• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.251-251
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• 2016

인공위성을 이용한 가뭄연구에는 전지구적으로 운용되는 GPM (Global Precipitation Measurement) 위성, AQUA/TERRA 위성의 MODIS (MODerate resolution Imaging Spectroradiometer) 센서 등에서 수집된 관측 자료가 이용된다. 그러나 전지국적으로 관측된 위성 자료는 자료를 생산 제공하는 기관에 따라 자료의 파일포맷 (NetCDF, HDF5, GeoTIFF 등), 자료의 투영법 (projection) 등이 상이하다. 그러므로 가뭄연구에 다중위성자료를 활용하고자 하는 지리정보시스템(Geographic Information System: GIS)에 대한 전문지식이 부족한 연구자는 자료의 표준화 (파일포맷과 투영변환 등) 과정으로 인해 원활한 연구수행이 어렵다. MODIS 위성자료의 경우에는 일반적으로 많이 사용되는 횡단메르카토르 도법 (Transverse Mercator Projection: TM) 대신 시뉴소이드 도법 (sinusoidal projection)을 이용한다. 그래서 미국 지질조사국은 MODIS 자료의 재투영(reprojection)을 위한 전용 소프트웨어인 MRT (MODIS Reprojection Tool)를 배포하고 있다. 본 연구에서는 무료/오픈소스 소프트웨어를 활용하여 시뉴소이드 도법이 적용된 MODIS 자료의 수집, 재투영, 파일포맷 변환 등을 자동으로 처리하는 기법을 개발하여 가뭄활용에 이용하고자 하였으며, MODIS MOD09GA/MOD11A1 자료를 이용하여 효율성을 검증하였다.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1301-1314
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• 2022

Normalized Difference Vegetation Index (NDVI) is utilized as an indicator to represent the vegetation condition on the land surface in various applications such as land cover, crop yield, agricultural drought, soil moisture, and forest disaster. However, satellite optical sensors for visible and infrared rays cannot see through the clouds, so the NDVI of the cloud pixel is not a valid value for the land surface. This study proposed a real-time correction of the underestimation noise for GEO-KOMPSAT-2A (GK2A) daily NDVI and made sure its feasibility through the quantitative comparisons with Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI and the qualitative interpretation of time-series changes. The underestimation noise was effectively corrected by the procedures such as the time-series correction considering vegetation phenology, the outlier removal using long-term climatology, and the gap filling using rigorous statistical methods. The correlation with MODIS NDVI was higher, and the difference was lower, showing a 32.7% improvement compared to the original NDVI product. The proposed method has an extensibility for use in other satellite products with some modification.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.10a
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• pp.386-388
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• 2008

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.243-246
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• 2007

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.597-609
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• 2012

Leaf area index (LAI) is important in explaining the ability of the crop to intercept solar energy for biomass production and in understanding the impact of crop management practices. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal series of IKONOS, Landsat TM, and MODIS satellite images using empirical models and demonstrates its use with data collected at Missouri field sites. LAI data were obtained several times during the 2002 growing season at monitoring sites established in two central Missouri experimental fields, one planted to soybean (Glycine max L.) and the other planted to corn (Zea mays L.). Satellite images at varying spatial and spectral resolutions were acquired and the data were extracted to calculate normalized difference vegetation index (NDVI) after geometric and atmospheric correction. Linear, exponential, and expolinear models were developed to relate temporal NDVI to measured LAI data. Models using IKONOS NDVI estimated LAI of both soybean and corn better than those using Landsat TM or MODIS NDVI. Expolinear models provided more accurate results than linear or exponential models.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.53-56
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• 2007

The Korean Geostationary satellite (COMS) to fly in year 2009 will carry a meteorological sensor from which visible channel measurements will be available. We developed a method utilizing satellite-derived BRDFs for the solar channel calibration over the bright desert area. The 6S model has been incorporated to account for directional effects of the surface using MODIS-derived BRDF parameters within the spectral interval in interest. Simulated radiances over the desert targets were compared with MODIS and SeaWiFS measured spectral radiances in order to examine the feasibility of the developed calibration algorithm. We also simulated TOA radiance over ocean targets to verify the consistency and reliability of the result. It was shown that simulated 16-day averaged radiances are in good agreement with the satellite-measured radiances within about ${\pm}5%$ uncertainty range for the year 2005, suggesting that the developed algorithm can be used for calibrating the COMS visible channel within about 5% uncertainty level.

