A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters
Abstract
:1. Introduction
2. GOCI Overview
3. Bibliometric Analysis
4. Inland and Coastal Waters Monitoring by the GOCI
4.1. Atmospheric Correction of GOCI Images
4.2. Algal Blooms
4.3. Water Quality Parameters
4.3.1. Chla
4.3.2. SPM
4.3.3. Water Clarity
4.3.4. Other Parameters
5. Discussions
5.1. The Limitations and Uncertainties of Current Studies for the GOCI
5.2. Integrating Geostationary Ocean Color Satellites, Unmanned Aerial Vehicles, and Ground Collaborative Observation
5.3. Fusion of Geostationary Ocean Color Satellites with Other Satellite Products
5.4. Improving Spectral, Spatial, and Temporal Resolution of Geostationary Ocean Color Sensors
5.4.1. Improving Spectral Resolution
5.4.2. Improving Spatial Resolution
5.4.3. Improving Temporal Resolution
5.5. Further Expansion of GOCI-II Products
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Schofield, O.; Arnone, R.A.; Bissett, W.P.; Dickey, T.D.; Davis, C.O.; Finkel, Z.; Oliver, M.; Moline, M.A. Watercolors in the coastal zone: What can we see? Oceanography 2004, 17, 30–37. [Google Scholar] [CrossRef]
- Shi, J.; Shen, Q.; Yao, Y.; Zhang, F.; Li, J.; Wang, L. Field Radiometric Calibration of a Micro-Spectrometer Based on Remote Sensing of Plateau Inland Water Colors. Appl. Sci. 2023, 13, 2117. [Google Scholar] [CrossRef]
- Morel, A.; Prieur, L. Analysis of variations in ocean color 1. Limnol. Oceanogr. 1977, 22, 709–722. [Google Scholar] [CrossRef]
- Gordon, H.R.; Morel, A.Y. Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery: A Review; Springer: Berlin/Heidelberg, Germany, 2012. [Google Scholar]
- Sathyendranath, S. Reports of the International Ocean-Colour Coordinating Group. IOCCG Proj. Off. Dartm. Nova Scotia IOCCG Rep. 2000, 3, 140. [Google Scholar]
- Schroeder, T.; Schaale, M.; Fischer, J. Retrieval of atmospheric and oceanic properties from MERIS measurements: A new Case-2 water processor for BEAM. Int. J. Remote Sens. 2007, 28, 5627–5632. [Google Scholar] [CrossRef]
- Kyryliuk, D.; Kratzer, S. Evaluation of Sentinel-3A OLCI products derived using the Case-2 Regional CoastColour processor over the Baltic Sea. Sensors 2019, 19, 3609. [Google Scholar] [CrossRef] [PubMed]
- Ding, X.; He, X.; Bai, Y.; Zhu, Q.; Gong, F.; Li, H.; Li, J. High-frequency and tidal period observations of suspended particulate matter in coastal waters by AHI/Himawari-8. Opt. Express 2020, 28, 27387–27404. [Google Scholar] [CrossRef] [PubMed]
- Li, J.; Li, Y.; Bi, S.; Xu, J.; Guo, F.; Lyu, H.; Dong, X.; Cai, X. Utilization of GOCI data to evaluate the diurnal vertical migration of Microcystis aeruginosa and the underlying driving factors. J. Environ. Manag. 2022, 310, 114734. [Google Scholar] [CrossRef]
- Mouw, C.B.; Greb, S.; Aurin, D.; DiGiacomo, P.M.; Lee, Z.; Twardowski, M.; Binding, C.; Hu, C.; Ma, R.; Moore, T.; et al. Aquatic color radiometry remote sensing of coastal and inland waters: Challenges and recommendations for future satellite missions. Remote Sens. Environ. 2015, 160, 15–30. [Google Scholar] [CrossRef]
