• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.707-723
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• 2022

Although satellite-based sea surface temperature (SST) is advantageous for monitoring large areas, spatiotemporal data gaps frequently occur due to various environmental or mechanical causes. Thus, it is crucial to fill in the gaps to maximize its usability. In this study, daily SST composite fields with a resolution of 4 km were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data. The first step was SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using multi-satellite-derived SST data. The second step improved the reconstructed SST targeting in situ measurements based on light gradient boosting machine (LGBM) to finally produce daily SST composite fields. The DINCAE model was validated using random masks for 50 days, whereas the LGBM model was evaluated using leave-one-year-out cross-validation (LOYOCV). The SST reconstruction accuracy was high, resulting in R² of 0.98, and a root-mean-square-error (RMSE) of 0.97℃. The accuracy increase by the second step was also high when compared to in situ measurements, resulting in an RMSE decrease of 0.21-0.29℃ and an MAE decrease of 0.17-0.24℃. The SST composite fields generated using all in situ data in this study were comparable with the existing data assimilated SST composite fields. In addition, the LGBM model in the second step greatly reduced the overfitting, which was reported as a limitation in the previous study that used random forest. The spatial distribution of the corrected SST was similar to those of existing high resolution SST composite fields, revealing that spatial details of oceanic phenomena such as fronts, eddies and SST gradients were well simulated. This research demonstrated the potential to produce high resolution seamless SST composite fields using multi-satellite data and artificial intelligence.

• 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 Soil Science and Fertilizer
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• v.18 no.3
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• pp.265-275
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• 1985

Present studies were carried out to investigate the distribution and formation of organic tiers contained paddy soils in Honam area characteristics to give basic informations on the effective utilization, management and improvement of the soils. The results obtained were summarized as follows; 1. The extent of organic tiers contained paddy soils in Honam area were 6.538㏊ and the amount of peat deposits were presumed about 2.41 million M/T. 2. Out of the total extent of the organic tiers contained paddy soils, about 97.6% was distributed in Honam plains (water-sheds of Mangyeong-Dongjin river), while about 1.5% in the Naju plains (water-sheds of Yeongsan river), and 0.9% in the Wando and Yeocheon areas. 3. The period of peat formation was presumed to be about the early of Seung Moon period (B.C. 4,250), and the Gongdeog series and the Bongnam series were formed in the bog conditions close to the valley mouth of near rolling and hill with small steram channels, and the Gimje series was formed in the out-skirts plains of the Gongdeog and Bongnam soils. 4. In the casue of peat formation, it was presumed to be the Gimje series that accumulated the fibrous peat out of the autochthonous peat such as reeds and grasses etc, to be the Gongdeog and Bongnam series that accumulated the autochtonous peat and the xylem and fibrous peat out of first allochthonous peat. 5. In the Organic horizons of these soils, the range of muck and peat horizons were in 62-68cm and 68-137cm of soil profile in the Gongdeog series, 52-84cm and 84-113cm in the Bongnam series respectively, one of muck horizon was in 46-71cm in the Gimje series. 6. The marks of soil horizons of the soils were expressed that the lower soils than the horizon of muck and peat were formed Cg, Aag for the muck horizon, 0 for the peat horizon, 0 of peat horizon were distingushed with Oag and Oig according to Organic forms. 7. The depthe occurred the muck and peat horizons were positively correlated with the width of local in the Gongdeog series ($r=0.881^{**}$, $r=0.827^{**}$), but not in the Bongnam series and Gimje series.