• 한국농공학회논문집
• /
• 제57권4호
• /
• pp.61-71
• /
• 2015

Quantitative variability of AMSR2 (Advanced Microwave Scanning Radiometer 2) soil moisture data shows that the remotely sensed soil moisture is underestimated during Spring and Winter seasons and is overestimated during Summer and Fall seasons. Therefore the bias correction of the remotely sensed data is essential for the purpose of water resource management. To enhance their applicability, the bias of AMSR2 soil moisture data was corrected using ground observation data at Cheorwon Chuncheon, Suwon, Cheongju, Jeonju, and Jinju sites. Test statistics demonstrated that the correlation coefficient R is improved from 0.107~0.328 to 0.286~0.559 and RMSE is improved from 9.46~14.36 % to 5.38~9.62 %. Bias correction using ground network data improved the applicability of remotely sensed soil moisture data.

• 한국토양비료학회지
• /
• 제50권6호
• /
• pp.674-683
• /
• 2017

The frequency and intensity of soil moisture stress associated with climate change has increasing, and the stability of field crop cultivation has decreasing. This experiment was conducted to investigate the effect of soil moisture management method on growth and yield of corn. Soil moisture was managed at the grade of WSM (wet soil moisture, 34.0~42.9%), OSM (optimum soil moisture, 27.8~34.0%), DSM (dry soil moisture, 20.3~27.8%), and ESM (extreme dry moisture, 16.6~20.3%) during V8 (8th leaf stage)-VT (tasseling stage). After VT, irrigation was limited. The treated amount of irrigation was 54.1, 47.7, 44.0 and 34.5% of total water requirement, respectively. The potential evapotranspiration during the growing period was $3.29mm\;day^{-1}$, and upward movement of soil water was estimated by the AFKAE 0.5 model in the order of ESM, DSM, OSM, and WSM. We could confirm this phenomenon from actual observations. There was no significant difference in leaf characteristics, dry matter, and primary productivity depending on the level of soil moisture, but leaf development was delayed and dry weight decreased in DSM. However, dry weight and individual productivity of DSM increased after irrigation withdrawal compared to that of OSM. In DSM, ear yield and number of kernels per ear decreased, but water use efficiency and harvest index were higher than other treatments. Therefore, it is considered that the soil moisture is concentratedly managed before the V8 period, the V8-VT period is controlled within the range of 100 to 500 kPa (20.3~27.8%), and no additional irrigation is required after the VT.

• 한국농공학회논문집
• /
• 제59권6호
• /
• pp.29-37
• /
• 2017

In this study, we analyzed the impacts of land surface characteristics on spatially and temporally distributed soil moisture values at the Yongdam and Soyang-river dam watersheds in 2014 and 2015. The soil moisture, NDVI (Normalized Difference Vegetation Index) and temperature values at the spatio-temporal scales were estimated using satellite-based MODIS (MODerate Resolution Imaging Spectroradiometer) products. Then the Pearson correlations between soil moisture and land surface characteristics (NDVI, temperature and DEM-digital elevation model) were estimated and analyzed, respectively. Overall, the monthly soil moisture values at the time step were highly influenced by the precipitation amounts. Also, the results showed that the soil moisture has the strong correlation with DEM while the temperature was inversely correlated with the soil moisture. However the monthly correlations between NDVI and soil moisture were highly varied along the time step. These findings indicated that water loss near the land surface are highly occurred by soil and plant activities as evapotranspiration and infiltration during the no/less precipitation period. But the high precipitation amounts reduce the impacts of land surface characteristics because of saturated condition of land surface. Thus these results demonstrated that soil moisture values are highly correlated with land surface characteristics. Our findings can be useful for water resources/environmental management, agricultural drought, etc.

• 한국작물학회지
• /
• 제45권5호
• /
• pp.282-287
• /
• 2000

Soybean [Glycine max (L.) Merrill] crops, grown in a rice soybean rotation, can suffer when grown in soil with excessive moisture. The objective of this work were to determine the reduction in growth and yield, responses of vegetative and reproductive growth of soybean to excessive soil moisture achieved by prolonged irrigation. Responses of different cultivars were determined at growth stages from V6 to R8 to clarify the sensitive growth stages or characteristics to excessive soil moisture. Cultivar differences in response to excessive soil moisture condition were conspicuous in seed dry weight and harvest index (HI) but not in the response of seed number or pod number per plant. The timing of irrigation causing the condition of excessive soil moisture influenced the vegetative or reproductive traits. Soybean plants were more affected by irrigation commencing at the pre-flowering than at the post-flowering stage. Post-flowering irrigation did not reduce growth of vegetative organs significantly; in fact the growth of stems and leaves was facilitated by the prolonged irrigation commencing at flowering. Differences between cultivar response to prolonged irrigation were assumed to relate to the reduced amount of assimilates translocated to the reproductive organ.