- Palmer, S.C.J.; Kutser, T.; Hunter, P.D. Remote sensing of inland waters: Challenges, progress and future directions. Remote Sens. Environ. 2015, 157, 1–8. [Google Scholar] [CrossRef]
- Wang, S.; Zhang, X.; Chen, N.; Tian, L.; Zhang, Y.; Nam, W.H. A systematic review and quantitative meta-analysis of the relationships between driving forces and cyanobacterial blooms at global scale. Environ. Res. 2023, 216, 114670. [Google Scholar] [CrossRef] [PubMed]
- Zeng, F.; Song, C.; Cao, Z.; Xue, K.; Lu, S.; Chen, T.; Liu, K. Monitoring inland water via Sentinel satellite constellation: A review and perspective. ISPRS J. Photogramm. Remote Sens. 2023, 204, 340–361. [Google Scholar] [CrossRef]
- Hovis, W.A.; Clark, D.; Anderson, F.; Austin, R.; Wilson, W.; Baker, E.; Ball, D.; Gordon, H.; Mueller, J.; El-Sayed, S. Nimbus-7 Coastal Zone Color Scanner: System description and initial imagery. Science 1980, 210, 60–63. [Google Scholar] [CrossRef] [PubMed]
- Conkright, M.; Gregg, W. Comparison of global chlorophyll climatologies: In situ, CZCS, Blended in situ-CZCS and SeaWiFS. Int. J. Remote Sens. 2003, 24, 969–991. [Google Scholar] [CrossRef]
- Cracknell, A.P. The development of remote sensing in the last 40 years. Int. J. Remote Sens. 2018, 39, 8387–8427. [Google Scholar] [CrossRef]
- Jiang, L.; Wang, M. Improved near-infrared ocean reflectance correction algorithm for satellite ocean color data processing. Opt. Express 2014, 22, 21657–21678. [Google Scholar] [CrossRef]
- Pan, Y.; Shen, F.; Wei, X. Fusion of Landsat-8/OLI and GOCI Data for Hourly Mapping of Suspended Particulate Matter at High Spatial Resolution: A Case Study in the Yangtze (Changjiang) Estuary. Remote Sens. 2018, 10, 158. [Google Scholar] [CrossRef]
- Salisbury, J.; Davis, C.; Erb, A.; Hu, C.; Gatebe, C.; Jordan, C.; Lee, Z.; Mannino, A.; Mouw, C.; Schaaf, C.; et al. Coastal Observations from a New Vantage Point. Eos 2016, 97. [Google Scholar] [CrossRef]
- Wu, J.; Chen, C.; Nukapothula, S. Atmospheric Correction of GOCI Using Quasi-Synchronous VIIRS Data in Highly Turbid Coastal Waters. Remote Sens. 2019, 12, 89. [Google Scholar] [CrossRef]
- Wang, M.; Shi, W. The NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing. Opt. Express 2007, 15, 15722–15733. [Google Scholar] [CrossRef]
- Shi, W.; Wang, M. Detection of turbid waters and absorbing aerosols for the MODIS ocean color data processing. Remote Sens. Environ. 2007, 110, 149–161. [Google Scholar] [CrossRef]
- Agarwal, N.; Sharma, R.; Thapliyal, P.; Gangwar, R.; Kumar, P.; Kumar, R. Geostationary satellite-based observations for ocean applications. Curr. Sci. 2019, 117, 506–515. [Google Scholar] [CrossRef]
- Bailey, S.W.; Werdell, P.J. A multi-sensor approach for the on-orbit validation of ocean color satellite data products. Remote Sens. Environ. 2006, 102, 12–23. [Google Scholar] [CrossRef]
- Cao, C.; Wang, S.; Li, J.; Zhao, H.; Shen, W.; Xie, Y. MODIS-based monitoring of spatial distribution of trophic status in 144 key lakes and reservoirs of China in summer of 2018. J. Lake Sci. 2021, 33, 405–413. [Google Scholar]
