• Korean Journal of Remote Sensing
• /
• v.26 no.2
• /
• pp.189-207
• /
• 2010

Several ocean color algorithms have been developed for GOCI (Geostationary Ocean Color Imager) using in-situ bio-optical data sets. These data sets collected around the Korean Peninsula between 1998 and 2009 include chlorophyll-a concentration (Chl-a), suspended sediment concentration (SS), absorption coefficient of dissolved organic matter ($a_{dom}$), and remote sensing reflectance ($R_{rs}$) obtained from 1348 points. The GOCI Chl-a algorithm was developed using a 4-band remote sensing reflectance ratio that account for the influence of suspended sediment and dissolved organic matter. The GOCI Chl-a algorithm reproduced in-situ chlorophyll concentration better than the other algorithms. In the SeaWiFS images, this algorithm reduced an average error of 46 % in chlorophyll concentration retrieved by standard chlorophyll algorithms of SeaWiFS. For the GOCI SS algorithm, a single band was used (Ahn et al., 2001) instead of a band ratio that is commonly used in chlorophyll algorithms. The GOCI $a_{dom}$ algorithm was derived from the relationship between remote sensing reflectance band ratio ($R_{rs}(412)/R_{rs}(555)$) and $a_{dom}(\lambda)$). The GOCI Chl-a fluorescence and GOCI red tide algorithms were developed by Ahn and Shanmugam (2007) and Ahn and Shanmugam (2006), respectively. If the launch of GOCI in June 2010 is successful, then the developed algorithms will be analyzed in the GOCI CAL/VAL processes, and improved by incorporating more data sets of the ocean optical properties data that will be obtained from waters around the Korean Peninsula.

• Korean Journal of Remote Sensing
• /
• v.12 no.1
• /
• pp.1-16
• /
• 1996

Korea Aerospace Research Institute(KARI) is developing a Korea Multi-Purpose Satellite I(KOMPSAT-I) which accommodates Electro-Optical Camera(EOC), Ocean Color Imager(OCI), Space Physics Sensor(SPS) for cartography, ocean color monitoring, and space environment monitoring respectively. The satellite has the weight of about 500 kg and is operated on the sun synchronized orbit with the altitude of 685km, the orbit period of 98 minutes, and the orbit revisit time of 28days. The satellite will be launched in the third quarter of 1999 and its lifetime is more than 3 years. EOC has cartography mission to provide images for the production of scale maps, including digital elevation models, of Korea from a remote earth view in the KOMPSAT orbit. EOC collects panchromatic imagery with the ground sample distance(GSD) of 6.6m and the swath width of 15km at nadir through the visible spectral band of 510-730 nm. EOC scans the ground track of 800km per orbit by push-broom and body pointed method. OCI mission is worldwide ocean color monitoring for the study of biological oceanography. OCI is a multispectral imager generating 6 color ocean images with and <1km GSD by whisk-broom scanning method. OCI is designed to provide on-orbit spectral band selectability in the spectral range from 400nm to 900nm. The color images are collected through 6 primary spectral bands centered at 443, 490, 510, 555, 670, 865nm or 6 spectral bands selected in the spectral range via ground commands after launch. SPS consists of High Energy Particle Detector(HEPD) and Ionosphere Measurement Sensor(IMS). HEPD has mission to characterize the low altitude high energy particle environment and to study the effects of radiation environment on microelectronics. IMS measures densities and temperature of electrons in the ionosphere and monitors the ionospheric irregularities in KOMPSAT orbit.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_2
• /
• pp.1317-1328
• /
• 2021

