• Title/Summary/Keyword: Soil Moisture

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Radar Remote Sensing of Soil Moisture and Surface Roughness for Vegetated Surfaces

  • Oh, Yi-Sok
    • Korean Journal of Remote Sensing
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    • v.24 no.5
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    • pp.427-436
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    • 2008
  • This paper presents radar remote sensing of soil moisture and surface roughness for vegetated surfaces. A precise volume scattering model for a vegetated surface is derived based on the first-order radiative transfer technique. At first, the scattering mechanisms of the scattering model are analyzed for various conditions of the vegetation canopies. Then, the scattering model is simplified step by step for developing an appropriate inversion algorithm. For verifying the scattering model and the inversion algorithm, the polarimetric backscattering coefficients at 1.85 GHz, as well as the ground truth data, of a tall-grass field are measured for various soil moisture conditions. The genetic algorithm is employed in the inversion algorithm for retrieving soil moisture and surface roughness from the radar measurements. It is found that the scattering model agrees quite well with the measurements. It is also found that the retrieved soil moisture and surface roughness parameters agree well with the field-measured ground truth data.

Measurements of dielectric constants of soil to develop a landslide prediction system

  • Rhim, Hong Chul
    • Smart Structures and Systems
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    • v.7 no.4
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    • pp.319-328
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    • 2011
  • In this study, the measurements of the dielectric constants of soil at 900 MHz and 1 GHz were made to relate those properties to the moisture content of the soil. This study's intention was to use the relationship between the dielectric constant and the moisture content to develop a landslide prediction system. By monitoring the change of the moisture content within the soil using ground penetrating radar (GPR) systems in the field, the possibility of a landslide is expected to be detected. To establish a database for the dielectric constants and the moisture content, the measurements of soil samples were made using both an open-ended dielectric coaxial probe and the GPR. Based on the measurement results, correlations between the GPR and reflector for each frequency at 900 MHz and 1 GHz were found for the dielectric constants and the moisture content. Finally, the mechanism of the measurement device to be implemented in the field is suggested.

Effects of Moisture, Temperature, and Characteristics of two Soils on Imazamethabenz Degradation (토양 수분, 온도, 특성이 imazamethabenz 분해에 미치는 영향)

  • Joo, Jin-H.
    • Korean Journal of Soil Science and Fertilizer
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    • v.34 no.4
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    • pp.245-254
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    • 2001
  • Effects of soil moisture and temperature on the degradation rate of imazamethabenz were studied in two soils, a Declo sandy loam soil with 1.5% organic matter and pH of 8.0, and a Pancheri silt loam soil with 2.1% organic matter and pH of 7.7. Soils were incubated for 12 weeks under controlled conditions. Treatments were a factorial arrangements with combinations of three soil moistures (45, 75, 100% of field capacity) and two soil temperatures (20, 30C). Imazamethabenz degradation followed first-order kinetics for all soil moisture-soil temperature combinations. Imazamethabenz degradation rate was proportional to increase of soil moisture and temperature. Soil moisture effect on imazamethabenz degradation was greater when soil moisture was increased from 45 to 75% of field capacity (half-life decreased 2.6 fold) than when moisture increased from 75 to 100% of field capacity (half-life decreased 1.2 fold). Imazamethabenz degradation occurred more rapidly in the Pancheri silt loam than the Declo sandy loam soil. Formation of imazamethabenz acid from imazamethabenz followed a quadratic trend for most soil-moisture-soil temperature combinations. Imazamethabenz acid formation initially increased at earlier stages, but later gradually decreased. In most cases, increasing soil moisture and temperature appeared to accelerate it's acid breakdown to other metabolites.

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Spatial Variability of Soil Moisture and Irrigation Scheduling for Upland Farming (노지 작물의 적정 관개계획을 위한 토양수분의 공간변이성 분석)

