics east of $140^{\circ}E$ during summer mostly due to low SST, weak convection, and strong vertical wind shear in those areas. The similar ENSO event (in shifting time and La Ni$\tilde{n}$a intensity) also occurred in 1998, which was the second smallest TC genesis year (16 TCs) since 1951. The common point of the two years suggests that the ENSO episode shifting from El Ni$\tilde{n}$o to strong La Ni$\tilde{n}$a in summer leads to extremely low TC genesis during La Ni$\tilde{n}$a although more samples are needed for confidence. In 2010, three TCs, DIANMU (1004), KOMPASU (1007) and MALOU (1009), influenced the Korean Peninsula (KP) in spite of low total TC genesis. These TCs were all generated at high latitude above $20^{\circ}N$ and arrived over the KP in short time. Among them, KOMPASU (1007) brought the most serious damage to the KP due to strong wind. For 14 TCs in 2010, mean official track forecast error of the Korea Meteorological Administration (KMA) for 48 hours was 215 km, which was the highest among other foreign agencies although the errors are generally decreasing for last 10 years, suggesting that more efforts are needed to improve the forecast skill.

• Journal of the Korean earth science society
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• v.33 no.3
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• pp.219-232
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• 2012

The intensive observation (ProbeX-2010) was performed to investigate an urban effect on summer rainfall over the Seoul metropolitan area from 13 August to 3 September 2010. Two kinds of urban effect were detected. First, weak rainfall (${\leq}1\;mm\;hr^{-1}$) was observed more frequently in the downwind area of Seoul than any other area of the country. The high frequency of weak rainfall in the downwind area was also confirmed from the recent five years of observational data (2006-2010). Because the high frequency was more apparent in mountainous regions during nighttime, the weak rainfall seems to be caused by a combined effect of urbanization and topography. Second, sporadically, a convective system was developed rapidly in the downwind area of Seoul, causing heavy rainfall (${\geq}10\;mm\;hr^{-1}$). It can be most clearly seen in series of radar images around 1300-1500 KST 27 August 2010. We investigated in detail the synoptic and local weather and upper air conditions. As a result, not only urban-induced high sensible heat but also conditionally unstable atmosphere (especially unstable in low level) and low level moisture were pointed out as important factors that contributed to urban-induced heavy rainfall.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.