• Korean Journal of Plant Resources
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• v.31 no.5
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• pp.498-514
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• 2018

A statistical analysis for 3651 genetic resources collected from China (1,542), Japan (1,409), Korea (413), and India (287) was conducted using normal distribution, variability index value (VIV), analysis of variation (ANOVA) and Ducan's multiple range test (DMRT) based on a data obtained from NIRS analysis. In normal distribution, the average protein content was 8.0%, whereas waxy type amylose and common rice amylose were found to be 8.7% and 22.7%, respectively. The protein contents ranged from 5.4 to 10.6% at the level of 95%. The waxy amylose and common rice amylose ranged from 5.9 to 11.5%, and from 16.9 to 28.5% at 95% confidence level, respectively. The VIV was 0.59 for protein, 0.64 for low amylose, and 0.81 for high amylose contents. The average amylose contents were 18.85% in Japanese, 19.99% in Korean, 20.27% in Chinese, and 25.46% in Indian resources, while the average protein contents were found to be 7.23% in Korean, 7.73% in Japanese, 8.01% in Chinese, and 8.17% in Indian resources. The ANOVA of amylose and protein content showed significant differences at the level of 0.01. The F-test for amylose content was 158.34, and for protein content 53.95 compared to critical value 3.78. The DMRT of amylose and protein content showed significant difference (p<0.01) between resources of different countries. Japanese resources had the lowest level of amylose contents, whereas, the lowest level of protein content was found in Korean resources compared to other origins. Indian resources showed the highest level of amylose and protein contents. It is recommended these results should be helpful to future breeding experiments.

• Journal of The Korean Society of Grassland and Forage Science
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• v.39 no.4
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• pp.258-263
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• 2019

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages. The objective of this study was to evaluate the potential of NIRS, applied to imported forage, to estimate the moisture and chemical parameters for imported hays. A population of 392 imported hay representing a wide range in chemical parameters was used in this study. Samples of forage were scanned at 1 nm intervals over the wavelength range 680-2500nm and the optical data was recorded as log 1/Reflectance(log 1/R), which scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares(PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation(R²) and the lowest standard error of cross-validation(SECV). The results of this study showed that NIRS predicted the chemical parameters with very high degree of accuracy. The R² and SECV for imported hay calibration were 0.92(SECV 0.61%) for moisture, 0.98(SECV 0.65%) for acid detergent fiber, 0.97(SECV 0.40%) for neutral detergent fiber, 0.99(SECV 0.06%) for crude protein and 0.97(SECV 3.04%) for relative feed value on a dry matter(%), respectively. Results of this experiment showed the possibility of NIRS method to predict the moisture and chemical composition of imported hay in Korea for routine analysis method to evaluate the feed value.

• Journal of Bio-Environment Control
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• v.27 no.4
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• pp.400-406
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• 2018

In this experiment the effect of supplemental lighting on the growth and yield of cucumber (Cucumis sativus L. 'Fresh') plants during low radiation period of winter season were investigated in glasshouses using common high-pressure sodium (HPS) lamps and newly developed plasma lighting system (PLS) lamps. Plants grown without supplemental lighting were considered as a control. Supplemental lighting was provided from November 20th, 2015 to March 15th, 2016 to ensure 14-hour photoperiod (natural+supplemental light), also lamps were operated automatically when the outside sun radiation levels were less than $100W{\cdot}m^{-2}$. Spectral analysis showed that HPS lamp had a discrete spectrum, lacked of the radiation in the 400-550 nm wave band (blue-green light), but had a high output in the orange-red region (550-650 nm). A higher red light output resulted in an increased red to far-red (R/FR) ratio in HPS lamp. PLS had a continuous spectrum and had a peak radiation in green region (490-550 nm). HPS has 12.6% lower output in photosynthetically active radiation (PAR) but 12.6% higher output in near infra-red (NIR) spectral regions compared to PLS. Both HPS and PLS lamps emitted very low levels of ultra-violet radiation (300-400 nm). Supplemental lighting both from HPS and PLS lamps increased plant height, leaf number, internode number and dry weight of cucumber plants compared to control. Photosynthetic activity of cucumber plants grown under two supplemental lighting systems was comparable. Number of fruits per cucumber plant (fruit weight per plant) in control, PLS, and HPS plots were 21.2 (2.9 kg), 38.7 (5.5 kg), and 40.4 (5.6 kg), respectively, thereby increasing yield by 1.8-1.9 times in comparison with control. An analysis of the economic feasibility of supplemental lighting in cucumber cultivation showed that considering lamp installation and electricity costs the income from supplemental lighting increased by 37% and 62% for PLS and HPS lamps, respectively.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.4
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• pp.353-365
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• 2019

