• Journal of the Korean earth science society
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• v.41 no.4
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• pp.356-366
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• 2020

The accuracy of satellite-observed sea surface salinity (SSS) was evaluated in comparison with in-situ salinity measurements from ARGO floats and buoys in the seas around the Korean Peninsula, the northwest Pacific, and the global ocean. Differences in satellite SSS and in-situ measurements (SSS errors) indicated characteristic dependences on geolocation, sea surface temperature (SST), and other oceanic and atmospheric conditions. Overall, the root-mean-square (rms) errors of non-averaged SMOS SSSs ranged from approximately 0.8-1.08 psu for each in-situ salinity dataset consisting of ARGO measurements and non-ARGO data from CTD and buoy measurements in both local seas and the ocean. All SMOS SSSs exhibited characteristic negative bias errors at a range of -0.50- -0.10 psu in the global ocean and the northwest Pacific, respectively. Both rms and bias errors increased to 1.07 psu and -0.17 psu, respectively, in the East Sea. An analysis of the SSS errors indicated dependence on the latitude, SST, and wind speed. The differences of SMOS-derived SSSs from in-situ salinity data tended to be amplified at high latitudes (40-60°N) and high sea water salinity. Wind speeds contributed to the underestimation of SMOS salinity with negative bias compared with in-situ salinity measurements. Continuous and extensive validation of satellite-observed salinity in the local seas around Korea should be further investigated for proper use.

• Journal of the Korean earth science society
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• v.41 no.5
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• pp.469-477
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• 2020

Salinity is not only an important variable that determines the density of the ocean but also one of the main parameters representing the global water cycle. Ocean salinity observations have been mainly conducted using ships, Argo floats, and buoys. Since the first satellite salinity was launched in 2009, it is also possible to observe sea surface salinity in the global ocean using satellite salinity data. However, the satellite salinity data contain various errors, it is necessary to validate its accuracy before applying it as research data. In this study, the salinity accuracy between the Soil Moisture Active Passive (SMAP) satellite salinity data and the in-situ salinity data provided by the Ieodo ocean research station was evaluated, and the error characteristics were analyzed from April 2015 to August 2020. As a result, a total of 314 match-up points were produced, and the root mean square error (RMSE) and mean bias of salinity were 1.79 and 0.91 psu, respectively. Overall, the satellite salinity was overestimated compare to the in-situ salinity. Satellite salinity is dependent on various marine environmental factors such as season, sea surface temperature (SST), and wind speed. In summer, the difference between the satellite salinity and the in-situ salinity was less than 0.18 psu. This means that the accuracy of satellite salinity increases at high SST rather than at low SST. This accuracy was affected by the sensitivity of the sensor. Likewise, the error was reduced at wind speeds greater than 5 m s^-1. This study suggests that satellite-derived salinity data should be used in coastal areas for limited use by checking if they are suitable for specific research purposes.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.649-654
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• 2002

In summer season of 1998, a huge flood occurred around the Yangtze River in the eastern China. The low salinity water less than 28 psu from the river was detected around the southeastern part of the Jeju Island which is located in the southern part of the Korean peninsula. We studied how to detect low salinity water from the Yangtze River, which gives terrible damages to the Korean fisheries. We got the relationships between low surface salinity, turbid water from the Yangtze River and digital ocean color using remote sensing of SeaWiFS satellite in the northern East China Sea in summer seanson of 1998, 1999, 2000 and 2001. The charts of salinity in the northern East China Sea were made by the regenerating of the satellite ocean color data with the formula from the relationships between low salinity, in situ turbid water (transparency) and satellite ocean color.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.51 no.2
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• pp.187-198
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• 2018

The monthly salinity maps from Aquarius satellite covering the entire East Sea were produced to analyze the low-salinity water appearing in fall every year. The low-salinity water in the northern East Sea began to appear in May-June, spreading southward along the coast and eastward north of the subpolar front. Low-salinity water from the East China Sea entered the East Sea through the Korea Strait from July to September and was mixed with low-salinity water from the northern East Sea in the Ulleung Basin. The strength of the low-salinity water from the East China Sea was dependent on the strength of the southerly wind of the East China Sea in July-August. The salinity reaches a minimum in September with a distribution parallel to the latitude of $37.5^{\circ}N$. In October, low salinity water is distributed along the mean current path and subpolar front and the entire East Sea is covered with the low salinity water in November. Water with salinity larger than 34 psu starts to flow into the East Sea through the Korea Strait in December and it expands gradually northward up to the subpolar front in January- February.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.6
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• pp.426-433
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• 2013

This study was aimed to classify soil desalination area for cultivation using NDVI (Normalized difference vegetation index) of high-resolution satellite image because the soil salinity affects the change of plant community in reclaimed lands. We measured the soil salinity and NDVI at 28 sites in the Saemangeum reclaimed land in June 2013. In halophyte and non-vegetation sites, no relation was found between NDVI and soil salinity. In glycophyte sites, however, we found that the soil salinity was below 0.1% and NDVI ranged from 0.11 to 0.57 which was greater than the other sites. So, we could distinguish the glycophyte sites from the halophyte sites and non-vegetation, and classify the area that soil salinty was below 0.1%. This technique could save the time and labor to measure the soil salinity in large area for agricultural utilization.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.188-191
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• 2004

