• Journal of Astronomy and Space Sciences
• /
• v.26 no.4
• /
• pp.547-554
• /
• 2009

The Atmospheric Infrared Sounder (AIRS) aboard the Aqua satellite, which is one of the Earth Observing System satellites managed by National Aeronautics and Space Administration, provides global measurements of the water vapor in the atmosphere using infrared (IR) channels. In this paper, we restored precipitable water vapor (PWV) over a permanent GPS station in Incheon using the IR measurements of AIRS and compared the result with GPS-based PWV estimates. As a result, AIRS PWV had similar trends with GPS PWV; the bias of AIRS PWV against GPS PWV is 0.3 cm and root mean square error (RMSE) 0.7 cm. In addition, the correlation coefficient between AIRS PWV and GPS PWV was 0.89. Thus we conclude that the AIRS PWV reflects local characteristics of the water vapor content.

• The Journal of the Petrological Society of Korea
• /
• v.26 no.4
• /
• pp.373-384
• /
• 2017

A new era with the $4^{th}$ Industrial Revolution certainly brings new opportunities for human to explore human's activities outside of the Earth. After the Apollo program, exploration for lunar resources and establishment of lunar base seem to be in reality. This could be due to new findings by the LCROSS and LRO proving the advanced scientific development and new scientific results about the moon from Asian countries including China with Chang'E missions. It is expected that fossil fuels will be in shortage in the near future and at this time, Helium-3 could be an energy resource as a replacement of the fossil fuels. At present it is well known that countries like Russia, USA, and Europe will continue to investigate on lunar exploration especially with landers toward future human activities on the moon to establish a lunar base. With this point of view, it is important for human to understand lunar resources and prepare for prospective utilization of lunar resources. This review paper considers on a point of view in both lunar resource exploration and establishment of lunar base.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.44 no.1
• /
• pp.70-79
• /
• 2016

Satrec Initiative successfully developed a high-resolution electro-optical camera system, EOS-D Ver.1.0. EOS-D Ver.1.0 is the main payload of DubaiSat-2 and Deimos-2, which are developed based on the SI-300 platform of Satrec Initiative. After the launch and early operation (LEOP) of DubaiSat-2 and Deimos-2, we performed refocusing for the telescope of EOS-D Ver.1.0 to compensate for the dimensional change of its metering structure by moisture out-gassing. Before and after refocusing, we conducted the performance evaluation of thermal control system(TCS) for EOS-D Ver.1.0 using the in-orbit telemetry data. The evaluation showed EOS-D Ver.1.0 was under well-controlled thermal environment, which demonstrates TCS was designed and developed to meet all requirements.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.33 no.4
• /
• pp.305-316
• /
• 2015

The GPS signal delays in troposphere, which are along the signal path between a transmitting satellite and GPS permanent station, can be used to retrieve the precipitable water vapor. The GPS remote sensing technique of atmospheric water vapor is capable of monitoring typhoon and detecting long term water vapor for tracking of earth’s climate change. In this study, we analyzed GPS precipitable water vapor variations during the heavy snowstorm event occurred in the Yeongdong area, 2014. The results show that the snowfall event were occurring after the GPS precipitable water vapor were increased, the maximum fresh snow depth was recorded after the maximum GPS precipitable water vapor was generated, in Kangneug and Wuljin, respectively. Also, we analyzed that the closely correlation among the GPS precipitable water vapor, the K-index and total index which was acquired by the upper air observation system during this snowstorm event was revealed.

• Journal of Space Technology and Applications
• /
• v.1 no.1
• /
• pp.49-63
• /
• 2021

The exploration of the solar system is carried out through various payloads, and accordingly, many research results are emerging. We tried to apply deep-learning as a method of studying the bodies of solar system. Unlike Earth observation satellite data, the data of solar system differ greatly from celestial bodies to probes and to payloads of each probe. Therefore, it may be difficult to apply it to various data with the deep-learning model, but we expect that it will be able to reduce human errors or compensate for missing parts. We have implemented a model that detects craters on the lunar surface. A model was created using the Lunar Reconnaissance Orbiter Camera (LROC) image and the provided shapefile as input values, and applied to the lunar surface image. Although the result was not satisfactory, it will be applied to the image of the permanently shadow regions of the Moon, which is finally acquired by ShadowCam through image pre-processing and model modification. In addition, by attempting to apply it to Ceres and Mercury, which have similar the lunar surface, it is intended to suggest that deep-learning is another method for the study of the solar system.