- He, M.; He, S.; Zhang, X.; Zhou, F.; Li, P. Assessment of Normalized Water-Leaving Radiance Derived from GOCI Using AERONET-OC Data. Remote Sens. 2021, 13, 1640. [Google Scholar] [CrossRef]
- Tan, Z.; Cao, Z.; Shen, M.; Chen, J.; Song, Q.; Duan, H. Remote Estimation of Water Clarity and Suspended Particulate Matter in Qinghai Lake from 2001 to 2020 Using MODIS Images. Remote Sens. 2022, 14, 3094. [Google Scholar] [CrossRef]
- Xiong, J.; Lin, C.; Ma, R.; Cao, Z. Remote Sensing Estimation of Lake Total Phosphorus Concentration Based on MODIS: A Case Study of Lake Hongze. Remote Sens. 2019, 11, 2068. [Google Scholar] [CrossRef]
- Baldwin, D.; Tschudi, M.; Pacifici, F.; Liu, Y. Validation of Suomi-NPP VIIRS sea ice concentration with very high-resolution satellite and airborne camera imagery. ISPRS J. Photogramm. Remote Sens. 2017, 130, 122–138. [Google Scholar] [CrossRef]
- Justice, C.O.; Román, M.O.; Csiszar, I.; Vermote, E.F.; Wolfe, R.E.; Hook, S.J.; Friedl, M.; Wang, Z.; Schaaf, C.B.; Miura, T. Land and cryosphere products from Suomi NPP VIIRS: Overview and status. J. Geophys. Res. Atmos. 2013, 118, 9753–9765. [Google Scholar] [CrossRef]
- Lin, L.; Hao, X.; Zhang, B.; Zou, C.-Z.; Cao, C. Assessment of the Reprocessed Suomi NPP VIIRS Enterprise Cloud Mask Product. Remote Sens. 2021, 13, 2502. [Google Scholar] [CrossRef]
- Wang, M.; Jiang, L. VIIRS-derived ocean color product using the imaging bands. Remote Sens. Environ. 2018, 206, 275–286. [Google Scholar] [CrossRef]
- Park, M.-S.; Lee, S.; Ahn, J.-H.; Lee, S.-J.; Choi, J.-K.; Ryu, J.-H. Decadal measurements of the first Geostationary Ocean Color Satellite (GOCI) compared with MODIS and VIIRS data. Remote Sens. 2021, 14, 72. [Google Scholar] [CrossRef]
- Xia, L.; Mao, K.; Ma, Y.; Zhao, F.; Jiang, L.; Shen, X.; Qin, Z. An algorithm for retrieving land surface temperatures using VIIRS data in combination with multi-sensors. Sensors 2014, 14, 21385–21408. [Google Scholar] [CrossRef] [PubMed]
- Morrison, J.M.; Jeffrey, H.; Gorter, H.; Anderson, P.; Clark, C.; Holmes, A.; Feldman, G.C.; Patt, F.S. SeaHawk: An advanced CubeSat mission for sustained ocean colour monitoring. In Proceedings of the Sensors, Systems, and Next-Generation Satellites XX, Edinburgh, UK, 26–29 September 2016; pp. 309–319. [Google Scholar]
- Arino, O.; Gross, D.; Ranera, F.; Leroy, M.; Bicheron, P.; Brockman, C.; Defourny, P.; Vancutsem, C.; Achard, F.; Durieux, L. GlobCover: ESA service for global land cover from MERIS. In Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 23–28 July 2007; pp. 2412–2415. [Google Scholar]
- Doerffer, R.; Schiller, H. The MERIS Case 2 water algorithm. Int. J. Remote Sens. 2007, 28, 517–535. [Google Scholar] [CrossRef]
- Rast, M.; Bezy, J.; Bruzzi, S. The ESA Medium Resolution Imaging Spectrometer MERIS a review of the instrument and its mission. Int. J. Remote Sens. 1999, 20, 1681–1702. [Google Scholar] [CrossRef]
