• Journal of Biosystems Engineering
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• v.35 no.3
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• pp.175-181
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• 2010

This study compared moisture content measured by moisture meter of microwave resistance type(MMMRT) and standard moisture content of paddy, and developed the correction equation using linear and curvilinear regression analysis, and to explore its significance test. The correction factor according to the range of moisture content was developed to improve the measurement precision of MMMRT. The results were as followings. The coefficients of determination of correction equation by linear and curvilinear regression analysis with comparing the MMMRT and standard moisture content were 0.946 and 0.968, respectively. The moisture content error of MMMRT and standard moisture content measured after the MMMRT were corrected by moisture content rate of every 5% using the correction equation by curvilinear regression analysis appeared with 0~0.5% and 0.9~1.8% respectively in the moisture content range of 15~20% and 20~25%.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.4
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• pp.61-71
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• 2015

Quantitative variability of AMSR2 (Advanced Microwave Scanning Radiometer 2) soil moisture data shows that the remotely sensed soil moisture is underestimated during Spring and Winter seasons and is overestimated during Summer and Fall seasons. Therefore the bias correction of the remotely sensed data is essential for the purpose of water resource management. To enhance their applicability, the bias of AMSR2 soil moisture data was corrected using ground observation data at Cheorwon Chuncheon, Suwon, Cheongju, Jeonju, and Jinju sites. Test statistics demonstrated that the correlation coefficient R is improved from 0.107~0.328 to 0.286~0.559 and RMSE is improved from 9.46~14.36 % to 5.38~9.62 %. Bias correction using ground network data improved the applicability of remotely sensed soil moisture data.

• Journal of the Korean Society of Tobacco Science
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• v.27 no.1 s.53
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• pp.94-99
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• 2005

To correct the difference of filling volumn for various cut tobacco and puffed stem according to moisture contents, correction equation was estamated by a simple regression analysis. The $R^2$(coefficient of determination) of correction equation was above 0.95. To verify the precision of correction equation, we predicted correction equation of other samples. The filling volumns by the difference of $1\%$ moisture content were $0.018\;~\;0.022cc/g$ (cut tobacco) and 0.060cc/g (puffed stem). The precision of correction equation for various cut tobacco was very high, but that of puffed stem was low due to quality deviation of row stem according to a season.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.625-636
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• 2019

Soil moisture is a representative factor that plays a key role in hydrological cycle. It is involved in the interaction between atmosphere and land surface, and is used in fields such as agriculture and water resources. Advanced Microwave Scanning Radiometer 2 (AMSR2), Advanced SCATterometer (ASCAT), and European Space Agency Climate Change Initiative (ESACCI) data were used to analyze the applicability and uncertainty of satellite soil moisture product in the Korean peninsula. Cumulative distribution function (CDF) matching and triple collocation (TC) analysis were carried out to investigate uncertainty and correction of satellite soil moisture data. Comparisons of pre-calibration satellite soil moisture data with the Automated Agriculture Observing System (AAOS) indicated that ESACCI and ASCAT data reflect the trend of AAOS well. On the other hand, AMSR2 satellite data showed overestimated values during the freezing period. Correction of satellite soil moisture data using CDF matching improved the error and correlation compared to those before correction. Finally, uncertainty analysis of soil moisture was carried out using TC method. Clearly, the uncertainty of the satellite soil moisture, corrected by CDF matching, was diminished in both freezing and thawing periods. Overall, it is expected that using ASCAT and ESACCI rather than AMSR2 soil moisture data will give more accurate soil moisture information when correction is performed on the Korean peninsula.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.1
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• pp.13-34
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• 2022

Soil moisture data have been collected at 11 agrometeorological stations operated by The Korea Meteorological Administration (KMA). This study aimed to verify the accuracy of soil moisture data of KMA and develop a correction formula to be applied to improve their quality. The soil of the observation field was sampled to analyze its physical properties that affect soil water content. Soil texture was classified to be sandy loam and loamy sand at most sites. The bulk density of the soil samples was about 1.5 g/cm³ on average. The content of silt and clay was also closely related to bulk density and water holding capacity. The EnviroSCAN model, which was used as a reference sensor, was calibrated using the self-manufactured "reference soil moisture observation system". Comparison between the calibrated reference sensor and the field sensor of KMA was conducted at least three times at each of the 11 sites. Overall, the trend of fluctuations over time in the measured values of the two sensors appeared similar. Still, there were sites where the latter had relatively lower soil moisture values than the former. A linear correction formula was derived for each site and depth using the range and average of the observed data for the given period. This correction formula resulted in an improvement in agreement between sensor values at the Suwon site. In addition, the detailed approach was developed to estimate the correction value for the period in which a correction formula was not calculated. In summary, the correction of soil moisture data at a regular time interval, e.g., twice a year, would be recommended for all observation sites to improve the quality of soil moisture observation data.

