• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1465-1483
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• 2020

Evapotranspiration is a concept that includes the evaporation from soil and the transpiration from the plant leaf. It is an essential factor for monitoring water balance, drought, crop growth, and climate change. Actual evapotranspiration (AET) corresponds to the consumption of water from the land surface and the necessary amount of water for the land surface. Because the AET is derived from multiplying the crop coefficient by the reference evapotranspiration (ET0), an accurate calculation of the ET0 is required for the AET. To date, many efforts have been made for gridded ET0 to provide multiple products now. This study presents a comparison between the ET0 products such as FAO56-PM, LDAPS, PKNU-NMSC, and MODIS to find out which one is more suitable for the local-scale hydrological and agricultural applications in Korea, where the heterogeneity of the land surface is critical. In the experiment for the period between 2016 and 2019, the daily and 8-day products were compared with the in-situ observations by KMA. The analyses according to the station, year, month, and time-series showed that the PKNU-NMSC product with a successful optimization for Korea was superior to the others, yielding stable accuracy irrespective of space and time. Also, this paper showed the intrinsic characteristics of the FAO56-PM, LDAPS, and MODIS ET0 products that could be informative for other researchers.

• Journal of Korea Water Resources Association
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• v.55 no.1
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• pp.1-10
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• 2022

Irrigation water supplied to the paddy field is consumed in the amount of evapotranspiration, underground infiltration, and natural and artificial drainage from the paddy field. Irrigation return flow is defined as the excess of irrigation water that is not consumed by evapotranspiration and crop, and which returns to an aquifer by infiltration or drainage. The research on estimating the return flow play an important part in water circulation management of agricultural watershed. However, the return flow rate calculations are needs because the result of calculating return flow is different depending on irrigation channel water loss, analysis methods, and local characteristics. In this study, the irrigation return flow rate of agricultural watershed was estimated using the monitoring and SWMM (Storm Water Management Model) modeling from 2017 to 2020 for the Heungeop reservoir located in Wonju, Gangwon-do. SWMM modeling was performed by weather data and observation data, water of supply and drainage were estimated as the result of SWMM model analysis. The applicability of the SWMM model was verified using RMSE and R-square values. The result of analysis from 2017 to 2020, the average annual quick return flow rate was 53.1%. Based on these results, the analysis of water circulation characteristics can perform, it can be provided as basic data for integrated water management.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1125-1135
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• 2022

Damages by heavy snow does not occur very often, but when it does, it causes damage to a wide area. To mitigate snow damage, it is necessary to know, in advance, the depth of snow that causes damage in each region. However, snow depths are measured at observatory locations, and it is difficult to understand the spatial distribution of snow depth that causes damage in a region. To understand the spatial distribution of snow depth, the point measurements are interpolated. However, estimating spatial distribution of snow depth is not easy when the number of measured snow depth is small and topographical characteristics such as altitude are not similar. To overcome this limit, satellite images such as Synthetic Aperture Radar (SAR) can be analyzed using Differential Interferometric SAR (DInSAR) method. DInSAR uses two different SAR images measured at two different times, and is generally used to track minor changes in topography. In this study, the spatial distribution of snow depth was estimated by DInSAR analysis using dual polarimetric IW mode C-band SAR data of Sentinel-1B satellite operated by the European Space Agency (ESA). In addition, snow depth was estimated using geostationary satellite Chollian-2 (GK-2A) to compare with the snow depth from DInSAR method. As a result, the accuracy of snow cover estimation in terms with grids was about 0.92% for DInSAR and about 0.71% for GK-2A, indicating high applicability of DInSAR method. Although there were cases of overestimation of the snow depth, sufficient information was provided for estimating the spatial distribution of the snow depth. And this will be helpful in understanding regional damage-causing snow depth.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.3
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• pp.172-179
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• 2022

