• Journal of Korea Water Resources Association
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• v.44 no.3
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• pp.169-177
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• 2011

The impacts of climate change on paddy irrigation water demands in Korea have been analyzed. High-resolution ($27{\times}27\;km$) climate data for the SRES A2 scenario produced by the Korean Meteorological Research Institute (METRI) and the observed baseline climatology dataset were used. The outputs from the ECHO-G GCM model were dynamically downscaled using the MM5 regional model by the METRI. The Geographic information system (GIS) was used to produce maps showing the spatial changes in irrigation water requirements for rice paddies. The results showed that the growing season mean temperature for future scenarios was projected to increase by $1.5^{\circ}C$ (2020s), $3.3^{\circ}C$ (2050s) and $5.3^{\circ}C$ (2080s) as compared with the baseline value (1971~2000). The growing season rainfall for future scenarios was projected to increase by 0.1% (2020s), 4.9% (2050s) and 19.3% (2080s). Assuming cropping area and farming practices remain unchanged, the total volumetric irrigation demand was projected to increase by 2.8% (2020s), 4.9% (2050s) and 4.5% (2080s). These projections are contrary to the previous study that used HadCM3 outputs and projected decreasing irrigation demand. The main reason for this discrepancy is the difference with the projected climate of the GCMs used. The temporal and spatial variations were large and should be considered in the irrigation water resource planning and management in the future.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1231-1244
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• 2022

The frequency of exposure of field crops to stress situations is increasing due to abnormal weather conditions. In South Korea, large-scale diseases in representative paddy rice cultivation area were happened. There are limits to field investigation on the crop damage due to large-scale. Satellite-based remote sensing techniques are useful for monitoring crops in cities and counties, but the sensitivity of vegetation index measured from satellite under abnormal growth of crop should be evaluated. The goal is to evaluate satellite-based normalized difference vegetation index (NDVI) retrieved from different spatial scales using drone imagery. In this study, Sentinel-2 and Landsat-8 satellites were used and they have spatial resolution of 10 and 30 m. Drone-based NDVI, which was resampled to the scale of satellite data, had correlation of 0.867-0.940 with Sentinel-2 NDVI and of 0.813-0.934 with Landsat-8 NDVI. When the effects of bias were minimized, Sentinel-2 NDVI had a normalized root mean square error of 0.2 to 2.8% less than that of the drone NDVI compared to Landsat-8 NDVI. In addition, Sentinel-2 NDVI had the constant error values regardless of diseases damage. On the other hand, Landsat-8 NDVI had different error values depending on degree of diseases. Considering the large error at the boundary of agricultural field, high spatial resolution data is more effective in monitoring crops.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.609-626
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• 2020

Satellite-based land surface temperature (LST) has been used as one of the major parameters in various climate and environmental models. Especially, Moderate Resolution Imaging Spectroradiometer (MODIS) LST is the most widely used satellite-based LST product due to its spatiotemporal coverage (1 km spatial and sub-daily temporal resolutions) and longevity (> 20 years). However, there is an increasing demand for LST products with finer spatial resolution (e.g., 10-250 m) over regions such as urban areas. Therefore, various methods have been proposed to produce high-resolution MODIS-like LST less than 250 m (e.g., 100 m). The purpose of this review is to provide a comprehensive overview of recent research trends and challenges for the downscaling of MODIS LST. Based on the recent literature survey for the past decade, the downscaling techniques classified into three groups-kernel-driven, fusion-based, and the combination of kernel-driven and fusion-based methods-were reviewed with their pros and cons. Then, five open issues and challenges were discussed: uncertainty in LST retrievals, low thermal contrast, the nonlinearity of LST temporal change, cloud contamination, and model generalization. Future research directions of LST downscaling were finally provided.

