• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.203-203
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• 2023

총일차생산량(GPP, Gross Primary Productivity)은 생태계의 유기물 생산량을 나타내는 지표로써 생태계 생산성과 안정성을 파악할 수 있는 중요한 지표로 알려져 있다. GPP를 산출하는 대표적인 방법에는 다중 센서를 탑재한 원격 탐사 자료를 활용하는 방법과 플럭스타워를 통해 관측한 에디공분산을 분석하는 방법이 있다. 본 연구에서는 Landsat과 MODIS와 같이 시공간 해상도가 다른 원격 탐사 자료들을 기반으로 초고해상도 GPP 자료를 산출하기 위한 공간자료 융합 연구를 수행하였다. 이를 위해 GAN(Generative Adversarial Networks)과 같은 머신러닝 알고리즘을 활용하였으며 최종적으로 산정된 GPP 정보는 설마천과 청미천 등에 설치된 플럭스타워로부터 획득한 자료와의 비교·검증을 통해 평가되었다. 본 연구의 성과는 향후 증발산 자료, 생태계 호흡량 자료 등과의 조합을 통해 얻을 수 있는 물이용효율(WUE, Water Use Efficiency), 탄소이용효율(CUE, Carbon Uptake Efficiency)과 같은 지표 산정 시 적극 활용될 수 있을 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.302-304
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• 2019

탄소 순환과 수문 순환의 관계를 이해하기 위해서는 효율적인 물 사용과 실제 물 사용 간의 비율로 정의되는 용수효율(Water Use Efficiency; WUE)을 정량화 하는 것이 필요하다. 특히 용수효율을 평가하기 위해서는 탄소 순환의 주요 인자인 총 1차 생산량(Gross Primary Productivity; GPP)과 순 1차 생산량(Net Primary Productivity; NPP)을 산정하는 것이 중요하다. 본 연구에서는 전 세계적으로 가장 많이 활용되고 있는 지면 모형 중 하나인 Community Land Model(CLM)을 활용하여 동아시아 지역에서의 GPP와 NPP를 산정하였다. 모형을 통해서 산정된 광역의 GPP와 NPP는 Flux tower에서 관측된 지점 자료를 활용하여 검증할 예정이다. 또한 지면 모형에서 획득한 동아시아 지역의 GPP와 NPP에 대한 공간 분포를 분석하여 탄소 순환 인자들에 대한 시공간적인 변화에 대하여 확인하고자 한다.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2005.09a
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• pp.37-40
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• 2005

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2005.09a
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• pp.19-20
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• 2005

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.1
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• pp.19-26
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• 2009

The standardization of eddy-covariance data processing is essential for the analysis and synthesis of vast amount of data being accumulated through continuous observations in various flux measurement networks. End users eventually benefit from the open and transparent standardization protocol by clear understanding of final products such as evapotranspiration and gross primary productivity. In this paper, we briefly introduced KoFlux efforts to standardize data processing methodologies and then estimated uncertainties of surface fluxes due to different processing methods. Based on our scrutiny of the data observed at Gwangneung KoFlux site, net ecosystem exchange and ecosystem respiration were sensitive to the selection of different processing methods. Gross primary production, however, was consistent within errors due to cancellation of the differences in NEE and Re, emphasizing that independent observation of ecosystem respiration is required for accurate estimates of carbon exchange. Nocturnal soil evaporation was small and thus the annually integrated evapotranspiration was not sensitive to the selection of different data processing methods. The implementation of such standardized data processing protocol to AsiaFlux will enable the establishment of consistent database for validation of models of carbon cycle, dynamic vegetation, and land-atmosphere interaction at regional scale.

• 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.12 no.2
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• pp.107-121
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• 2010

We conducted a sensitivity test of Joint UK Land Environment Simulator (JULES), in which the influence of biophysical parameters on the simulation of gross primary productivity (GPP) and ecosystem respiration (RE) was investigated for two typical ecosystems in Korea. For this test, we employed the whole-year observation of eddy-covariance fluxes measured in 2006 at two KoFlux sites: (1) a deciduous forest in complex terrain in Gwangneung and (2) a farmland with heterogeneous mosaic patches in Haenam. Our analysis showed that the simulated GPP was most sensitive to the maximum rate of RuBP carboxylation and leaf nitrogen concentration for both ecosystems. RE was sensitive to wood biomass parameter for the deciduous forest in Gwangneung. For the mixed farmland in Haenam, however, RE was most sensitive to the maximum rate of RuBP carboxylation and leaf nitrogen concentration like the simulated GPP. For both sites, the JULES model overestimated both GPP and RE when the default values of input parameters were adopted. Considering the fact that the leaf nitrogen concentration observed at the deciduous forest site was only about 60% of its default value, the significant portion of the model's overestimation can be attributed to such a discrepancy in the input parameters. Our finding demonstrates that the abovementioned key biophysical parameters of the two ecosystems should be evaluated carefully prior to any simulation and interpretation of ecosystem carbon exchange in Korea.

• Journal of Korea Water Resources Association
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• v.45 no.12
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• pp.1293-1307
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• 2012

This study is to evaluate the RHESSys (Regional Hydro-Ecological Simulation System) simulated streamflow (Q), evapotranspiration (ET), soil moisture (SM), gross primary productivity (GPP) and photosynthetic productivity (PSNnet) with the measured data. The RHESSys is a hydro-ecological model designed to simulate integrated water, carbon, and nutrient cycling and transport over spatially variable terrain. A 8.5 $km^2$ Seolma-cheon catchment located in the northwest of South Korea was adopted. The catchment covers 90.0% forest and the dominant soil is sandy loam. The model was calibrated with 2 years (2007-2008) daily Q at the watershed outlet and MODIS (Moderate Resolution Imaging Spectroradiometer) GPP, PSNnet and 3 year (2007～2009) daily ET data measured at flux tower using the eddy-covariance technique. The coefficient of determination ($R^2$) and the Nash-Sutcliffe model efficiency (ME) for Q were 0.74 and 0.63, and the average $R^2$ for ET and GPP were 0.54 and 0.93 respectively. The model was validated with 1 year (2009) Q and GPP. The $R^2$ and the ME for Q were 0.92 and 0.84, the $R^2$ for GPP were 0.93.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.182-196
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• 2023

Accurate and timely estimation of crop yields is crucial for various purposes, including global food security planning and agricultural policy development. Remote sensing techniques, particularly using vegetation indices (VIs), have show n promise in monitoring and predicting crop conditions. However, traditional VIs such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) have limitations in capturing rapid changes in vegetation photosynthesis and may not accurately represent crop productivity. An alternative vegetation index, the near-infrared reflectance of vegetation (NIRv), has been proposed as a better predictor of crop yield due to its strong correlation with gross primary productivity (GPP) and its ability to untangle confounding effects in canopies. In this study, we investigated the potential of NIRv in estimating crop yield, specifically for corn and soybean crops in major crop-producing regions in 14 states of the United States. Our results demonstrated a significant correlation between the peak value of NIRv and crop yield/area for both corn and soybean. The correlation w as slightly stronger for soybean than for corn. Moreover, most of the target states exhibited a notable relationship between NIRv peak and yield, with consistent slopes across different states. Furthermore, we observed a distinct pattern in the yearly data, where most values were closely clustered together. However, the year 2012 stood out as an outlier in several states, suggesting unique crop conditions during that period. Based on the established relationships between NIRv peak and yield, we predicted crop yield data for 2022 and evaluated the accuracy of the predictions using the Root Mean Square Percentage Error (RMSPE). Our findings indicate the potential of NIRv peak in estimating crop yield at the county level, with varying accuracy across different counties.