A near-infrared reflectance spectroscopy (NIRS) prediction model was set to establish a rapid analysis system of wheat germplasm and provide statistical information on the characteristics of protein contents. The variability index value (VIV) of calibration resources was 0.80, the average protein content was 13.2%, and the content range was from 7.0% to 13.2%. After measuring the near-infrared spectra of calibration resources, the NIRS prediction model was developed through a regression analysis between protein content and spectra data, and then optimized by excluding outliers. The standard error of calibration, R², and the slope of the optimized model were 0.132, 0.997, and 1.000 respectively, and those of external validation results were 0.994, 0.191, and 1.013, respectively. Based on these results, a developed NIRS model could be applied to the rapid analysis of protein in wheat. The distribution of NIRS protein content of 6,794 resources were analyzed using a normal distribution analysis. The VIV was 0.79, the average protein was 12.1%, and the content range of resources accounting for 42.1% and 68% of the total accessions were 10-13% and 9.5-14.6%, respectively. The composition of total resources was classified into breeding line (3,128), landrace (2,705), and variety (961). The VIV in breeding line was 0.80, the protein average was 11.8%, and the contents of 68% of total resources ranged from 9.2% to 14.5%. The VIV in landrace was 0.76, the protein average was 12.1%, and the content range of resources of 68% of total accessions was 9.8-14.4%. The VIV in variety was 0.80, the protein average was 12.8%, and the accessions representing 68% of total resources ranged from 10.2% to 15.4%. These results should be helpful to the related experts of wheat breeding.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.968-974
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• 2010

We assessed the feasibility of discrete wavelet transform (DWT) applied for the spectral processing to enhance the estimation performance quality of soil organic matters using visible-near infrared spectra and mapped their distribution via block Kriging model. Continuum-removal and $1^{st}$ derivative transform as well as Haar and Daubechies DWT were used to enhance spectral variation in terms of soil organic matter contents and those spectra were put into the PLSR (Partial Least Squares Regression) model. Estimation results using raw reflectance and transformed spectra showed similar quality with $R^2$ > 0.6 and RPD> 1.5. These values mean the approximation prediction on soil organic matter contents. The poor performance of estimation using DWT spectra might be caused by coarser approximation of DWT which not enough to express spectral variation based on soil organic matter contents. The distribution maps of soil organic matter were drawn via a spatial information model, Kriging. Organic contents of soil samples made Gaussian distribution centered at around 20 g $kg^{-1}$ and the values in the map were distributed with similar patterns. The estimated organic matter contents had similar distribution to the measured values even though some parts of estimated value map showed slightly higher. If the estimation quality is improved more, estimation model and mapping using spectroscopy may be applied in global soil mapping, soil classification, and remote sensing data analysis as a rapid and cost-effective method.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1559-1572
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• 2021

The response of vegetation under the crop stress condition was evaluated using structural, biochemical, and physiological vegetation indices based on unmanned aerial vehicle (UAV) images and field-spectrometer data. A high concentration of herbicide was sprayed at the different growth stages of garlic to process crop stress, the above ground dry matter of garlic at experimental area (EA) decreased about 46.2~84.5% compared to that at control area. The structural vegetation indices clearly responded to these crop damages. Spectral reflectance at near-infrared wavelength consistently decreased at EA. Most biochemical vegetation indices reflected the crop stress conditions, but the meaning of physiological vegetation indices is not clear due to the effect of vinyl mulching. The difference of the decreasing ratio of vegetation indices after the herbicide spray was 2.3% averagely in the case of structural vegetation indices and 1.3~4.1% in the case of normalization-based vegetation indices. These results meant that appropriate vegetation indices should be utilized depending on the types of crop stress and the cultivation environment and the normalization-based vegetation indices measured from the different spatial scale has the minimized difference.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1029-1042
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• 2021

As part of the next-generation Compact Advanced Satellite 500 (CAS500) project, CAS500-4 is scheduled to be launched in 2025 focusing on the remote sensing of agriculture and forestry. To obtain quantitative information on vegetation from satellite images, it is necessary to acquire surface reflectance through atmospheric correction. Thus, it is essential to develop an atmospheric correction method suitable for CAS500-4. Since the absorption and scattering characteristics in the atmosphere vary depending on the wavelength, it is needed to analyze the sensitivity of atmospheric correction parameters such as aerosol optical depth (AOD) and water vapor (WV) considering the wavelengths of CAS500-4. In addition, as CAS500-4 has only five channels (blue, green, red, red edge, and near-infrared), making it difficult to directly calculate key parameters for atmospheric correction, external parameter data should be used. Therefore, thisstudy performed a sensitivity analysis of the key parameters (AOD, WV, and O₃) using the simulated images based on Sentinel-2 satellite data, which has similar wavelength specifications to CAS500-4, and examined the possibility of using the products of GEO-KOMPSAT-2A (GK2A) as atmospheric parameters. The sensitivity analysisshowed that AOD wasthe most important parameter with greater sensitivity in visible channels than in the near-infrared region. In particular, since AOD change of 20% causes about a 100% error rate in the blue channel surface reflectance in forests, a highly reliable AOD is needed to obtain accurate surface reflectance. The atmospherically corrected surface reflectance based on the GK2A AOD and WV was compared with the Sentinel-2 L2A reflectance data through the separability index of the known land cover pixels. The result showed that two corrected surface reflectance had similar Seperability index (SI) values, the atmospheric corrected surface reflectance based on the GK2A AOD showed higher SI than the Sentinel-2 L2A reflectance data in short-wavelength channels. Thus, it is judged that the parameters provided by GK2A can be fully utilized for atmospheric correction of the CAS500-4. The research findings will provide a basis for atmospheric correction of the CAS500-4 in the future.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1165-1178
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• 2018

