• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5B
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• pp.437-444
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• 2010

Soil moisture has been recognized as the essential parameter when understanding the complicated relationship between land surface and atmosphere in water and energy recycling system. It has been generally known that it is related with the temperature, wind, evaporation dependent on soil properties, transpiration due to vegetations and other constituents. There is, however, little research concerned about the relationship between soil moisture and these constitutes, thus it is needed to investigate it in detail. We estimated the soil moisture and then compared with field data using the hydrometerological data such as atmospheric temperature, specific humidity, and wind obtained from the Flux tower in Selmacheon, Korea. In the winter season, subterranean temperature showed highly positive correlation with soil moisture while it was negatively correlated from the spring to the fall. Estimation of seasonal soil moisture was compared with field measurements with the correlation of determination, R=0.82, 0.81, 0.82, and 0.96 for spring, summer, fall, and winter, respectively. Comprehensive relationship from this study can supply useful information about the downscaling of soil moisture with relatively large spatial resolutions, and will help to deepen the understanding of the water and energy recycling on the earth's surface.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.6
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• pp.430-438
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• 2009

An attempt was made to establish the suitability classes of lands for the cultivation of ginseng(Panax ginseng C. A. Meyer). For this study, the relationships between various soil characteristics and ginseng yields were investigated on altogether 450 ginseng fields (150 sites in paddy and 300 sites in upland), across Kangwon, Kyunggi, Chungbug, Chungnam, Jonbug and Kyungbug Provinces, where ginseng is widely cultivated. In the paddy fields, most influential properties of soil on the ginseng yields was found to be the drainage class. Texture of surface soil and available soil depths affected the ginseng yields to some extents. However, the topography, slope, and the gravel content were found not to affect the ginseng yields. In the uplands, the texture of surface soil was most influential and the topography, slope, and occurrence depth of hard-pan were least influential on the performance of the crop. Making use of multiple regression, by SAS, the contribution of soil morphological and physical properties such as, topography, surface soil texture, drainage class, slope, available soil depth, gravel content, and appearance depth of hard-pan, for the suitability of land for ginseng cultivation was analyzed. Based on the results of above analysis, adding up all of the suitability indices, land suitability classes for ginseng cultivation were proposed. On top of this, taking the weather conditions into consideration, suitability of land for ginseng cultivation was established in paddy field and in uplands. As an example, maps showing the distribution of suitable land for ginseng cultivation were drawn, adopting the land suitability classes obtained through current study, soil map, climate map, and GIS information, for Eumsung County, Chungbug Province. Making use of the information on the land suitability for ginseng cultivation obtained from current study, the suitability of lands currently under cultivation of ginseng was investigated. The results indicate that 74.0% of them in paddy field and 88.3% in upland are "highly suitable" and "suitable".

• Journal of Korean Society of Forest Science
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• v.107 no.1
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• pp.50-58
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• 2018

The purpose of this study was to investigate the effects of Aruncus dioicus on annual soil erosion reduction effect. Based on the measured soil erosion data, the cover factor of RUSLE was calculated. Comparing calculated the cover factor and Chewings fescue cover factor for soil erosion reduction, It found that cover crop Aruncus dioicus of reducing soil erosion was effective. The amount of soil erosion according to the type of Aruncus dioicus covering was 2.22 Mg/ha, Chewings fescue was 1.85 Mg/ha, 10.60 Mg/ha was produced in the Bare ground. Cover factor of Aruncus dioicus was $0.09{\pm}0.03$ according to the type of covering, Chewings fescue was $0.08{\pm}0.03$, Bare ground was $0.35{\pm}0.10$. Weeds control Bare ground was $0.83{\pm}0.14$. The results of the variance analysis of the cover factor for each covering were different according to the cover type. As a result of the classification of the same group through post - analysis, it was found that the Aruncus dioicus and Chewings fescue were similar to each other. Therefore, the Aruncus dioicus was effective to reduce the soil erosion to the extent that it was comparable to the Chewings fescue.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.19-19
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• 2019

