• Journal of the Korean earth science society
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• v.40 no.6
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• pp.613-623
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• 2019

Over the past decades, daily sea surface temperature (SST) composite data have been produced using periodically and extensively observed satellite SST data, and have been used for a variety of purposes, including climate change monitoring and oceanic and atmospheric forecasting. In this study, we evaluated the accuracy and analyzed the error characteristic of the SST composite data in the sea around the Korean Peninsula for optimal utilization in the regional seas. We evaluated the four types of multi-satellite SST composite data including OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis), OISST (Optimum Interpolation Sea Surface Temperature), CMC (Canadian Meteorological Centre) SST, and MURSST (Multi-scale Ultra-high Resolution Sea Surface Temperature) collected from January 2016 to December 2016 by using in-situ temperature data measured from the Ieodo Ocean Research Station (IORS). Each SST composite data showed biases of the minimum of 0.12℃ (OISST) and the maximum of 0.55℃ (MURSST) and root mean square errors (RMSE) of the minimum of 0.77℃ (CMC SST) and the maximum of 0.96℃ (MURSST) for the in-situ temperature measurements from the IORS. Inter-comparison between the SST composite fields exhibited biases of -0.38-0.38℃ and RMSE of 0.55-0.82℃. The OSTIA and CMC SST data showed the smallest error while the OISST and MURSST data showed the most obvious error. The results of comparing time series by extracting the SST data at the closest point to the IORS showed that there was an apparent seasonal variation not only in the in-situ temperature from the IORS but also in all the SST composite data. In spring, however, SST composite data tended to be overestimated compared to the in-situ temperature observed from the IORS.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1767-1772
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• 2021

Global satellite observation surface albedo data over a long period of time are actively used to monitor changes in the global climate and environment, and their utilization and importance are great. Through the generational shift of geostationary satellites COMS (Communication, Ocean and Meteorological Satellite)/MI (Meteorological Imager sensor) and GK-2A (GEO-KOMPSAT-2A)/AMI (Advanced Meteorological Imager sensor), it is possible to continuously secure surface albedo outputs. However, the surface albedo outputs of COMS/MI and GK-2A/AMI differ between outputs due to Differences in retrieval algorithms. Therefore, in order to expand the retrieval period of the surface albedo of COMS/MI and GK-2A/AMI to secure continuous climate change monitoring linkage, the analysis of the two satellite outputs and errors should be preceded. In this study, error characteristics were analyzed by performing comparative analysis with ground observation data AERONET (Aerosol Robotic Network) and other satellite data GLASS (Global Land Surface Satellite) for the overlapping period of COMS/MI and GK-2A/AMI surface albedo data. As a result of error analysis, it was confirmed that the RMSE of COMS/MI was 0.043, higher than the RMSE of GK-2A/AMI, 0.015. In addition, compared to other satellite (GLASS) data, the RMSE of COMS/MI was 0.029, slightly lower than that of GK-2A/AMI 0.038. When understanding these error characteristics and using COMS/MI and GK-2A/AMI's surface albedo data, it will be possible to actively utilize them for long-term climate change monitoring.

• Journal of the Korean earth science society
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• v.43 no.5
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• pp.604-617
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• 2022

Sea surface temperature (SST) is a key variable that can be used to understand ocean-atmosphere phenomena and predict climate change. Satellite microwave remote sensing enables the measurement of SST despite the presence of clouds and precipitation in the sensor path. Therefore, considering the high utilization of microwave SST, it is necessary to continuously verify its accuracy and analyze its error characteristics. In this study, the validation of the microwave global precision measurement (GPM)/GPM microwave imager (GMI) SST around the Northwest Pacific and Korean Peninsula was conducted using surface drifter temperature data for approximately eight years from March 2014 to December 2021. The GMI SST showed a bias of 0.09K and an average root mean square error of 0.97K compared to the actual SST, which was slightly higher than that observed in previous studies. In addition, the error characteristics of the GMI SST were related to environmental factors, such as latitude, distance from the coast, sea wind, and water vapor volume. Errors tended to increase in areas close to coastal areas within 300 km of land and in high-latitude areas. In addition, relatively high errors were found in the range of weak wind speeds (<6 m s^-1) during the day and strong wind speeds (>10 m s^-1) at night. Atmospheric water vapor contributed to high SST differences in very low ranges of <30 mm and in very high ranges of >60 mm. These errors are consistent with those observed in previous studies, in which GMI data were less accurate at low SST and were estimated to be due to differences in land and ocean radiation, wind-induced changes in sea surface roughness, and absorption of water vapor into the microwave atmosphere. These results suggest that the characteristics of the GMI SST differences should be clarified for more extensive use of microwave satellite SST calculations in the seas around the Korean Peninsula, including a part of the Northwest Pacific.