• Ocean Systems Engineering
• /
• v.14 no.1
• /
• pp.85-99
• /
• 2024

Ocean surface currents have an essential role in the Earth's climate system and significantly impact the marine ecosystem, weather patterns, and human activities. However, predicting ocean surface currents remains challenging due to the complexity and variability of the oceanic processes involved. This review article provides an overview of the current research status, challenges, and opportunities in the prediction of ocean surface currents. We discuss the various observational and modelling approaches used to study ocean surface currents, including satellite remote sensing, in situ measurements, and numerical models. We also highlight the major challenges facing the prediction of ocean surface currents, such as data assimilation, model-observation integration, and the representation of sub-grid scale processes. In this article, we suggest that future research should focus on developing advanced modeling techniques, such as machine learning, and the integration of multiple observational platforms to improve the accuracy and skill of ocean surface current predictions. We also emphasize the need to address the limitations of observing instruments, such as delays in receiving data, versioning errors, missing data, and undocumented data processing techniques. Improving data availability and quality will be essential for enhancing the accuracy of predictions. The future research should focus on developing methods for effective bias correction, a series of data preprocessing procedures, and utilizing combined models and xAI models to incorporate data from various sources. Advancements in predicting ocean surface currents will benefit various applications such as maritime operations, climate studies, and ecosystem management.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_2
• /
• pp.1553-1563
• /
• 2023

The Geostationary Ocean Color Imager-II (GOCI-II) is a satellite designed for ocean color observation, covering the Northeast Asian region and the entire disk of the Earth. It commenced operations in 2020, succeeding its predecessor, GOCI, which had been active for the previous decade. In this study, we aimed to enhance the atmospheric correction algorithm, a critical step in producing satellite-based ocean color data, by performing cross-calibration on the GOCI-II near-infrared (NIR) band using the GOCI NIR band. To achieve this, we conducted a cross-calibration study on the top-of-atmosphere (TOA) radiance of the NIR band and derived a vicarious calibration gain for two NIR bands (745 and 865 nm). As a result of applying this gain, the offset of two sensors decreased and the ratio approached 1. It shows that consistency of two sensors was improved. Also, the Rayleigh-corrected reflectance at 745 nm and 865 nm increased by 5.62% and 9.52%, respectively. This alteration had implications for the ratio of Rayleigh-corrected reflectance at these wavelengths, potentially impacting the atmospheric correction results across all spectral bands, particularly during the aerosol reflectance correction process within the atmospheric correction algorithm. Due to the limited overlapping operational period of GOCI and GOCI-II satellites, we only used data from March 2021. Nevertheless, we anticipate further enhancements through ongoing cross-calibration research with other satellites in the future. Additionally, it is essential to apply the vicarious calibration gain derived for the NIR band in this study to perform vicarious calibration for the visible channels and assess its impact on the accuracy of the ocean color products.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.32 no.1
• /
• pp.19-27
• /
• 2014

Kompsat-3 is an optical high-resolution earth observation satellite launched in May 2012. In addition to its 0.7m spatial resolution, Kompsat-3 is capable of in-track stereo acquisition enabling quality Digital Elevation Model(DEM) generation. Typical DEM generation procedure requires accurate control points well-distributed over the entire image region. But we often face difficult situations especially when the area of interests is oversea or inaccessible area. One solution to this is to use existing geospatial data even though they only cover a part of the image. This paper aimed to assess accuracy of DEM from Kompsat-3 with different scenarios including no control point, Rational Polynomial Coefficients(RPC) relative adjustment, and RPC adjustment with control points. Experiments were carried out for Kompsat-3 stereo data in USA. We used Digital Orthophoto Quadrangle(DOQ) and Shuttle Radar Topography Mission(SRTM) as control points sources. The generated DEMs are compared to a LiDAR DEM for accuracy assessment. The test results showed that the relative RPC adjustment significantly improved DEM accuracy without any control point. And comparable DEM could be derived from single control point from DOQ and SRTM, showing 7 meters of mean elevation error.

• Journal of Aerospace System Engineering
• /
• v.12 no.4
• /
• pp.64-74
• /
• 2018

Small high-resolution Earth observation satellites require precise optical alignment at the submicron level. However, misalignments can occur due to the influence of external factors during the launch and operation despite the sufficient alignment processes that take place before the launch. Thus, satellites need to realign their optical elements in orbit in what is known as a refocusing process to compensate for any misalignments. Refocusing algorithms developed for satellites have only considered de-space, which is the most sensitive factor with respect to image quality. However, the existing algorithms can cause correction error when inner and external forces generate tilt amount in an optical system. The present work suggests an improved online refocusing algorithm by considering the tilting effect for application in the case of a de-spaced and tilted optical system. In addition, the algorithm is considered to be efficient in terms of time and cost because it is designed to be used as an online method that does not require ground communication.

• Korean Journal of Remote Sensing
• /
• v.21 no.3
• /
• pp.189-211
• /
• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.