• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.1029-1040
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• 2017

Wildfires release a large amount of greenhouse gases (GHGs) into the atmosphere. Fire radiative power (FRP) data obtained from geostationary satellites can play an important role for tracing the GHGs. This paper describes an estimation of the Himawari-8 FRP and fire emissions for Samcheock and Gangnueng wildfire in 6 May 2017. The FRP estimated using Himawari-8 well represented the temporal variability of the fire intensity, which cannot be captured by MODIS (Moderate Resolution Imaging Spectroradiometer) because of its limited temporal resolution. Fire emissions calculated from the Himwari-8 FRP showed a very similar time-series pattern compared with the AirKorea observations, but 1 to 3 hour's time-lag existed because of the distance between the station and the wildfire location. The estimated emissions were also compared with those of a previous study which analyzed fire damages using high-resolution images. They almost coincided with 12% difference for Samcheock and 2% difference for Gangneung, demonstrating a reliability of the estimation of fire emissions using our Himawari-8 FRP without high-resolution images. This study can be a reference for estimating fire emissions using the current and forthcoming geostationary satellites in East Asia and can contribute to improving accuracy of meteorological products such as AOD (aerosol optical depth).

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.287-298
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• 2020

It is predicted that future climate warming will occur, and the subtropical climate zone currently confined to the south coast of Korea will gradually rise north. The shift of climate zone implies a change in area for cultivating crops. This study aimed to evaluate the current and future status of climate zones based on the high-resolution climate data of South Korea to prepare adaptation measures for cultivating crops under changing agricultural climate conditions. First, the climatic maps of South and North Korea were produced by using the high-resolution monthly maximum and minimum daily temperature and monthly cumulative precipitation produced during the past 30 years (1981-2010) covering South and North Korea. Then the climate zones of the Korean Peninsula were classified based on the Köppen climate classification. Second, the changes in climate zones were predicted by using the corrected monthly climate data of the Korean Peninsula (grid resolution 30-270m) based on the RCP8.5 scenario of the Korea Meteorological Administration. Köppen climate classification was applied based on the RCP8.5 scenario, the temperature and precipitation of the Korean Peninsula would continue to increase and the climate would become simpler. It was predicted that the temperate climate, appearing in the southern region of Korea, would be gradually expanded and the most of the Korean Peninsula, excluding some areas of Hamgkyeong and Pyeongan provinces in North Korea, would be classified as a temperate climate zone between 2071 and 2100. The subarctic climate would retreat to the north and the Korean Peninsula would become warmer and wetter in general.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.446-454
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• 2023

The impact of climate change on agriculture is substantial, especially as global warming is projected to lead to varying temperature and humidity patterns in the future. These changes pose a higher risk for both crops and livestock, exposing them to environmental stressors under altered climatic conditions. Specifically, as temperatures are expected to rise, the risk of heat stress is assessable through the Temperature-Humidity Index (THI), derived from temperature and relative humidity data. This study involved the comparison of THI collected from 10 Korea Meteorological Administration ASOS stations spanning a 60-year period from 1961 to 2020. Moreover, high-resolution temperature and humidity distribution data from 1981 to 2020 were employed to generate high-resolution TH I distributions, analyzing temporal changes. Additionally, the number of days characterized by heat stress, derived from TH I, was compared over different time periods. Generally, TH I showed an upward trend over the past, albeit with varying rates across different locations. As TH I increased, the frequency of heat stress days also rose, indicating potential future cost increases in the livestock industry due to heat-related challenges. The findings emphasize the feasibility of evaluating heat stress risk in livestock using THI and underscore the need for research analyzing THI under future climate change scenarios.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1803-1818
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• 2021

As the frequency of drought increases due to climate change, it is very important to have a monitoring system that can accurately determine the situation of widespread drought. However, while ground-based meteorological data has limitations in identifying all the complex droughts in Korea, satellite remote sensing data can be effectively used to identify the spatial characteristics of drought in a wide range of regions and to detect drought. This study attempted to analyze the possibility of using remote sensing data for drought identification in South Korea. In order to monitor various aspects of drought, remote sensing and ground observation data of precipitation and potential evapotranspiration, which are major variables affecting drought, were collected. The evaluation of the applicability of remote sensing data was conducted focusing on the comparison with the observation data. First, to evaluate the applicability and accuracy of remote sensing data, the correlations with observation data were analyzed, and drought indices of various aspects were calculated using precipitation and potential evapotranspiration for meteorological drought monitoring. Then, to evaluate the drought monitoring ability of remote sensing data, the drought reproducibility of the past was confirmed using the drought index. Finally, a high-resolution drought map using remote sensing data was prepared to evaluate the possibility of using remote sensing data for actual drought in South Korea. Through the application of remote sensing data, it was judged that it would be possible to identify and understand various drought conditions occurring in all regions of South Korea, including unmeasured watersheds in the future.

