• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.2
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• pp.161-170
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• 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.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.183-197
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• 2006

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.6
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• pp.173-183
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• 2015

The aim of this study was to develop a soil moisture simulation model equipped with meteorological data enhanced by WRF (Weather Research and Forecast) model, and this soil moisture model was applied for quantifying soil moisture content and irrigation requirement. The WRF model can provide grid based meteorological data at various resolutions. For applicability assessment, comparative analyses were conducted using WRF data and weather data obtained from weather station located close to test bed. Water balance of each upland grid was assessed for soils represented with four layers. The soil moisture contents simulated using the soil moisture model were compared with observed data to evaluate the capacity of the model qualitatively and quantitatively with performance statistics such as correlation coefficient (R), coefficient of determination (R2) and root mean squared error (RMSE). As a result, R is 0.76, $R^2$ is 0.58 and RMSE 5.45 mm in soil layer 1 and R 0.61, $R^2$ 0.37 and RMSE 6.73 mm in soil layer 2 and R 0.52, $R^2$ 0.27 and RMSE 8.64 mm in soil layer 3 and R 0.68, $R^2$ 0.45 and RMSE 5.29 mm in soil layer 4. The estimated soil moisture contents and irrigation requirements of each soil layer showed spatiotemporally varied distributions depending on weather and soil texture data incorporated. The estimated soil moisture contents using weather station data showed uniform distribution about all grids. However the estimated soil moisture contents from WRF data showed spatially varied distribution. Also, the estimated irrigation requirements applied WRF data showed spatial variabilities reflecting regional differences of weather conditions.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.154-154
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• 2019

Characterization of reliable field-scale root-zone soil moisture (RZSM) variability contribute to effective hydro-meterological monitoring. Although a promising cosmic-ray neutron probe (CRNP) holds the pontential for field-scale RZSM measurement, it is often restricted at deeper depths due to the non-unique sensitivity of CRNP-measured fast neutron signal to other hydrogen pools. In this study, a merging framework relied on coupling cosmic-ray soil moisture with a representative additional RZSM, was introduced to scale shallower CRNP effective depth to represent root-zone layer. We tested our proposed framework over a densely vegetated region in South Korea covering a network of one CRNP and nine in-situ point measurements. In particular, cosmic-ray soil moisture and ancillary RZSM retrieved from the most time stable location were considered as input datasets; whereas the remaining point locations were used to generate a reference RZSM product. The errors between these two input datasets and the reference were forecasted by a linear autoregressive model. A linear combination of forecasts was then employed to compute a suitable weight for merging two input products from the predicted errors. The performance of merging framework was evaluated against reference RZSM in comparison to the two original products and a commonly used exponential filter technique. The results of this study showed that merging framework outperformed other products, demonstrating its robustness in improving field-scale RZSM. Moreover, a strong relationship between the quality of input data and the performance merging framework in light of CRNP effective depth variation has been also underlined via the merging framework.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.995-1010
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• 2020

In this study, a multivariate analysis model of partial least square regression (PLSR) was developed to predict the moisture content of green peppers using hyperspectral imaging (HSI). In HSI, illumination is essential for high-quality image acquisition and directly affects the analytical performance of the visible near-infrared hyperspectral imaging (VIS/NIR-HSI) system. When green pepper images were acquired using a direct lighting system, the specular reflection from the surface of the objects and their intensities fluctuated with time. The images include artifacts on the surface of the materials, thereby increasing the variability of data and affecting the obtained accuracy by generating false-positive results. Therefore, images without glare on the surface of the green peppers were created using a polarization filter at the front of the camera lens and by exposing the polarizer sheet at the front of the lighting systems simultaneously. The results obtained from the PLSR analysis yielded a high determination coefficient of 0.89 value. The regression coefficients yielded by the best PLSR model were further developed for moisture content mapping in green peppers based on the selected wavelengths. Accordingly, the polarization filter helped achieve an uniform illumination and the removal of gloss and artifact glare from the green pepper images. These results demonstrate that the HSI technique with a polarized lighting system combined with chemometrics can be effectively used for high-throughput prediction of moisture content and image-based visualization.

• Journal of Forest and Environmental Science
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• v.39 no.1
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• pp.13-19
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• 2023