• The Korean Journal of Ecology
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• v.28 no.5
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• pp.245-254
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• 2005

This paper presents a paleoenvironmental study on Hoya Rincon do Parangueo, a maar lake in Valle de Santiago in Central Mexican Bajio. Maar lake sediments have been widely used for high-resolution reconstruction of paleoenvironment. Many different paleoenvironmental proxy data such as stable isotopes, pollen, sediment chemistry, and dung fungus spore were produced in this study. The pine-oak ratio, stable isotopes, and sediment chemistry help to reveal paleoenviromental changes throughout the whole period covered by sediment materials from this study site. The evidence I found indicates that during ca. 9,500 $\sim$ ca. 8,300 cal yr B.P. there was dry climate; during ca. 8,300 $\sim$ ca. 6,300 cal yr B.P. it was wetter; during ca. 6,300 $\sim$ ca. 4,000 cal yr B.P. drier and cooler; during ca. 4,000 $\sim$ ca. 1,100 cal yr B.P. milder and wetter. The presence of Chupicuaro culture between ca. 2,500 $\sim$ 1,100 cal yr B.P. is implied by the high frequencies of Amaranthaceae and Zea mars. It seems that man left this lake around 1,100 cal yr B.P. due to a dry climate after 1,300 cal yr B.P. Spanish arrival around 400 cal yr B.P. is implied by the fact that fe3 mars reappears and Sporormiella spp. become significant around 120 cm, whereas Poaceae drops sharply.

• Korean Journal of Remote Sensing
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• v.31 no.4
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• pp.347-351
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• 2015

Sea ice is a key parameter for understanding the climate change in cryosphere. In this study, we investigated the correlation with the factors that influenced change of the sea ice extent. We used the Sea Ice Concentration (SIC) from Ocean and Sea Ice Satellite Application Facility (OSI-SAF), and surface albedo provided by The Satellite Application Facility on Climate Monitoring (CM SAF). We converted the same temporal and spatial resolution of the data and detected the sea ice using SIC data. We performed the relationship analysis between SIC and sea ice albedo. As a result, we found they have a strong positive correlation. We performed the linear regression between SIC and sea ice albedo, and found they have high-level coefficient of determination. It shows using either SIC or sea ice albedo is possible to estimate the sea ice products.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.366-372
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• 2019

The PRISM model has been used to estimate precipitation in South Korea where observation data are readily available at a large number of weather station. However, it is likely that the PRISM model would result in relatively low reliability of precipitation estimates in North Korea where weather data are available at a relatively small number of weather stations. Alternatively, a hybrid method has been developed to estimate the precipitation distribution in area where availability of climate data is relatively low. In the hybrid method, Regression coefficients between the precipitation-terrain relationships are applied to a low-resolution precipitation map produced using the PRISM. In the present study, a hybrid approach was applied to North Korea for estimation of precipitation distribution at a high spatial resolution. At first, the precipitation distribution map was produced at a low-resolution (2,430m) using the PRISM model. Secondly, a deviation map was prepared calculating difference between altitudes of synoptic stations and virtual terrains produced using 270m-resolution digital elevation map (DEM). Lastly, another deviation map of precipitation was obtained from the maps of virtual precipitation produced using observation data from the synoptic weather stations and both synoptic and automated weather station (AWS), respectively. The regression equation between precipitation and terrain was determined using these deviation maps. The high resolution map of precipitation distribution was obtained applying the regression equation to the low-resolution map. It was found that the hybrid approach resulted in better representation of the effects of the terrain. The precipitation distribution map for the hybrid approach had similar spatial pattern to that for the existing method. It was estimated that the mean annual cumulative precipitation of entire territory of North Korea was 1,195mm with a standard deviation of 253mm.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.665-673
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• 2010

The role of remote sensing in phenological studies is increasingly regarded as a key in understanding large area seasonal phenomena. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for vegetation classification using seasonal variation patterns. The vegetation seasonal variation phase of Seoul and provinces in Korea was inferred using 8 day composite MODIS NDVI (Normalized Difference Vegetation Index) dataset of 2006. The seasonal vegetation classification approach is performed with reclassification of 4 categories as urban, crop land, broad-leaf and needle-leaf forest area. The BISE (Best Index Slope Extraction) filtering algorithm was applied for a smoothing processing of MODIS NDVI time series data and fuzzy classification method was used for vegetation classification. The overall accuracy of classification was 77.5% and the kappa coefficient was 0.61%, thus suggesting overall high classification accuracy.

