• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.4
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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.

• Proceedings of the KSRS Conference
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• 2008.10a
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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.

• 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).

• Korean Journal of Remote Sensing
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• v.30 no.3
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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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.3
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• pp.535-542
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• 1994

Remote sensing technique is based on the estimation of land surface characteristics from the measurement of the emitted radiation from the earth. The hydrologically related parameters studied using this approach include surface temperature, evapotranspiration, soil moisture, precipitation and snow. This study introduces a method for estimating moisture content of a bare soil from the observed and simulated brightness temperature. In a bare soil, microwave emission depends on moisture content, soil temperature, and surface roughness. The method is based on a radiative transfer model with some modifications of Fresnel reflection coefficient to take into account the effect of surface roughness. One smooth bare field and two fields with different surface roughness are prepared for the study. The results indicate that the effect of surface roughness is to increase the soil's brightness temperature and to reduce the slope of regression between brightness temperature and moisture contents.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2B
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• pp.155-163
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• 2011

In this study, a soil moisture estimation model was developed using a decision tree model, an artificial neural networks (ANN) model, remotely sensed data, and ground network data of daily precipitation, soil moisture and surface temperature. Soil moisture data of the Yongdam dam basin (5 sites) were used for model validation. Satellite remote sensing data and geographical data and meteorological data were used in the classification and regression tree (CART) model for data classification and the ANNs model was applied for clustered data to estimate soil moisture. Soil moisture data of Jucheon, Bugui, Sangjeon, Ahncheon sites were used for training and the correlation coefficient between soil moisture estimates and observations was between 0.92 to 0.96, root mean square error was between 1.00 to 1.88%, and mean absolute error was between 0.75 to 1.45%. Cheoncheon2 site was used for validation. Test statistics showed that the correlation coefficient, the root mean square error, the mean absolute error were 0.91, 3.19%, and 2.72% respectively. Results demonstrated that the developed soil moisture model using CART and ANN was able to apply for the estimation of soil moisture distribution.

• Korean Journal of Soil Science and Fertilizer
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• v.47 no.4
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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.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.5
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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.

• Journal of Korean Society on Water Environment
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• v.32 no.1
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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.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.1
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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.