토양수분은 지표수가 증발, 유출, 침투되는 과정에 중요한 역할을 하는 수문 인자로, 수문학적인 관점에서 물 순환을 이해하는 데 필수적인 요소이다. 그럼에도 불구하고 토양 내 수분을 측정하는 데 어려움이 많아 국내에서는 토양수분의 지속적인 관측을 위한 관측소 운영이 원활하게 이루어지지 않고 있으며, 주로 유전율식 계측 방식을 통해 지점 기반의 토양수분 자료를 생산하는데 그치고 있다. 최근 발사된 토양수분 위성인 SMAP (Soil Moisture Active Passive)을 비롯한 위성기반의 토양수분 자료와 융합하여 사용하기 위해서는 지점에서의 토양수분 네트워크가 우선적으로 구축되어야 하나, 관측소의 수도 부족할 뿐 아니라, 지형이 복잡하고 산지가 많은 한반도에서는 점 단위의 토양수분 자료의 공간적 대표성이 부족하여 활용에 어려움이 많다. 따라서 본 연구에서는 운영중인 지점 기반의 토양수분 관측소의 FDR (Frequency Domain Reflectometry), TDR (Time Domain Reflectometry) 센서를 함께 활용하여 산악지형에서의 Cosmic-ray 기반 토양수분자료를 생산하고자 한다. 산악지형에서의 Cosmic-ray 센서는 토양 유기물과 식생 차단 등에 의한 영향이 많으므로 평지에서 토양수분을 산정하기 위한 교정 함수들의 비교 및 평가를 실시하였다. 일반적으로 평지에서 활용되는 교정 함수들은 강우에 따른 토양수분의 거동을 잘 나타내고 있는 것으로 확인되었으나, 갑작스러운 강우로 인한 식생 차단과 토양 유기물의 영향이 커지는 경우 토양수분의 급격한 변동성을 표현하기에는 한계가 있는 것으로 나타났다. 이러한 연구를 기반으로 산악지형에서 Cosmic-ray 센서에 영향을 미치는 인자들을 분석할 수 있으며, 추후 산악지형에서 지역 규모의 토양수분을 관측할 수 있는 관측소를 구축하는데 활용될 것으로 기대된다.

• Spatial Information Research
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• v.20 no.3
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• pp.57-66
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• 2012

Gangwon-do has been suffering extensive landslide dam age, because its geography consists mainly of mountains. Analyzing the related factors is crucial for landslide prediction. We digitized the landslide and non-landslide spots on an aerial photo obtained right after a disaster in Inje, Gangwon-do. Three landslide factors-topographic, forest type, and soil factors-w ere statistically analyzed through GIS overlap analysis between topographic map, forest type map, and soil map. The analysis showed that landslides occurred mainly between the inclination of $20^{\circ}$ and $35^{\circ}$, and needleleaf tree area is more vulnerable to a landslide. About soil properties, an area with shallow effective soil depth and parent material of acidic rock has a greater chance of landslide.

• Korean Journal of Soil Science and Fertilizer
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• v.16 no.2
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• pp.112-118
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• 1983

Rainfall factor (R-factor), which is an index for the prediction of soil erosion in the Universal Soil Loss Equation (USLE), was computed from 21 years rainfall data at 51 locations in Korea. The values of R-factor are from 200 to 300 in the eastern part, and 300 to 700 in the western and southern part of the peninsula. Curvilinear regressions exist between annual rainfall and annual R-factor or between monthly rainfall and monthly R-factor. The R-factor can be estimated from the regression equation as a function of the amount of rainfall. According to the comparison between the actual soil loss measured by lysimeter and the soil loss predicted by the USLE, EI 30 for R-factor was recognized as a suitable factor for the USLE in korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.53-53
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• 2023

본 연구에서는 토양의 동결-융해 상태 구분을 위해 영상레이더(Synthetic Aperture Radar) 자료를 활용해 지표상태인자(Surface State Factor, SSF)를 산정하고, 관측 토양수분 자료 및 지표면 온도(Land Surface Temperature, LST) 자료와의 비교를 통해 SSF의 정확도를 분석하였다. SSF 산정은 용담댐 유역을 포함한 인근 40×50 km²의 영역(N35°35'~36°00', E127°20'~127°45')에 대한 9개의 토양수분 관측지점(계북, 천천, 상전, 안천, 부귀, 주천, 장수읍, 진안읍, 무주읍)을 대상으로 연구를 수행하였으며, 이를 위해 2015년부터 2019년까지의 해당 지점의 토양수분 관측자료와 Sentinel-1A Interferometric Wide swath (IW) 모드의 Ground Range Detected (GRD) product를 구축하여 활용하였다. SSF 자료의 정확도 분석을 위한 토양수분 관측지점에 대한 LST 자료는 인근 7개 기상관측소 지점(전주, 금산, 임실, 남원, 장수, 함양군, 거창)의 관측자료로부터 역거리가중법을 통해 산정하였다. Receiver Operating Characteristic (ROC) 분석을 통한 겨울철(12-2월)의 SSF 산정 정확도를 평가한 결과, 지표면 온도 자료와의 평균 정확도는 0.75(0.48-0.87)로 나타났다. 그러나, 지표면 온도가 0℃ 이상일 때 SSF가 동결 상태로 나타나는 오차가 관측되었으며, 이는 여름철 후방산란계수의 평균값과 겨울철 후방산란계수의 평균값을 통해 산정하는 SSF 산정 수식의 특성 때문으로 이 값의 조정을 통해 오차를 개선할 수 있음을 보였다.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.26-67
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• 2020