• Korean Journal of Remote Sensing
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• v.32 no.1
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• pp.25-38
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• 2016

With the increasing need for high temporal resolution satellite imagery for monitoring land surfaces, this study evaluated the temporal resolution of the NDVI composites from Geostationary Ocean Color Imager (GOCI) data. The GOCI is the first geostationary satellite sensor designed to provide continuous images over a $2,500{\times}2,500km^2$ area of the northeast Asian region with relatively high spatial resolution of 500 m. We used total 2,944 hourly images of the GOCI level 1B radiance data obtained during the one-year period from April 2011 to March 2012. A daily NDVI composite was produced by maximum value compositing of eight hourly images captured during day-time. Further NDVI composites were created with different compositing periods ranging from two to five days. The cloud coverage of each composite was estimated by the cloud detection method developed in study and then compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua cloud product and 16-day NDVI composite. The GOCI NDVI composites showed much higher temporal resolution with less cloud coverage than the MODIS NDVI products. The average of cloud coverage for the five-day GOCI composites during the one year was only 2.5%, which is a significant improvement compared to the 8.9%~19.3% cloud coverage in the MODIS 16-day NDVI composites.

• Korean Journal of Geomatics
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• v.3 no.1
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• pp.59-65
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• 2003

The merging of different scales or multi-sensor image data is becoming a widely used procedure of the complementary nature of various data sets. Ideally, the merging method should not distort the characteristics of the high-spatial and high-spectral resolution data used. To present an effective merging method for survey of reclaimed land using the high-resolution (6.6 m) Electro-Optical Camera (EOC) panchromatic image of the first Korea Multi-Purpose Satellite 1 (KOMPSA T-l) and the multispectral Moderate Resolution Imaging Spectroradiometer (MODIS) image data, this paper compares the results of Intensity Hue Saturation (IHS) and Principal Component Analysis (PCA) methods. The comparison is made by statistical and visual evaluation of three-color combination images of IHS and PCA results based on spatial and spectral characteristics. The use of MODIS bands 1, 2, and 3 with a contrast stretched EOC panchromatic image as a substitute for intensity was found to be particularly effective in this study.

• Journal of Environmental Science International
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• v.18 no.4
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• pp.355-367
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• 2009

Comparison study on the land surface temperatures, which are calculated from four different algorithms for MODIS data, was carried out and the characteristics of each algorithm on land surface temperature estimation were also analysed in this study. Algorithms, which are well used for various satellite data analysis, in the comparisons are proposed by Price, Becker and Li, Ulivieri et al., and Wan. Verification of estimated land surface temperature from each algorithm is also performed using observation based regression data. The coefficient of determination ($R^2$) for daytime land surface temperature estimated from Wan's algorithm is higher than that of another algorithms at all seasons and the value of $R^2$ reach on 0.92 at spring. Although $R^2$ for Ulivieri's algorithm is slightly lower than that for Wan's algorithm, the variation pattern of land surface temperature for two algorithms are similar. However, the difference of estimated values among four algorithms become small at the region of high land surface temperature.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.254-259
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• 2009

In this study, Simplified Method for the Atmospheric Correction (SMAC) radiative transfer model (RTM) used to retrieve surface reflectance from MODIS Top Of Atmosphere (TOA) reflectance (MOD02). SMAC code provides coefficients which were previously yielded by Second Simulation of the Satellite Signal in the Solar Spectrum (6S) for each satellite sensor. We conducted error analysis of SMAC RTM using MOD02 over comparison with MODIS surface reflectance (MOD09) which was provided from 6S. It showed that low accuracy values such as, $R^2$ : 0.6196, Root Means Square Error (RMSE) : 0.00031, bias : - 0.0859. Thus sensitivity analysis of input parameters and coefficients was conducted to searching error sources. Coefficients about $\tau_p$ (average AOD) are more influence than any other coefficients of $\tau_{a550}$ (Aerosol Optical Depth at 550nm) from sensitivity test. Calibrated coefficients of $\tau_p$ from regression analysis were used to surface reflectance which showed that improve accuracy of surface reflectance ($R^2$ : 0.827, RMSE : 0.00672, bias : - 0.000762).