• 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.

• Journal of the Korean Geotechnical Society
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• v.28 no.11
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• pp.53-67
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• 2012

In this research, stainless steel and heat-shrinkage tube were used for new TDR sensing line to solve the problems of ordinary TDR system. The new TDR line improved the sensitivity of water content and endurance, and reduced the data noise. The saturation degree test and acryl model test were done by revised TDR sensor. From the results, without additional data filtering and quantitative analysis, the raw data were separated into 3 zones; saturated, unsaturated and dried zones easily. In addition, the revised TDR sensor was installed in large-scale embankment model to perform the seepage test. The raw data of the model tests showed the distributed seepage behaviors and separated zones clearly, which were almost the same tendencies as the lab test results.

• 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.).

• Journal of the Korean GEO-environmental Society
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• v.17 no.7
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• pp.5-13
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• 2016

This research is focused on the seepage behavior of embankment which had the weak zone with big permeability. The distributed TDR (Time Domain Reflectometer) and point sensors such as settlement gauge, pore water pressuremeter, vertical total stressmeter, and FDR (Frequency Domain Reflectometer) sensor were used to measure the seepage characteristics and embankment behavior. Also, the measured data were compared to the data of 2-D and 3-D numerical analysis. The dimension of model embankment was 7 m length, 5 m width and 1.5 m height, which is composed of fine-grained sands and the water level of embankment was 1.3 m height. The seepage behavior of measuring and numerical analysis were very similar, it means that the proper sensing system can monitor the real-time safety of embankment. The result by 2-D and 3-D numerical analysis showed similar saturation processing, however in case of weak zone, the phreatic lines of 2-D showed faster movement than that of 3-D analysis, and finally they converged.

• 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.

• 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 The Korean Society of Agricultural Engineers
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• v.60 no.1
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• pp.21-30
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• 2018

In this study, we developed the Soil Moisture Data Assimilation (SMDA) scheme to extract Effective Soil Characteristics-ESC (Sand, Silt, Clay %) from MODerate resolution Imaging Spectroradiometer (MODIS) products. The SMDA scheme was applied to the MODIS-based Soil Moisture (SM) data during the summer (July to September) period. Then the ESC and soil erosion factors (K) were predicted, respectively. Several numerical experiments were conducted to test the performance of SMDA at the study sites under the synthetic and field validation conditions. In the synthetic experiment, the estimated soil moistures values(R: >0.990 and RMSE: <0.005) were identified well with the synthetic observations. The field validation results at the Bangdongri and Chungmicheon sites were also comparable to the TDR-based measurements with the statistics (R: 0.772/0.000 and RMSE: 0.065/0.000). The estimated ESC values were also matched well with the measurements for the synthetic and field validation conditions. Then we tested the SMDA scheme to extract the ESC from the MODIS-based soil moisture products. Although uncertainties exist in the results, the estimated soil moisture and ESC based on the SMDA were comparable to the measurements. Overall, the K factors were similarly distributed based on the derived ESC. Also, the K factors in the mountainous regions were higher than those of the relatively flat areas. Thus, the newly developed SMDA scheme can be useful to estimate spatially and temporally-distributed soil erosion and establish soil erosion management plans.