• 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.54 no.4
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• pp.105-114
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• 2012

Soil moisture plays a pivotal role in hydrological processes, especially in the forest which covers more than 64% of the national land. Soil moisture was monitored to analyze soil moisture change characteristics in terms of time and soil layers in this study. 2 Years soil moisture change data was obtained from the experimental nut pine forest and statistical analysis including auto-correlation and cross-corelation among soil moisture data from different soil layers was conducted. Using the monitored soil moisture data, a relationship between soil moisture change and precipitation was analyzed and seasonal soil moisture change characteristics were analyzed. From the result of inter-relationships among soil layers in terms of season and time lag, soil moisture change characteristics in the nut pine forest were upper soil layers were much sensitive than lowers, and seasonal variation if soil moisture for upper soil layers were bigger than lowers showing low correlation with precipitation in winter and spring due to freezing and snowfalls.

• 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.

• Journal of Ecology and Environment
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• v.33 no.1
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• pp.47-57
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• 2010

To understand day-to-day fluctuations in soil moisture content in Seoul, I simulated daily soil moisture content from 1908 to 2009 using long-term climatic precipitation and temperature data collected at the Surface Synoptic Meteorological Station in Seoul for the last 98 years with a hydrological simulation model, BROOK. The output data set from the BROOK model allowed me to examine day-to-day fluctuations and the severity and duration of droughts in the Seoul area. Although the soil moisture content is highly dependent on the occurrence of precipitation, the pattern of changes in daily soil moisture content was clearly quite different from that of precipitation. Generally, there were several phases in the dynamics of daily soil moisture content. The period from mid-May to late June can be categorized as the initial period of decreasing soil moisture content. With the initiation of the monsoon season in late June, soil moisture content sharply increases until mid-July. From the termination of the rainy season in mid-July, daily soil moisture content decreases again. Highly stochastic events of typhoons from late June to October bring large amount of rain to the Korean peninsula, culminating in late August, and increase the soil moisture content again from late August to early September. From early September until early October, another sharp decrease in soil moisture content was observed. The period from early October to mid-May of the next year can be categorized as a recharging period when soil moisture content shows an increasing trend. It is interesting to note that no statistically significant increase in mean annual soil moisture content in Seoul, Korea was observed over the last 98 years. By simulating daily soil moisture content, I was also able to reconstruct drought phenomena to understand the severity and duration of droughts in Seoul area. During the period from 1908 to 2009, droughts in the years 1913, 1979, 1939, and 2006 were categorized as 'severe' and those in 1988 and 1982 were categorized as 'extreme'. This information provides ecologists with further potential to interpret natural phenomenon, including tree growth and the decline of tree species in Korea.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.4
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• pp.49-54
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• 2018

With increasing satellite-based RS(Remotely Sensed) techniques, RS soil moisture footprints have been providing for various purposes at the spatio-temporal scales in hydrology, agriculture, etc. However, their coarse resolutions still limit the applicability of RS soil moisture to field regions. To overcome these drawbacks, the LDA(Landsat-based Downscaling Algorithm) was developed to downscale RS soil moisture footprints from the coarse- to finer-scales. LDA estimates Landsat-based soil moisture($30m{\times}30m$) values in a spatial domain, and then the weighting values based on the Landsat-based soil moisture estimates were derived at the finer-scale. Then, the coarse-scale RS soil moisture footprints can be downscaled based on the derived weighting values. The LW21(Little Washita) site in Oklahoma(USA) was selected to validate the LDA scheme. In-situ soil moisture data measured at the multiple sampling locations that can reprent the airborne sensing ESTAR(Electronically Scanned Thinned Array Radiometer, $800m{\times}800m$) scale were available at the LW21 site. LDA downscaled the ESTAR soil moisture products, and the downscaled values were validated with the in-situ measurements. The soil moisture values downscaled from ESTAR were identified well with the in-situ measurements, although uncertainties exist. Furthermore, the SMAP(Soil Moisture Active & Passive, $9km{\times}9km$) soil moisture products were downscaled by the LDA. Although the validation works have limitations at the SMAP scale, the downscaled soil moisture values can represent the land surface condition. Thus, the LDA scheme can downscale RS soil moisture products with easy application and be helpful for efficient water management plans in hydrology, agriculture, environment, etc. at field regions.

