• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.65-78
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• 2008

This study compares the relative accuracy and consistency of four split-window land surface temperature (LST) algorithms (Becker and Li, Kerr et ai., Price, Ulivieri et al.) using 24 sets of Terra (Aqua)/Moderate Resolution Imaging Spectroradiometer (MODIS) data, observed ground grass temperature and air temperature over South Korea. The effective spectral emissivities of two thermal infrared bands have been retrieved by vegetation coverage method using the normalized difference vegetation index. The intercomparison results among the four LST algorithms show that the three algorithms (Becker-Li, Price, and Ulivieri et al.) show very similar performances. The LST estimated by the Becker and Li's algorithm is the highest, whereas that by the Kerr et al.'s algorithm is the lowest without regard to the geographic locations and seasons. The performance of four LST algorithms is significantly better during cold season (night) than warm season (day). And the LST derived from Terra/MODIS is closer to the observed LST than that of Aqua/MODIS. In general, the performances of Becker-Li and Ulivieri et al algorithms are systematically better than the others without regard to the day/night, seasons, and satellites. And the root mean square error and bias of Ulivieri et al. algorithm are consistently less than that of Becker-Li for the four seasons.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.554-556
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• 2005

Meteorological imager on the Multi-functional Transport Satellite (MTSAT-IR), which has been operating formally since 28 June 2005, was intercalibrated with a polar orbit satellite [Aqua Moderate Resolution Imaging Spectroradiometer (Aqua/MODIS)] as a well-calibrated instrument. The intercalibration method used in this study was developed by the Cooperative Institute for Meteorological Satellite Studies (CIMSS). This was done for the infrared window channels. The differences of MTSAT-IR and MODIS were are -0.26 K for $11\;\mu m-IR$ window channel, 0.40 K for $12\;\mu m-IR$, window channel, and -0.67 K for $6.7\;\mu m-water$ vapor channel.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.113-126
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• 2009

With high temporal resolution, four times receiving during a day, MODIS images from Terra and Aqua satellites provide several advantages for monitoring spacious land. Especially, diverse MODIS products related to land, atmosphere, and ocean have been provided with radiance MODIS images. The products such as surface reflectance, NDVI, cloud mask, aerosol etc. are based on theoretical algorithms developed in academic areas. Comparing with other change detection studies mainly using the vegetation index, this study investigated temporal surface reflectance of landcovers for five years from 2004 to 2008. The near infrared (NIR) reflectance in urbanized and burned areas showed considerable difference before and after events. The specific characteristics of surface reflectance temporal profiles are possibly useful for the detection of landcover changes and classification.

• 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.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.271-276
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• 2003

The Moderate Resolution Imaging Spectroradiometer (MODIS) on the Earth Observing System (EOS) of Terra and Aqua satellites, launched in December 1999 and May 2002, has been directly received by Korea Aerospace Research Institute (KARI) ground station facility from July 2002. MODIS scans a swath width of 2330 km that is sufficiently wide to cover Korean peninsular, Yellow and East Sea at once. The MODIS has 36 spectral bands between 0.415 $\mu\textrm{m}$ and 14.235 $\mu\textrm{m}$, i.e., through the visible into the thermal infrared. MODIS has been observed active fires, floods, smoke transport, dust storms, severe storms since February of 2000. The satellite imagery obtained through the MODIS will be utilized for many application such as national territorial management, agriculture, natural environment, atmosphere and ocean, etc. In this study is to introduce various application field of MODIS imagery and data processing system.

• Korean Journal of Remote Sensing
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• v.23 no.3
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• pp.161-169
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• 2007

