• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1039-1041
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• 2003

Ocean color products derived from remote sensing satellite data are useful for monitoring the sea water quality such as the concentrations of chlorophyll, sediments and dissolved organic matter. Currently, ocean color products derived from MODIS data can be requested from NASA over the internet. However, due to the bandwidth limitation of most users in this region, and the time delay in data delivery, the products cannot be use for near-real time monitoring of sea water chlorophyll. CRISP operates a MODIS data receiving station for environmental monitoring purposes. MODIS data have been routinely received and processed to level 1B. We have adapted the higher level processing algorithms from the Institutional Algorithms provided by NASA to run in a standalone environment. The implemented algorithms include the MODIS ocean color algorithms. Seasonal chlorophyll concentration composite can be compiled for the region. By comparing the near-real time chlorophyll product with the seasonal composite, anomaly in chlorophyll concentration can be detected.

• Journal of the Korean association of regional geographers
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• v.19 no.2
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• pp.352-361
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• 2013

This paper aims to analyze the root mean square errors of MODIS LST data and inland water temperature measurement data in order to use MODIS LST data as an input of numerical weather prediction model. MODIS LST data from July 2011 to June 2012 were compared to water temperature measurement data in the automated water quality measurement network. MODIS data have two composites: day-time and night-time. Monthly errors of day-time and night-time LST range $2{\sim}8^{\circ}C$ and $3{\sim}12^{\circ}C$, respectively. Temporally, monthly errors of day-time LST are less in fall and those of night-time LST are less in summer. Spatially, on the four major rivers including the Han, Nakdong, Geum, and Yeongsan rivers, the errors of Yeongsan river were the smallest, which location is the south-most among them. In this study, the errors of MODIS LST as an input of numerical weather prediction model were analyzed and the results can be used as an error level of MODIS LST data for inaccessible areas such as North Korea.

• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.305-316
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• 2010

The MODerate Resolution Imaging Spectroradiometer (MODIS) sensor has 36 bands at 250m, 500m, 1km spatial resolution. However, 500m or 1km MODIS data exhibits a few limitations when low resolution data is applied at small areas that possess complex land cover types. In this study, we produce seven 250m spectral bands by fusing two MODIS 250m bands into five 500m bands. In order to recommend the best fusion method by which one acquires MODIS data, we compare seven fusion methods including the Brovey transform, principle components algorithm (PCA) fusion method, the Gram-Schmidt fusion method, the least mean and variance matching method, the least square fusion method, the discrete wavelet fusion method, and the wavelet-PCA fusion method. Results of the above fusion methods are compared using various evaluation indicators such as correlation, relative difference of mean, relative variation, deviation index, peak signal-to-noise ratio index and universal image quality index, as well as visual interpretation method. Among various fusion methods, the local mean and variance matching method provides the best fusion result for the visual interpretation and the evaluation indicators. The fusion algorithm of 250m MODIS data may be used to effectively improve the accuracy of various MODIS land products.

• Korean Journal of Remote Sensing
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• v.19 no.1
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• pp.1-9
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• 2003

MODIS LAI product has been one of key variable for analyzing the quantitative aspects of terrestrial ecology at global scale. This study was designed to validate MODIS global LAI product for regional application. To examine the quality of MODIS LAI data, we developed a reference LAI surface that was derived by relating the ground LAI measurements to Landsat ETM+ reflectance. The study area, the Kwangneung Experiment Forest in Korea, covers mixed deciduous and coniferous species of temperate forest. Ground measurements of LAI were conducted at 30 sample plots by using a photo-optical instrument during the growing season of 2002. Ground measured LAI data were then related to the ETM+ reflectance to produce a continuous map of LAI surface over the study area. From the comparison between the MODIS LAI and the reference LAI, it was found that the MODIS LAI values were slightly higher at the forestland. Considering the limitations of producing the reference LAI surface and the uncertainty of the input variable for the MODIS LAI algorithm, such small discrepancy mal not be significant.

• Journal of Korean Society for Atmospheric Environment
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• v.25 no.4
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• pp.264-274
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• 2009

Concentration of Air Quality Models (CMAQ) has a deep connection with emissions and wind fields. In particular the wind field is highly affected by local topography and plays an important role in transport and dispersion of contaminants from the pollution sources. The purpose of this study is to examine the impact of interpolation on Air quality model. This study was designed to evaluate enhancement of MM5 and CMAQ predictions by using Four Dimensional Data Assimilation (FDDA), the SONDE data and the national meteorological station and the MODerate resolution Imaging Spectroradiometer (MODIS). The alternative meteorological fields predicted with and without MODIS data were used to simulate spatial and temporal variations of ozone in combined with CMAQ on June 2006. The result of this study indicated that data assimilation using MODIS data provided an attractive method for generating realistic meteorological fields and dispersion fields of ozone in the Korea peninsular, because MODIS data in 10 km domain are grid horizontally and vertically. In order to ensure the success of Air quality model, it is necessary to FDDA using MODIS data.

• 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.

• 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.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.920-922
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• 2003

Large plumes of yellow send or yellow dust blow out over the Sea of Japan and the Japanese archipelago from mainland of China. In this study, the methodology to capture the perspective on the large Yellow dust storm by using MODIS data is discussed. As the typical image of yellow send, MODIS data obtained of April 8, 2002 were used in this study.

• Journal of Environmental Impact Assessment
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• v.20 no.2
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• pp.199-205
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• 2011

The Saemangeum embankment construction have changed the flowing on the topography of the coastal marine environment. However, the variety of ecological factors are changing from outside of Saemangeum embankment area. The ecosystem of various marine organisms have led to changes by sea surface temperature. The aim of this study is to monitoring of sea surface temperature(SST) changes were measured by using thermal infrared satellite imagery, MODIS and Landsat. The MODIS data have the high temporal resolution and Landsat satellite data with high spatial resolution was used for time series monitoring. The extracted informations from sea surface temperature changes were compared with the dyke to allow them inside and outside of Saemangeum embankment. The spatial extent of the spread of sea water were analyzed by SST using MODIS and Landsat thermal channel data. The difference of sea surface temperature between inland and offshore waters of Saemangeum embankment have changed by seasonal flow and residence time of sea water in dyke.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.9
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• pp.24-33
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• 2017

Multitemporal MODIS vegetation index (VI) data are widely used in vegetation monitoring research into environmental and climate change, since they provide a profile of vegetation activity. However, MODIS data inevitably contain disturbances caused by the presence of clouds, atmospheric variability, and instrument problems, which impede the analysis of the NDVI time series data and limit its application utility. For this reason, preprocessing to reduce the noise and reconstruct high-quality temporal data streams is required for VI analysis. In this study, a data reconstruction method for MODIS NDVI is proposed to restore bad or missing data based on the statistical properties of the oscillations in the NDVI temporal dynamics. The first derivatives enable us to examine the monotonic properties of a function in the data stream and to detect anomalous changes, such as sudden spikes and drops. In this approach, only noisy data are corrected, while the other data are left intact to preserve the detailed temporal dynamics for further VI analysis. The proposed method was successfully tested and evaluated with simulated data and NDVI time series data covering Baekdu Mountain, located in the northern part of North Korea, over the period of interest from 2006 to 2012. The results show that it can be effectively employed as a preprocessing method for data reconstruction in MODIS NDVI analysis.