Appearance and disappearance of the China Coastal Waters(CCW) in the neighbouring sea of Cheju Island was very different yearly but usually appeared strongly in summer. At this time, sea level and salinity were varied in this area by the influence of the CCW. Satellite data(T/P;Topex/Poseidon) and Salinity (NFRID;National Fisheries Research and Development Institute) were used from 1993 to 2001. We compared with TG data of NOR I and TIP data in the observed station(33 31'N, 12632'E). Coefficient of correlation was 0.6~0.8 with the exception of 1993 and 1995. And variations of salinity was higher than $32.00\%_{\circ}$ in the southwestern part of Cheju Island and the southern part of the South Sea of Korea during June-October and SLA(Sea level Anomaly) was 10-11cm. Salinity of the southeastern part was higher than those of the southwestern part and SLA was 12~13cm because of the influence of Tsushima Current.

• Journal of Korea Water Resources Association
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• v.44 no.1
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• pp.23-30
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• 2011

Any significant inflow of fresh water due to flood or snow melting can rapidly reduce salinity at the bay. In such a case, the habitat environment in the sea near river mouth can be partly destroyed. Therefore, research to understand the salinity distribution quantitatively at the bay for the utilization of natural environment and for the inhabitant conservation must be very important. In this study, the investigation on the relationship between satellite image and turbidity is carried out first, and then the salinity distribution at the bay using the relationship between turbidity and salinity is derived. The main results are as follows. First the reappearance ability of RGB bands respectively of the satellite image is investigated, and then it was confirmed that the combination of band2 and band3 expressed best the movement characteristics of turbid water at the bay is chopped up into 4 small areas. Second the turbidity of river mouth is estimated using the travel time from the upward monitoring station to the river mouth. Finally the satellite image is converted into the salinity distribution by the correlation of salinity and turbidity. It is confirmed that the salinity distributions obtained from above three investigation methods are quite reasonable and clear.

• Korean Journal of Remote Sensing
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• v.26 no.1
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• pp.59-64
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• 2010

The second satellite in ESA's Earth Explorer series, the Soil Moisture and Ocean Salinity (SMOS) mission was launched into orbit at November 1, 2009. The SMOS will play a key role in the monitoring of climate change on a global scale using the payload of L-band synthetic aperture radiometer. It is the first ever satellite designed both to map sea surface salinity and to monitor soil moisture on a global scale, and will provide the important data to study the water cycle among oceans, the atmosphere and land. To introduce the operation of the SMOS, this paper shows brief summary of appearance and current operation.

• Ocean and Polar Research
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• v.38 no.4
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• pp.259-270
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• 2016

Sea Surface Salinity (SSS) observations from the Aquarius satellite in the East Sea show large systematic biases mainly caused by the surrounding lands and Radio Frequency Interferences (RFI) along the descending orbits on which the satellite travels from the Asian continent to the East Sea. To develop a technique for correcting the systematic biases unique to the East Sea, the least square regression between in situ observations of salinity and the reanalyzed salinities by HYCOM is first performed. Then monthly mean reanalyzed salinities fitted to the in situ salinities are compared with monthly mean Aquarius salinities to calculate mean biases in $1^{\circ}{\times}1^{\circ}$ boxes. Mean biases in winter (December-March) are found to be considerably larger than those in other seasons possibly caused by the inadequate correction of surface roughness in the sea surrounded by the land, and thus the mean bias corrections are performed using two bias tables. Large negative biases are found in the area near the coast of Japan and in the areas with islands. In the northern East Sea, data sets using the ascending orbit only (SCIA) are chosen for correction because of large RFI errors on the descending orbit (SCID). Resulting mean biases between the reanalysis salinities fitted to in situ observations and the bias corrected Aquarius salinities are less than 0.2 psu in all areas. The corrected mean salinity distributions in March and September demonstrate marked improvements when compared with mean salinities from the World Ocean Atlas (WOA [2005-2012]). In September, salinity distributions based on the corrected Aquarius and on the WOA (2005-2012) show similar distributions of Changjiang Diluted Water (CDW) in the East Sea.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.397-402
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• 2006

China Coastal Water (CCW) usually appears in the seas surrounding Jeju Island annually (June-October) and is very pronounced in August. The power spectrum density (PSD), sea level anomalies (SLAs), and sea surface temperatures (SSTs) were found to peak annually and semiannually. The peaks at intervals of 80-, 60-, and 43-days are considered to be influenced by CCW and the Kuroshio Current. Generally, low-salinity water appears to the west of Jeju Island from June through October and gradually propagates to the east, where CCW meets the Tsushima Current. Empirical orthogonal function (EOF) analysis of SLAs and SSTs indicated that the variance in SLAs and SSTs was 55.70 and 98.09% in the first mode, respectively. The PSD for the first mode of EOF analysis of SLAs was stronger in the western than in the eastern waters because of the influence of CCW. The PSD for the EOF analysis of SSTs was similar in all areas (the Yangtze Estuary and the waters to the west and east of Jeju Island), with a period of approximately 260 days.