• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.151-160
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• 2013

Recently, although many efforts have been made to estimate insolation over Korean Peninsula based on satellite imagery, most of them have utilized overseas satellite imagery. This paper aims to estimate insolation over the Korean Peninsula based on the Korean stationary orbit satellite imagery. It utilizes level 1 data and level 2 cloud image of COMS MI, the first meteorological satellite of Korea, and OMI image of NASA as input data. And Kawamura physical model which has been known to be suitable for East Asian area is applied. Daily global horizontal insolation was estimated by using satellite images of every fifteen minutes for the period from May 2011 to April 2012, and the estimates were compared to the ground based measurements. The estimated and observed daily insolations are highly correlated as the $R^2$ value is 0.86. The error rates of monthly average insolation was under ${\pm}15%$ in most stations, and the annual average error rate of horizontal global insolation ranged from -5% to 5% except for Seoul. The experimental results show that the COMS MI based approach has good potential for estimating insolation over the Korean Peninsula.

• Journal of the Korean Solar Energy Society
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• v.38 no.6
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• pp.27-36
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• 2018

Typical Meteorological Year Dataset is necessary for the renewable energy feasibility study. Since National Renewable Energy Laboratory has been built Typical Meteorological Year Dataset in 1978, gridded datasets taken from numerical weather prediction or satellite imagery are employed to produce Typical Meteorological Year Dataset. In general, Typical Meteorological Year Dataset is generated by using long-term in-situ observations. However, solar insolation is not usually measured at synoptic observing stations and therefore it is limited to build the Typical Meteorological Year Dataset with only in-situ observation. This study attempts to build the Typical Meteorological Year Dataset with satellite derived solar insolation as an alternative and then we evaluate the Typical Meteorological Year Dataset made by using satellite derived solar irradiance at Daejeon ground station. The solar irradiance is underestimated when satellite imagery is employed.

• Korean Journal of Agricultural and Forest Meteorology
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• v.13 no.1
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• pp.41-46
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• 2011

Reliable information on the surface solar radiation is indispensable for rebuilding food production system in the famine plagued North Korea. However, transfer of the related modeling technology of South Korea is not possible simply because raw data such as solar radiation or sunshine duration are not available. The objective of this study is restoring solar radiation data at 27 synoptic stations in North Korea by using satellite remote sensing data. We derived relationships between MODIS radiation estimates and the observed solar radiation at 18 locations in South Korea. The relationships were used to adjust the MODIS based radiation data and to restore solar radiation data at those pixels corresponding to the 27 North Korean synoptic stations. Inverse distance weighted averaging of the restored solar radiation data resulted in gridded surfaces of monthly solar radiation for 4 decadal periods (1983-1990, 1991-2000 and 2001-2010), respectively. For a direct application of these products, we produced solar irradiance estimates for each sub-grid cell with a 30 m spacing based on a sun-slope geometry. These products are expected to assist planning of the North Korean agriculture and, if combined with the already prepared South Korean data, can be used for climate change impact assessment across the whole Peninsula.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.569-575
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• 2021

Deep learning shows differences in prediction performance depending on data quality and model. This study uses various input data and multiple deep learning models to build an optimal deep learning model for predicting solar radiation, which has the most influence on power generation prediction. did. As the input data, the weather data of the Korea Meteorological Administration and the clairvoyant meteorological image were used by segmenting the image of the Korea Meteorological Agency. , comparative evaluation, and predicting solar radiation by constructing multiple deep learning models connecting the models with the best error rate in each model. As an experimental result, the RMSE of model A, which is a multiple deep learning model, was 0.0637, the RMSE of model B was 0.07062, and the RMSE of model C was 0.06052, so the error rate of model A and model C was better than that of a single model. In this study, the model that connected two or more models through experiments showed improved prediction rates and stable learning results.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.103-103
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• 2019

본 연구에서는 Terra MODIS 위성영상과 Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) 모형을 이용하여 2012년부터 2017년까지 한반도 전국의 증발산량을 산정하고 플럭스 타워 실측 증발산량과 비교하였다. METRIC은 전 세계에 널리 적용된 바 있는 에너지 수지 기반의 Surface Energy Balance Algorithm for Land (SEBAL) 모형의 개념과 기술을 기반으로 현열(Sensible Heat Flux) 추정 모듈을 개선한 모형이다. 본 연구에서 METRIC 모형은 기존 C#으로 개발되어 있던 SEBAL 코드에서 현열 추정 모듈을 수정하였고 연산 속도 개선을 위해 Python으로 재작성하였다. METRIC 모형의 위성 자료로 Terra MODIS 위성의 MOD13A2(16day, 1km) NDVI, MOD11A1(Daily, 1km) Land Surface Temperature (LST) 및 MCD43A3(Daily, 500m) Albedo를 구축하였으며 500m 공간해상도의 Albedo는 1000m 해상도로 resample하여 활용하였다. 기상자료는 기상청 기상관측소의 풍속, 풍속측정높이, 습도, 10분 간격 이슬점 온도, 일사량 자료를 위성 자료와 같은 공간해상도로 내삽(Interpolation)하여 구축하였다. 모형결과 검증을 위해 국내 플럭스 타워 (설마천, 청미천, 덕유산) 증발산량 관측 자료와의 결정계수(Coefficient of determination, $R^2$), RMSE(Root mean square error) relative RMSE (RMSE%), Nash-Sutcliffe efficiency (NSE) 및 IOA(Index of Agreement)를 산정하고, 기존 SEBAL 모형 결과와의 비교를 통해 본 모형의 개선점을 보이고자 한다.

