Remote sensing of sea surface salinity: challenges and research directions

Kim, Young Jun; Han, Daehyeon; Jang, Eunna; Im, Jungho; Sung, Taejun

doi:10.1080/15481603.2023.2166377

Cited by 13 publications

(6 citation statements)

References 141 publications

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“…Given that SSS calculations are based on R rs , the GII-C SSS (alongside GOCI-derived SSS) closely follows the distribution pattern observed in the GOCI-II R rs data (Figure 10). To counterbalance R rs influences, it is proposed that additional salinity-related variables, such as SST, which has a temperature-dependent sensitivity to seawater salinity at the L-band [13], be included in the model. This approach is supported by the strong correlation between SSS and SST, making SST a frequently used input in various SSS estimation studies [19,38,41,47].…”

Section: Discussion For Limitations and Future Improvements Of Goci-i...mentioning

confidence: 99%

“…To overcome these limitations, numerous studies have explored the potential of ocean color reflectance and related ocean products for detailed SSS pattern estimation within LSW plumes and coastal regions [13]. Despite cloud interference, ocean color-derived SSS estimations have proven more effective than L-band microwave radiometer-derived SSS estimations, especially for coastal monitoring [8].…”

Section: Introductionmentioning

confidence: 99%

“…Globally, regions heavily influenced by LSW, such as the Gulf of Mexico, the Arabian Gulf, the ECS, and the Bohai Sea, have seen SSS estimation through regional algorithms utilizing data from instruments like the Landsat-8 Operational Land Imager (OLI), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) [14][15][16][17][18]. Differing from L-band microwave radiometry, ocean color or optical sensor-based SSS estimation does not depend on direct ocean color signals but rather on indirect signals, notably the correlation between colored dissolved organic matter (CDOM) and salinity [13]. In coastal areas, the riverine discharge of various organic materials results in areas with diminished salinity and heightened CDOM concentrations; these substances influence the ocean's color and, by extension, are detectable as satellite-based ocean color signals [19].…”

Section: Introductionmentioning

confidence: 99%

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Continuity and Enhancements in Sea Surface Salinity Estimation in the East China Sea Using GOCI and GOCI-II: Challenges and Further Developments

Jang,

Choi,

Ahn

2024

Remote Sensing

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During the summer, substantial freshwater discharge from the Changjiang River into the East China Sea (ECS) results in extensive low-salinity water (LSW) plumes that significantly affect regions along the southern Korean Peninsula and near Jeju Island. Previous research developed an empirical equation to estimate sea surface salinity (SSS) in the ECS during the summer season using remote-sensing reflectance (Rrs) data from bands 3–6 (490, 555, 660, and 680 nm) of the Geostationary Ocean Color Imager (GOCI). With the conclusion of the GOCI mission in March 2021, this study aims to ensure the continuity of SSS estimation in the ECS by transitioning to its successor, the GOCI-II. This transition was facilitated through two approaches: applying the existing GOCI-based equation and introducing a new machine learning method using a random forest model. Our analysis demonstrated a high correlation between SSS estimates derived from the GOCI and GOCI-II when applying the equation developed for the GOCI to both satellites, as indicated by a robust R2 value of 0.984 and a low RMSD of 0.8465 psu. This study successfully addressed the challenge of maintaining continuous SSS estimation in the ECS post-GOCI mission and evaluated the accuracy and limitations of the GOCI-II-derived SSS, proposing future strategies to enhance its effectiveness.

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Section: Discussion For Limitations and Future Improvements Of Goci-i...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Continuity and Enhancements in Sea Surface Salinity Estimation in the East China Sea Using GOCI and GOCI-II: Challenges and Further Developments

Jang,

Choi,

Ahn

2024

Remote Sensing

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“…Mapping Ulva prolifera green tides from space: A revisit on algorithm design and data products 81 13 14 Kim et al [42] 2023…”

Section: Introductionmentioning

confidence: 99%

Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Cavalli

2024

Remote Sensing

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Since 1971, remote sensing techniques have been used to map and monitor phenomena and parameters of the coastal zone. However, updated reviews have only considered one phenomenon, parameter, remote data source, platform, or geographic region. No review has offered an updated overview of coastal phenomena and parameters that can be accurately mapped and monitored with remote data. This systematic review was performed to achieve this purpose. A total of 15,141 papers published from January 2021 to June 2023 were identified. The 1475 most cited papers were screened, and 502 eligible papers were included. The Web of Science and Scopus databases were searched using all possible combinations between two groups of keywords: all geographical names in coastal areas and all remote data and platforms. The systematic review demonstrated that, to date, many coastal phenomena (103) and parameters (39) can be mapped and monitored using remote data (e.g., coastline and land use and land cover changes, climate change, and coastal urban sprawl). Moreover, the authors validated 91% of the retrieved parameters, retrieved from remote data 39 parameters that were mapped or monitored 1158 times (88% of the parameters were combined together with other parameters), monitored 75% of the parameters over time, and retrieved 69% of the parameters from several remote data and compared the results with each other and with available products. They obtained 48% of the parameters using different methods, and their results were compared with each other and with available products. They combined 17% of the parameters that were retrieved with GIS and model techniques. In conclusion, the authors addressed the requirements needed to more effectively analyze coastal phenomena and parameters employing integrated approaches: they retrieved the parameters from different remote data, merged different data and parameters, compared different methods, and combined different techniques.

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“…Remote sensing technologies such as optical, radar, and LiDAR, offer unique advantages for studying sediment dynamic processes on deltas, beaches, dunes, and barrier islands; coastal processes in erosional landscapes, such as rocky cliffs, forcing mechanisms of coastal processes such as waves, tides, and currents; and coastal geomorphology and evolution. Additionally, close-range remote sensing applications, such as planes or UAVs, as well as onsite cameras or webcams, offer valuable data for studying the coastal environment at a high spatiotemporal resolution and accuracy [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%