We investigated Soil respiration in Bluepine forest of western Bhutan, in relation to soil temperature, moisture content and soil pH and it was aimed at establishing variability in space and time. The Bluepine forest thrives in the typical shallow dry valleys in the inter-montane Bhutan Himalaya, which is formed by ascending wind from the valley bottom, which carries moisture from the river away to the mountain ridges. Stratified random sampling was applied and the study site was classified into top, mid, low slope and further randomized sample of n=20 from 30 m×30 m from each altitude. The overall soil respiration mean for the forest was found 2248.17 CO₂ g yr^-1 and it is ~613.58 C g yr^-1. The RS from three sites showed a marginal variation amongst sites, lower slope (2,309 m) was 4.64 μ mol m^-2 s^-1, mid slope (2,631 m) was 6.78 μ mol m^-2 s^-1 and top slope (3,027 m) was 6.33 μ mol m^-2 s^-1 and mean of 5.92 μ mol m^-2 s^-1, SE=0.25 for the forest. Temporal distribution and variations were observed more pronounced than in the space variation. Soil respiration was found highest during March and lowest in September. Soil temperature had almost inverse trend against soil respiration and dropped a low in February and peak in July. The moisture in the soil changed across months with precipitation and pH remained almost consistent across the period. The soil respiration and soil temperature had significant relationship R²=-0.61, p=0.027 and other variables were found insignificant. Similar relationship are reported for dry season in a tropical forest soil respiration. Soil temperature was found to have most pronounced effect on the soil respiration of the forest under study.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.2
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• pp.203-209
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• 2009

Yield monitoring is one of a precision agriculture technology that is used most widely. It is spatial variability analysis of yield information that should be attained with yield monitoring system development. This experiment was conducted to evaluate spatial variability of yield and grain moisture content in rice paddy field, and their relationships to rice productivity. It is necessary to minimize sampling interval for accurate yield map making or to control cutting width of rice combine. Considering small rice plots such as $0.2{\sim}0.4$ ha, optimum size of sampling plot was below 15 m more than 5 m in with and length. In variable rate treatment field, average yield was similar, but yield variation was reduced than conventional field. Gap of yield by another plot in same field was bigger than half of average yield than yield variation was significantly big. Therefore yield measuring flow sensor must be able to measure at least 300 kg/10a more than 1000 kg/10a. Variation of moisture content in same field was not big and spatial dependance did not appear greatly. But, variation between different field is appeared difference according to weather circumstance before harvesting. Change of spatial dependence of yield was not big, because of field variation of moisture content is not big.

• Journal of Ecology and Environment
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• v.43 no.2
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• pp.246-253
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• 2019

Background: Water use efficiency (WUE) is an indicator of the trade-off between carbon uptake and water loss to the atmosphere at the plant or ecosystem level. Understanding temporal dynamics and the response of WUE to climatic variability is an essential part of land degradation assessments in water-limited dryland regions. Alternative definitions of and/or alternative methodologies used to measure WUE, however, have hampered intercomparisons among previous studies of different biomes and regions. The present study aims to clarify semantic differences among WUE parameters applied in previous studies and summarize these parameters in terms of their definition and methodology. Additionally, the consistency of the responses of alternative WUE parameters to interannual changes in moisture levels in Northeast Asia dryland regions (NADRs) was tested. Results: The literature review identified more than five different WUE parameters defined at leaf and ecosystem levels and indicates that major conclusions regarding the WUE response to climatic variability were partly inconsistent depending on the parameters used. Our demonstration of WUE in NADR again confirmed regional inconsistencies and further showed that inconsistencies were more distinct in hyper- and semi-arid climates than in arid climates, which might reflect the different relative roles of physical and biological processes in the coupled carbon-water process. Conclusions: The responses of alternative WUE parameters to drying and wetting may be different in different regions, and regionally different response seems to be related to aridity, which determines vegetation coverage.

• Journal of Soil and Groundwater Environment
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• v.21 no.6
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• pp.36-45
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• 2016

Variability in precipitation due to climate change causes difficulties in securing stable surface water resource, which requires understanding of relation between precipitation and stream discharge. This study simulated stream discharge in a small mountainous forested catchment using antecedent precipitation index (API) models which represent variability of saturation conditions of soil layers depending on rainfall events. During 13 months from May 2015 to May 2016, stream discharge and rainfall were measured at the outlet and in the central part of the watershed, respectively. Several API models with average recession coefficients were applied to predict stream discharge using measured rainfall, which resulted in the best reflection time for API model was 1 day in terms of predictability of stream discharge. This indicates that soil water in riparian zones has fast response to rainfall events and its storage is relatively small. The model can be improved by employing seasonal recession coefficients which can consider seasonal fluctuation of hydrological parameters. These results showed API models can be useful to evaluate variability of streamflow in ungauged small forested watersheds in that stream discharge can be simulated using only rainfall data.

• Journal of Korea Water Resources Association
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• v.49 no.5
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• pp.423-429
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• 2016

This study aims at assessing the quality of the Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture products onboard GCOM-W1 satellite based on Land Parameter Retrieval Model (LPRM) soil moisture retrieval algorithm with field measurements in South Korea from March to September, 2014. Results of mean bias and root mean square error between AMSR2 LPRM soil moisture products (X-band) and ground measurements showed reasonable value of 0.03 and 0.16. Also, the maximum of the Pearson correlation coefficients was 0.67, which showed good agreement in terms of temporal variability with ground measurements. By comparing AMSR2 soil moisture with in-situ measurement according to the overpass time and band frequency, X-band products on the ascending time outperformed than those of C1-band and C2-band. Furthermore, this study offers an insight into the applicability of the AMSR2 soil moisture products for monitoring various natural disasters at a large scale such as drought and flood.