• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.509-520
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• 2012

The objective of this study was to estimate rice yield in Korea using satellite and meteorological data such as sunshine hours or solar radiation, and rainfall. Terra and Aqua MODIS (The MOderate Resolution Imaging Spectroradiometer) products; MOD13 and MYD13 for NDVI and EVI, MOD15 and MYD15 for LAI, respectively from a NASA web site were used. Relations of NDVI, EVI, and LAI obtained in July and August from 2000 to 2011 with rice yield were investigated to find informative days for rice yield estimation. Weather data of rainfall and sunshine hours (climate data 1) or solar radiation (climate data 2) were selected to correlate rice yield. Aqua NDVI at DOY 233 was chosen to represent maximum vegetative growth of rice canopy. Sunshine hours and solar radiation during rice ripening stage were selected to represent climate condition. Multiple regression based on MODIS NDVI and sunshine hours or solar radiation were conducted to estimate rice yields in Korea. The results showed rice yield of $494.6kg\;10a^{-1}$ and $509.7kg\;10a^{-1}$ in 2011, respectively and the difference from statistics were $1.1kg\;10a^{-1}$ and $14.1kg\;10a^{-1}$, respectively. Rice yield distributions from 2002 to 2011 were presented to show spatial variability in the country.

• Korean Journal of Agricultural and Forest Meteorology
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• v.7 no.2
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• pp.171-183
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• 2005

In order to monitor the global terrestrial carbon cycle, NASA (National Aeronautics and Space Administration) provides 8-day GPP images by use of satellite remote-sensing reflectance data from MODIS (Moderate Resolution Imaging Spectroradiometer) at l-km nadir spatial resolution since December, 1999. MODIS GPP algorithm adopts DAO (Data Assimilation Office) meteorological data to calculate daily GPP. By evaluating reliability of DAO data with respect to surface weather station data, we examined the effect of errors from DAO data on MODIS GPP estimation in the Korean Peninsula from 2001 to 2003. Our analyses showed that DAO data underestimated daily average temperature, daily minimum temperature, and daily vapor pressure deficity (VPD), but overestimated daily shortwave radiation during the study period. Each meteorological variable resulted in different spatial patterns of error distribution across the Korean Peninsula. In MODIS GPP estimation, DAO data resulted in overestimation of GPP by $25\%$ for all biome types but up to $40\%$ for forest biomes, the major biome type in the Korean Peninsula. MODIS GPP was more sensitive to errors in solar radiation and VPD than in temperatures. Our results indicate that more reliable gridded meteorological data than DAO data are necessary for satisfactory estimation of MODIS GPP in the Korean Peninsula.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.64.1-64.1
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• 2019

Thermal fracture and cracking near the perihelion are found to be a possible mechanism to produce the dust trail of the near-Earth asteroid, (3200) Phaethon (Jewitt and Li, 2013, ApJ 771, L36). It is, however, not well understood how the debris particles were escalated from the regolith against the asteroid's gravity. Thus, the scenario that these debris particles are responsible for the detected activities (Li and Jewitt, 2013, ApJ, 145, 154), is not complete yet. Here, we hypothesize that the thermal radiation pressure around the perihelion passage would exert substantial force outwards from the regolith on dust grains, and they can be lifted up and contributes the dust tail formation with further help of solar radiation pressure. Our modeling indicates that particles with sizes of roughly ~1-10 micron can be ejected from Phaethon by the mechanism, while a detailed model of gravitational field is required for accurate estimation of the particle size range. Our idea is not necessarily limited to Phaethon case, but is applicable to any atmosphereless bodies.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.3
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• pp.123-125
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• 2009

I thank Kang and Kim (this issue) for their interests and constructive comments on Kwon (2009) in which the objectives were to find the site-specific relationship between net radiation ($R_N$) and solar radiation ($R_S$) in major plant functional types in Korea and to examine its applicability for estimating $R_N$. The authors addressed six major points requesting clarification and suggesting improvement. Below, my replies are provided.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.256-266
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• 2022

