• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1935-1943
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• 2022

The development and application of a high-resolution soil moisture mapping method using satellite imagery has been considered one of the major research themes in remote sensing. In this study, soil moisture mapping in the test area of Jeju Island was performed. The soil moisture was calculated with optical images using linearly adjusted Synthetic Aperture Radar (SAR) polarization images and incident angle. SAR Backscatter data, Analysis Ready Data (ARD) provided by Google Earth Engine (GEE), was used. In the soil moisture processing process, the optical image was applied to normalized difference vegetation index (NDVI) by surface reflectance of KOMPSAT-3 satellite images and the land cover map of Environmental Systems Research Institute (ESRI). When the SAR image and the optical images are fused, the reliability of the soil moisture product can be improved. To validate the soil moisture mapping product, a comparative analysis was conducted with normalized difference water index (NDWI) products by the KOMPSAT-3 image and those of the Landsat-8 satellite. As a result, it was shown that the soil moisture map and NDWI of the study area were slightly negative correlated, whereas NDWI using the KOMPSAT-3 images and the Landsat-8 satellite showed a highly correlated trend. Finally, it will be possible to produce precise soil moisture using KOMPSAT optical images and KOMPSAT SAR images without other external remotely sensed images, if the soil moisture calculation algorithm used in this study is further developed for the KOMPSAT-5 image.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.599-608
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• 2023

The increasing interest in soil moisture data using satellite data for applications of hydrology, meteorology, and agriculture has led to the development of methods for generating soil moisture maps of variable resolution. This study demonstrated the capability of generating soil moisture maps using Sentinel-1 and Sentinel-2 data provided by Google Earth Engine (GEE). The soil moisture map was derived using synthetic aperture radar (SAR) image and optical image. SAR data provided by the Sentinel-1 analysis ready data in GEE was applied with normalized difference vegetation index (NDVI) based on Sentinel-2 and Environmental Systems Research Institute (ESRI)-based Land Cover map. This study produced a soil moisture map in the research area of Victoria, Australia and compared it with field measurements obtained from a previous study. As for the validation of the applied method's result accuracy, the comparative experimental results showed a meaningful range of consistency as 4-10%p between the values obtained using the algorithm applied in this study and the field-based ones, and they also showed very high consistency with satellite-based soil moisture data as 0.5-2%p. Therefore, public open data provided by GEE and the algorithm applied in this study can be used for high-resolution soil moisture mapping to represent regional land surface characteristics.

• Journal of Korea Water Resources Association
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• v.33 no.1
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• pp.61-72
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• 2000

The purpose of this study is to improve the method of evaluating groundwater recharge by using grid-based soil moisture routing technique. A model which predicts temporal variation and spatial distribution of soil moisture on a daily time step was developed. The model uses ASCII-formatted map data supported by the irregular gridded map of the GRASS(Geographic Resources Analysis Support System)-GIS and can generate daily and monthly spatial distribution map of surface runoff, soil moisture content, evapotranspiration within the watershed. The model was applied to Ipyunggyo watershed($75.6\;\textrm{km}^2$) located in the upstream of Bocheongchun watershed. Seven maps; DEM(Digital Elevation Mode]), stream, flow path, soil, land use, Thiessen network and free groundwater level, were used for input data. Predicted streamflows resulting from two years (l995, 1996) daily data were compared with the observed values at the watershed outlet. The results of temporal variations and spatial distributions of soil moisture are presented by using GRASS GIS. As a final result, the monthly predicted groundwater recharge was presented.sented.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1185-1193
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• 2023

