• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.4
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• pp.297-306
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• 2023

Saemangeum Lake is the largest artificial lake in Korea. The continuous deterioration of lake water quality necessitates the introduction of novel water quality management strategies. Therefore, this study aims to identify the spatiotemporal water quality characteristics of Saemangeum Lake using data from the National Water Quality Measurement Network and provide basic information for water quality management. In the water quality parameters of Saemangeum Lake, water temperature and total phosphorous content were correlated, and salt, total nitrogen content, pH, and chemical oxygen demand were significantly correlated. Other parameters showed a low correlation. The spatial principal component analysis of Saemangeum Lake showed the characteristics of its four zones. The mid-to-downstream section of the river affected by freshwater inflow showed a high nutrient salt concentration, and the deep-water section of the drainage gate and the lake affected by seawater showed a high salt concentration. Two types of water qualities were observed in the intermediate water area where river water and outer sea water were mixed: waters with relatively low salt and high chemical oxygen demand, and waters with relatively low salt and high pH concentration. In the principal component analysis by time, the water quality was divided into four groups based on the observation month. Group I occurred during May and June in late spring and early summer, Group II was in early spring (March-April) and late autumn (November-December), Group III was in winter (January-February), and Group IV was in summer (July-October) during high temperatures. The water quality characteristics of Saemangeum Lake were found to be affected by the inflow of the upper Mangyeong and Dongjin rivers, and the seawater through the Garuk and Shinshi gates installed in the Saemangeum Embankment. In order to achieve the target water quality of Saemangeum Lake, it is necessary to establish water quality management measures for Saemangeum Lake along with pollution source management measures in the upper basin.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.29 no.1
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• pp.56-76
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• 2024

We for the first time made a successful longest continuous sectional observation in the East Sea by an underwater glider during 95 days from September 18 to December 21 2020 in the Korea along the 106 Line (129.1 °E ~ 131.5 °E at 37.9 °N) of the regular shipboard measurements by the National Institute of Fishery Science (NIFS) and obtained twelve hydrographic sections with high spatiotemporal resolution. The glider was deployed at 129.1 °E in September 18 and conducted 88-days flight from September 19 to December 15 2020, yielding twelve hydrographic sections, and then recovered at 129.2 °E in December 21 after the last 6 days virtual mooring operation. During the total traveled distance of 2550 km, the estimated deviation from the predetermined zonal path had an average RMS distance of 262 m. Based on these high-resolution long-term glider measurements, we conducted a comparative study with the bi-monthly NIFS measurements in terms of spatial and temporal resolutions, and found distinguished features. One is that spatial features of sub-mesoscale such as sub-mesoscale frontal structure and intensified thermocline were detected only in the glider measurements, mainly due to glider's high spatial resolution. The other is the detection of intramonthly variations from the weekly time series of temperature and salinity, which were extracted from glider's continuous sections. Lastly, there were deviations and bias in measurements from both platforms. We argued these deviations in terms of the time scale of variation, the spatial scale of fixed-point observation, and the calibration status of CTD devices of both platforms.