• KOREAN JOURNAL OF CROP SCIENCE
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• v.65 no.4
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• pp.377-385
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• 2020

Recently, wheat consumption has been increasing in Korea, requiring increased production. Nitrogen fertilization is a critical determinant in crop yield; therefore, it is necessary to optimize the nitrogen fertilization regime with current trends that emphasize the minimum impact of nitrogen fertilizer on the environment. In this study, both nondestructive spectral analysis using a hyperspectral camera and growth analysis were performed to determine the optimal N top-dressing rates after heading. The nitrogen application regimes consisted of three conditions according to the secondary top-dressing rate: N_4:3:0 (0 kg 10 a^-1), N_4:3:3 (2.73 kg 10 a^-1), and N_4:3:6 (5.46 kg 10 a^-1). Subsequently, growth and physiological investigations were performed at the jointing, heading, and ripening stages of wheat, and spectral investigations were conducted. On April 29, as the nitrogen fertilization rate was increased to N_4:3:3 and N_4:3:6, plant height and grain yield increased by 4% and 8%, and 8% and 52%, respectively, compared to those under N_4:3:0. Leaf area index and SPAD value also increased by 13% and 24%, and 32% and 43%, respectively. The R (red), G (green), and B (blue) of leaf color were lowered by 15, 11, and 4 in N_4:3:3 and 44, 34, and 18 in N_4:3:6, respectively, as compared to the control. Grain yield was the highest at high top-dressing (N_4:3:6), however, there was no difference between no top-dressing (N_4:3:0) and intermediat top-dressing (N_4:3:3). The reflectance analyzed using a hyperspectral camera showed a difference in the near-infrared (NIR) region on March 19, and on April 29, there was a difference both in the visible light region greater than 550 nm and the NIR region. Vegetation indices differed according to fertilization regime, except for the greenness index (GI). The results of this study showed that not only growth and physiological analysis but also spectral indices can be used to optimize the nitrogen top-dressing rate.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.221-234
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• 2016

In Korea, most industrial parks and major cities are located in coastal areas, which results in serious environmental problems in both coastal land and ocean. In order to effectively manage such problems especially in coastal ocean, water quality should be monitored. As there are many factors that influence water quality, the Korean Government proposed an integrated Water Quality Index (WQI) based on in situmeasurements of ocean parameters(bottom dissolved oxygen, chlorophyll-a concentration, secchi disk depth, dissolved inorganic nitrogen, and dissolved inorganic phosphorus) by ocean division identified based on their ecological characteristics. Field-measured WQI, however, does not provide spatial continuity over vast areas. Satellite remote sensing can be an alternative for identifying WQI for surface water. In this study, two schemes were examined to estimate coastal WQI around Korea peninsula using in situ measurements data and Geostationary Ocean Color Imager (GOCI) satellite imagery from 2011 to 2013 based on machine learning approaches. Scheme 1 calculates WQI using estimated water quality-related factors using GOCI reflectance data, and scheme 2 estimates WQI using GOCI band reflectance data and basic products(chlorophyll-a, suspended sediment, colored dissolved organic matter). Three machine learning approaches including Random Forest (RF), Support Vector Regression (SVR), and a modified regression tree(Cubist) were used. Results show that estimation of secchi disk depth produced the highest accuracy among the ocean parameters, and RF performed best regardless of water quality-related factors. However, the accuracy of WQI from scheme 1 was lower than that from scheme 2 due to the estimation errors inherent from water quality-related factors and the uncertainty of bottom dissolved oxygen. In overall, scheme 2 appears more appropriate for estimating WQI for surface water in coastal areas and chlorophyll-a concentration was identified the most contributing factor to the estimation of WQI.

• Korean Journal of Food Science and Technology
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• v.40 no.6
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• pp.603-610
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• 2008

The principal objective of current study was to evaluate the potential of near infrared reflectance spectroscopy (NIRS) as a non-destructive method for the prediction of the amylose and protein contents of un-hulled and brown rice in broad-based calibration models. The average amylose and protein content of 75 rice accessions were 20.3% and 7.1%, respectively. Additionally, the range of amylose and protein content were 16.6-24.5% and 3.8-9.3%, respectively. In total, 79 rice germplasms representing a wide range of chemical characteristics, variable physical properties, and origins were scanned via NIRS for calibration and validation equations. The un-hulled and brown rice samples evidenced distinctly different patterns in a wavelength range from 1,440 nm to 2,400 nm in the original NIR spectra. The optimal performance calibration model could be obtained by MPLS (modified partial least squares) using the first derivative method (1:4:4:1) for un-hulled rice and the second derivative method (2:4:4:1) for brown rice. The correlation coefficients $(r^2)$ and standard error of calibration (SEC) of protein and amylose contents for the un-hulled rice were 0.86, 2.48, and 0.84, 1.13, respectively. The $r^2$ and SEC of protein and amylose content for brown rice were 0.95, 1.09 and 0.94, 0.42, respectively. The results of this study suggest that the NIRS technique could be utilized as a routine procedure for the quantification of protein and amylose contents in large accessions of un-hulled rice germplasms.