This study analyzes the early satellite mission marine fog detection results from Geostationary Ocean Color Imager-II (GOCI-II). We investigate optical characteristics of the GOCI-II spectral bands for marine fog between October 2020 and March 2021 during the overlapping mission period of Geostationary Ocean Color Imager (GOCI) and GOCI-II. For Rayleigh-corrected reflection (R_rc) at 412 nm band available for the input of the GOCI-II marine fog algorithm, the inter-comparison between GOCI and GOCI-II data showed a small Root Mean Square Error (RMSE) value (0.01) with a high correlation coefficient (0.988). Another input variable, Normalized Localization Standard (NLSD), also shows a reasonable correlation (0.798) between the GOCI and GOCI-II data with a small RMSE value (0.007). We also found distinctive optical characteristics between marine fog and clouds by the GOCI-II observations, showing the narrower distribution of all bands' R_rc values centered at high values for cloud compared to marine fog. The GOCI-II marine fog detection distribution for actual cases is similar to the GOCI but more detailed due to the improved spatial resolution from 500 m to 250 m. The validation with the automated synoptic observing system (ASOS) visibility data confirms the initial reliability of the GOCI-II marine fog detection. Also, it is expected to improve the performance of the GOCI-II marine fog detection algorithm by adding sufficient samples to verify stable performance, improving the post-processing process by replacing real-time available cloud input data and reducing false alarm by adding aerosol information.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_3
• /
• pp.1405-1423
• /
• 2021

Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellite-based quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ㎛), infrared channel (10.8 ㎛), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the Z-R relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_1
• /
• pp.647-660
• /
• 2022

To prepare an atmospheric correction model suitable for CAS500-4 (Compact Advanced Satellite 500-4), this letter examined an atmospheric correction experiment using Sentinel-2 images having similar spectral characteristics to CAS500-4. Studies to compare the atmospheric correction results depending on different Aerosol Optical Depth (AOD) data are rarely found. We conducted a comparison of Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Sen2Cor, and Second Simulation of the Satellite Signal in the Solar Spectrum - Vector (6SV) version 1.1 and 2.1, using Geo-Kompsat 2A (GK2A) Advanced Meteorological Imager (AMI) and Aerosol Robotic Network (AERONET) AOD data. In this experiment, 6SV2.1 seemed more stable than others when considering the correlation matrices and the output images for each band and Normalized Difference Vegetation Index (NDVI).

• Korean Journal of Remote Sensing
• /
• v.33 no.2
• /
• pp.159-169
• /
• 2017

The Satellite Electro-Optic Payload System needsspecial requirements with the conditions of limited power consumption and the space environment of solar radiation. The acquired image quality should be mainly depend on the GSD (Ground Sampled Distance), SNR (Signal to Noise Ratio), and MTF (Modulation Transfer Function). On the well-manufactured sensor level, the thermal noise is removed on ASP (Analog Signal Processing) using the CDS (Corrective Double Sampling); the noise signal from the image sensor can be reduced from the offset signals based on the pre-pixels and the dark-pixels. The non-uniformity shall be corrected with gain, offset, and correction parameter of the image sensor pixel characteristic on the sensor control system. This paper describes the SNR enhancement method of the satellite EOS payload using the mentioned noise remove processes on the system design and operation, which is verified by tests and simulations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.4
• /
• pp.291-302
• /
• 2017

Radiometric calibration is a prerequisite to quantitative remote sensing, and its accuracy has a direct impact on the reliability and accuracy of the quantitative application of remotely sensed data. This paper presents absolute radiometric calibration of the KOMPSAT-3 (KOrea Multi Purpose SATellite-3) and cross calibration using the Landsat-8 OLI (Operational Land Imager). Absolute radiometric calibration was performed using a reflectance-based method. Correlations between TOA (Top Of Atmosphere) radiances and the spectral band responses of the KOMPSAT-3 sensors in Goheung, South Korea, were significant for multispectral bands. A cross calibration method based on the Landsat-8 OLI was also used to assess the two sensors using near simultaneous image pairs over the Libya-4 PICS (Pseudo Invariant Calibration Sites). The spectral profile of the target was obtained from EO-1 (Earth Observing-1) Hyperion data over the Libya-4 PICS to derive the SBAF (Spectral Band Adjustment Factor). The results revealed that the TOA radiance of the KOMPSAT-3 agree with Landsat-8 within 5.14% for all bands after applying the SBAF. The radiometric coefficient presented here appears to be a good standard for maintaining the optical quality of the KOMPSAT-3.