The increasing interest in soil moisture data from satellite imagery for applications in hydrology, meteorology, and agriculture has led to the development of methods to produce variable-resolution soil moisture maps. Research on accurate soil moisture estimation using satellite imagery is essential for remote sensing applications. The purpose of this study is to generate a soil moisture estimation map for a test area using KOMPSAT-3/3A and KOMPSAT-5 SAR imagery and to quantitatively compare the results with soil moisture data from the Soil Moisture Active Passive (SMAP) mission provided by NASA, with a focus on accuracy validation. In addition, the Korean Environmental Geographic Information Service (EGIS) land cover map was used to determine soil moisture, especially in agricultural and forested regions. The selected test area for this study is the western part of Jeju, South Korea, where input data were available for the soil moisture estimation algorithm based on the Water Cloud Model (WCM). Synthetic Aperture Radar (SAR) imagery from KOMPSAT-5 HV and Sentinel-1 VV were used for soil moisture estimation, while vegetation indices were calculated from the surface reflectance of KOMPSAT-3 imagery. Comparison of the derived soil moisture results with SMAP (L-3) and SMAP (L-4) data by differencing showed a mean difference of 4.13±3.60 p% and 14.24±2.10 p%, respectively, indicating a level of agreement. This research suggests the potential for producing highly accurate and precise soil moisture maps using future South Korean satellite imagery and publicly available data sources, as demonstrated in this study.

• Journal of Korea Water Resources Association
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• v.43 no.7
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• pp.597-608
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• 2010

In this study, a method for soil moisture estimation was proposed to obtain the nationwide soil moisture distribution map using on-site soil moisture observations, rainfall, surface temperature, NDVI, land cover, effective soil depth, and CART (Classification And Regression Tree) algorithm. The method was applied to the Yong-dam dam basin since the soil moisture data (4 sites) of the basin were reliable. Soil moisture observations of 3 sites (Bu-gui, San-jeon, Cheon-cheon2) were used for training the algorithm and 1 site (Gye-buk2) was used for the algorithm validation. The correlation coefficient between the observed and estimated data of soil moisture in the validation sites is about 0.737. Results show that even though there are limitations of the lack of reliable soil moisture observation for various land use, soil type, and topographic conditions, the soil moisture estimation method using ancillary data and CART algorithm can be a reasonable approach since the algorithm provided a fairly good estimation of soil moisture distribution for the study area.

• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.6
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• pp.33-43
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• 1999

Drought is a serious diaster in agriculutre, especially to upland crops. Hence, the Agricultural Drought Analysis Model (ADAM) that is integratable with GIS was applied to analyae agriculture drought in upland. ADAM is composed of two sub-models , one is a Soil Water Balance Model (SWBM) and the other is a Drougth Analysis Model (DAM) that is based on the Runs theory. The ADAM needs weather data, rainfall data and soil physical characteristics data as input and calculates daily soil moisture contents. GIS was integrated to the ADAM for the calculation of regional soil moisture using digitized landuse map, detaile dsoil map, thiessen network and district boundary . For the agriculutral drought analysis, the ADAM adapt the Runs theory for analyzing drought duration, severity and magnitude . Log-Pearson Type-III probability distribution function and Kolmogorov-Smirnov test were used to test the fitness of good of the model. The integration of ADAM with GIS was successfully implemented and would be operated effectively for the regional drought analysis.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.497-500
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• 2005

Research and development of remote sensing technique is necessary so that more accurate and extensive information may be obtained. To achieve this goal, the synthesized technique which integrates the high resolution optic and SAR image, and topographical information was examined to investigate the quantitative/qualitative characteristics of the Earth's surface environment. For this purpose, high-precision DEMs of Jeju-Island was generated and data fusion algorithm was developed in order to integrate the multi-spectral optic and polarimetric SAR image. Three dimensional land-cover and two dimensional soil moisture maps were generated conclusively so as to investigate the Earth's surface environments and extract the geophysical parameters.

