• 한국농공학회논문집
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• 제53권4호
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• pp.1-9
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• 2011

Forest area covers 64 % of the national land of Korea and the forest plays a pivotal role in the hydrological process such as flood, drought, runoff, infiltration, evapotranspiration, etc. In this study, soil moisture monitoring for conifer forest in experimental forest of Seoul National University has been conducted using FDR (Frequency Domain Reflection) for 6 different soil layers, 10, 20, 30, 60, 90 and 120 cm during 2009~2010, and precipitation data was collected from nearby AWS (Automatic Weather Station). Soil moisture monitoring data were used to estimate soil moisture recession constant (SMRC) for analyzing soil moisture recession characteristics. From the results, empirical soil moisture recession equations were estimated and validated to determine the feasibility of the result, and soil moisture contents of measured and calculated showed a similar tendency from April to November. Thus, the results can be applied for soil moisture estimation and provided the basic knowledge in forest soil moisture consumption. Nevertheless, this approach demonstrated applicability limitations during winter and early spring season due to freezing and melting of snow and ice causing peculiar change of soil moisture contents.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.26-28
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• 2008

Soil moisture plays an important role in the land-atmosphere energy balance because it governs the partitioning of energy through latent heat fluxes or evapotranspiration. From the numerous studies, it is evident that the L-band radiometer is a useful and effective tool to measure soil moisture. The objective of the study is to develop and to verify the soil moisture retrieval algorithms for the L-band radiometer system. Through the radiometer-observed brightness temperature, surface emissivity and reflectivity can be derived, and, hence, soil moisture. We collect field and L-band airborne radiometer data from washita92, SGP97 and SGP99 experiments to assist the development of the retrieval algorithms. Upon launching the satellite L-band radiometer such as ESA-sponsored SMOS (Soil Moisture and Ocean Salinity) mission, the developed algorithms may be used to study and monitor globe soil moisture change.

• 한국농공학회논문집
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• 제62권6호
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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).

• 대한원격탐사학회지
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• 제30권3호
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• pp.375-381
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• 2014

Soil moisture is an essential satellite-driven variable for understanding hydrologic, pedologic and geomorphic processes. The European Space Agency (ESA) has endorsed soil moisture as one of Climate Change Initiates (CCI) and had merged multi-satellites over 30 years. The $0.25^{\circ}$ coarse resolution soil moisture satellite data showed correlations with variables of a water stress index, Temperature-Vegetation Dryness Index (TVDI), from a stepwise regression analysis. The ancillary data from TVDI, Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from MODIS were inputted to a multi-regression analysis for estimating the surface soil moisture. The estimated soil moisture was validated with in-situ soil moisture data from April, 2012 to March, 2013 at Andong observation sites in South Korea. The soil moisture estimated using satellite-based LST and NDVI showed a good agreement with the observed ground data that this approach is plausible to define spatial distribution of surface soil moisture.

• 대한토목학회논문집
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• 제14권3호
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• pp.535-542
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• 1994

원격 측정 방법은 지표로부터 방출된 방사선의 관측으로부터 지표면의 특성을 추정하는데 기초를 두고 있으며 이 방법을 사용하여 연구되어지는 수문학적 인자에는 지표면 온도, 증발산, 토양 함수비, 강우 그리고 강설 등이 있다. 본 연구에서는 관측된 광명온도와 모의된 광명온도로부터 피복이 안된 토양의 함수비 산정에 대한 방법을 소개한다. 피복이 안된 토양에서의 초단파 방출은 함수비, 토양의 온도와 표면 조도에 의존하게 된다. 이 방법은 표면 조도의 영향을 고려하기 위한 Fresnel 반사 계수의 수정과 함께 방사선 전달 모델(radiative transfer model)에 기본을 두고 있는데 피복이 안된 매끈한 표면과 서로 다른 표면 조도를 가진 표면에 대해 분석을 실시한다. 연구의 결과는 표면 조도의 영향이 토양의 광명온도를 증가시키고 광명온도와 함수비 사이의 감소경사를 작게함을 보여주고 있다.

• 대한토목학회논문집
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• 제31권2B호
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• pp.155-163
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• 2011

본 연구에서는 의사결정나무(CART)기법, 인공신경망모형, 인공위성 원격탐사자료와 지형자료 및 지상 기상관측망자료를 이용하여 토양수분을 산정하는 모형을 개발하였다. 본 모형의 검증을 위하여 사용된 토양수분 관측자료는 용담댐 유역에서 관측된 5개 지점의 토양수분자료를 사용하였다. 가용자료에 대해 CART기법을 적용하여 자료를 분류한 다음 분류된 각 자료집단에 대하여 인공신경망(Artificial Neural Networks)모형을 적용하여 토양수분 분포를 예측하였다. 모형의 학습에 사용된 주천, 부귀, 상전, 안천 지점의 토양수분 산정치는 관측치와 약 0.92-0.96의 상관계수, 약 1.00-1.88%의 평균제곱근오차와 약 0.75-1.45%의 평균절대오차를 보여주었다. 토양수분 추정모형을 검증하기 위해 천천2의 지점에 적용한 결과 약 0.91의 상관계수, 약 3.19%의 평균제곱근오차, 약 2.72%의 평균절대오차를 보여 CART기법과 인공신경망모형을 연계한 토양수분 산정모형이 토양수분 분포제시 활용에 적절한 것으로 판단된다.