Sweet potato varieties with high fiber content in the storage root have poor texture when steamed or roasted. This study investigates the difference in fiber content among sweet potato varieties by soil and climate. The average fiber content of 'Hogammi', 'Sodammi', 'Pungwonmi', 'Danjami', and 'Jinyulmi' cultivars from the samples collected at farms in Haenam, Muan, and Unbong, Korea were 95.71, 66.73, 44.55, 40.55, and 38.53 mg/100g FW, respectively. There was no significant difference between site-specific conditions and varieties. Based on the degree of visual fibrousness, 'Hogammi' has an average of 3.6-4.0 with many thick stringy fibers. The fiber content of the 'Hogammi' cultivar was measured across 19 sites representing the main sweet potato growing regions of Korea. The fiber content was between 115.82 and 114.6 mg/100g in Haenam 2 and Boryeong 1, and 87.46 mg/100g in Hamyang. However, the fiber content at the remaining 16 sites was within the range of 94.63-108.52 mg/100g, although there were some site-level differences. The fiber content of the sweet potato storage roots were positively correlated with soil phosphorus (R² = 0.58^**), organic matter (R² = 0.52^*), and pH (R² = 0.51^*), which had a significance of 1% and 5%. The fiber content of sweet potato storage roots was found to have increased with increasing phosphorus content, organic matter and pH in the soil. However, there was no correlation with the amount of precipitation, days of precipitation and hours of sunshine with the fiber content of sweet potato at the selected sites.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.246-254
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• 2022

Domestic facility agriculture grows rapidly, such as modernization and large-scale. And the production scale increases significantly compared to the area, accounting for about 60% of the total agricultural production. Greenhouses require energy input to create an appropriate environment for stable mass production throughout the year, but the energy load per unit area is large because of low insulation properties. Through the rooftop greenhouse, one of the types of urban agriculture, energy that is not discarded or utilized in the building can be used in the rooftop greenhouse. And the cooling and heating load of the building can be reduced through optimal greenhouse operation. Dynamic energy analysis for various environmental conditions should be preceded for efficient operation of rooftop greenhouses, and about 40% of the solar energy introduced in the greenhouse is energy exchange for crops, so it should be considered essential. A major analysis is needed for each sensible heat and latent heat load by leaf surface temperature and evapotranspiration, dominant in energy flow. Therefore, an experiment was conducted in a rooftop greenhouse located at the Korea Institute of Machinery and Materials to analyze the energy exchange according to the growth stage of crops. A micro-meteorological and nutrient solution environment and growth survey were conducted around the crops. Finally, a regression model of leaf temperature and evapotranspiration according to the growth stage of leafy vegetables was developed, and using this, the dynamic energy model of the rooftop greenhouse considering heat transfer between crops and the surrounding air can be analyzed.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.4
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• pp.199-210
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• 2022

This study aimed to develop a cropping system to use limited crop-land with optimum efficiency, while considering management from farmers. To establish the cropping system involving a two-year rotation of three crops, three types of cropping system were evaluated in Suwon (Seogcheon series) and Anseong (Geumcheon series) in the middle plain area using six crops from 2018 to 2019: maize-perilla-onion, potato-sesame-garlic, and maize-sesame-onion. The crop productivity and income of the cropping systems involving food-, oilseed-, and horticultural crops were analyzed, and the optimal cropping system was reviewed. The total yield of each crop was as follows: maize 1,281 kg, potato 4,837 kg, perilla 125 kg, sesame 120 kg, onion 6,503 kg, and garlic 1,027 kg per 10a. However, in terms of gross profit, the potato was more than 3.8 times more profitable than corn, sesame was 1.8 times more profitable than perilla, and garlic was more than 2.8 times more profitable than onions. As a result, in terms of net income, the potato-sesame-garlic cropping system produced the highest income per unit area. Sesame seedlings were planted after the potato harvest, thereby solving the problem of competition between the first and last crops. Overall, this study confirmed that the potato-sesame-garlic cropping system, a two-year rotation of three crops, contributed to the improvement of upland crop productivity and farmers' income and was an overall effective cropping system.

• Korean Journal of Environment and Ecology
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• v.36 no.6
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• pp.592-602
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• 2022

