• Journal of Korea Water Resources Association
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• v.53 no.1
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• pp.57-70
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• 2020

This study is to establish the situation of inter-basin transfer from Seomjin river basin to Yeongsan river basin using SWAT (Soil and Water Assessment Tool). Firstly, the SWAT modeling was conducted for each river basin. After, the inter-basin transfer was established using SWAT reservoir operating parameters WURESN (Water Use Reservoir Withdrawn) and inlet function from Juam dam of Seomjin river basin to Gwangju stream of Yeongsan river basin respectively. Each river basin was calibrated and validated using 13 years (2005~2017) data of Seomjin- Juam dam reservoir storage (JAD), release, transfer and Yeongsan-Mareuk (MR) stream gauge station. The results of root mean square error RMSE, Nash-Sutcliffe efficiency NSE, and determination coefficient R² of JAD were 2.22 mm/day, 0.62 and 0.86 respectively. The RMSE, NSE, and R² of MR were 1.38 mm/day, 0.69 and 0.84 respectively. To evaluate the downstream effects by the transferred water, the water levels of 2 multi-function weirs (SCW, JSW) in Yeongsan river basin and the Gokseong (GS) and Gurye (GR) stream gauge stations in Seomjin river basin were also calibrated. The RMSE, NSE, and R² of SCW, JSW, GS and GR were 1.49~2.49 mm/day, 0.45~0.76, 0.81~0.90 respectively.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.209-218
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• 2013

The Sonoran Desert, which is located in North America, has been frequently used for vicarious calibration of many optical sensors in satellites. Although the desert area has good conditions for vicarious calibration (e.g. high reflectance, little vegetation, large area, low precipitation), its adjacency to the sea and large variability in atmospheric water vapor are the disadvantages for vicarious calibration. For vicarious calibration using top-of-atmospheric (TOA) reflectance, the atmospheric variability brings about degraded precision in vicarious calibration results. In this paper, the location with the smallest radiometric variability in TOA reflectance is sought by using 12-year Landsat 5 data, and corrected the TOA reflectance for bidirectional reflectance distribution function (BRDF) which is another major source of variability in TOA reflectance. Experiments show that the mid-western part of the Sonoran Desert has the smallest variability collectively for visible and near-infrared bands, and the variability from the sunarget-sensor geometry can be reduced by the BRDF correction for the visible bands, but not sufficiently for the infrared bands.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.251-262
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• 2010

In order to provide quantitative control of the standard products of Geostationary Ocean Color Imager (GOCI), on-board radiometric correction, atmospheric correction, and bio-optical algorithm are obtained continuously by comprehensive and consistent calibration and validation procedures. The calibration/validation for radiometric, atmospheric, and bio-optical data of GOCI uses temperature, salinity, ocean optics, fluorescence, and turbidity data sets from buoy and platform systems, and periodic oceanic environmental data. For calibration and validation of GOCI, we compared radiometric data between in-situ measurement and HyperSAS data installed in the Ieodo ocean research station, and between HyperSAS and SeaWiFS radiance. HyperSAS data were slightly different in in-situ radiance and irradiance, but they did not have spectral shift in absorption bands. Although all radiance bands measured between HyperSAS and SeaWiFS had an average 25% error, the 11% absolute error was relatively lower when atmospheric correction bands were omitted. This error is related to the SeaWiFS standard atmospheric correction process. We have to consider and improve this error rate for calibration and validation of GOCI. A reference target site around Dokdo Island was used for studying calibration and validation of GOCI. In-situ ocean- and bio-optical data were collected during August and October, 2009. Reflectance spectra around Dokdo Island showed optical characteristic of Case-1 Water. Absorption spectra of chlorophyll, suspended matter, and dissolved organic matter also showed their spectral characteristics. MODIS Aqua-derived chlorophyll-a concentration was well correlated with in-situ fluorometer value, which installed in Dokdo buoy. As we strive to solv the problems of radiometric, atmospheric, and bio-optical correction, it is important to be able to progress and improve the future quality of calibration and validation of GOCI.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1565-1576
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• 2023

An atmospheric correction algorithm based on the radiative transfer model is required to obtain remote-sensing reflectance (R_rs) from the Geostationary Ocean Color Imager-II (GOCI-II) observed at the top-of-atmosphere. This R_rs derived from the atmospheric correction is utilized to estimate various marine environmental parameters such as chlorophyll-a concentration, total suspended materials concentration, and absorption of dissolved organic matter. Therefore, an atmospheric correction is a fundamental algorithm as it significantly impacts the reliability of all other color products. However, in clear waters, for example, atmospheric path radiance exceeds more than ten times higher than the water-leaving radiance in the blue wavelengths. This implies atmospheric correction is a highly error-sensitive process with a 1% error in estimating atmospheric radiance in the atmospheric correction process can cause more than 10% errors. Therefore, the quality assessment of R_rs after the atmospheric correction is essential for ensuring reliable ocean environment analysis using ocean color satellite data. In this study, a Quality Assurance (QA) algorithm based on in-situ R_rs data, which has been archived into a database using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Archive and Storage System (SeaBASS), was applied and modified to consider the different spectral characteristics of GOCI-II. This method is officially employed in the National Oceanic and Atmospheric Administration (NOAA)'s ocean color satellite data processing system. It provides quality analysis scores for R_rs ranging from 0 to 1 and classifies the water types into 23 categories. When the QA algorithm is applied to the initial phase of GOCI-II data with less calibration, it shows the highest frequency at a relatively low score of 0.625. However, when the algorithm is applied to the improved GOCI-II atmospheric correction results with updated calibrations, it shows the highest frequency at a higher score of 0.875 compared to the previous results. The water types analysis using the QA algorithm indicated that parts of the East Sea, South Sea, and the Northwest Pacific Ocean are primarily characterized as relatively clear case-I waters, while the coastal areas of the Yellow Sea and the East China Sea are mainly classified as highly turbid case-II waters. We expect that the QA algorithm will support GOCI-II users in terms of not only statistically identifying R_rs resulted with significant errors but also more reliable calibration with quality assured data. The algorithm will be included in the level-2 flag data provided with GOCI-II atmospheric correction.