• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1255-1272
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• 2023

Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of time-synthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.111-121
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• 2019

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.

• Journal of Climate Change Research
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• v.5 no.1
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• pp.71-81
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• 2014

Using the Eddy Covariance technique, we analyzed seasonal variation in net ecosystem $CO_2$ exchange (NEE) and investigated the effects of environmental factors and aboveground biomass of rice on the $CO_2$ fluxes in a rice-barley double cropping paddy field of Gimje, Korea. Quality control and gap-filling were conducted before this investigation of the effects. The results have been showed that NEE, gross primary production (GPP), and ecosystem respiration (Re) during the rice growing period were -215.6, 763.9, and $548.3g\;C\;m^{-2}$, respectively. Relation between NEE and net radiation (Rn) could be described by a quadratic equation, and about 65 % of variation in NEE was explained by changes in Rn. On the other hand, an exponential function relating Re to soil temperature accounted for approximately 43 % of variation in Re under the flooded condition of paddy field. Aboveground biomass showed significant linear relationships with NEE ($r^2=0.93$), GPP ($r^2=0.96$), and Re ($r^2=0.95$), respectively.

• Journal of Nutrition and Health
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• v.55 no.1
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• pp.120-140
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• 2022

Purpose: The purpose of the study was to investigate whether adherence to the Korea Healthy Eating Index (KHEI) was associated with metabolic syndrome and risk markers. Methods: The participants included 8,345 adults, aged 20-59 years, who took part in the 7th Korea National Health and Nutrition Examination Survey (KNHANES). The data were analyzed using a complex-sample t-test, the Rao Scott χ²-test, and logistic regression analysis on the SPSS v. 26.0 software. The participants were divided into four groups by quartiles of KHEI scores. Results: The average KHEI score was 61.06 points out of 100, and the women's score (62.50 points) was significantly higher than that of men (59.63 points). The KHEI quartiles status showed significant differences by age (p < 0.001), household income (p < 0.001), smoking status (p < 0.001), and food security. Specifically, the KHEI quartiles in the men showed significant differences in education (p < 0.001) and economic activity (p < 0.001) whereas those of women showed significant differences in alcohol-consumption (p < 0.001), depression (p < 0.01) and eating-out (p < 0.001). As the KHEI scores increased, the proportion of subjects with an energy intake below the estimated energy requirement (EER) was lower, and significantly better levels of intake were observed for carbohydrate, protein, vitamin C, calcium, vitamin B₁, vitamin B₂, and niacin. The incidence of the metabolic syndrome risk factors, hypertriglyceridemia and hyperglycemia for men and hypertension, and hyperglycemia for women showed significant differences. The KHEI scores were inversely associated with abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol, hyperglycemia, hypertension, and metabolic syndrome. Conclusion: Based on these results, we conclude that higher adherence to the KHEI was associated with lower metabolic syndrome risk factors and incidence of the metabolic syndrome.