• Korean Journal of Optics and Photonics
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• v.28 no.3
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• pp.108-115
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• 2017

We report the assembly procedure and performance evaluation of a visible and near-infrared spectrometer in the wavelength region of 400-900 nm, which is later to be combined with fore-optics (a telescope) to form a f/2.5 imaging spectrometer with a field of view of ${\pm}7.68^{\circ}$. The detector at the final image plane is a $640{\times}480$ charge-coupled device with a $24{\mu}m$ pixel size. The spectrometer is in an Offner relay configuration consisting of two concentric, spherical mirrors, the secondary of which is replaced by a convex grating mirror. A double-pass test method with an interferometer is often applied in the assembly process of precision optics, but was excluded from our study due to a large residual wavefront error (WFE) in optical design of 210 nm ($0.35{\lambda}$ at 600 nm) root-mean-square (RMS). This results in a single-path test method with a Shack-Hartmann sensor. The final assembly was tested to have a RMS WFE increase of less than 90 nm over the entire field of view, a keystone of 0.08 pixels, a smile of 1.13 pixels and a spectral resolution of 4.32 nm. During the procedure, we confirmed the validity of using a Shack-Hartmann wavefront sensor to monitor alignment in the assembly of an Offner-like spectrometer.

• Journal of Plant Biotechnology
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• v.42 no.1
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• pp.60-70
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• 2015

The aim of this study was to investigate whether fourier transform infrared (FT-IR) spectroscopy can be applied to simultaneous determination of fatty acids contents in different soybean cultivars. Total 153 lines of soybean (Glycine max Merrill) were examined by FT-IR spectroscopy. Quantification of fatty acids from the soybean lines was confirmed by quantitative gas chromatography (GC) analysis. The quantitative spectral variation among different soybean lines was observed in the amide bond region ($1,700{\sim}1,500cm^{-1}$), phosphodiester groups ($1,500{\sim}1,300cm^{-1}$) and sugar region ($1,200{\sim}1,000cm^{-1}$) of FT-IR spectra. The quantitative prediction modeling of 5 individual fatty acids contents (palmitic acid, stearic acid, oleic acid, linoleic acid, linolenic acid) from soybean lines were established using partial least square regression algorithm from FT-IR spectra. In cross validation, there were high correlations ($R^2{\geq}0.97$) between predicted content of 5 individual fatty acids by PLS regression modeling from FT-IR spectra and measured content by GC. In external validation, palmitic acid ($R^2=0.8002$), oleic acid ($R^2=0.8909$) and linoleic acid ($R^2=0.815$) were predicted with good accuracy, while prediction for stearic acid ($R^2=0.4598$), linolenic acid ($R^2=0.6868$) had relatively lower accuracy. These results clearly show that FT-IR spectra combined with multivariate analysis can be used to accurately predict fatty acids contents in soybean lines. Therefore, we suggest that the PLS prediction system for fatty acid contents using FT-IR analysis could be applied as a rapid and high throughput screening tool for the breeding for modified Fatty acid composition in soybean and contribute to accelerating the conventional breeding.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1117-1132
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• 2019

Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of Bio-Environment Control
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• v.32 no.1
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• pp.48-56
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• 2023

