• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.195-200
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• 2016

Water vapor leading various scale of atmospheric circulation and accounting for about 60% of the naturally occurring warming effect is important climate variables. Using the Total Precipitable Water (TPW) from Moderate Resolution Imaging Spectroradiometer (MODIS) operating on both Terra and Aqua, we study long-term Variation of TPW and define relationship among TPW and climatic parameters such as temperature and precipitation to quantitatively demonstrate the impact on climate change over East Asia focusing on the Korea peninsula. In this study, we used linear regression analysis to detect the correlation of TPW and temperature/precipitation and harmonic analysis to analyze changeable aspects of periodic characteristics. A result of analysis using linear regression analysis between TPW and climate elements, TPW shows a high determination coefficient ($R^2$) with temperature and precipitation (determination coefficient between TPW and temperature: 0.94, determination coefficient between TPW anomaly and temperature anomaly: 0.8, determination coefficient between TPW and precipitation: 0.73, determination coefficient between TPW anomaly and precipitation anomaly: 0.69). A result of harmonic analysis of TPW and precipitation of two-year to five-year cycle, amplitude contribution ratio of 3.5-year cycle are much higher and two phases are similar in 3.5-year cycle.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.511-524
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• 2014

In this study, we investigated the statistical occupations and interannual variations of land cover types over Northeast Asian region using the 12 years (2001-2012) MODerate Resolution Imaging Spectroradiometer(MODIS) land cover data sets. The spatial resolution and land cover types of MODIS land cover data sets are 500 m and 17, respectively. The 12-year average shows that more than 80% of the analysis region is covered by only 3 types of land cover, cropland (36.96%), grasslands (23.14%) and mixed forests (22.97%). Whereas, only minor portion is covered by cropland/natural vegetation mosaics (6.09%), deciduous broadleaf forests (4.26%), urban and built-up (2.46%) and savannas (1.54%). Although sampling period is small, the regression analysis showed that the occupations of evergreen needleleaf forests, deciduous broadleaf forests and mixed forests are increasing but the occupations of woody savannas and savannas are decreasing. In general, the pixels where the land cover types are classified differently with year are amount to more than 10%. And the interannual variations in the occupations of land cover types are most prominent in cropland (1.41%), mixed forests (0.82%) and grasslands (0.73%). In addition, the percentage of pixels classified as 1 type for 12 years is only 57% and the other pixels are classified as more than 2 types, even 9 types. The annual changes in the classification of land cover types are mainly occurred at the almost entire region, except for the eastern and northwestern parts of China, where the single type of land cover located. When we take into consider the time scale needed for the land cover changes, the results indicate that the MODIS land cover data sets over the Northeast Asian region should be used with caution.

• Journal of Korea Water Resources Association
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• v.49 no.11
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• pp.903-912
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• 2016

Accurate understanding and estimating Evapotranspiration (ET) is essential for understanding the mechanism of water cycle and water budget. ET has been analyzed by many researchers in worldwide while Ground-based ET has limiation in analyzing the spatio-temporal pattrens of ET. Thus, many researches have been conducted to represent the spatio-temporal variation of ET by using hydrometeorological variables estimated from remote sensing datasets. Previous remote sensing based ET algorithms, however, have disadvantage in that various hydrometeological input datasets were required. In this study, actual ET was estimated by MODIS-based Rn and MS-PT algorithm requiring relatively less input data than previous method. The result confirmed that the observed $R_N$ and latent heat flux from the eddy-covariance based fluxtowers located at CFK and SMK showed high correlation with the estimated $R_N$ and ET. The average determination coefficients ($R^2$) of ET estimated from satellite dataset over study periods were 0.77 (0.72-0.81) in Cheongmi (CFK) and 0.70 (0.67-0.78) in Sulma (SMK), respectively. Comparing with the actual ET of two flux tower sites, however, SMK showed more overestimated patterns than CFK due to the vegetation and radiation related errors.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.197-214
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• 2019

In this study, the correlation analysis was conducted between observed air temperature (maximum, minimum, and mean air temperature) and the daytime and nighttime data of Terra/Aqua MODIS LST(Moderate Resolution Imaging Spectroradiometer Land Surface Temperature) for 86 weather stations. All the data of the recent 11 years from 2008 to 2018 were prepared with daily base. In particular, the characteristics of the cold and heat waves incidence period in 2018 were analyzed. The correlation analysis was performed using the Pearson correlation coefficient(R) and root mean square error(RMSE). As a result of time series analysis, the trend between observed air temperature and MODIS LST were similar, showing the correlation above 0.9 in maximum temperature, above 0.8 in mean and minimum temperature. Especially, the maximum temperature was found to have the highest accuracy with Terra MODIS LST daytime, and the minimum temperature had the highest correlation with Terra MODIS LST nighttime. During the cold wave period, both Terra and Aqua MODIS LST showed higher correlations with nighttime data than daytime data. For the heat wave period, the Aqua MODIS LST daytime data was good, but the overall R was below 0.5. Additional analysis is necessary for further study considering such as land cover and elevation characteristics.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.51-64
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• 2015

We improved the MODerate resolution Imaging Spectroradiometer (MODIS) land cover map over the Asia-Oceania region through the reclassification of the misclassified pixels. The misclassified pixels are defined where the number of land cover types are greater than 3 from the 12 years of MODIS land cover map. The ratio of misclassified pixels in this region amounts to 17.53%. The MODIS Normalized Difference Vegetation Index (NDVI) time series over the correctly classified pixels showed that continuous variation with time without noises. However, there are so many unreasonable fluctuations in the NDVI time series for the misclassified pixels. To improve the quality of input data for the reclassification, we corrected the MODIS NDVI using Correction based on Spatial and Temporal Continuity (CSaTC) developed by Cho and Suh (2013). Iterative Self-Organizing Data Analysis (ISODATA) was used for the clustering of NDVI data over the misclassified pixels and land cover types was determined based on the seasonal variation pattern of NDVI. The final land cover map was generated through the merging of correctly classified MODIS land cover map and reclassified land cover map. The validation results using the 138 ground truth data showed that the overall accuracy of classification is improved from 68% of original MODIS land cover map to 74% of reclassified land cover map.

