Atmospheric correction under cloud edge effects for Geostationary Ocean Color Imager through deep learning

Men, Jilin; Feng, Lian; Chen, Xi; Tian, Liqiao

doi:10.1016/j.isprsjprs.2023.05.023

Cited by 6 publications

(3 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many researchers have also developed AC methods for GOCI under non-ideal observation conditions. [84] proposed a novel AC algorithm based on deep learning (denoted as DLACC) to improve the quality of 𝑅 𝑟𝑠 (λ) data contaminated by cloud edge effects, including stray light, cloud shadows, and cloud adjacent effects. The observation characteristics of GOCI images from morning to evening also determine that many images have a larger solar zenith angle (SZA) (>70°).…”

Section: Atmospheric Correction Over Inland and Coastal Watersmentioning

confidence: 99%

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: Atmospheric Correction Over Inland and Coastal Watersmentioning

confidence: 99%

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

“…Many researchers have also developed AC methods for the GOCI under non-ideal observation conditions. Men [84] proposed a novel AC algorithm based on deep learning (denoted as DLACC) to improve the quality of R rs (λ) data contaminated by cloud edge effects, including stray light, cloud shadows, and cloud adjacent effects. The observation characteristics of GOCI images from morning to evening also determine that many images have a larger solar zenith angle (SZA) (>70 • ).…”

Section: Atmospheric Correction Of Goci Imagesmentioning

confidence: 99%

“…We are unsure whether it can determine which water pixels are affected by the adjacency effects. There are still too few studies on the impact of adjacency effects on GOCI nearshore water monitoring [56,84,223]. The improved spatial resolution of the GOCI-II (250 m) provides the possibility for monitoring smaller bays, lakes, reservoirs, and rivers.…”

Section: The Limitations and Uncertainties Of Current Studies For The...mentioning

confidence: 99%

A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters

Shao,

Wang,

Liu

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

In recent decades, eutrophication in inland and coastal waters (ICWs) has increased due to anthropogenic activities and global warming, thus requiring 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), aboard the Communication Ocean and Meteorological Satellite (COMS) from the Republic of Korea, marked a significant milestone as the world’s inaugural geostationary ocean color observation satellite. Its operational tenure spanned from 1 April 2011 to 31 March 2021. Over ten years, the GOCI has observed oceans, coastal waters, and inland waters within its 2500 km × 2500 km target area centered on the Korean Peninsula. The most attractive feature of the GOCI, compared with other commonly used water color sensors, was its high temporal resolution (1 h, eight times daily from 0 UTC to 7 UTC), providing an opportunity to monitor ICWs, where their water quality can undergo significant changes within a day. This study 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 the GOCI’s strength and performance with different processing methods. These articles reveal that the GOCI played an essential role in monitoring the ecological health of ICWs in its observation coverage (2500 km × 2500 km) in East Asia. The 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 Ocean Color Sensors in monitoring water quality and provide suggestions for future Geostationary Ocean Color Sensors to better monitor the ICWs.

show abstract

Atmospheric correction algorithm based on deep learning with spatial-spectral feature constraints for broadband optical satellites: Examples from the HY-1C Coastal Zone Imager

Zhao,

Ma,

Xiao

et al. 2023

ISPRS Journal of Photogrammetry and Remote Sensing

View full text Add to dashboard Cite

Atmospheric correction under cloud edge effects for Geostationary Ocean Color Imager through deep learning

Cited by 6 publications

References 52 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

A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters

Atmospheric correction algorithm based on deep learning with spatial-spectral feature constraints for broadband optical satellites: Examples from the HY-1C Coastal Zone Imager

Contact Info

Product

Resources

About