• Proceedings of the KSRS Conference
• /
• 2007.10a
• /
• pp.195-198
• /
• 2007

GOCI(Geostationary Ocean Color Imager) is the core paryload of the geostationary satellite COMS(Communication, Ocean and Meteological Satellite) for ocean monitoring. It is scheduled to be launched at the end of 2008. GOCI observes ocean color around the Korean Peninsula over $2500km{\times}2500km$ area. Whole field of view is divided into 16 solts and scan mechanism enables to point each slot position. Tilted two-axis scan method is used to observe entire field of view with great pointing stability. Vignetting of the optical system appears when the partial obscuration by intermediate optical components occurs. It leads to the variation of the illumination in the image and gradual fading near the edge of the field. It should be prohibited for the stable radiometric performances. In this work, vignetting analysis of GOCI optical system is performed. For the systematic approach, GOCI optical system is divided into scan mechanism part and telescope part. Vignetting analysis of each part is performed and each result is combined for the overall vignetting performances. The analyzed results can be applied to the selection of slot acquisition angle of scan mechanism to minimize vignetting effects.

• Proceedings of the KSRS Conference
• /
• 1998.09a
• /
• pp.387-392
• /
• 1998

To establish a monthly data collection planning for the Ocean Scanning Multispectral Imager (OSMI), we have examined the global patterns of three impacting factors: pigment concentration, cloud cover, and sun glint. Other than satellite mission constraints (e.g., duty cycle), these three factors are considered critical for the OSMI data collection. The Nimbus-7 Coastal Zone Color Scanner (CZCS) monthly mean products and the International Satellite Cloud Climatology Project (ISCCP) monthly mean products (C2) were used for the analysis of pigment concentration and cloud cover distributions, respectively. And the monthly simulated patterns of sun glint were produced by performing the OSMI orbit prediction and the calculation of sun glint radiances at the top-of-atmosphere (TOA). Using monthly statistics (mean and/or standard deviation) of each factor in the above for a given 10$^{\circ}$ latitude by 10$^{\circ}$ longitude grid, we generated the priority map for each month. The priority maps of three factors for each month were subsequently superimposed to visualize the impact of three factors in all. The initial results illustrated that a large part of oceans in the summer hemisphere was classified into the low priority regions because of seasonal changes of clouds and sun illumination. Sensitivity tests were performed to see how cloud cover and sun glint affect the priority determined by pigment concentration distributions, and consequently to minimize their seasonal effects upon the data collection planning.

• Korean Journal of Remote Sensing
• /
• v.35 no.1
• /
• pp.83-92
• /
• 2019

Land cover (LC) is an important factor in socioeconomic and environmental studies. According to various studies, a number of LC maps, including global land cover (GLC) datasets, are made using polar orbit satellite data. Due to the insufficiencies of reference datasets in Northeast Asia, several LC maps display discrepancies in that region. In this paper, we performed a feasibility assessment of LC mapping using Geostationary Ocean Color Imager (GOCI) data over Northeast Asia. To produce the LC map, the GOCI normalized difference vegetation index (NDVI) was used as an input dataset and a level-2 LC map of South Korea was used as a reference dataset to evaluate the LC map. In this paper, 7 LC types(urban, croplands, forest, grasslands, wetlands, barren, and water) were defined to reflect Northeast Asian LC. The LC map was produced via principal component analysis (PCA) with K-means clustering, and a sensitivity analysis was performed. The overall accuracy was calculated to be 77.94%. Furthermore, to assess the accuracy of the LC map not only in South Korea but also in Northeast Asia, 6 GLC datasets (IGBP, UMD, GLC2000, GlobCover2009, MCD12Q1, GlobeLand30) were used as comparison datasets. The accuracy scores for the 6 GLC datasets were calculated to be 59.41%, 56.82%, 60.97%, 51.71%, 70.24%, and 72.80%, respectively. Therefore, the first attempt to produce the LC map using geostationary satellite data is considered to be acceptable.