• Korean Journal of Remote Sensing
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• v.32 no.5
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• pp.413-421
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• 2016

In this study, correlations between Moderate Resolution Imaging Spectroradiometer (MODIS) derived Aerosol Optical Depth (AOD) values and surface-level $PM_{2.5}$ concentrations at Gosan, Korea have been investigated. For this purpose, data from various instruments, such as satellite, sunphotometer, Optical Particle Counter (OPC), and Micro Pulse Lidar (MPL) on 14-24 October 2009 were used. Direct comparison between sunphotometer measured AOD and surface-level $PM_{2.5}$ concentrations showed a $R^2=0.48$. Since the AERONET L2.0 data has significant number of observations with high AOD values paired to low surface-level $PM_{2.5}$ values, which were believed to be the effect of thin cloud or Asian dust. Correlations between MODIS AOD and $PM_{2.5}$ concentration were increased by screening thin clouds and Asian dust cases by use of aerosol profile data on Micro-Pulse Lidar Network (MPLNet) as $R^2$ > 0.60. Our study clearly demonstrates that satellite derived AOD is a good surrogate for monitoring atmospheric PM concentration.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.2
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• pp.70-78
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• 2009

Remote sensing technique in agriculture can be used to identify chlorophyll content, biomass, and yield caused from N stress level. This study was conducted to evaluate biomass, N stress levels, and yield of tobacco (Nicotiana tabacum L.) under sand culture in a plastic film house using ground-based remote sensors. Nitrogen rates applied were 40, 60, 80, 100, 120, and 140 percent of N concentration in the Hoagland's nutrient solution. Sensor readings for reflectance indices were taken at 30, 35, 40, 45, 50 and 60 days after transplanting(DAT). Reflectance indices measured at 40th DAT were highly correlated with dry weight(DW) of tobacco leaves and N uptake by leaves. Especially, green normalized difference vegetation index(gNDVI) from spectroradiometer and aNDVI from Crop Circle passive sensor were able to explain 85% and 84% of DW variability and 85% and 92% of N uptake variability, respectively. All the reflectance indices measured at each sampling date during the growing season were significantly correlated with tobacco yield. Especially the gNDVI derived from spectroradiometer readings at the 40th DAT explained 72% of yield variability. N rates of tobacco were distinguished by sufficiency index calculated using the ratio of reflectance indices of stress to optimum plot of N treatment. Consequently results indicate that the reflectance indices by ground-based remote sensor can be used to predict tobacco yield and recommend the optimum application rate of N fertilizer for top dressing of tobacco.

• Journal of Wetlands Research
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• v.13 no.2
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• pp.171-179
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• 2011

The main objective of this study is to estimate of the vegetation response induced by climate change to soil moisture. We investigated a relationship between vegetation activity and climate variables using Moderate Resolution Imaging Spectroradiometer (MODIS)-retrieved Normalized Difference Vegetation Index (NDVI) and soil moisture. NDVI which extracted from MODIS 13 Vegetation Indices Product was considered as an useful parameter to figure out a relationship with two types of soil moisture, which were observed at Rural Development Administration sites and estimated from Advanced Microwave Scanning Radiometer E (AMSR-E) satellite imagery. The correlation of MODIS-NDVI and ground measured soil moisture were observed, became much stronger when compared to soil moisture values with time lag (5days, 10days, 15days). The correlation patterns between NDVI and soil moisture with different time lag were related to soil texture. The results from this study will be useful to understand the role of vegetation in water balance control in various scales from regional to global climate change.

• 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 Remote Sensing
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• v.31 no.6
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• pp.523-530
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• 2015

A Multiple Regression Method (MRM) is used for the first time with Ozone Monitoring Instrument (OMI) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimate formaldehyde (HCHO) Vertical Column Density (VCD). For a 3.5-year period from January 2005 through July 2008, HCHO VCD estimation is investigated in cities over Asia in two categorized areas: (1) Major cities in Northeast Asia (Beijing, Seoul, and Tokyo), (2) Major cities in Southeast Asia (New Delhi, Dhaka, and Bangkok). In the Major cities in Northeast Asia, there are good agreements between HCHO estimated by the multiple linear regression method ($HCHO_{MRM}$) and HCHO measured by OMI ($HCHO_{OMI}$) (0.78 < $R^2$ < 0.82). However, in Major cities in Southeast Asia, there were poor agreements between $HCHO_{OMI}$ and $HCHO_{MRM}$ (0.24 < $R^2$ < 0.39). In addition, an unbiased assessment of the MRM performance using modeling and validation groups shows that the performance of the MRM based on separate modeling and validation groups is comparable to that using all the data for deriving Multiple Regression Equations (MREs). This study demonstrates that MRM can be an alternative tool for HCHO estimation in certain areas over Asia.