- Hammond, M.L.; Henson, S.A.; Lamquin, N.; Clerc, S.; Donlon, C. Assessing the Effect of Tandem Phase Sentinel-3 OLCI Sensor Uncertainty on the Estimation of Potential Ocean Chlorophyll-a Trends. Remote Sens. 2020, 12, 2522. [Google Scholar] [CrossRef]
- Moses, W.J.; Gitelson, A.A.; Berdnikov, S.; Povazhnyy, V. Estimation of chlorophyll-aconcentration in case II waters using MODIS and MERIS data—Successes and challenges. Environ. Res. Lett. 2009, 4, 045005. [Google Scholar] [CrossRef]
- Xu, Y.; He, X.; Bai, Y.; Wang, D.; Zhu, Q.; Ding, X. Evaluation of Remote-Sensing Reflectance Products from Multiple Ocean Color Missions in Highly Turbid Water (Hangzhou Bay). Remote Sens. 2021, 13, 4267. [Google Scholar] [CrossRef]
- Tilstone, G.H.; Pardo, S.; Dall’Olmo, G.; Brewin, R.J.W.; Nencioli, F.; Dessailly, D.; Kwiatkowska, E.; Casal, T.; Donlon, C. Performance of Ocean Colour Chlorophyll a algorithms for Sentinel-3 OLCI, MODIS-Aqua and Suomi-VIIRS in open-ocean waters of the Atlantic. Remote Sens. Environ. 2021, 260, 112444. [Google Scholar] [CrossRef]
- Deuzé, J.; Bréon, F.; Devaux, C.; Goloub, P.; Herman, M.; Lafrance, B.; Maignan, F.; Marchand, A.; Nadal, F.; Perry, G. Remote sensing of aerosols over land surfaces from POLDER-ADEOS-1 polarized measurements. J. Geophys. Res. Atmos. 2001, 106, 4913–4926. [Google Scholar] [CrossRef]
- Leroy, M.; Deuzé, J.; Bréon, F.; Hautecoeur, O.; Herman, M.; Buriez, J.; Tanré, D.; Bouffies, S.; Chazette, P.; Roujean, J.-L. Retrieval of atmospheric properties and surface bidirectional reflectances over land from POLDER/ADEOS. J. Geophys. Res. Atmos. 1997, 102, 17023–17037. [Google Scholar] [CrossRef]
- Shimoda, H. ADEOS overview. IEEE Trans. Geosci. Remote Sens. 1999, 37, 1465–1471. [Google Scholar] [CrossRef]
- Kurihara, Y.; Murakami, H.; Ogata, K.; Kachi, M. A quasi-physical sea surface temperature method for the split-window data from the Second-generation Global Imager (SGLI) onboard the Global Change Observation Mission-Climate (GCOM-C) satellite. Remote Sens. Environ. 2021, 257, 112347. [Google Scholar] [CrossRef]
- Matsuoka, A.; Campbell, J.W.; Hooker, S.B.; Steinmetz, F.; Ogata, K.; Hirata, T.; Higa, H.; Kuwahara, V.S.; Isada, T.; Suzuki, K. Performance of JAXA’s SGLI standard ocean color products for oceanic to coastal waters: Chlorophyll a concentration and light absorption coefficients of colored dissolved organic matter. J. Oceanogr. 2022, 78, 187–208. [Google Scholar] [CrossRef]
- Tanaka, K.; Okamura, Y.; Mokuno, M.; Amano, T.; Yoshida, J. First year on-orbit calibration activities of SGLI on GCOM-C satellite. In Proceedings of the Earth Observing Missions and Sensors: Development, Implementation, and Characterization V, Honolulu, HI, USA, 25–26 September 2018; pp. 101–110. [Google Scholar]
- Qu, L.; Liu, M.; Guan, L. Simulation of Thermal Infrared Brightness Temperatures from an Ocean Color and Temperature Scanner Onboard a New Generation Chinese Ocean Color Observation Satellite. Remote Sens. 2023, 15, 5059. [Google Scholar] [CrossRef]
- Heales, C.J.; Lloyd, E. Play simulation for children in magnetic resonance imaging. J. Med. Imaging Radiat. Sci. 2022, 53, 10–16. [Google Scholar] [CrossRef]