• 한국농공학회논문집
• /
• 제61권1호
• /
• pp.1-8
• /
• 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.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2022년도 학술발표회
• /
• pp.8-12
• /
• 2022

Water loss is one of the typical but challenging problems in water management. To reduced water loss or increase water efficiency, the pilot projects were implemented in the TTD's irrigation area. Modern soil moisture technology and local level water user training were conducted together as a mean to achieve improved water efficiency. In terms of technology, soil moisture sensors and monitoring system were used to estimate crop water requirement to reduce unnecessary irrigation. This was found to save 16.47% of irrigated water and 25.20% of irrigation supply. Further improvement of water efficiency was gained by means of local level water user training in which stakeholders were engaged in the network of communications and co-planning. The lessons learnt from the TTD pilot project was translated into good water management practices at local level.

• 한국물환경학회지
• /
• 제32권1호
• /
• pp.70-79
• /
• 2016

Based on the soil moisture data assimilation suggested in the first paper (I), we estimated root zone soil moisture and evaluated drought severity using remotely sensed (RS) data. We tested the impacts of various spatial resolutions on soil moisture variations, and the model outputs showed that resolutions of more than 2-3 km resulted in over-/under-estimation of soil moisture values. Thus, we derived the 2 km resolution-scaled soil moisture dynamics and assessed the drought severity at the study sites (Chungmi-cheon sites 1 and 2) based on the estimated soil/root parameters and weather forcings. The drought indices at the sites were affected mainly by precipitation during the spring season, while both the precipitation and land surface characteristics influence the spatial distribution of drought during the rainy season. Also, the drought severity showed a periodic cycle, but additional research on drought cycles should be conducted using long-term historical data. Our proposed approach enabled estimation of daily root zone soil moisture dynamics and evaluation of drought severity at various spatial scales using MODIS data. Thus, this approach will facilitate efficient management of water resources.

• 한국농공학회논문집
• /
• 제62권5호
• /
• pp.93-103
• /
• 2020

In this study, we estimated soil moisture values using the Deep Neural Network(DNN) scheme at the mountainous regions. In order to test the sensitive analysis of DNN scheme, we collected the measured(at the soil depths of 10 cm and 30 cm) soil moisture and DNN input(weather and land surface) data at the Pyeongchang-gun(relatively flat) and Geochang-gun(steep slope) sites. Our findings indicated that the soil moisture estimates were sensitive to the weather variables(5 days-averaged rainfall, 5 days precedent rainfall, accumlated rainfall) and DEM. These findings showed that the DEM and weather variables play the key role in the processes of soil water flow at the mountainous regions. We estimated the soil moisture values at the soil depths of 10 cm and 30 cm using DNN at two study sites under different climate-landsurface conditions. The estimated soil moisture(R: 0.890 and RMSE: 0.041) values at the soil depth of 10 cm were comparable with the measured data in Pyeongchang-gun site while the soil moisture estimates(R: 0.843 and RMSE: 0.048) at the soil depth of 30 cm were relatively biased. The DNN-based soil moisture values(R: 0.997/0.995 and RMSE: 0.014/0.006) at the soil depth of 10 cm/30 cm matched well with the measured data in Geochang-gun site. Although uncertainties exist in the results, our findings indicated that the DNN-based soil moisture estimation scheme demonstrated the good performance in estimating soil moisture values using weather and land surface information at the monitoring sites. Our proposed scheme can be useful for efficient land surface management in various areas such as agriculture, forest hydrology, etc.

• 한국작물학회지
• /
• 제53권2호
• /
• pp.161-170
• /
• 2008

Interest is growing in applying simulation models for the South Texas conditions, to better assess crop water use and production with different crop management practices. The Environmental Policy Integrated Climate (EPIC) model was used to evaluate its application as a decision support tool for irrigation management of com (Zea mays L.) in South Texas of the U.S. We measured actual crop evapotranspiration (ETc) using a weighing lysimeter, soil moisture using a neutron probe, and grain yield by field sampling. The model was then validated using the measured data. Simulated ETc using the Hargreaves-Samani equation was in agreement with the lysimeter measured ETc. Simulated soil moisture generally matched with the measured soil moisture. The EPIC model simulated the variability in grain yield with different irrigation regimes with $r^2$value of 0.69 and root mean square error of $0.5\;ton\;ha^{-1}$. Simulation results with farm data demonstrate that EPIC can be used as a decision support tool for com under irrigated conditions in South Texas. EPIC appears to be effective in making long term and pre-season decisions for irrigation management of crops, while reference ET and phenologically based crop coefficients can be used for inseason irrigation management.

• 한국농공학회논문집
• /
• 제64권1호
• /
• pp.79-89
• /
• 2022

Soil moisture is significantly related to crop growth and plays an important role in irrigation management. To predict soil moisture, various process-based model has been developed and used in the world. Various models (Land surface model) may have different performance depending on the model parameters and structures that causes the different model output for the same modeling condition. In this study, the three land surface models (Noah Land Surface Model, Soil Water Atmosphere Plant, Community Land Model) were used to compare the model performance (soil moisture prediction) and develop the multi-model simulation. At first, the genetic algorithm was used to estimate the optimal soil parameters for each model, and the parameters were used to predict soil moisture in the study area. Then, we used the multi-model approach based on Bayesian model averaging (BMA). The results derived from this approach showed a better match to the measurements than the results from the original single land surface model. In addition, identifying the strengths and weaknesses of the single model and utilizing multi-model methods can help to increase the accuracy of soil moisture prediction.