• 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 Remote Sensing
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• v.31 no.5
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• pp.385-394
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• 2015

Pine wilt disease has greatly damaged pine forests not only in East Asia including South Korea and China, but also in European region. The damage caused by pine wood nematode (Bursaphelenchus xylophilus) is expressed in bundles within stands and rapidly spreading, however, present field survey methods have limitations to detecting damaged trees at regional level. This study extracted the damaged trees by pine wilt disease using time series hyperspectral aerial photographs, and analyzed their distribution characteristics. Hyperspectral aerial photographs of 1 meter spatial resolution were obtained in June, September, and October. Damaged trees by pine wilt disease were extracted using Normalized Difference Vegetation Index (NDVI) and Vegetation Index green (VIgreen) of the September photograph. Among extracted damaged trees, dead trees with leaves and without leaves were classified, and the spectral reflectance values from the photographs obtained in June, September, and October were compared to extract new outbreaks in September and October. Based on the time series dispersion of extracted damaged trees, nearest neighbor analysis was conducted to analyze distribution characteristics of the damaged trees within the region where hyperspectral aerial photographs were acquired. As a result, 2,262 damaged trees were extracted in the study area, and 604 dead trees (dead trees in last year) with leaves in relation to the damaged time and 300 and 101 newly damaged trees in September and October were classified. The result of nearest neighbor analysis using the data shows that aggregated distribution was the dominant pattern both previous and current year in the study area. Also, 80% of the damaged trees in current year were found within 60 m of dead trees in previous year.

• Economic and Environmental Geology
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• v.52 no.1
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• pp.13-28
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• 2019

This study analyzed precipitation environment, heavy metal contamination, and mineral composition of white, reddish brown and mixed precipitates occurring at the Osip stream drainage, Gangwondo. Furthermore, spectral characteristics of the precipitates associated with heavy metal contamination and mineral composition was investigated based on spectroscopic analysis. The pH range of the precipitates was 4.43-6.91 for white precipitates, 7.74-7.94 for reddish brown precipitates, and 7.59-7.9 for the mixed precipitates, respectively. XRF analysis revealed that these precipitates were contaminated with Ni, Cu, Zn, and As. The white precipitates showed high Al concentration compared to reddish brown precipitates as much as 3.3 times, and the reddish brown precipitates showed high Fe concentration compared to white precipitates as much as 15 times. XRD analysis identified that the mineral composition of the white participates was aluminocoquimbite, gibbsite, quartz, saponite, and illite, and that of reddish brown precipitates was aluminum isopropoxide, kaolinite, goethite, dolomite, pyrophyllite, magnetite, quartz, calcite, pyrope. The mineral composition of the mixed precipitates was quartz, albite, and calcite. The spectral characteristics of the precipitates was manifested by gibbsite, saponite, illite for white precipitates, goethite, kaolinite, pyrophyllite for reddish brown precipitates, and albite for the mixed precipitates, respectively. The spectral reflectance of the precipitates decreased with increase in heavy metal contamination, and absorption depth of the precipitates indicated that the heavy metal ions were adsorbed to saponite and illite for white precipitates, and goethite and magnetite for reddish brown precipitates.

• Journal of Sensor Science and Technology
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• v.6 no.1
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• pp.24-34
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• 1997

In this study, two different types of complementary electrochromic devices using amorphous $WO_{3}$ films as a working electrode, $V_{2}O_{5}$ film and NiO film as counter electrodes respectively were investigated. For the devices using amorphous and crystalline $V_{2}O_{5}$ films of $100{\sim}150nm$ thickness with $ITO/WO_{3}/LiClO_{4}-PC/V_{2}O_{5}/ITO$ structure, an optical modulation of $50{\sim}60%$ were obtained at a potential range of $1{\sim}2V$. It has been shown that transmittance and reflectance of light could be electrically controlled by low applied voltage. For the devices with $ITO/WO_{3}/LiClO_{4}-PC/NiO/ITO$ structure in which NiO film was deposited by a RF reactive sputtering, the optical modulation in visible light region (${\lambda}=550nm$) and in near infrared light region (${\lambda}=850nm$) were 25% and 30%, respectively.

• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.421-434
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• 2012

Colored Dissolved Organic Matter (CDOM) is one of the important components of optical properties of seawater to determine ecosystem dynamics in a given marine area. The optical characteristics of CDOM may depend on the various ecosystem and environmental variables in the sea and those variables may vary region to region. Therefore, the retrieval algorithm for determining light absorption coefficient of CDOM ($a_{CDOM}$) using satellite remote sensing reflectance ($R_{rs}$) developed from other region may not be directly applicable to the other region, and it must be validated using an in-situ ground-truth observation. We have tested 6 known CDOM algorithms (three Semi-analytical and three Empirical CDOM algorithms) developed from other regions of the world ocean with laboratory determined in-situ values for the East Sea using field data collected during seven oceanographic cruises in the period of 2009~2011. Our field measurements extended from the coastal waters to the open oceanic type CASE-1 Waters. Our study showed that Quasi-Analytical Algorithm (QAA_v5) derived $a_{CDOM}$(412) appears to match in-situ $a_{CDOM}$(412) values statistically. Semi-analytical algorithms appeared to underestimate and empirical ones overestimated $a_{CDOM}$ in the East Sea. $a_{CDOM}$(412) value was found to be relatively high in the relatively high satellite derived-chlorophyll-a area. $a_{CDOM}$(412) value appears to be influenced by the amount of chlorophyll-a in seawater. The outcome of this work may be referenced to develop $a_{CDOM}$ algorithm for the new Korean Geostationary Ocean Color Imager (GOCI).

• Journal of Biosystems Engineering
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• v.37 no.3
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• pp.177-183
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• 2012

Purpose: In near infrared spectroscopy, interactance configuration of a light source and a spectrometer probe can provide more information regarding fruit internal attributes, compared to reflectance and transmittance configuration. However, there is no through study on the parameters of interactance measurement setup. The objective of this study was to investigate the effect of the parameters on the estimation of soluble solids content (SSC) and firmness of muskmelons. Methods: Melon samples were taken from greenhouses at three different harvesting seasons. The prediction models were developed at three distances of 2, 5, and 8 cm between the light source and the spectrometer probe, three measurement points of 2, 3, and 6 evenly distributed on each sample, and different number of fruit samples for calibration models. The performance of the models was compared. Results: In the test at the three distances, the best results were found at a 5 cm distance. The coefficient of determination ($R_{cv}{^2}$) values of the cross-validation were 0.717 (standard error of prediction, SEP=$1.16^{\circ}Brix$) and 0.504 (SEP=4.31 N) for the estimation of SSC and firmness, respectively. The minimum measurement point required to fully represent the spectral characteristics of each fruit sample was 3. The highest $R_{cv}{^2}$ values were 0.736 (SEP=$0.87^{\circ}Brix$) and 0.644 (SEP=4.16 N) for the estimation of SSC and firmness, respectively. The performance of the models began to be saturated when 60 fruit samples were used for developing calibration models. The highest $R_{cv}{^2}$ of 0.713 (SEP=$0.88^{\circ}Brix$) and 0.750 (SEP=3.30 N) for the estimation of SSC and firmness, respectively, were achieved. Conclusions: The performance of the prediction models was quite different according to the condition of interactance measurement setup. In designing a fruit grading machine with interactance configuration, the parameters for interactance measurement setup should be chosen carefully.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.4 s.31
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• pp.3-12
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• 2004

It is necessary to manage water resources in rural areas in order to achieve proper development of new water resources, sustainable usage and reasonable distribution. This paper aims to analyze multi-temporal Landsat-7 ETM+data for soil moisture that is essential for crops in Ahnsung area. The ETM data was also fused with KOMPSAT-1 images in order to be used as backdrop watershed maps at first. Multi-temporal Images showed also the characteristics of soil moisture distribution. Images taken in April showed that rice paddy had as low reflectance as artificial features. Compared with April scenes, those taken in Hay and June showed wetness index increased in the rice paddies. The mountainous areas have almost constant moisture index, so the difference between the dates was very low while reservoirs and livers had dramatic changes. We can calculate total potential areas of distribution of moisture content within the basin and estimate the areas being sensitive to drought. Finally we can point out the sites of small rice paddies lack of water and visualize their distribution within the same basin. It can be said that multi-temporal Landsat-7 ETM+ and KOMPSAT data can be used to show broad drought with quick and simple analysis. Drought sensitiveness maps may enable the decision makers on rural water to evaluate the risk of drought and to measure mitigation, accompanied with proper data on the hydrological and climatic drought.