- Chakraborty, A.; Kumar, R.; Stoffelen, A. Validation of ocean surface winds from the OCEANSAT-2 scatterometer using triple collocation. Remote Sens. Lett. 2013, 4, 84–93. [Google Scholar] [CrossRef]
- Parmar, R.; Arora, R.; Rao, M.V.; Thyagarajan, K. OCEANSAT 2: Mission and its applications. In Proceedings of the GEOSS and Next-Generation Sensors and Missions, Goa, India, 13–14 November 2006; pp. 62–73. [Google Scholar]
- Singh, R.; Kumar, P.; Pal, P.K. Assimilation of Oceansat-2-scatterometer-derived surface winds in the weather research and forecasting model. IEEE Trans. Geosci. Remote Sens. 2011, 50, 1015–1021. [Google Scholar] [CrossRef]
- Lee, S.-J.; Lee, D.-E.; Choi, S.-Y.; Kwon, O.-S. OSMI-1 enhances TRAIL-induced apoptosis through ER stress and NF-κB signaling in colon cancer cells. Int. J. Mol. Sci. 2021, 22, 11073. [Google Scholar] [CrossRef]
- Hamacher, K.; Buchkremer, R. Measuring online sensory consumer experience: Introducing the Online Sensory Marketing Index (OSMI) as a structural modeling approach. J. Theor. Appl. Electron. Commer. Res. 2022, 17, 751–772. [Google Scholar] [CrossRef]
- Lamquin, N.; Mazeran, C.; Doxaran, D.; Ryu, J.-H.; Park, Y.-J. Assessment of GOCI radiometric products using MERIS, MODIS and field measurements. Ocean Sci. J. 2012, 47, 287–311. [Google Scholar] [CrossRef]
- Shin, J.; Lee, J.-S.; Jang, L.-H.; Lim, J.; Khim, B.-K.; Jo, Y.-H. Sargassum detection using machine learning models: A case study with the first 6 months of GOCI-II imagery. Remote Sens. 2021, 13, 4844. [Google Scholar] [CrossRef]
- Warren, M.; Quartly, G.D.; Shutler, J.; Miller, P.I.; Yoshikawa, Y. Estimation of ocean surface currents from maximum cross correlation applied to GOCI geostationary satellite remote sensing data over the Tsushima (Korea) Straits. J. Geophys. Res. Ocean. 2016, 121, 6993–7009. [Google Scholar] [CrossRef]
- Paduan, J.D.; Washburn, L. High-frequency radar observations of ocean surface currents. Annu. Rev. Mar. Sci. 2013, 5, 115–136. [Google Scholar] [CrossRef] [PubMed]
- Ruddick, K.; Neukermans, G.; Vanhellemont, Q.; Jolivet, D. Challenges and opportunities for geostationary ocean colour remote sensing of regional seas: A review of recent results. Remote Sens. Environ. 2014, 146, 63–76. [Google Scholar] [CrossRef]
- Ryu, J.-H.; Han, H.-J.; Cho, S.; Park, Y.-J.; Ahn, Y.-H. Overview of geostationary ocean color imager (GOCI) and GOCI data processing system (GDPS). Ocean Sci. J. 2012, 47, 223–233. [Google Scholar] [CrossRef]
- Schaeffer, B.A.; Whitman, P.; Vandermeulen, R.; Hu, C.; Mannino, A.; Salisbury, J.; Efremova, B.; Conmy, R.; Coffer, M.; Salls, W.; et al. Assessing potential of the Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) for water quality monitoring across the coastal United States. Mar. Pollut. Bull. 2023, 196, 115558. [Google Scholar] [CrossRef] [PubMed]
- Son, S.; Kim, Y.H.; Kwon, J.-I.; Kim, H.-C.; Park, K.-S. Characterization of spatial and temporal variation of suspended sediments in the Yellow and East China Seas using satellite ocean color data. GIScience Remote Sens. 2014, 51, 212–226. [Google Scholar] [CrossRef]
