Atmospheric Correction of GOCI Using Quasi-Synchronous VIIRS Data in Highly Turbid Coastal Waters

Wu, Jie; Chen, Chuqun; Nukapothula, Sravanthi

doi:10.3390/rs12010089

Cited by 7 publications

(7 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[162] developed a semi-analytical SPM model for estimating SPM using the satellite-in situ matchup data collected in Lake Taihu and Hangzhou Bay. [163] calibrated the SPM model proposed by [83] to map SPM products using GOCI in Lake Taihu as Equation ( 20): 𝑆𝑃𝑀 = 10 0.61+1.43 * 𝑅 𝑟𝑠 (745)/𝑅 𝑟𝑠 (490) (20) The GOCI-derived SPM products are consistent with a two-dimensional model that couples shallow water and SPM transport dynamics. The high-spatial-resolution GOCI SPM product combined with the hydrodynamic model has essential application value in high-time frequency suspended solids transport monitoring.…”

Section: Spmmentioning

confidence: 82%

“…The main sources of SPM are surface runoff, sediment resuspension, and organic debris composed of decaying organisms and excrements [147]. SPM directly affects the propagation of light in the water body, which in turn affects the aquatic ecological environment and ultimately determines the primary productivity of the lake [20,[148][149][150].…”

Section: Spmmentioning

confidence: 99%

“…Polarization and Directionality of the Earth's Reflectances (POLDER) in 1996 and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) in 1997. SeaWiFS was the first watercolor sensor that could provide continuous data on the bio-optical properties (i.e., those properties of organisms that can be visually recorded) of the oceans [18][19][20]. In 1999, the United States successfully launched the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite, the first advanced polarorbiting environmental remote-sensing satellite with nine specific watercolor bands from 412 to 865 nm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Review of the Application of GOCI to Water Quality Monitoring of Inland and Coastal Waters

Shao,

Wang,

Liu

et al. 2024

Preprint

View full text Add to dashboard Cite

In recent decades, as eutrophication in inland and coastal waters (ICWs) has increased due to anthropogenic activities and global warming, so has the need for timely monitoring. Compared with traditional sampling and laboratory analysis methods, satellite remote sensing technology can provide macro-scale, low-cost, and near real-time water quality monitoring services. The Geostationary Ocean Color Imager (GOCI), integrated onboard the Communication Ocean and Meteorological Satellite (COMS) from the Republic of Korea, was the first geostationary ocean color observation satellite and was operational from April 1, 2011 to March 31, 2021. Over ten years, GOCI has observed oceans, coastal waters, and inland waters within its 2,500 km×2,500 km target area centered on the Korean Peninsula. The most attractive feature of GOCI, compared with other commonly used watercolor sensors, was its high temporal resolution (1h, eight times daily from 0 UTC to 7 UTC), providing the opportunity to monitor the quality of ICWs, where optical properties can change rapidly throughout the day. This systematic review aims to comprehensively review GOCI features and applications in ICWs, analyzing progress in atmospheric correction algorithms and water quality monitoring. Analyzing 123 articles from the Web of Science and China National Knowledge Infrastructure (CNKI) through a bibliometric quantitative approach, we examined GOCI’s strength and performance with different processing methods. These articles reveal that GOCI played an essential role in monitoring the ecological health of ICWs in its observation area in East Asia. GOCI has led the way to a new era of geostationary ocean satellites, providing new technical means for monitoring water quality in oceans, coastal zones, and inland lakes. We also discuss the challenges encountered by geostationary satellites in monitoring water quality and provide suggestions for improvements.

show abstract

Section: Spmmentioning

confidence: 82%

Section: Spmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Review of the Application of GOCI to Water Quality Monitoring of Inland and Coastal Waters

Shao,

Wang,

Liu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The Visible Infrared Imaging Radiometer Suite (VIIRS), which is on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite, has three SWIR bands with band centers 1238, 1601, and 2257 nm. The quasi-synchronous VIIRS (QSV) data can be used for the atmospheric correction of other sensors in highly turbid waters [41].…”

Section: Gf-4 Remote Sensing Data Processingmentioning

confidence: 99%

Detection of the Minute Variations of Total Suspended Matter in Strong Tidal Waters Based on GaoFen-4 Satellite Data