• Journal of Korean Society of Rural Planning
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• v.27 no.4
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• pp.33-41
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• 2021

Ammonia emission from the agricultural sector contributes almost 78% of total ammonia emission in Korea. The current ammonia emission estimation method from fertilizer application has high uncertainty and needs to be improved. In this study, we propose an improvement method for estimating the amount of ammonia emission from agricultural land with improved spatiotemporal resolution using Farm Manager Registration Information System and criteria for the fertilizer. We calculated ammonia emissions by utilizing the 2020 cultivation area provided by Farm Manager Registration Information System for 55 kinds of upland crops cultivated in the field area of the farmland. As a result, soybeans were the most cultivated field crop in 2020, and the area of cultivated land was surveyed at about 77,021 ha, followed by sweet potatoes 22,057 ha, garlic 20004 ha, potatoes 17,512 ha, and corn 16,636 ha. The month with the highest ammonia emissions throughout the year was calculated by emitting 590.01 ton yr-1 in May, followed by 486.55 ton yr-1 in March. Hallim-eup in Jeju showed the highest ammonia emission at 117.50 ton yr-1.

• Journal of the Korean earth science society
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• v.45 no.4
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• pp.327-337
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• 2024

The ocean is linked to long-term climate variability, but there are very few methods to assess the short-term performance of forecast models. This study analyzes the short-term prediction performance regarding ocean temperature and salinity of the Global Seasonal prediction system version 5 (GloSea5). GloSea5 is a historical climate re-creation (2001-2010) performed on the 1st, 9th, 17th, and 25th of each month. It comprises three ensembles. High-resolution hindcasts from the three ensembles were compared with the Array for Real-Time Geostrophic Oceanography (ARGO) float data for the period 2001-2010. The horizontal position was preprocessed to match the ARGO float data and the vertical layer to the GloSea5 data. The root mean square error (RMSE), Brier Score (BS), and Brier Skill Score (BSS) were calculated for short-term forecast periods with a lead-time of 10 days. The results show that sea surface temperature (SST) has a large RMSE in the western boundary current region in Pacific and Atlantic Oceans and Antarctic Circumpolar Current region, and sea surface salinity (SSS) has significant errors in the tropics with high precipitation, with both variables having the largest errors in the Atlantic. SST and SSS had larger errors during the fall for the NINO3.4 region and during the summer for the East Sea. Computing the BS and BSS for ocean temperature and salinity in the NINO3.4 region revealed that forecast skill decreases with increasing lead-time for SST, but not for SSS. The preprocessing of GloSea5 forecasts to match the ARGO float data applied in this study, and the evaluation methods for forecast models using the BS and BSS, could be applied to evaluate other forecast models and/or variables.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.3
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• pp.175-190
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• 2024

In order to project changes in climate zones across the Korean Peninsula, the Trewartha's climate classification was applied to the SSP-RCP scenario data with a 1km resolution produced by the National Institute of Agricultural Sciences of the Rural Development Administration. Currently, most of the Korean Peninsula (92.3%) belongs to the temperate climate type (D), whereas only some areas (4.9%), such as Jeju Island, belongs to the subtropical climate type (C). According to SSP-RCP scenarios, the temperature is expected to gradually increase due to the influence of global warming during the 21st century, and the subtropical climate type is expected to expand to 14.1 to 48.6% of the total area of the Korean Peninsula in the far future. On the other hand, the temperate zone, which is currently most dominant on the Korean Peninsula, is expected to shrink by 85.8 to 51.4% in the late 21st century. If carbon dioxide emissions continue at the current rate, the entire Korean Peninsula will likely be dominated by subtropical and temperate regions in the distant future. In particular, the subtropical climate type is expected to dominate most of South Korea in the high-carbon scenario, except for highlands.