• Korean Journal of Remote Sensing
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• v.26 no.1
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• pp.59-64
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• 2010

The second satellite in ESA's Earth Explorer series, the Soil Moisture and Ocean Salinity (SMOS) mission was launched into orbit at November 1, 2009. The SMOS will play a key role in the monitoring of climate change on a global scale using the payload of L-band synthetic aperture radiometer. It is the first ever satellite designed both to map sea surface salinity and to monitor soil moisture on a global scale, and will provide the important data to study the water cycle among oceans, the atmosphere and land. To introduce the operation of the SMOS, this paper shows brief summary of appearance and current operation.

• Korean Journal of Soil Science and Fertilizer
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• v.39 no.3
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• pp.123-135
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• 2006

Spatial variability and distribution map of soil properties and the relationships between soil properties and crop yields are not well characterized in agroecosystems that have been land leveled to facilitate more cultivation of the new reclaimed sloping highland. Potato, onion, carrot, Chinese cabbage and radish were grown on the coarse sandy loam soil in 2004. Soil moisture content, soil penetration resistance and crop yield were sampled in the $10m{\times}50m$ field consisted of five plots. Sampling sites of each cultivation plot were 33 for the soil moisture, 11 for the soil penetration and 33 for the crop yield. The results of semivariance analysis, most of models were shown spherical equation. The significant ranges of each spatial variability model for the soil moisture, soil penetration and crop yield were broad as 33-35 meters in the potato cultivation plot, and that in the Chinese cabbage cultivation plot was narrow as 5-6 meters. The coefficient of variances (C.V.) of moisture, penetration and yield were various from 14 to 59 percents in five cultivation plots. The highest C.V. of potato yield was 59 percents, and that of the radish cultivation plot was as low as 14 percents. The required sample numbers for the determination of soil moisture content, soil penetration resistance and crop yield with error 10% at 0.05 significant level were ranged 8-40 for soil moisture, 7-25 for soil penetration and 424-4,678 for crop yield. The variogram and distribution map by kriging described field characteristics well so that the spatial variability would be useful for soil management for better efficiency and precision agriculture in the reclaimed highland.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.315-315
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• 2017

In Japan, more than 80 % of soybean growing area is converted fields and excess water is one of the major problems in soybean production. For example, recent study (Yoshifuji et al., 2016) suggested that in the fields of shallow groundwater level (GWL) (< 1m depth), rising GWL even in a short period (e.g. 1 day) causes inhibition of soybean growth. Thus it becomes more and more important to predict GWL and soil moisture in detail. In addition to conventional surface drainage and underdrain, FOEAS (Farm Oriented Enhancing Aquatic System), which is expected to control GWL in fields adequately, has been developed recently. In this study we attempted to predict GWL and soil moisture condition at the converted field with FOEAS in Biwa lake reclamation area, Shiga prefecture, near the center of the main island of Japan. Two dimensional HYDRUS model (Simuinek et al., 1999) based on common Richards' equation, was used for the calculation of soil water movement. The calculation domain was considered to be 10 and 5 meter in horizontal and vertical direction, respectively, with two layers, i.e. 20cm-thick of plowed layer and underlying subsoil layer. The center of main underdrain (10 cm in diameter) was assumed to be 5 meter from the both ends of the domain and 10-60cm depth from the surface in accordance with the field experiment. The hydraulic parameters of the soil was estimated with the digital soil map in "Soil information web viewer" and Agricultural soil-profile physical properties database, Japan (SolphyJ) (Kato and Nishimura, 2016). Hourly rainfall depth and daily potential evapo-transpiration rate data were given as the upper boundary condition (B.C.). For the bottom B.C., constant upward flux, which meant the inflow flux to the field from outside, was given. Seepage face condition was employed for the surrounding of the underdrain. Initial condition was employed as GWL=60cm. Then we compared the simulated and observed results of volumetric water content at depth of 15cm and GWL. While the model described the variation of GWL well, it tended to overestimate the soil moisture through the growing period. Judging from the field condition, and observed data of soil moisture and GWL, consideration of soil structure (e.g. cracks and clods) in determination of soil hydraulic parameters at the plowed layer may improve the simulation results of soil moisture.