  • Choi, Yonghun;Kim, Minyoung;Kim, Youngjin;Jeon, Jonggil;Seo, Myungchul
    • Journal of The Korean Society of Agricultural Engineers
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    • v.58 no.5
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    • pp.81-90
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    • 2016
  • Due to droughts and water shortages causing severe damage to crops and other vegetations, much attention has been given to efficient irrigation for upland farming. However, little information has been known to measure soil moisture levels in a field scale and apply their spatial variability for proper irrigation scheduling. This study aimed to characterize the spatial variability and temporal stability of soil water contents at depths of 10 cm, 20 cm and 30 cm on flat (loamy soil) and hill-slope fields (silt-loamy soil). Field monitoring of soil moisture contents was used for variogram analysis using GS+ software. Kriging produced from the structural parameters of variogram was applied for the means of spatial prediction. The overall results showed that the surface soil moisture presented a strong spatial dependence at the sampling time and space in the field scale. The coefficient variation (CV) of soil moisture was within 7.0~31.3 % in a flat field and 8.3~39.4 % in a hill-slope field, which was noticeable in the dry season rather than the rainy season. The drought assessment analysis showed that only one day (Dec. 21st) was determined as dry (20.4 % and 24.5 % for flat and hill-slope fields, respectively). In contrary to a hill-slope field where the full irrigation was necessary, the centralized irrigation scheme was appeared to be more effective for a flat field based on the spatial variability of soil moisture contents. The findings of this study clearly showed that the geostatistical analysis of soil moisture contents greatly contributes to proper irrigation scheduling for water-efficient irrigation with maximal crop productivity and environmental benefits.

Verification of Mid-/Long-term Forecasted Soil Moisture Dynamics Using TIGGE/S2S (TIGGE/S2S 기반 중장기 토양수분 예측 및 검증)

  • Shin, Yonghee;Jung, Imgook;Lee, Hyunju;Shin, Yongchul
    • Journal of The Korean Society of Agricultural Engineers
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    • v.61 no.1
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    • pp.1-8
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    • 2019
  • Developing reliable soil moisture prediction techniques at agricultural regions is a pivotal issue for sustaining stable crop productions. In this study, a physically-based SWAP(Soil-Water-Atmosphere-Plant) model was suggested to estimate soil moisture dynamics at the study sites. ROSETTA was also integrated to derive the soil hydraulic properties(${\alpha}$, n, ${\Theta}_r$, ${\Theta}_s$, $K_s$) as the input variables to SWAP based on the soil information(Sand, Silt and Clay-SSC, %). In order to predict the soil moisture dynamics in future, the mid-term TIGGIE(THORPEX Interactive Grand Global Ensemble) and long-term S2S(Subseasonal to Seasonal) weather forecasts were used, respectively. Our proposed approach was tested at the six study sites of RDA(Rural Development Administration). The estimated soil moisture values based on the SWAP model matched the measured data with the statistics of Root Mean Square Error(RMSE: 0.034~0.069) and Temporal Correlation Coefficient(TCC: 0.735~0.869) for validation. When we predicted the mid-/long-term soil moisture values using the TIGGE(0~15 days)/S2S(16~46 days) weather forecasts, the soil moisture estimates showed less variations during the TIGGE period while uncertainties were increased for the S2S period. Although uncertainties were relatively increased based on the increased leading time of S2S compared to those of TIGGE, these results supported the potential use of TIGGE/S2S forecasts in evaluating agricultural drought. Our proposed approach can be useful for efficient water resources management plans in hydrology, agriculture, etc.

Effects of Temperature, Soil Moisture, Soil pH and Light on Root Gall Development of Chinese Cabbage by Plasmodiophora brassicae (배추무사마귀병 뿌리혹의 형성에 미치는 온도, 토양수분, 토양 pH, 광의 영향)

  • 김충회
    • Plant Disease and Agriculture
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    • v.5 no.2
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    • pp.84-89
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    • 1999
  • Development of root galls of clubroot disease on Chinese cabbage seedlings was first observed 17days after inoculation of Plasmodiophora brassicae at $25^{\circ}C$ 4-11days earlier than at 5, 20, 3$0^{\circ}C$ and 35$^{\circ}C$. Subsequent enlargement of root galls was also fastest at $25^{\circ}C$ and 2$0^{\circ}C$ but delayed at 15$^{\circ}C$ and 3$0^{\circ}C$ or above. Chinese cabbage seedlings with root gall formation showed reduction in number of leaves above ground fresh weight and amount of root hairs but increase in root weight, Root galls development was highest at soil moisture level of 80% of maximum soil moisture capacity than at 60% and 100%. Optimum soil pH for root gall development was pH 6 although root galls were formed at a range of pH 5 to 8. Period of light illumination also affected root gall development with the greatest gall development at 12hr/12hr in light/dark period and the least at 8hr/16hr. Site of root gall formation and gall shape did not differ greatly among treatments of temperature soil moisture pH and light experiments.