A study was conducted in the Hong Kong with the aim of deriving an algorithm for the retrieval of suspended sediment (SS) and sea surface salinity (SSS) concentrations from Aqua/MODIS level 1B reflectance data with 250m and 500m spatial resolutions. 'In-situ' measurements of SS and SSS were also compared with coincident MODIS spectral reflectance measurements over the ocean surface. This is the first study of SSS modeling in Southeast Asia using earth observation satellite images. Three analysis techniques such as multiple regression, linear regression, and principal component analysis (PCA) were performed on the MODIS data and the 'in-situ' measurement datasets of the SS and SSS. Correlation coefficients by each analysis method shows that the best correlation results are multiple regression from the 500m spatial resolution MODIS images, $R^2$= 0.82 for SS and $R^2$ = 0.81 for SSS. The Root Mean Square Error (RMSE) between satellite and 'in-situ' data are 0.92mg/L for SS and 1.63psu for SSS, respectively. These suggest that 500m spatial resolution MODIS data are suitable for water quality modeling in the study area. Furthermore, the application of these models to MODIS images of the Hong Kong and Pearl River Delta (PRO) Region are able to accurately reproduce the spatial distribution map of the high turbidity with realistic SS concentrations.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.415-418
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• 2004

The Studying is on developing precision of the moisture information on a soil. We used the data of AQUA AMSR-E which were obtained by Direct Receiving System in Korea Meteorological Administration(KMA). Although we know the Soil Moisture Information(SMI) helps the numerical weather model to produce the realistic results, we couldn't do it for the problem on a spatial resolution of the data is too low to apply. So we've tried to develop in a spatial resolution by using the AMSR-E data with a Digital Elevation Model(DEM) and Normal Difference Vegetation Index(NDVI) from AQUA MODIS and compared the difference between their information in statics. The result is more precise than the simple algorithm by a polarization ratio, and we could get the better result to use in forecast practically, if it's apply to get more detail in the vegetation temperature.

• Proceedings of the KSRS Conference
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• v.1
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• pp.487-490
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• 2006

Ocean Color Monitor (OCM) onboard the Indian Remote Sensing Satellite IRS-P4 has been used to retrieve chlorophyll concentration in the Bay of Bengal and the Arabian Sea using a bio-optical algorithm. Cloud masking and atmospheric corrections have been performed before applying mapping function to derive chlorophyll concentration from IRS-P4 OCM data. We have retrieved chlorophyll concentration from OCM, and MODIS during the summer and winter season along the eastern and western coast of India at every 1 degree latitude at increasing distance (25, 50, 100, 150 and 200km) away from the coast as well as near river mouths for the period 2000-2003. We have also studied spatial and temporal dynamics of monthly MODIS Aqua (for period July 2002-April 2004). The seasonal dynamics of chlorophyll concentration over the Bay of Bengal and the Arabian Sea have been discussed using OCM and MODIS for both the coastal region and the open sea.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.607-615
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• 2017

The aerosol optical thickness data retrieved by Moderate Resolution Imaging Spectrometer (MODIS) of Terra & Aqua and Meteorological Imager (MI) of Communication Ocean and Meteorological Satellite (COMS) are analyzed and compared with the measurement data of Aerosol Robotic Network (AERONET) in Northeast Asia. As the result, the aerosol optical thickness retrieved by MODIS and MI were well agreed at ocean region but quite different at cloud edge and barren surface. The reason was that MODIS aerosol optical thickness was retrieved using the visible and infrared channels but MI was retrieved with the visible channel only. Consequentially, the thin cloud be misinterpreted as aerosol by MI and the difference between MODIS and MI aerosol optical thicknesses could be occurred with Normal Distribution Vegetation Index (NDVI) and land surface property. Therefore, the accuracies of clear/cloud region and surface reflectivity are required in order to improve the aerosol optical thickness algorithm by MI.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.1 s.39
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• pp.23-29
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• 2007

Spatial outliers in remotely sensed imagery represent observed quantities showing unusual values compared to their neighbor pixel values. There have been various methods to detect the spatial outliers based on spatial autocorrelations in statistics and data mining. These methods may be applied in detecting forest fire pixels in the MODIS imageries from NASA's AQUA satellite. This is because the forest fire detection can be referred to as finding spatial outliers using spatial variation of brightness temperature. In this paper, we propose a new forest fire detection algorithm which is based on local spatial association analysis, and test the proposed algorithm to evaluate its applicability. In order to evaluate the proposed algorithm, the results were compared with the MODIS fire product provided by the NASA MODIS Science Team, which showed the possibility of the proposed algorithm in detecting the fire pixels.