Qiong

Zhou

et al. 2021

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Hangzhou Bay (HZB) is the largest macro-tidal bay in China, where suspended sediment concentrations are significantly modulated by tidal oscillations. This makes it an ideal area for the study of the impact of tide on temporal–spatial variation in suspended sediment. The GaoFen-4 (GF-4) satellite is the first high-resolution geosynchronous orbiting satellite of China. It exhibits the unique advantages of capturing minute variations and finer details of total suspended matter (TSM) due to the enhancement in spatial resolution (50 m) and observation time interval (20 s). In this study, TSM concentration of the HZB was retrieved based on the GF-4 satellite. The spatial distribution and minute variations of TSM concentration under the ebb tide from 7:30 to 7:40 a.m. on 28 August 2017, were analyzed. The results showed that the average TSM concentration inside HZB was (371.8 ± 1.8) mg/L. There was a linearly increasing trend of TSM concentration at ebb tide, with an increment of (3.96 ± 0.31) (mg/L)/min, and a more significant increase was observed in the high TSM areas. This increase in TSM concentration was associated with both the bottom topography and tide processes. The tidal potential energy generated by the tidal range and the strong shear stress generated by the high current velocity both led to the re-suspension of the sedimentary particles, which affected the variation of TSM concentration. In addition, the influence of bottom topography changed the intensity of re-suspension and also affected the distribution of TSM concentration in HZB.

show abstract

“…Qi et al [24] determined typical types of diurnal changing patterns in cyanobacteria blooms of Taihu Lake using SeaDAS algorithm-based GOCI products. However, Wu et al [25] declared that either the GDPS algorithm or SeaDAS algorithm-based Rrs could not be retrieved successfully in the highly turbid coastal waters. Moreover, many studies have used the GDPS algorithm or SeaDAS algorithm, as well as improved algorithms, to retrieve other products, such as colored dissolved organic matter (CDOM) absorption coefficient, the diffuse attenuation coefficient, and chlorophyll-a concentration [6,16,21,22,26,27].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Normalized Water-Leaving Radiance Derived from GOCI Using AERONET-OC Data

Zhang

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

The geostationary ocean color imager (GOCI), as the world’s first operational geostationary ocean color sensor, is aiming at monitoring short-term and small-scale changes of waters over the northwestern Pacific Ocean. Before assessing its capability of detecting subdiurnal changes of seawater properties, a fundamental understanding of the uncertainties of normalized water-leaving radiance (nLw) products introduced by atmospheric correction algorithms is necessarily required. This paper presents the uncertainties by accessing GOCI-derived nLw products generated by two commonly used operational atmospheric algorithms, the Korea Ocean Satellite Center (KOSC) standard atmospheric algorithm adopted in GOCI Data Processing System (GDPS) and the NASA standard atmospheric algorithm implemented in Sea-Viewing Wide Field-of-View Sensor Data Analysis System (SeaDAS/l2gen package), with Aerosol Robotic Network Ocean Color (AERONET-OC) provided nLw data. The nLw data acquired from the GOCI sensor based on two algorithms and four AERONET-OC sites of Ariake, Ieodo, Socheongcho, and Gageocho from October 2011 to March 2019 were obtained, matched, and analyzed. The GDPS-generated nLw data are slightly better than that with SeaDAS at visible bands; however, the mean percentage relative errors for both algorithms at blue bands are over 30%. The nLw data derived by GDPS is of better quality both in clear and turbid water, although underestimation is observed at near-infrared (NIR) band (865 nm) in turbid water. The nLw data derived by SeaDAS are underestimated in both clear and turbid water, and the underestimation worsens toward short visible bands. Moreover, both algorithms perform better at noon (02 and 03 Universal Time Coordinated (UTC)), and worse in the early morning and late afternoon. It is speculated that the uncertainties in nLw measurements arose from aerosol models, NIR water-leaving radiance correction method, and bidirectional reflectance distribution function (BRDF) correction method in corresponding atmospheric correction procedure.

show abstract

Atmospheric Correction of GOCI Using Quasi-Synchronous VIIRS Data in Highly Turbid Coastal Waters

Cited by 7 publications

References 50 publications

A Systematic Review of the Application of GOCI to Water Quality Monitoring of Inland and Coastal Waters

A Systematic Review of the Application of GOCI to Water Quality Monitoring of Inland and Coastal Waters

Detection of the Minute Variations of Total Suspended Matter in Strong Tidal Waters Based on GaoFen-4 Satellite Data

Assessment of Normalized Water-Leaving Radiance Derived from GOCI Using AERONET-OC Data

Contact Info

Product

Resources

About