• Journal of the Korean Solar Energy Society
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• v.39 no.3
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• pp.47-57
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• 2019

Clear sky indices were estimated by various ways based on in-situ observation and satellite-derived solar irradiance. In principle, clear sky index defined by clear sky solar irradiance indicates the impacts of cloud on the incoming solar irradiance. However, clear sky index widely used in energy sciences is formulated by extraterrestrial irradiance, which implies the extinction of solar irradiance due to mainly aerosol, water vapor and clouds drops. This study examined the relative difference of clear sky indices and then major characteristics of clear sky irradiance when sky is clear are investigated. Clear sky is defined when clear sky index based on clear sky irradiance is higher than 0.9. In contrast, clear sky index defined by extraterrestrial irradiance is distributed between 0.4 and 0.8. When aerosol optical depth and air mass coefficient are relative larger, solar irradiance is lower due to enhanced extinction, which leads to the lower value of clear sky index defined by extraterrestrial irradiance.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.470-470
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• 2015

본 연구의 목적은 위성영상 기반의 SEBAL(Surface Energy Balance Algorithm for Land) 모형과 SWAT(Soil and Water Assessment Tool) 수문모형을 용담댐 유역($922.3km^2$)에 적용하여 증발산량을 산정하고 모형 간 공간 증발산량의 비교를 통해 각 모형의 적용성을 평가하는데 있다. 이를 위해 SEBAL모형의 입력자료로 Terra MODIS(Moderate Resolution Imaging Spectrometer) Product 중 Normalized Distribution Vegetation Index(NDVI), Albedo 영상을 2012년부터 2013년까지 월단위로 구축하고, 일단위의 Land Surface Temperature(LST) 영상을 구축하였다. 지형자료로는 Digital Elevation Model(DEM)과 Land use를 구축하였으며 SEBAL 모형의 구동을 위한 위성영상 및 지형자료는 500 m의 공간해상도로 재구축하였다. SWAT 모형의 모의를 위해 기상 및 유량 자료를 2000년부터 2013년까지 일단위로 구축하였고, DEM, Land use, 토양도의 지형자료를 30 m의 공간해상도로 구축하였다. SWAT 모형의 유출 검보정 후 수위관측소 지점에서 평균 $R^2$를 산정한 결과 도치(0.80), 동향(0.72), 석정(0.64), 주천(0.80), 천천(0.80), 용담댐(0.72)로 높은 상관성을 나타냈으며, 유출 검보정 후 SWAT 모형의 증발산량 모의 결과를 바탕으로 SEBAL 모형과의 공간 증발산량을 비교하였다. 두 모형의 증발산량은 SEBAL 모형의 경우 지형에 따라 SWAT 모형은 토양 특성에 따라 분포하는 경향이 다르게 나타났다. SEBAL 모형은 주로 저지대에서 증발산량이 높게 산정되며 고지대로 갈수록 감소하여 증발산량이 지형의 고저차에 따라 분포하는 모습을 보였다. SWAT 모형은 토양 특성에 따라 증발산량이 분포하며 유역 내에서 뚜렷한 차이를 나타내지는 않았다. 월별 총 증발산량은 SWAT 모형의 경우 7~8월에 약 90 mm/mon로 가장 높게 나타나고 1~2월은 0 mm/mon로 계절별 변화폭이 컸으나, SEBAL 모형의 경우 5~6월에 증발산량이 약 60 mm/mon로 가장 높게 나타났고 계절별 변화 폭이 SWAT 모형에 비해 적은 모습을 보였다. 이는 위성영상을 기반으로 하는 SEBAL 모형의 특성상 장마 기간에 해당하는 7~8월은 구름으로 인해 일사량이 적게 계산되고, 그 결과 5~6월에 비해 증발산량이 작게 산정되는 것으로 판단된다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.481-494
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• 2011

Investigation of the $CO_2$ exchange between biosphere and atmosphere at regional, continental, and global scales can be directed to combining remote sensing with carbon cycle process to estimate vegetation productivity. NASA Earth Observing System (EOS) currently produces a regular global estimate of gross primary productivity (GPP) and annual net primary productivity (NPP) of the entire terrestrial earth surface at 1 km spatial resolution. While the MODIS GPP algorithm uses meteorological data provided by the NASA Data Assimilation Office (DAO), the sub-pixel heterogeneity or complex terrain are generally reflected due to coarse spatial resolutions of the DAO data (a resolution of $1{\circ}\;{\times}\;1.25{\circ}$). In this study, we estimated inputs retrieved from MODIS products of the AQUA and TERRA satellites with 5 km spatial resolution for the purpose of finer GPP and/or NPP determinations. The derivatives included temperature, VPD, and solar radiation. Seven AmeriFlux data located in the Corn Belt region were obtained to use for evaluation of the input data from MODIS. MODIS-derived air temperature values showed a good agreement with ground-based observations. The mean error (ME) and coefficient of correlation (R) ranged from $-0.9^{\circ}C$ to $+5.2^{\circ}C$ and from 0.83 to 0.98, respectively. VPD somewhat coarsely agreed with tower observations (ME = -183.8 Pa ~ +382.1 Pa; R = 0.51 ~ 0.92). While MODIS-derived shortwave radiation showed a good correlation with observations, it was slightly overestimated (ME = -0.4 MJ $day^{-1}$ ~ +7.9 MJ $day^{-1}$; R = 0.67 ~ 0.97). Our results indicate that the use of inputs derived MODIS atmosphere and land products can provide a useful tool for estimating crop GPP.

• 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.