This study aimed to check habitat distribution and analyze influencing factors by analyzing the mating calls of Auritibicen intermedius inhabiting limited locations in South Korea by applying bioacoustic detection techniques. The study sites were 20 protection areas nationwide. The mating call analysis period was 4 years from 2017 to 2021, excluding 2020. The bioacoustic recording system installed at each study site collected recordings of mating calls every day for 1 minute per hour. Climate data received from the Meteorological Agency, such as temperature, humidity, rainfall, cloudiness, and sunshine, were analyzed. The results of this study identified A. intermedius habitat only in four national parks in the highlands of Gangwon Province (Mt. Seorak, Mt. Odae, Mt. Chiak, and Mt. Taebak) out of 20 study sites. During the four years of study, the mating call period of A. intermedius was between August 5 and September 28, and the duration of the mating call was 31 to 52 days. The temperature analysis during the appearance period of A. intermedius showed that A. intermedius mainly produced mating calls at temperatures between 13.1℃ and 35.3℃, and the average temperature during the circadian cycle of mating calls (09:00 to 16:00) was 24.4 to 24.9℃. The analysis of the circadian cycle of mating calls at four study sites where A. intermedius appeared in 2019 showed that A. intermedius produced mating calls from 06:00 to 16:00 and that they peaked around 11:00 to 12:00. During the appearance period of A. intermedius, four species appeared in common: Hyalessa maculaticollis, Meimuna opalifera, Graptopsaltria nigrofuscata, and Suisha coreana. A logistic regression analysis confirmed that sunlight was the environmental factor affecting the mating call of A. intermedius. Regarding interspecific influence, it was confirmed that A. intermedius exchanged interspecific influence with 4 other common species (H. maculaticollis, M. opalifera, G. nigrofuscata, and S. coreana). The above results confirmed that A. intermedius habitats were limited in the highlands of Gangwon Province highlands in Korea and produced mating calls at a lower temperature compared to other species. These results can be used as basic data for future research on A. intermedius in Korea.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.526-535
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• 2021

Spatial sampling design plays an important role in GIS-based modeling studies because it increases modeling efficiency while reducing the cost of sampling. In the field of agricultural systems, research demand for high-resolution spatial databased modeling to predict and evaluate climate change impacts is growing rapidly. Accordingly, the need and importance of spatial sampling design are increasing. The purpose of this study was to design spatial sampling of paddy fields (11,386 grids with 1 km spatial resolution) in Korea for use in agricultural spatial modeling. A stratified random sampling design was developed and applied in 2030s, 2050s, and 2080s under two RCP scenarios of 4.5 and 8.5. Twenty-five weather and four soil characteristics were used as stratification variables. Stratification and sample allocation were optimized to ensure minimum sample size under given precision constraints for 16 target variables such as crop yield, greenhouse gas emission, and pest distribution. Precision and accuracy of the sampling were evaluated through sampling simulations based on coefficient of variation (CV) and relative bias, respectively. As a result, the paddy field could be optimized in the range of 5 to 21 strata and 46 to 69 samples. Evaluation results showed that target variables were within precision constraints (CV<0.05 except for crop yield) with low bias values (below 3%). These results can contribute to reducing sampling cost and computation time while having high predictive power. It is expected to be widely used as a representative sample grid in various agriculture spatial modeling studies.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.2
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• pp.47-58
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• 2023

The quality and yield of crops produced using field cultivation are expected to decrease due to the recent global climate change caused by extreme weather. The plant reproductive stage associated with crop yields is a highly vulnerable period to global warming caused by high temperatures. This study analyzed the adzuki bean's yield properties, antioxidant contents, and pollen viability of adzuki bean (Vigna angularis L.) under high-temperature stress and growth periods in a temperature gradient greenhouse that forms 0 to 4℃ above the outside temperatures. As the main variety of red beans cultivated in Korean farms, the "Arari" red bean was grown in the rain shield greenhouse and the temperature gradient greenhouse from 2021 to 2022 in Milyang, Korea. Compared to 2022, it showed a 0 - 1.0℃ lower temperature during the whole growth period in 2021. However, its average temperatures were 0 - 3.7℃ higher in the vegetative stage and 0.4 - 2.4℃ higher in the anthesis stage in 2021. The lowest yield (6.8 ± 0.7 g) was at the highest temperature (T4: low, 23.6℃; average, 28.5℃; high, 35.8℃) during the anthesis stage in 2021. The temperatures of the mature stage were 1.7 - 3.9℃, which were higher in 2022 than in 2021, although the low temperatures of 2022 were lower than in 2021. The yields of the mature stage in 2022 increased more than in 2021 because of the high temperature of the mature stage. The growth and yield were good at 40.5℃ in the vegetative stage. However, growth was poor when the average temperature was 27.0℃ or higher, and yields decreased during the flowering period. Total polyphenol and flavonoid contents were increased, and the pollen viability was 40.75% in the whole growth period at high temperature (T4: low, 22.9℃; average, 28.8℃; high, 36.9℃). These results showed that the antioxidant levels increased when the antioxidant component was affected at higher temperatures than at normal. In contrast, the pollen viability-related yield decreased as the temperature increased. Our results are the basic data for field growers and the breeding of thermos-tolerance in adzuki beans to prepare for the changeable future climate.