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Estimation of Spatial Distribution of Soil Moisture at Yongdam Dam Watershed Using Artificial Neural Networks (인공신경망을 이용한 용담댐 유역 공간 토양수분 분포도 산정)

  • Park, Jung-A;Kim, Gwang-Seob
    • Journal of the Korean Geographical Society
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    • v.46 no.3
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    • pp.319-330
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    • 2011
  • In this study, a soil moisture estimation model was proposed using the ground observation data of soil moisture, precipitation, surface temperature, MODIS NDVI and artificial neural networks. The model was calibrated and verified on the Yongdam dam watershed which has reliable ground soil moisture networks. The test statistics of calibration sites, Jucheon, Bugui, Sangjeon, showed that the correlation coefficients between observations and estimations are about 0.9353 and RMSE is about 1.4957%. Also that of the verification site, Cheoncheon2, showed that the correlation coefficient is about 0.8215 and RMSE is about 4.2077%. The soil moisture estimation model was applied to estimate the spatial distribution of soil moisture in the Yongdam dam watershed and results showed improved spatial soil moisture distribution since the model used satellite information of NDVI and artificial neural networks which can represent the nonlinear relationships between data well. The model should be useful to estimate wide range soil moisture information.

A study on the impact on predicted soil moisture based on machine learning-based open-field environment variables (머신러닝 기반 노지 환경 변수에 따른 예측 토양 수분에 미치는 영향에 대한 연구)

  • Gwang Hoon Jung;Meong-Hun Lee
    • Smart Media Journal
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    • v.12 no.10
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    • pp.47-54
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    • 2023
  • As understanding sudden climate change and agricultural productivity becomes increasingly important due to global warming, soil moisture prediction is emerging as a key topic in agriculture. Soil moisture has a significant impact on crop growth and health, and proper management and accurate prediction are key factors in improving agricultural productivity and resource management. For this reason, soil moisture prediction is receiving great attention in agricultural and environmental fields. In this paper, we collected and analyzed open field environmental data using a pilot field through random forest, a machine learning algorithm, obtained the correlation between data characteristics and soil moisture, and compared the actual and predicted values of soil moisture. As a result of the comparison, the prediction rate was about 92%. It was confirmed that the accuracy was . If soil moisture prediction is carried out by adding crop growth data variables through future research, key information such as crop growth speed and appropriate irrigation timing according to soil moisture can be accurately controlled to increase crop quality and improve productivity and water management efficiency. It is expected that this will have a positive impact on resource efficiency.

A Study on the Development of a Simulation Model for Predicting Soil Moisture Content and Scheduling Irrigation (토양수분함량 예측 및 계획관개 모의 모형 개발에 관한 연구(I))

