• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.5
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• pp.89-99
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• 2019

In this study, we estimated the spatially-distributed soil moisture at the high resolution ($10m{\times}10m$) using the satellite-based Sentinel-1A/B SAR (Synthetic Aperture Radar) sensor images. The Sentinel-1A/B raw data were pre-processed using the SNAP (Sentinel Application Platform) tool provided from ESA (European Space Agency), and then the pre-processed data were converted to the backscatter coefficients. The regression equations were derived based on the relationships between the TDR (Time Domain Reflectometry)-based soil moisture measurements and the converted backscatter coefficients. The TDR measurements from the 51 RDA (Rural Development Administration) monitoring sites were used to derive the regression equations. Then, the soil moisture values were estimated using the derived regression equations with the input data of Sentinel-1A/B based backscatter coefficients. Overall, the soil moisture estimates showed the linear trends compared to the TDR measurements with the high Pearson's correlations (more than 0.7). The Sentinel-1A/B based soil moisture values matched well with the TDR measurements with various land surface conditions (bare soil, crop, forest, and urban), especially for bare soil (R: 0.885~0.910 and RMSE: 3.162~4.609). However, the Mandae-ri (forest) and Taean-eup (urban) sites showed the negative correlations with the TDR measurements. These uncertainties might be due to limitations of soil surface penetration depths of SAR sensors and complicated land surface conditions (artificial constructions near the TDR site) at urban regions. These results may infer that qualities of Sentinel-1A/B based soil moisture products are dependent on land surface conditions. Although uncertainties exist, the Sentinel-1A/B based high-resolution soil moisture products could be useful in various areas (hydrology, agriculture, drought, flood, wild fire, etc.).

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1998.10a
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• pp.65-70
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• 1998

Soil moisture measuring is important for irrigation scheduling of upland crops, estimation of evapotranspiration, and hydrologic modeling. Hence, the comparative study was implemented for the soil moisture measuring instruments, Neutron probe and TDR with soil sampling methods, and the result was represented.

• Journal of Korea Water Resources Association
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• v.32 no.3
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• pp.215-223
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• 1999

Recently, measurement of soil moisture contents using TDR (time domain reflectometry) has been proven to be viable technique. The first empirical model proposed by Topp et al. (1980) has been widely used to determine moisture contents of soils from the TDR-measured dielectric constants. However, applicability of the model was limited to medium-textured soils. In this study, we investigate the applicability of the model to sandy-gravelly soils. Calibration experiments consisted of measurement on travel time of electromagnetic waveform along the parallel TDR rods inserted into samples and gravimetric determination of soil moisture contents. The experiments are performed for two sets of samples different in the length and each set consisted of seven different particle size distributions with various gavel contents. The calibration results show that the Topp equation overestimated the measured moisture content for a given dielectric constant by 3 to 8%. We therefore propose new empirical relationships valid for sandy-gravelly soils.

• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.1
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• pp.86-96
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• 1999

Laboratory experiment was performed for the TDR to measure the soil moisture, and the results, were compared with the design water content and the one measured by oven-try method. Sand and kaolinite were used . Varaiables for the experiment were water content (10-50%), void ration (0.7 -1.3), mixture rate of kaolinite (10-30%), and measurement methods (TDR and oven-dry). In all cases , TDR method showed very accurage and reliable results , and average error and error range were far lews than the oven-dry method which is widely used. Considerable error was noticed when water contnet was 50% where saturation was achieved for both methods. Therefore, TDR was thought to be applicable to the field moisture measurement if it is unsaturated. For field scale application of TDR, more research and verification of the accuracy with diverse soil conditions including physical ,chemical and mineral properties are recommended.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1998.10a
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• pp.407-413
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• 1998

Considerable progress has been made in the application of time-domain reflectrometry(TDR) to measurement of soil water content. The TDR provides a means of monitoring the water content of soil over a wide range of values, in the field and in the laboratory The TDR measures the volumetric moisture content of the soil via a bureid sensor(probe). Probes can be buried and monitored remotely and an immediate result can be obtained. In addition to, the results are very reliable.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.1
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• pp.57-70
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• 2017

In this study, we evaluated daily root zone soil moisture dynamics and agricultural drought using a near-surface soil moisture data assimilation scheme with Soil Moisture Active & Passive (SMAP, $3km{\times}3km$) soil moisture footprints under different hydro-climate conditions. Satellite-based LANDSAT and MODIS image footprints were converted to spatially-distributed soil moisture estimates based on the regression model, and the converted soil moisture distributions were used for assessing uncertainties and applicability of SMAP data at fields. In order to overcome drawbacks of the discontinuity of SMAP data at the spatio-temporal scales, the data assimilation was applied to SMAP for estimating daily soil moisture dynamics at the spatial domain. Then, daily soil moisture values were used to estimate weekly agricultural drought based on the Soil Moisture Deficit Index (SMDI). The Yongdam-dam and Soyan river-dam watersheds were selected for validating our proposed approach. As a results, the MODIS/SMAP soil moisture values were relatively overestimated compared to those of the TDR-based measurements and LANDSAT data. When we applied the data assimilation scheme to SMAP, uncertainties were highly reduced compared to the TDR measurements. The estimated daily root zone soil moisture dynamics and agricultural drought from SMAP showed the variability at the sptio-temporal scales indicating that soil moisture values are influenced by not only the precipitation, but also the land surface characteristics. These findings can be useful for establishing efficient water management plans in hydrology and agricultural drought.

• Smart Structures and Systems
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• v.6 no.9
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• pp.1007-1023
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• 2010

This paper describes the development and evaluation of an innovative TDR distributed moisture sensor. This sensor features advantages of being responsive to the spatial variations of the soil moisture content. The geometry design of the sensor makes it rugged for field installation. Good linear calibration is obtained between the sensor measured dielectric constant and soil physical properties. Simulations by the finite element method (FEM) are conducted to assist the design of this sensor and to determine the effective sampling range. Compared with conventional types of moisture sensor, which only makes point measurement, this sensor possesses distributed moisture sensing capability. This new sensor is not only easy to install, but also measures moisture distribution with much lower cost. This new sensor holds promise to significantly improve the current field instruments. It will be a useful tool to help study the influence of a variety of moisture-related phenomena on infrastructure performance.

• Korean Journal of Agricultural and Forest Meteorology
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• v.7 no.1
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• pp.78-90
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• 2005

The spatial and temporal distribution of soil moisture was characterized from soil moisture data through the intensive monitoring using Time Domain Reflectometry (TDR). The recession of soil moisture after a rainfall event was characterized and the empirical equation was used in the recession curve analysis. Recession analysis provides features of soil moisture variation such as recharge and stability depending upon locations of monitoring. The wetness index was useful for explaining spatial and temporal distributions of soil moisture and recession characteristics at hillslope scale.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09a
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• pp.404-415
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• 2010

In this paper, using the TDR sensors, variation of soil water content changes were measured as TDR data. Then filtering technique was determined using Fourier transform. Determine the moisture content of soil and ground water level and tried to determine unsaturated zone. First, variation of water content changes were measured TDR data by indoor experiment. Then as a function of TDR data made for water content of soil. Next, through Acrylic indoor laboratory model experiments, changes in ground water levels and lateral penetration of the field conditions were reproduced in an indoor. Field applicability of the TDR sensor was demonstrated by analysis of this. TDR sensor was installed in the embankment, TDR data were measured by TDR sensor.

• 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).