• Korean Journal of Remote Sensing
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• v.34 no.2_1
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• pp.237-248
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• 2018

The vertical distribution of hydrometeor before precipitation near the cloud base has been analyzed using a scanning lidar, rawinsonde data, and Cloud-Resolving Storm Simulator (CReSS). This study mostly focuses on 13 Desember 2016 only. The typical synoptic pattern of lake-effect snowstorm induced easterly in the Yeongdong region. Clouds generated due to high temperature difference between 850 hPa and sea surface (SST) penentrated in the Yeongdong region along with northerly and northeasterly, which eventually resulted precipitation. The cloud base height before the precipitation changed from 750 m to 1,280 m, which was in agreement with that from ceilometer at Sokcho. However, ceilometer tended to detect the cloud base 50 m ~ 100 m below strong signal of lidar backscattering coefficient. As a result, the depolarization ratio increased vertically while the backscattering coefficient decreased about 1,010 m~1,200 m above the ground. Lidar signal might be interpreted to be attenuated with the penetration depth of the cloud layer with of nonspherical hydrometeor (snow, ice cloud). An increase in backscattering signal and a decrease in depolarization ratio occured in the layer of 800 to 1,010 m, probably being associated with an increase in non-spherical particles. There seemed to be a shallow liquid layer with a low depolarization ratio (<0.1) in the layer of 850~900 m. As the altitude increases in the 680 m~850 m, the backscattering coefficient and depolarization ratio increase at the same time. In this range of height, the maximum value (0.6) is displayed. Such a result can be inferred that the nonspherical hydrometeor are distributed by a low density. At this time, the depolarization ratio and the backscattering coefficient did not increase under observed melting layer of 680 m. The lidar has a disadvantage that it is difficult for its beam to penetrate deep into clouds due to attenuation problem. However it is promising to distinguish hydrometeor morphology by utilizing the depolarization ratio and the backscattering coefficient, since its vertical high resolution (2.5 m) enable us to analyze detailed cloud microphysics. It would contribute to understanding cloud microphysics of cold clouds and snowfall when remote sensings including lidar, radar, and in-situ measurements could be timely utilized altogether.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.953-966
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• 2022

Since aerosols adversely affect human health, such as deteriorating air quality, quantitative observation of the distribution and characteristics of aerosols is essential. Recently, satellite-based Aerosol Optical Depth (AOD) data is used in various studies as periodic and quantitative information acquisition means on the global scale, but optical sensor-based satellite AOD images are missing in some areas with cloud conditions. In this study, we produced gap-free GeoKompsat 2A (GK-2A) Advanced Meteorological Imager (AMI) AOD hourly images after generating a Random Forest based gap-filling model using grid meteorological and geographic elements as input variables. The accuracy of the model is Mean Bias Error (MBE) of -0.002 and Root Mean Square Error (RMSE) of 0.145, which is higher than the target accuracy of the original data and considering that the target object is an atmospheric variable with Correlation Coefficient (CC) of 0.714, it is a model with sufficient explanatory power. The high temporal resolution of geostationary satellites is suitable for diurnal variation observation and is an important model for other research such as input for atmospheric correction, estimation of ground PM, analysis of small fires or pollutants.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.6
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• pp.11-20
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• 2020

We estimated the spatio-temporally distributed soil moisture using Sentinel-1A/B SAR (Synthetic Aperture Radar) sensor images and soil moisture data assimilation technique in South Korea. Soil moisture data assimilation technique can extract the hydraulic parameters of soils using observed soil moisture and GA (Genetic Algorithm). The SWAP (Soil Water Atmosphere Plant) model associated with a soil moisture assimilation technique simulates the soil moisture using the soil hydraulic parameters and meteorological data as input data. The soil moisture based on Sentinel-1A/B was validated and evaluated using the pearson correlation and RMSE (Root Mean Square Error) analysis between estimated soil moisture and TDR soil moisture. The soil moisture data assimilation technique derived the soil hydraulic parameters using Sentinel-1A/B based soil moisture images, ASOS (Automated Synoptic Observing System) weather data and TRMM (Tropical Rainfall Measuring Mission)/GPM (Global Precipitation Measurement) rainfall data. The derived soil hydrological parameters as the input data to SWAP were used to simulate the daily soil moisture values at the spatial domain from 2001 to 2018 using the TRMM/GPM satellite rainfall data. Overall, the simulated soil moisture estimates matched well with the TDR measurements and Sentinel-1A/B based soil moisture under various land surface conditions (bare soil, crop, forest, and urban).