High-pressure sodium (HPS) lamps have been widely used as a useful supplemental light source to emit sufficient photosynthetically active radiation and provide a radiant heat, which contribute the heat requirement in greenhouses. The objective of this study to analyze the thermal characteristics of HPS lamp and thermal behavior in supplemented greenhouse, and evaluate the performance of a horizontal leaf temperature of sweet pepper plants using computational fluid dynamics (CFD) simulation. We simulated horizontal leaf temperature on upper canopy according to three growth stage scenarios, which represented 1.0, 1.6, and 2.2 plant height, respectively. We also measured vertical leaf and air temperature accompanied by heat generation of HPS lamps. There was large leaf to air temperature differential due to non-uniformity in temperature. In our numerical calculation, thermal energy of HPS lamps contributed of 50.1% the total heat requirement on Dec. 2022. The CFD model was validated by comparing measured and simulated data at the same operating condition. Mean absolute error and root mean square error were below 0.5, which means the CFD simulation values were highly accurate. Our result about vertical leaf and air temperature can be used in decision making for efficient thermal energy management and crop growth.

• Journal of the Korean Society of Radiology
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• v.17 no.4
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• pp.541-549
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• 2023

The aim of this study wasto conduct a quantitative analysis of CT image quality according to an algorithm designed to reduce metal artifacts induced by metal components. Ten baseline images were obtained with the standard filtered back-projection algorithm using spectral detector-based CT and CT ACR 464 phantom, and ten images were also obtained on the identical phantom with the standard filtered back-projection algorithm after inducing metal artifacts. After applying the to raw data from images with metal artifacts, ten additional images for each were obtained by applying the virtual monoenergetic algorithm. Regions of interest were set for polyethylene, bone, acrylic, air, and water located in the CT ACR 464 phantom module 1 to conduct compare the Hounsfield units for each algorithm. The algorithms were individually analyzed using root mean square error, mean absolute error, signal-to-noise ratio, peak signal-to-noise ratio, and structural similarity index to assess the overall image quality. When the Hounsfield units of each algorithm were compared, a significant difference was found between the images with different algorithms (p < .05), and large changes were observed in images using the virtual monoenergetic algorithm in all regions of interest except acrylic. Image quality analysis indices revealed that images with the metal artifact reduction algorithm had the highest resolution, but the structural similarity index was highest for images with the metal artifact reduction algorithm followed by an additional virtual monoenergetic algorithm. In terms of CT images, the metal artifact reduction algorithm was shown to be more effective than the monoenergetic algorithm at reducing metal artifacts, but to obtain quality CT images, it will be important to ascertain the advantages and differences in image qualities of the algorithms, and to apply them effectively.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.477-487
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• 2012

Sea surface winds in the sea off the east coast of Korea were derived from L-band ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar) data and their characteristics of errors were analyzed. We could retrieve high-resolution wind vectors off the east coast of Korea including the coastal region, which has been substantially unavailable from satellite scatterometers. Retrieved SAR-wind speeds showed a good agreement with in-situ buoy measurement by showing relatively small an root-mean-square (RMS) error of 0.67 m/s. Comparisons of the wind vectors from SAR and scatterometer presented RMS errors of 2.16 m/s and $19.24^{\circ}$, 3.62 m/s and $28.02^{\circ}$ for L-band GMF (Geophysical Model Function) algorithm 2009 and 2007, respectively, which tended to be somewhat higher than the expected limit of satellite scatterometer winds errors. L-band SAR-derived wind field exhibited the characteristic dependence on wind direction and incidence angle. The previous version (L-band GMF 2007) revealed large errors at small incidence angles of less than $21^{\circ}$. By contrast, the L-band GMF 2009, which improved the effect of incidence angle on the model function by considering a quadratic function instead of a linear relationship, greatly enhanced the quality of wind speed from 6.80 m/s to 1.14 m/s at small incident angles. This study addressed that the causes of wind retrieval errors should be intensively studied for diverse applications of L-band SAR-derived winds, especially in terms of the effects of wind direction and incidence angle, and other potential error sources.