  • 김철회;고재군
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.19 no.1
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    • pp.4279-4295
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    • 1977
  • Two types of model were established in order to product the soil moisture content by which information on irrigation could be obtained. Model-I was to represent the soil moisture depletion and was established based on the concept of water balance in a given soil profile. Model-II was a mathematical model derived from the analysis of soil moisture variation curves which were drawn from the observed data. In establishing the Model-I, the method and procedure to estimate parameters for the determination of the variables such as evapotranspirations, effective rainfalls, and drainage amounts were discussed. Empirical equations representing soil moisture variation curves were derived from the observed data as the Model-II. The procedure for forecasting timing and amounts of irrigation under the given soil moisture content was discussed. The established models were checked by comparing the observed data with those predicted by the model. Obtained results are summarized as follows: 1. As a water balance model of a given soil profile, the soil moisture depletion D, could be represented as the equation(2). 2. Among the various empirical formulae for potential evapotranspiration (Etp), Penman's formula was best fit to the data observed with the evaporation pans and tanks in Suweon area. High degree of positive correlation between Penman's predicted data and observed data with a large evaporation pan was confirmed. and the regression enquation was Y=0.7436X+17.2918, where Y represents evaporation rate from large evaporation pan, in mm/10days, and X represents potential evapotranspiration rate estimated by use of Penman's formula. 3. Evapotranspiration, Et, could be estimated from the potential evapotranspiration, Etp, by introducing the consumptive use coefficient, Kc, which was repre sensed by the following relationship: Kc=Kco$.$Ka+Ks‥‥‥(Eq. 6) where Kco : crop coefficient Ka : coefficient depending on the soil moisture content Ks : correction coefficient a. Crop coefficient. Kco. Crop coefficients of barley, bean, and wheat for each growth stage were found to be dependent on the crop. b. Coefficient depending on the soil moisture content, Ka. The values of Ka for clay loam, sandy loam, and loamy sand revealed a similar tendency to those of Pierce type. c. Correction coefficent, Ks. Following relationships were established to estimate Ks values: Ks=Kc-Kco$.$Ka, where Ks=0 if Kc,=Kco$.$K0$\geq$1.0, otherwise Ks=1-Kco$.$Ka 4. Effective rainfall, Re, was estimated by using following relationships : Re=D, if R-D$\geq$0, otherwise, Re=R 5. The difference between rainfall, R, and the soil moisture depletion D, was taken as drainage amount, Wd. {{{{D= SUM from { {i }=1} to n (Et-Re-I+Wd)}}}} if Wd=0, otherwise, {{{{D= SUM from { {i }=tf} to n (Et-Re-I+Wd)}}}} where tf=2∼3 days. 6. The curves and their corresponding empirical equations for the variation of soil moisture depending on the soil types, soil depths are shown on Fig. 8 (a,b.c,d). The general mathematical model on soil moisture variation depending on seasons, weather, and soil types were as follow: {{{{SMC= SUM ( { C}_{i }Exp( { - lambda }_{i } { t}_{i } )+ { Re}_{i } - { Excess}_{i } )}}}} where SMC : soil moisture content C : constant depending on an initial soil moisture content $\lambda$ : constant depending on season t : time Re : effective rainfall Excess : drainage and excess soil moisture other than drainage. The values of $\lambda$ are shown on Table 1. 7. The timing and amount of irrigation could be predicted by the equation (9-a) and (9-b,c), respectively. 8. Under the given conditions, the model for scheduling irrigation was completed. Fig. 9 show computer flow charts of the model. a. To estimate a potential evapotranspiration, Penman's equation was used if a complete observed meteorological data were available, and Jensen-Haise's equation was used if a forecasted meteorological data were available, However none of the observed or forecasted data were available, the equation (15) was used. b. As an input time data, a crop carlender was used, which was made based on the time when the growth stage of the crop shows it's maximum effective leaf coverage. 9. For the purpose of validation of the models, observed data of soil moiture content under various conditions from May, 1975 to July, 1975 were compared to the data predicted by Model-I and Model-II. Model-I shows the relative error of 4.6 to 14.3 percent which is an acceptable range of error in view of engineering purpose. Model-II shows 3 to 16.7 percent of relative error which is a little larger than the one from the Model-I. 10. Comparing two models, the followings are concluded: Model-I established on the theoretical background can predict with a satisfiable reliability far practical use provided that forecasted meteorological data are available. On the other hand, Model-II was superior to Model-I in it's simplicity, but it needs long period and wide scope of observed data to predict acceptable soil moisture content. Further studies are needed on the Model-II to make it acceptable in practical use.

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Watershed Scale Drought Assessment using Soil Moisture Index (토양수분지수를 이용한 유역단위 가뭄 평가)

  • Kim, Ok-Kyoung;Choi, Jin-Yong;Jang, Min-Won;Yoo, Seung-Hwan;Nam, Won-Ho;Lee, Joo-Heon;Noh, Jae-Kyoung
    • Journal of The Korean Society of Agricultural Engineers
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    • v.48 no.6
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    • pp.3-13
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    • 2006
  • Although the drought impacts are comparably not catastrophic, the results from the drought are fatal in various social and economical aspects. Different from other natural hazards including floods, drought advances slowly and spreads widely, so that the preparedness is quite important and effective to mitigate the impacts from drought. Soil moisture depletion directly resulted from rainfall shortage is highly related with drought, especially for crops and vegetations, therefore a drought can be evaluated using soil moisture conditions. In this study, SMI (Soil Moisture Index) was developed to measure a drought condition using soil moisture model and frequency analysis for return periods. Runs theory was applied to quantify the soil moisture depletions for the drought condition in terms of severity, magnitude and duration. In 1994, 1995, 2000, and 2001, Korea had experienced several severe droughts, so the SMI developed was applied to evaluate applicability in the mid-range hydrologic unit watershed scale. From the results, SMI demonstrated the drought conditions with a quite sensitive manner and can be used as an indicator to measure a drought condition.