Empirical models including the Angstrom-Prescott (AP) model have been used to estimate solar radiation at sites, which would support a wide use of crop models. The objective of this study was to estimate two sets of solar radiation estimates using the AP coefficients derived for climate zone (AP_Frere) and specific site (AP_Choi), respectively. The daily solar radiation was estimated at 18 sites in Korea where long-term measurements of solar radiation were available. In the present study, daily solar radiation and sunshine duration were collected for the period from 2012 to 2021. Daily weather data including maximum and minimum temperatures and rainfall were also obtained to prepare input data to a process-based crop model, CERES-Rice model included in Decision Support System for Agrotechnology Transfer (DSSAT). It was found that the daily estimates of solar radiation using the climate zone specific coefficient, SFrere, had significantly less error than those using site-specific coefficients S_Choi (p<0.05). The cumulative values of S_Frere for the period from march to September also had less error at 55% of study sites than those of S_Choi. Still, the use of S_Frere and S_Choi as inputs to the CERES-Rice model resulted in slight differences between the outcomes of crop growth simulations, which had no significant difference between these outputs. These results suggested that the AP coefficients for the temperate climate zone would be preferable for the estimation of solar radiation. This merits further evaluation studies to compare the AP model with other sophisticated approaches such as models based on satellite data.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.197-198
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• 2021

The temperature and humidity inside concrete affects the depth of carbonation. In this study, the temperature and humidity inside concrete were predicted by the numerical method under the boundary conditions of ambient temperature, humidity, solar radiation, and wind. Using the results of the thermal humidity analysis, diffusion of carbon dioxide and the reaction of cement hydration products were calculated for carbonation depth.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1273-1281
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• 1993

Estimation of daily and seasonal evaportranspiration is essential for water resource planning irrigation feasibility study, and real-time irrigation water management . This paper is to evaluate the applicability of neural networks to the estimation of evapotranspiration . A neural network was developed to forecast daily evapotranspiration of the rice crop. It is a three-layer network with input, hidden , and output layers. Back-propagation algorithm with delta learning rule was used to train the neural network. Training neural network wasconducted usign daily actural evapotranspiration of rice crop and daily climatic data such as mean temperature, sunshine hours, solar radiation, relative humidity , and pan evaporation . During the training, neural network parameters were calibrated. The trained network was applied to a set of field data not used in the training . The created response of the neural network was in good agreement with desired values. Evaluating the neural networ performance indicates that neural network may be applied to the estimation of evapotranspiration of the rice crop.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.155-156
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• 2019

• Journal of Environmental Science International
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• v.9 no.2
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• pp.109-114
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• 2000

The purpose of the present study is to develope the estimation scheme for sensible heat flux by semi-empirical approach using routine meteorological data such as solar radiation and air temperature. To compare observed sensible heat flux with estimated sensible heat flux, the sensible heat fluxes were measured by three dimensional sonic anemometer-thermometer. The field observation was performed during 1 year from December 1, 1995 to November 30, 1996 on a rice paddy field in Chunchon basin. The heat fluxes were measured at a heights of 5m and mean meteorological variables were obtained at two levels, 2.5m(or 1.5m) and 10m. Since condition of rice paddy field such as, wetness of the field, roughness length, vary widely, we devided annual data to 5 periods. Comparing with two sensible heat fluxes, the results showed that the correlation coefficients were more than 0.86. Thus, we can conclude that the estimation method of sensible heat fluxes using routine meteorological data is practical and reliable enough.

• New & Renewable Energy
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• v.20 no.1
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• pp.95-101
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• 2024

Since 1990, the Renewable Big Data Research Lab at the Korea Institute of Energy Technology has been observing solar radiation at 16 sites across South Korea. Serving as the National Reference Standard Data Center for Renewable Energy since 2012, it produces essential data for the sector. By 2020, it standardized meteorological year data from 22 sites. Despite user demand for data from approximately 260 sites, equivalent to South Korea's municipalities, this need exceeds the capability of measurement-based data. In response, our team developed a method to derive solar radiation data from satellite images, covering South Korea in 400,000 grids of 500 m × 500 m each. Utilizing satellite-derived data and ERA5-Land reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), we produced standard meteorological year data for 1,000 sites. Our research also focused on data measurement traceability and uncertainty estimation, ensuring the reliability of our model data and the traceability of existing measurement-based data.