• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.2
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• pp.1-8
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• 2011

In this study, In order to evaluate reliable of MODIS GPP, the MODIS GPP and Flux tower measured GPP were compared to evaluate the use of method on 8 days composite MODIS GPP. The 2008 Flux data ($CO_2$ Flux and air temperature) measured in Seolmacheon watershed ($8.48\;km^2$) were used. The Flux tower GPP was estimated as the sum of $CO_2$ Flux and $R_{ec}$ (ecosystem respiration) by Lloyd and Taylor method (1994). The summer Monsoon period from June to August mostly contributed the underestimation of MODIS GPP by cloud contamination on MODIS pixels. The 2008 MODIS GPP and Flux tower GPP of the watershed were $1133.2\;g/m^2/year$ and $1464.3\;g/m^2/year$ respectively and the determination coefficient ($R^2$) after correction of cloud-originated errors was 0.74 (0.63 before correction). Even though effect of Cloud-Originated Errors was eliminated, Solar radiation and Temperature are affected at GPP. Measurement of correct GPP is difficult. But, If errors of MODIS GPP analyze on Cloud Moonsoon Climate in korea and eliminated effect of Cloud-Originated Errors, MODIS GPP will be considered GPP increasing of 9 %. There, Our results indicate that MODIS GPP show reliable and useful data except for summer period in Moonsoon Climate.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.2
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• pp.132-139
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• 2007

The Moderate Resolution Imaging Spectroradiometer (MODIS) regularly provides the eight-day gross primary productivity (GPP) at 1 km resolution. In this study, we evaluated the uncertainties of MODIS GPP caused by errors associated with the Data Assimilation Office (DAO) meteorology and a biophysical variable (fraction of absorbed photosynthetically active radiation, FPAR). In order to recalculate the improved GPP estimate, we employed ground weather station data and reconstructed cloud-free FPAR. The official MODIS GPP was evaluated as +17% higher than the improved GPP. The error associated with DAO meteorology was identified as the primary and the error from the cloud-contaminated FPAR as the secondary constituent in the integrative uncertainty. Among various biome types, the highest relative error of the official MODIS GPP to the improved GPP was found in the mixed forest biome with RE of 20% and the smallest errors were shown in crop land cover at 11%. Our results indicated that the uncertainty embedded in the official MODIS GPP product was considerable, indicating that the MODIS GPP needs to be reconstructed with the improved input data of daily surface meteorology and cloud-free FPAR in order to accurately monitor vegetation productivity in Korea.

• 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.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.779-793
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• 2021

Water stress and environmental drivers are important factors to explain the variance of gross primary production (GPP). Environmental drivers are used to generate GPP in Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm and process-based model. However, MODIS algorithm only consider the vapor pressure deficit (VPD) data while the process-based biogeochemical model also uses limited data to express water stress. We compared the relationship between environmental drivers and GPP from eddy covariance method, MODIS algorithm, and Community Land Model 4 (CLM 4) simulation in normal years and drought years. To consider water stress specifically, we used VPD and evaporative fraction (EF). We evaluated the effects from environmental drivers and EF towards GPP products using the structural equation modeling (SEM) in South Korea. We found that GPP products from MODIS algorithm and model simulation results were not restricted from VPD data if VPD was underestimated. We also found that in the cropland area, irrigation effects can relieve VPD effects to GPP. However, GPP products derived from MODIS and CLM 4 had limitation to explain the irrigation effects to GPP. Overall, these results will enhance the understanding of GPP products derived from MODIS and CLM 4.

• The Korean Journal of Ecology
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• v.28 no.4
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• pp.215-222
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• 2005

MODIS (Moderate Resolution Image Spectrometer) is a core satellite sensor boarded on Terra and Aqua satellite of NASA Earth Observing System since 1999 and 2001, respectively. MODIS LAI, FPAR, and GPP provide useful means to monitor plant phonology and material cycles in terrestrial ecosystems. In this study, LAI, FPAR, and GPP in Korea were evaluated and errors associated with cloud contamination on MODIS pixels were eliminated for years $2001\sim2003$. Three-year means of cloud-corrected annual GPP were 1836, 1369, and 1460g C $m^{-2}y^{-1}$ for evergreen needleleaf forest, deciduous broadleaf forest, and mixed forest, respectively. The cloud-originated errors were 8.5%, 13.1%, and 8.4% for FPAR, LAI, and GPP, respectively. Summertime errors from June to September explained by 78% of the annual accumulative errors in GPP. This study indicates that cloud-originated errors should be mitigated for practical use of MODIS vegetation products to monitor seasonal and annual changes in plant phonology and vegetation production in Korea.