Collapse of an Antarctic ice shelf and its flow velocity changes has the potential to reduce the restraining stress to the seaward flow of the Antarctic Ice Sheet, which can cause sea level rising. In this study, variations in ice velocity from 2000 to 2017 for the Nansen Ice Shelf in East Antarctica that experienced a large-scale collapse in April 2016 were analyzed using Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images. To extract ice velocity, image matching based on orientation correlation was applied to the image pairs of blue, green, red, near-infrared, panchromatic, and the first principal component image of the Landsat multispectral data, from which the results were combined. The Landsat multispectral image matching produced reliable ice velocities for at least 14% wider area on the Nansen Ice Shelf than for the case of using single band (i.e., panchromatic) image matching. The ice velocities derived from the Landsat multispectral image matching have the error of $2.1m\;a^{-1}$ compared to the in situ Global Positioning System (GPS) observation data. The region adjacent to the Drygalski Ice Tongue showed the fastest increase in ice velocity between 2000 and 2017. The ice velocity along the central flow line of the Nansen Ice Shelf was stable before 2010 (${\sim}228m\;a^{-1}$). In 2011-2012, when a rift began to develop near the ice front, the ice flow was accelerated (${\sim}255m\;a^{-1}$) but the velocity was only about 11% faster than 2010. Since 2014, the massive rift had been fully developed, and the ice velocity of the upper region of the rift slightly decreased (${\sim}225m\;a^{-1}$) and stabilized. This means that the development of the rift and the resulting collapse of the ice front had little effect on the ice velocity of the Nansen Ice Shelf.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.2
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• pp.70-88
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• 2019

A statistical analysis of 4,380 non-glutinous landrace rice germplasm collected from four East Asian countries namely South Korea (1,032), North Korea (994), Japan (800), and China (528) was conducted using normal distribution, variability index value (VIV), analysis of variation (ANOVA), and Duncan's multiple range test (DMRT) based on a data obtained from Near-Infrared Reflectance Spectroscopy (NIRS) analysis. In normal distribution, the average protein content was 8.2%, and the non-glutinous rice amylose, ranging over 10%, was found to be 22.0%. Protein content in most gremplasm was between 5.4 and 10.9%, and amylose content was between 15.0 and 28.9%. The VIV was 0.50 for protein, and 0.81 for non-glutinous rice amylose content. The average amylose content was 23.34% in Chinese, 21.55% in South Korean, 21.45% in Japanese, and 20.48% in North Korean resources, while the average protein content was found to be 9.02% in Chinese, 8.06% in Japanese, 8.04% in North Korean, and 7.99% in South Korean resources. ANOVA of amylose and protein content showed significant differences at p=0.01. The F-test value for amylose content was 94.92, and for protein content was 81.82 compared to the critical value of 3.79. DMRT of amylose and protein content revealed significant differences (p<0.01). Among the various germplasm obtained from different countries, that from North Korean had the lowest level of amylose content, whereas that from South Korea had the lowest level of protein content than all other resources. Chinese resources had the highest level of amylose and protein content. It is recommended to use these results in breeding fields.

• Korean Journal of Plant Resources
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• v.32 no.2
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• pp.124-143
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• 2019

This study was carried out to build a database system for amylose and protein contents of rice germplasm based on NIRS (Near-Infrared Reflectance Spectroscopy) analysis data. The average waxy type amylose contents was 8.7% in landrace, variety and weed type, whereas 10.3% in breeding line. In common rice, the average amylose contents was 22.3% for landrace, 22.7% for variety, 23.6% for weed type and 24.2% for breeding line. Waxy type resources comprised of 5% of the total germplasm collections, whereas low, intermediate and high amylose content resources share 5.5%, 20.5% and 69.0% of total germplasm collections, respectively. The average percent of protein contents was 8.2 for landrace, 8.0 for variety, and 7.9 for weed type and breeding line. The average Variability Index Value was 0.62 in waxy rice, 0.80 in common rice, and 0.51 in protein contents. The accession ratio in arbitrary ranges of landrace was 0.45 in amylose contents ranging from 6.4 to 8.7%, and 0.26 in protein ranging from 7.3 to 8.2%. In the variety, it was 0.32 in amylose ranging from 20.1 to 22.7%, and 0.51 in protein ranging from 6.1 to 8.3%. And also, weed type was 0.67 in amylose ranging from 6.6 to 9.7%, and 0.33 in protein ranging from 7.0 to 7.9%, whereas, in breeding line it was 0.47 in amylose ranging from 10.0 to 12.0%, and 0.26 in protein ranging from 7.0 to 7.9%. These results could be helpful to build database programming system for germplasm management.