• Proceedings of the KSRS Conference
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• v.2
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• pp.888-891
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• 2006

SAR image is not dependent on the weather condition and Sun's electromagnetic energy. But geometric distortions exist in almost all radar image, it need to be correction. The Radarsat-1 SAR images are used to monitoring of moisture acquired in May 1/1998 and May 25/1998. Radarsat-1 C band data is sensitive on moisture condition. Study area is located in Non-san site. It is made up almost agricultural area and a little of forest area. In May, Rice-planting is started in the midland of Korea. So moisture condition is undergoing many changes. Forest area need to be terrain effect removal for accurately results because it is included in layover, shadow, and so on. Results of land-cover moisture condition map are useful tool for fields of agriculture, forestry industry, and disaster.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.433-437
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• 2006

SAR image is not dependent on the weather condition and Sun's electromagnetic energy. But geometric distortions exist in almost all radar image, it need to be correction. The Radarsat-1 SAR images are used to monitoring of moisture acquired in May 1/1998 and May 25/1998. Radarsat-1 C band data is sensitive on moisture condition. Study area is located in Non-san site. It is made up almost agricultural area and a little of forest area. In May, Rice-planting is started in the midland of Korea. So moisture condition is undergoing many changes. Forest area need to be terrain effect removal for accurately results because it is included in layover, shadow, and so on. Results of land-cover moisture condition map are useful tool for fields of agriculture, forestry industry, and disaster.

• Journal of Biosystems Engineering
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• v.7 no.1
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• pp.1-16
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• 1982

The thermal conductivies of grain are influenced by many physical factors such as' initial temperature, moisture content, composition, bulk density or porosity of grain. However, not only few researchers considered all these factors in determining thermal conductivities of grain but also many researchers considered only moisture content as a major effective factor on the thermal conductivity. This study was conducted to experimentally determine the thermal conductivities of rough rice (3 Japonica-type, 3 Indica-type) and barley(covered, naked) as a function of initial temperature, moisture content and porosity of grain, and to investigate the effect of those physical factors on the thermal conductivities of grain. The results of this study are summarized as follows; 1. The average time correction value for this experimental apparatus was 7 sec, which. was insignificant to the calculated thermal conductivity. The resulting conductivity for considering time correction value was only 4.9 percent higher than that calculated by the non-corrected equation. 2. The thermal conductivity was in the range of 0.1208~0.2058W/$m^{\circ}K$ for naked barley, 0.1138~0.1724W/$m^{\circ}K$ for covered barley, 0.0912~0.1864W/$m^{\circ}K$ for Japonica-type rice and 0.086~0.1774W/$m^{\circ}K$ for Indica-type rice. 3. The thermal conductivities of grain increased with initial temperature and moisture content of grain but decreased with porosity of grain. 4. The regression equations of the thermal conductivity of each grain were determined as a function of initial temperature, moisture content and porosity. The regression equations of the thermal conductivity of both Japonica-type and Indica-type rough rice were also determined.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.233-246
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• 2023

In terms of understanding the water cycle and efficient water resource management, the importance of soil moisture has been highlighted. However, in Korea, the lack of qualified in-situ soil moisture data results in very limited utility. Even if satellite-based data are applied, the absence of ground reference data makes objective evaluation and correction difficult. The cosmic-ray neutron probe (CRNP) can play a key role in producing data for satellite data calibration. The installation of CRNP is non-invasive, minimizing damage to the soil and vegetation environment, and has the advantage of having a spatial representative for the intermediate scale. These characteristics are advantageous to establish an observation network in Korea which has lots of mountainous areas with dense vegetation. Therefore, this study was conducted to evaluate the applicability of the CRNP soil moisture observatory in Korea as part of the establishment of a Korean cOsmic-ray Soil Moisture Observing System (KOSMOS). The CRNP observation station was installed with the Gunup-ri observation station, considering the ease of securing power and installation sites and the efficient use of other hydro-meteorological factors. In order to evaluate the CRNP soil moisture data, 12 additional in-situ soil moisture sensors were installed, and spatial representativeness was evaluated through a temporal stability analysis. The neutrons generated by CRNP were found to be about 1,087 counts per hour on average, which was lower than that of the Solmacheon observation station, indicating that the Hongcheon observation station has a more humid environment. Soil moisture was estimated through neutron correction and early-stage calibration of the observed neutron data. The CRNP soil moisture data showed a high correlation with r=0.82 and high accuracy with root mean square error=0.02 m³/m³ in validation with in-situ data, even in a short calibration period. It is expected that higher quality soil moisture data production with greater accuracy will be possible after recalibration with the accumulation of annual data reflecting seasonal patterns. These results, together with previous studies that verified the excellence of CRNP soil moisture data, suggest that high-quality soil moisture data can be produced when constructing KOSMOS.

• Journal of Biosystems Engineering
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• v.23 no.1
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• pp.49-56
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• 1998

The objectives of this study were to develop models to predict the contents of moisture and protein of single kernel of brown rice based on visible/NIR (near-infrared) spectroscopic technique. The reflectance spectra of rice were obtained in the range of the wavelength 400 to 2,500 nm with 2 nm intervals. Multiple linear regression(MLR) and partial least squares (PLS) were used to develop the models. The MLR model using the first derivative spectra(10 nm of gap) with Standard Normal Variate and Detrending (SNV and Drt.) preprocessing showed the best results to predict moisture content of the sin린e kernel brown rice. To predict the protein content of a single kernel of brown ricer the PLS model used the raw spectra with multiplicative scatter correction(MSC) preprocessing over the wavelength of 1,100~1,500 nm.