• Korean Journal of Remote Sensing
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• v.29 no.3
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• pp.275-291
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• 2013

Monitoring the global Gross Primary Pproduction (GPP) is relevant to understanding the global carbon cycle and evaluating the effects of interannual climate variation on food and fiber production. GPP, the flux of carbon into ecosystems via photosynthetic assimilation, is an important variable in the global carbon cycle and a key process in land surface-atmosphere interactions. The Moderate-resolution Imaging Spectroradiometer (MODIS) is one of the primary global monitoring sensors. MODIS GPP has some of the problems that have been proven in several studies. Therefore this study was to solve the regional mismatch that occurs when using the MODIS GPP global product over Korea. To solve this problem, we estimated each of the GPP component variables separately to improve the GPP estimates. We compared our GPP estimates with validation GPP data to assess their accuracy. For all sites, the correlation was close with high significance ($R^2=0.8164$, $RMSE=0.6126g{\cdot}C{\cdot}m^{-2}{\cdot}d^{-1}$, $bias=-0.0271g{\cdot}C{\cdot}m^{-2}{\cdot}d^{-1}$). We also compared our results to those of other models. The component variables tended to be either over- or under-estimated when compared to those in other studies over the Korean peninsula, although the estimated GPP was better. The results of this study will likely improve carbon cycle modeling by capturing finer patterns with an integrated method of remote sensing.

• Korean Journal of Remote Sensing
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• v.28 no.2
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• pp.215-226
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• 2012

NASA MODIS GPP provides a useful tool to monitor global terrestrial vegetation productivity. Two major problems of NASA GPP in regional applications are coarse spatial resolution ($1.25^{\circ}{\times}1^{\circ}$) of DAO meteorological data and cloud contamination of MODIS FPAR product. In this study, we improved the NASA GPP by using enhanced input data of high spatial resolution (3 km${\times}$3 km) WRF meteorological data and cloud-corrected FPAR over the North Korea. The improved GPP was utilized to investigate characteristics of GPP interannual variation and spatial patterns from 2000 to 2008. The GPP varied from 645 to 863 $gC\;m^{-2}\;y^{-1}$ in 2000 and 2008, respectively. Mixed forest showed the highest GPP (1,076 $gC\;m^{-2}\;y^{-1}$). Compared to NASA GPP (790 $gC\;m^{-2}\;y^{-1}$);FPAR enhancement increased GPP (861) but utilization of WRF data decreased GPP (710). Enhancements of both FPAR and meteorological input resulted in GPP increase (809) and the improvement was the greatest for mixed forest regions (+10.2%). The improved GPP showed better spatial heterogeneity reflecting local topography due to high resolution WRF data. It is remarkable that the improved and NASA GPPs showed distinctly different interannual variations with each other. Our study indicates improvement of NASA GPP by enhancing input variables is necessary to monitor region-scale terrestrial vegetation productivity.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.3
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• pp.109-120
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• 2015

Spatial heterogeneous characteristics of solar radiation energy from Climate Change gives rise to energy imbalance in the general ecological system including water resources. This study is to estimate the $CO_2$ flux of South Korea using Terra MODIS image and to assess the reliability of MODIS data from the ground measured $CO_2$ flux by eddy covariance flux tower data at 3 locations (two at mixed forest area and one at rice paddy area). The MODIS Gross Primary Productivity (GPP) product (MOD17A2), 8-day composite at 1-km spatial resolution was adopted for the spatial $CO_2$ flux generation. The MOD17A2 data by noise like cloud and snow in a day were tried to fill by Inverse Distance Weighted (IDW) method from valid pixels and the damping effect of MOD17A2 data were corrected by Quality Control (QC) flag. The MODIS $CO_2$ flux was estimated as the sum of GPP and Re (ecosystem respiration) by Lloyd and Taylor method (1994). The determination coefficient ($R^2$) between MODIS $CO_2$ and flux tower $CO_2$ for 3 years (2011~2013) showed 0.55 and 0.60 in 2 mixed forests and 0.56 in rice paddy respectively. The $CO_2$ flux generally fluctuated showing minus values during summer rainy season (from July to August) and maintaining plus values for other periods. The MODIS $CO_2$ flux can be a useful information for extensive area, for example, as a reliable indicator on ecological circulation system.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.140-140
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• 2016

수분 이용효율(Water Use Efficiency, WUE)은 식생이 흡수한 수분 중 증발산에 이용한 비율로, 육상생태계의 수문학적 순환 및 탄소 순환의 기능을 측정할 수 있는 중요한 지표이다. 또한 이는 특정 지역의 기후 조건이나 토지 피복과 같은 다양한 요인에 따라 변할 수 있다. 본 연구에서는 한반도에서 2010년부터 2014년까지의 기후 요인(강수, 기온)과 토지 피복에 따른 WUE의 변화를 살펴본다. MODerate resolution Imaging Spectroradiometer (MODIS) 기반 총일차생산성(Gross Primary Productivity, GPP), 증발산량(Evapotranspiration, ET), 잎면적지수(Leaf Area Index, LAI)와 Willmot의 기상자료 등 대상지역에 대한 격자 자료를 사용한다. 구체적으로 숲, 초원, 농지와 같이 다양한 토지 피복 조건에 대한 월간 및 연간 WUE(=GPP/ET)를 산정하고, 이의 LAI나 기후 조건(강수, 기온)과의 관계를 분석하고자 상관성 및 요인 분석을 진행한다. 기후 조건 및 토지 피복 조건을 고려한 15년간 한반도에서의 WUE의 변화에 대한 분석은 앞으로의 수자원 관리 및 산림 정책에 활용할 수 있을 것으로 판단된다.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1117-1132
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• 2019

Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.