• Journal of Astronomy and Space Sciences
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• v.26 no.4
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• pp.555-566
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• 2009

Precipitable water vapor (PWV) derived from a numerical weather prediction (NWP) model were compared to observations derived from ground-based Global Positioning System (GPS) receivers. The model data compared were from the Weather Research and Forecasting (WRF) model short-range forecasts on nested grids. The numerical experimets were performed by selecting the cloud microphysics schemes and for the comparisons, the Changma period of 2008 was selected. The observational data were derived from GPS measurements at 9-sites in South Korea over a 1-month period, in the middle of June-July 2008. In general, the WRF model demonstrated considerable skill in reproducing the temporal and spatial evolution of the PWV as depicted by the GPS estimations. The correlation between forecasts and GPS estimates of PWV depreciated slowly with increasing forecast times. Comparing simulations with a resolution of 18 km and 6 km showed no obvious PWV dependence on resolution. Besides, GPS and the model PWV data were found to be in quite good agreement with data derived from radiosondes. These results indicated that the GPS-derived PWV data, with high temporal and spatial resolution, are very useful for meteorological applications.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.687-701
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• 2014

Sea ice is generally accepted as an important factor to understand the process of earth climate changes and is the basis of earth system models for analysis and prediction of the climate changes. To continuously monitor sea ice changes at kilometer scale, it is demanded to create more accurate grid data from the current, limited sea ice data. In this paper we described a downscaling method for Advanced Microwave Scanning Radiometer 2 (AMSR2) Sea Ice Concentration (SIC) from 10 km to 1 km resolution using a weighting scheme of sea ice days ratio derived from Moderate Resolution Imaging Spectroradiometer (MODIS) sea ice cover product that has a high correlation with the SIC. In a case study for Okhotsk Sea, the sea ice areas of both data (before and after downscaling) were identical, and the monthly means and standard deviations of SIC exhibited almost the same values. Also, Empirical Orthogonal Function (EOF) analyses showed that three kinds of SIC data (ERA-Interim, original AMSR2, and downscaled AMSR2) had very similar principal components for spatial and temporal variations. Our method can apply to downscaling of other continuous variables in the form of ratio such as percentage and can contribute to monitoring small-scale changes of sea ice by providing finer SIC data.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.111-124
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• 2010

To monitor the environment of land surface change is considered as an important research field since those parameters are related with land use, climate change, meteorological study, agriculture modulation, surface energy balance, and surface environment system. For the change detection, many different methods have been presented for distributing more detailed information with various tools from ground based measurement to satellite multi-spectral sensor. Recently, using high resolution satellite data is considered the most efficient way to monitor extensive land environmental system especially for higher spatial and temporal resolution. In this study, we use two different spatial resolution satellites; the one is SPOT/VEGETATION with 1 km spatial resolution to detect coarse resolution of the area change and determine objective threshold. The other is Landsat satellite having high resolution to figure out detailed land environmental change. According to their spatial resolution, they show different observation characteristics such as repeat cycle, and the global coverage. By correlating two kinds of satellites, we can detect land surface change from mid resolution to high resolution. The K-mean clustering algorithm is applied to detect changed area with two different temporal images. When using solar spectral band, there are complicate surface reflectance scattering characteristics which make surface change detection difficult. That effect would be leading serious problems when interpreting surface characteristics. For example, in spite of constant their own surface reflectance value, it could be changed according to solar, and sensor relative observation location. To reduce those affects, in this study, long-term Normalized Difference Vegetation Index (NDVI) with solar spectral channels performed for atmospheric and bi-directional correction from SPOT/VEGETATION data are utilized to offer objective threshold value for detecting land surface change, since that NDVI has less sensitivity for solar geometry than solar channel. The surface change detection based on long-term NDVI shows improved results than when only using Landsat.