• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.458-461
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• 2005

Surface Solar Insolation is important for vegetation productivity, hydrology, crop growth, etc. However, ground base measurement stations installed pyranometer are often sparsely distributed, especially over oceans. In this study, Surface Solar Insolation is estimated using the visible and infrared spin scan radiometer(VISSR) data on board Geostationary Meteorological Satellite (GMS)-S covering from March 2001 to December 2001 in clear and cloudy conditions. To retrieve atmospheric factor, such as, optical depth, the amount of ozone, H20, and aerosol, SMAC (Simplified Method for Atmospheric Correction) code, is adopted. The hourly Surface Solar Insolation is estimated with a spatial resolution of $5km\;\times\;5km$ grid. The daily Surface Solar Insolation is derived from the available hourly Surface solar irradiance, independently for every pixel. The pyranometer by the Korea Meteorological Agency (KMA) is used to validate the estimated Surface Solar Insolation with a spatial resolution of $3\;\times\;3Pixels.$

• Proceedings of the KSRS Conference
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• v.2
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• pp.950-953
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• 2006

SIMP is an international project funded by INTAS aimed at improving the information content, which can be inferred from multi-sensor satellite imagery of marine coastal areas. Scientific teams from Germany, UK, Portugal, and Russia focus on the development of novel tools for marine remote sensing of the coastal zone. In particular, the project teams' benefit from the fact that surface films may enhance the signatures of hydrodynamic processes such as plumes, internal waves, eddies, etc., on microwave, optical, and infrared imagery. The project's objectives are to develop a robust methodology for identifying slick-related phenomena/processes through their surface signatures and thereby, to improve the discrimination capabilities between slicks and other oceanic and atmospheric phenomena by taking into account information gained from satellite imagery quasi-simultaneously recorded at microwave, visible and IR wavelengths. The results of the two project years are summarized. Examples are given for the project’s web presentation, laboratory and field experiments, and of the analyses of various satellite data.

• Atmosphere
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• v.22 no.1
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• pp.1-12
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• 2012

The Atmospheric motion vectors (AMVs) derived using infrared (IR) channel imagery of geostationary satellites have been utilized widely for real-time weather analysis and data assimilation into global numerical prediction model. As the horizontal resolution of sensors on-board satellites gets higher, it becomes possible to identify atmospheric motions induced by convective clouds ($meso-{\beta}$ and $meso-{\gamma}$ scales). The National Institute of Meteorological Research (NIMR) developed the high resolution visible (HRV) AMV algorithm to detect mesoscale atmospheric motions including ageostrophic flows. To retrieve atmospheric motions smaller than $meso-{\beta}$ scale effectively, the target size is reduced and the visible channel imagery of geostationary satellite with 1 km resolution is used. For the accurate AMVs, optimal conditions are decided by investigating sensitivity of algorithm to target selection and correction method of height assignment. The results show that the optimal conditions are target size of 32 km ${\times}$ 32 km, the grid interval as same as target size, and the optimal target selection method. The HRV AMVs derived with these conditions depict more effectively tropical cyclone OMAIS than IR AMVs and the mean speed of HRV AMVs in OMAIS is slightly faster than that of IR AMVs. Optimized mesoscale AMVs are derived for 6 months (Feb. 2010-Jun. 2010) and validated with radiosonde observations, which indicates NIMR's HRV AMV algorithm can retrieve successfully mesoscale atmospheric motions.

• Proceedings of the KSRS Conference
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• v.1
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• pp.86-89
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• 2006

Ocean color remote sensing community currently uses the different solar irradiance spectra covering the visible and near-infrared in the calibration/validation and deriving products of ocean color instruments. These spectra derived from single and / or multiple measurements sets or models have significant discrepancies, primarily due to variation of the solar activity and uncertainties in the measurements from various instruments and their different calibration standards. Thus, it is prudent to examine model-to-model differences and select a standard reference spectrum that can be adopted in the future calibration and validation processes, particularly of the first Geostationary Ocean Color Imager (GOCI) onboard its Communication Ocean and Meterological Satellite (COMS) planned to be launched in 2008. From an exhaustive survey that reveals a variety of solar spectra in the literature, only eight spectra are considered here seeing as reference in many remote sensing applications. Several criteria are designed to define the reference spectrum: i.e., minimum spectral range of 350-1200nm, based completely or mostly on direct measurements, possible update of data and less errors. A careful analysis of these spectra reveals that the Thuillier 2004 spectrum seems to be very identical compared to other spectra, primarily because it represents very high spectral resolution and the current state of the art in solar irradiance spectra of exceptionally low uncertainty ${\sim}0.1%.$ This study also suggests use of the Gueymard 2004 spectrum as an alternative for applications of multispectral/multipurpose satellite sensors covering the terrestrial regions of interest, where it provides spectral converge beyond 2400nm of the Thuillier 2004 spectrum. Since the solar-activity induced spectral variation is about less than 0.1% and a large portion of this variability occurs particularly in the ultraviolet portion of the electromagnetic spectrum that is the region of less interest for the ocean color community, we disregard considering this variability in the analysis of solar irradiance spectra, although determine the solar constant 1366.1 $Wm^{-2}$ to be proposed for an improved approximation of the extraterrestrial solar spectrum in the visible and NIR region.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.2
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• pp.159-171
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• 2012