Synthetic Aperture Radar(SAR) is able to photograph the earth's surface regardless of weather conditions, day and night. Because of its possibility to search for hydrological factors such as soil moisture and groundwater, and its importance is gradually increasing in the field of water resources. SAR began to be mounted on satellites in the 1970s, and about 15 or more satellites were launched as of 2020, which around 10 satellites will be launched within the next 5 years. Recently, various types of SAR technologies such as enhancement of observation width and resolution, multiple polarization and multiple frequencies, and diversification of observation angles were being developed and utilized. In this paper, a brief history of the SAR system, as well as studies for estimating soil moisture and hydrological components were investigated. Up to now hydrological components that can be estimated using SAR satellites include soil moisture, subsurface groundwater discharge, precipitation, snow cover area, leaf area index(LAI), and normalized difference vegetation index(NDVI) and among them, soil moisture is being studied in 17 countries in South Korea, North America, Europe, and India by using the physical model, the IEM(Integral Equation Model) and the artificial intelligence-based ANN(Artificial Neural Network). RADARSAT-1, ENVISAT, ASAR, and ERS-1/2 were the most widely used satellite, but the operation has ended, and utilization of RADARSAT-2, Sentinel-1, and SMAP, which are currently in operation, is gradually increasing. Since Korea is developing a medium-sized satellite for water resources and water disasters equipped with C-band SAR with the goal of launching in 2025, various hydrological components estimation researches using SAR are expected to be active.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.103-104
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• 2017

본 연구에서는 다중회귀분석모형(MLRM)과 MODIS (MODerate resolution Imaging Spectroradiometer) LST (Land Surface Temperature) 자료를 이용하여 전국 공간토양수분을 산정하였다. 공간토양수분을 산정하기 위한 과정은 크게 두가지로 구분된다. 첫 번째로 기존의 MODIS LST 자료를 조건부 합성 보정기법을 적용하여 실측 LST 자료와 비교하여 위성 LST 자료가 갖고 있는 오차를 보정하였다. 그 결과, 조건부 합성 보정기법을 적용하기전 전국 71개 지상 관측지점에서 관측한 실측 LST와 MODIS LST의 R2는 전체 평균 0.70으로 어는정도 유의성 있는 상관관계를 나타냈으나 조건부 합성 보정기법을 적용한 후 실측 LST와 MODIS LST의 R2는 전체 평균 0.92로 상당히 크게 향상됨을 알 수 있었다. 두 번째로 보정된 MODIS LST를 이용하여 다중회귀분석 모형을 개발하고 토양수분을 예측하는 단계로 입력자료로 위성영상 자료와 관측자료를 융합하여 사용하였다. 위성영상 자료로는 보정된 MODIS LST와 MODIS NDV를 구축하였고 일단위 강수량 및 일조시간의 기상자료는 기상청으로부터 전국 68개 지점에 대해 구축하여 IDW 공간보간기법을 이용한 공간자료로 구축하였다. 토양수분 결과를 비교하기 위한 관측 토양수분은 자동농업기상관측(Automated Agriculture Observing System, AAOS)지점에서 2013년 1월부터 2015년 12월까지의 실측 일단위 토양수분 자료를 구축하여 사용하였다. 다중회귀분석 모형은 각각의 입력자료를 독립인자로서 조합하여 12개의 시나리오를 만들었다. 시공간적 경향을 고려하기 위하여 계절별, 토양 토성(soil texture)를 구분하여 회귀분석을 실시하였다. 관측 토양수분과 모의 토양수분을 비교한 결과 $R^2$가 0.80 (철원), 0.90 (춘천), 0.80 (수원), 0.63 (서산), 0.77 (청주), 0.82 (전주), 0.52 (순천), 0.63 (진주), 0.99 (보성)로 높은 상관성을 보였다. 본 연구에서는 토양수분을 예측하기 위한 인자 중 가장 민간함 LST를 보정하지 않는 토양수분 예측 방법은 상당한 오차를 포함하게 되어 실측 토양수분 결과와 크게 차이가 나타남을 보여주었다.

• Economic and Environmental Geology
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• v.56 no.3
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• pp.331-341
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• 2023

When creating a soil contamination map using geostatistical techniques, there are various sources that can affect prediction errors. In this study, a grid-based soil contamination map was created from the sampling data of heavy metal concentrations in soil in abandoned mine areas using Ordinary Kriging. Five factors that were judged to affect the prediction error of the soil contamination map were selected, and the variation of the root mean squared error (RMSE) between the predicted value and the actual value was analyzed based on the Leave-one-out technique. Then, using a machine learning algorithm, derived the top three factors affecting the RMSE. As a result, it was analyzed that Variogram Model, Minimum Neighbors, and Anisotropy factors have the largest impact on RMSE in the Standard interpolation. For the variogram models, the Spherical model showed the lowest RMSE, while the Minimum Neighbors had the lowest value at 3 and then increased as the value increased. In the case of Anisotropy, it was found to be more appropriate not to consider anisotropy. In this study, through the combined use of geostatistics and machine learning, it was possible to create a highly reliable soil contamination map at the local scale, and to identify which factors have a significant impact when interpolating a small amount of soil heavy metal data.