• 한국농공학회논문집
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• 제62권5호
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• pp.93-103
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• 2020

In this study, we estimated soil moisture values using the Deep Neural Network(DNN) scheme at the mountainous regions. In order to test the sensitive analysis of DNN scheme, we collected the measured(at the soil depths of 10 cm and 30 cm) soil moisture and DNN input(weather and land surface) data at the Pyeongchang-gun(relatively flat) and Geochang-gun(steep slope) sites. Our findings indicated that the soil moisture estimates were sensitive to the weather variables(5 days-averaged rainfall, 5 days precedent rainfall, accumlated rainfall) and DEM. These findings showed that the DEM and weather variables play the key role in the processes of soil water flow at the mountainous regions. We estimated the soil moisture values at the soil depths of 10 cm and 30 cm using DNN at two study sites under different climate-landsurface conditions. The estimated soil moisture(R: 0.890 and RMSE: 0.041) values at the soil depth of 10 cm were comparable with the measured data in Pyeongchang-gun site while the soil moisture estimates(R: 0.843 and RMSE: 0.048) at the soil depth of 30 cm were relatively biased. The DNN-based soil moisture values(R: 0.997/0.995 and RMSE: 0.014/0.006) at the soil depth of 10 cm/30 cm matched well with the measured data in Geochang-gun site. Although uncertainties exist in the results, our findings indicated that the DNN-based soil moisture estimation scheme demonstrated the good performance in estimating soil moisture values using weather and land surface information at the monitoring sites. Our proposed scheme can be useful for efficient land surface management in various areas such as agriculture, forest hydrology, etc.

• 한국토양비료학회지
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• 제47권4호
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• pp.235-241
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• 2014

A ground-based microwave scatterometer has an advantage for monitoring soil moisture content using multi-polarization, multi-frequencies and various incidence angles. In this paper, ground-based multi-frequency (L-, C-, and X-band) polarimetric scatterometer system capable of making observations every 10 min was used to monitor the soil moisture conditions in a corn field over an entire growth cycle. Measurements of volumetric soil moisture were obtained and their relationships to the backscatter observations were examined. Time series of soil moisture content was not corresponding with backscattering coefficient pattern over the whole growth stage, although it increased until early July (Day Of Year, DOY 160). We examined the relationship between the backscattering coefficients from each band and soil moisture content of the field. Backscattering coefficients for all bands were not correlated with soil moisture content when considered over the entire stage ($r{\leq}0.48$). However, L-band Horizontal transmit and Horizontal receive polarization (HH) had a good correlation with soil moisture ($r=0.85^{**}$) when LAI was lower than 2. Prediction equations for soil moisture were developed using the L-HH data. Relation between L-HH and soil moisture shows linear pattern and related with soil moisture content ($R^2=0.77$). Results from this study show that backscattering coefficients of microwave scatterometer appear to be effective to estimate soil moisture content in the field level.

• 한국물환경학회지
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• 제32권1호
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• pp.60-69
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• 2016

In this study, we estimated root zone soil moisture dynamics using remotely sensed (RS) data. A soil moisture data assimilation scheme was used to derive the soil and root parameters from MODerate resolution Imaging Spectroradiometer (MODIS) data. Based on the estimated soil/root parameters and weather forcings, soil moisture dynamics were simulated at spatio-temporal scales based on a hydrological model. For calibration/validation, the Little Washita (LW13) in Oklahoma and Chungmi-cheon/Seolma-cheon sites were selected. The derived water retention curves matched the observations at LW 13. Also, the simulated soil moisture dynamics at these sites was in agreement with the Time Domain Reflectrometry (TDR)-based measurements. To test the applicability of this approach at ungauged regions, the soil/root parameters at the pixel where the Seolma-cheon site is located were derived from the calibrated MODIS-based (Chungmi-cheon) soil moisture data. Then, the simulated soil moisture was validated using the measurements at the Seolma-cheon site. The results were slightly overestimated compared to the measurements, but these findings support the applicability of this proposed approach in ungauged regions with predictable uncertainties. These findings showed the potential of this approach in Korea. Thus, this proposed approach can be used to assess root zone soil moisture dynamics at spatio-temporal scales across Korea, which comprises mountainous regions with dense forest.

• 한국농공학회논문집
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• 제59권1호
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• pp.11-20
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• 2017

This study is to estimate the spatial soil moisture using multiple linear regression model (MLRM) and 15 minutes interval Land Surface Temperature (LST) data of Communication, Ocean and Meteorological Satellite (COMS). For the modeling, the input data of COMS LST, Terra MODIS Normalized Difference Vegetation Index (NDVI), daily rainfall and sunshine hour were considered and prepared. Using the observed soil moisture data at 9 stations of Automated Agriculture Observing System (AAOS) from January 2013 to May 2015, the MLRMs were developed by twelve scenarios of input components combination. The model results showed that the correlation between observed and modelled soil moisture increased when using antecedent rainfalls before the soil moisture simulation day. In addition, the correlation increased more when the model coefficients were evaluated by seasonal base. This was from the reverse correlation between MODIS NDVI and soil moisture in spring and autumn season.