Aerosol direct radiative forcing (ADRF) retrieval method was developed by combining data from passive and active satellite sensors. Aerosol optical thickness (AOT) retrieved form the Moderate Resolution Imaging Spectroradiometer (MODIS) as a passive visible sensor and aerosol vertical profile from to the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) as an active laser sensor were investigated an application possibility. Especially, space-born Light Detection and Ranging (Lidar) observation provides a specific knowledge of the optical properties of atmospheric aerosols with spatial, temporal, vertical, and spectral resolutions. On the basis of extensive radiative transfer modeling, it is demonstrated that the use of the aerosol vertical profiles is sensitive to the estimation of ADRF. Throughout the investigation of relationship between aerosol height and ADRF, mean change rates of ADRF per increasing of 1 km aerosol height are smaller at surface than top-of-atmosphere (TOA). As a case study, satellite data for the Asian dust day of March 31, 2007 were used to estimate ADRF. Resulting ADRF values were compared with those retrieved independently from MODIS only data. The absolute difference values are 1.27% at surface level and 4.73% at top of atmosphere (TOA).

• Proceedings of the KSRS Conference
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• v.2
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• pp.598-601
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• 2006

Satellite imagery may contain large regions covered with atmospheric aerosol. A high-resolution satellite imagery affected by non-homogenous aerosol cover should be processed for land cover study and perform the radiometric calibration that will allow its future application for Korea Multi-Purpose Satellite (KOMPSAT) data. In this study, aerosol signal was separated from high resolution satellite data based on the reflectance separation method. Since aerosol removal has a good sensitivity over bright surface such as man-made targets, aerosol optical thickness (AOT) retrieval algorithm could be used. AOT retrieval using Look-up table (LUT) approach for utilizing the transformed image to radiometrically compensate visible band imagery is processed and tested in the correction of satellite scenery. Moderate Resolution Imaging Spectroradiometer (MODIS), EO-1/HYPERION data have been used for aerosol correction and AOT retrieval with different spatial resolution. Results show that an application of the aerosol detection for HYPERION data yields successive aerosol separation from imagery and AOT maps are consistent with MODIS AOT map.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.379-383
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• 2006

Satellite imagery may contain large regions covered with atmospheric aerosol. A highresolution satellite imagery affected by non-homogenous aerosol cover should be processed for land cover study and perform the radiometric calibration that will allow its future application for Korea Multi-Purpose Satellite (KOMPSAT) data. In this study, aerosol signal was separated from high resolution satellite data based on the reflectance separation method. Since aerosol removal has a good sensitivity over bright surface such as man-made targets, aerosol optical thickness (AOT) retrieval algorithm could be used. AOT retrieval using Look-up table (LUT) approach for utilizing the transformed image to radiometrically compensate visible band imagery is processed and tested in the correction of satellite scenery. Moderate Resolution Imaging Spectroradiometer (MODIS), EO-l/HYPERION data have been used for aerosol correction and AOT retrieval with different spatial resolution. Results show that an application of the aerosol detection for HYPERION data yields successive aerosol separation from imagery and AOT maps are consistent with MODIS AOT map.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5095-5111
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• 2019

A multi-rotor Unmanned Aerial Vehicle (UAV) system is developed to solve the manhole cover detection problem for the infrastructure maintenance in the suburbs of big city. The visible light sensor is employed to collect the ground image data and a series of image processing and machine learning methods are used to detect the manhole cover. First, the image enhancement technique is employed to improve the imaging effect of visible light camera. An imaging environment perception method is used to increase the computation robustness: the blind Image Quality Evaluation Metrics (IQEMs) are used to percept the imaging environment and select the images which have a high imaging definition for the following computation. Because of its excellent processing effect the adaptive Multiple Scale Retinex (MSR) is used to enhance the imaging quality. Second, the Single Shot multi-box Detector (SSD) method is utilized to identify the manhole cover for its stable processing effect. Third, the spatial coordinate of manhole cover is also estimated from the ground image. The practical applications have verified the outdoor environment adaptability of proposed algorithm and the target detection correctness of proposed system. The detection accuracy can reach 99% and the positioning accuracy is about 0.7 meters.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.3
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• pp.323-332
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• 2023

Today, services using Positioning, Navigation, and Timing (PNT) technology are provided in various fields, such as smartphone Location-Based Service (LBS) and autonomous driving. Generally, outdoor positioning techniques depend on the Global Navigation Satellite System (GNSS), and the need for positioning techniques that guarantee positioning accuracy, availability, and continuity is emerging with advances in service. In particular, continuity is not guaranteed in urban canyons where it is challenging to secure visible satellites with standalone GNSS, and even if more than four satellites are visible, the positioning accuracy and stability are reduced due to multipath channels. Research using Low Earth Orbit (LEO) satellites is already underway to overcome these limitations. In this study, we conducted a trend analysis of LEO-PNT research, an LEO satellite-based navigation and augmentation system. Through comparison with GNSS, the differentiation of LEO-PNT was confirmed, and the system design and receiver processing were analyzed according to LEO-PNT classification. Lastly, the current status of LEO-PNT development by country and institution was confirmed.