• Journal of Environmental Science International
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• v.4 no.1
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• pp.1-12
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• 1995

The assessment for a degree of drought has been carried out based on the soil moisture index in Korea. The soil moisture index(Im) was calculated assuming of constant evapotranspiration until wilting point as Im = w2/wf $.$100. The soil moisture content(W2) at the final of a period is obtained from W2 : Pe + Wl - E, here the effective precipitation amount is Pe, evapotranspiration E, and the soil moisture content at the beginning of a period is Wl. The filed capacity(Wf), as a mean value of fine sandy loams, was reduced to 92 mm/ft when we accept the wilting point and the available soil moisture content of 42 mm/ft, respectively. The drought begins in Korea when the soil moisture index drops to less than 50%. The value coincides the isoline of 11 or more consecutive days without measureable precipitation. The soil moisture index frequently drops in the northern part of Youngnam area and Honam area so that both areas are well known as the areas of drought. Key word : Droughts, Soil Moisture Index.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.195-197
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• 2003

Soil moisture is a crucial variable in research works of hydrology, meteorology and plant sciences. Adequate soil moisture is essential for plant growth; excesses and deficits of soil moisture must be considered in agricultural practices. There are already several remote sensing methods used for monitoring soil moisture, such as thermal inertia, vegetation water-supplying index, crop water stress index and multi-factor regression. In this paper, an improved method has been discussed which is based on the thermal inertia. We analyzed the problems of monitoring soil moisture using satellites at first, and then put forward an simplified method which directly uses land surface temperature differences to measure soil moisture. Also we have taken the influence of vegetation into account, and import NDVI into the model. The method was used in the study of soil moisture in Heilongjiang Province, China, and we draw the conclusion by the experiments that the model can evidently increase the precision of monitoring soil moisture.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.2
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• pp.37-46
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• 2014

Water for crop growth were supplied by irrigation in protected cultivation and these are accumulated in the soil and utilized for crop evapotranspiration. The study for analyzing soil moisture characteristics is necessary for adequate irrigation water and soil water management in protected cultivation. Soil moisture content, irrigation water quantity and meteorological data were monitored to analyze soil moisture increment and extraction characteristics in terms of soil layers and cucumber crop growth stages. In first cropping period, the total amount of irrigation water was 5.07 mm/day, soil moisture increment was 4.82 mm/day and soil moisture extraction was 5.56 mm/day. In second cropping period, the total amount of irrigation water was 4.82 mm/day, soil moisture increment was 4.65 mm/day and soil moisture extraction was 4.73 mm/day. Soil moisture extraction rate from 0 to 75 cm is 90.3 % in first cropping period and 79.1 % in second cropping period. The majority irrigation water were consumed in root zone, however, about 15 % of soil moisture were losses by infiltration in lower soil layer. Soil moisture extraction and extraction pattern of cucumber crop calculated in this study can be utilized as a basic data for irrigation water management in protected cultivation.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.5
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• pp.77-84
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• 2003

Recent advances in remote sensing techniques provide the potential for monitoring soil color as well as soil moisture conditions at the spatial and temporal scales required for detailed local modeling efforts. Soil moisture as well as soil color is a key feature used in the identification and classification of soils. Soil spectral reflectance has a direct relationship with soil color, as well as to other parameters such as soil moisture, soil texture. and organic matter. We evaluate the influence of seven soil properties, soil color and soil moisture, on soil spectral reflectance. This paper presents the results obtained from the ground-truth spectral reflectance measurements in the 300-1100 nm wavelength range for various land surfaces. The results suggest that the reflectance properties of soils are related to soil color, soil texture, and soil moisture. Increasing soil moisture content generally decreases soil reflectance which leads to parallel curves of soil reflectance spectra across the entire shortwave spectrum. We discuss the relationships between the soil reflectance and the Munsell Soil Color Charts which contain standard color chips with colors specified by designations for hue, value, and chroma.

• Water Engineering Research
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• v.3 no.1
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• pp.31-44
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• 2002

The effect of diurnal cycle, intermittent visit of observation satellite, sensor installation, partial coverage of remote sensing, heterogeneity of soil properties and precipitation to the soil moisture estimation error were analyzed to present the global sampling strategy of soil moisture. Three models, the theoretical soil moisture model, WGR model proposed Waymire of at. (1984) to generate rainfall, and Turning Band Method to generate two dimensional soil porosity, active soil depth and loss coefficient field were used to construct sufficient two-dimensional soil moisture data based on different scenarios. The sampling error is dominated by sampling interval and design scheme. The effect of heterogeneity of soil properties and rainfall to sampling error is smaller than that of temporal gap and spatial gap. Selecting a small sampling interval can dramatically reduce the sampling error generated by other factors such as heterogeneity of rainfall, soil properties, topography, and climatic conditions. If the annual mean of coverage portion is about 90%, the effect of partial coverage to sampling error can be disregarded. The water retention capacity of fields is very important in the sampling error. The smaller the water retention capacity of the field (small soil porosity and thin active soil depth), the greater the sampling error. These results indicate that the sampling error is very sensitive to water retention capacity. Block random installation gets more accurate data than random installation of soil moisture gages. The Walnut Gulch soil moisture data show that the diurnal variation of soil moisture causes sampling error between 1 and 4 % in daily estimation.