- Duan, H.; Ma, R.; Hu, C. Evaluation of remote sensing algorithms for cyanobacterial pigment retrievals during spring bloom formation in several lakes of East China. Remote Sens. Environ. 2012, 126, 126–135. [Google Scholar] [CrossRef]
- Li, J.; Gao, M.; Feng, L.; Zhao, H.; Shen, Q.; Zhang, F.; Wang, S.; Zhang, B. Estimation of chlorophyll-a concentrations in a highly turbid eutrophic lake using a classification-based MODIS land-band algorithm. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2019, 12, 3769–3783. [Google Scholar] [CrossRef]
- Liu, H.; He, X.; Li, Q.; Kratzer, S.; Wang, J.; Shi, T.; Hu, Z.; Yang, C.; Hu, S.; Zhou, Q. Estimating ultraviolet reflectance from visible bands in ocean colour remote sensing. Remote Sens. Environ. 2021, 258, 112404. [Google Scholar] [CrossRef]
- Lou, X.; Hu, C. Diurnal changes of a harmful algal bloom in the East China Sea: Observations from GOCI. Remote Sens. Environ. 2014, 140, 562–572. [Google Scholar] [CrossRef]
- Wang, J.; Tang, J.; Wang, W.; Wang, Y.; Wang, Z. Quantitative Retrieval of Chlorophyll-a Concentrations in the Bohai–Yellow Sea Using GOCI Surface Reflectance Products. Remote Sens. 2023, 15, 5285. [Google Scholar] [CrossRef]
- Yang, H.; Choi, J.K.; Park, Y.J.; Han, H.J.; Ryu, J.H. Application of the Geostationary Ocean Color Imager (GOCI) to estimates of ocean surface currents. J. Geophys. Res. Ocean. 2014, 119, 3988–4000. [Google Scholar] [CrossRef]
- Li, G.; Wang, L.; Wang, X.; Wang, X.; Sun, G. Geostationary ocean color imager and application progress. Mar. Environ. Sci. 2014, 33, 966–971. [Google Scholar]
- Yin, W.; Huang, D. Applications of geostationary satellite data in the study of ocean and coastal short-term processes: Two cases in the East China Sea. In Remote Sensing of Ocean and Coastal Environments; Elsevier: Amsterdam, The Netherlands, 2021; pp. 139–154. [Google Scholar]
- Chen, J.; He, X.; Quan, W.; Ma, L.; Jia, M.; Pan, D. A statistical analysis of residual errors in satellite remote sensing reflectance data from oligotrophic open oceans. IEEE Trans. Geosci. Remote Sens. 2021, 60, 4203912. [Google Scholar] [CrossRef]
- Gong, S.; Huang, J.; Li, Y.; Wang, H. Comparison of atmospheric correction algorithms for TM image in inland waters. Int. J. Remote Sens. 2008, 29, 2199–2210. [Google Scholar] [CrossRef]
- Zeng, Q.; Zhao, Y.; Tian, L.-Q.; Chen, X.-L. Evaluation on the atmospheric correction methods for water color remote sensing by using HJ-1A/1B CCD image-taking Poyang Lake in China as a case. Spectrosc. Spectr. Anal. 2013, 33, 1320–1326. [Google Scholar]
- Ahn, J.-H.; Park, Y.-J.; Ryu, J.-H.; Lee, B.; Oh, I.S. Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI). Ocean Sci. J. 2012, 47, 247–259. [Google Scholar] [CrossRef]
- Concha, J.; Mannino, A.; Franz, B.; Kim, W. Uncertainties in the Geostationary Ocean Color Imager (GOCI) Remote Sensing Reflectance for Assessing Diurnal Variability of Biogeochemical Processes. Remote Sens. 2019, 11, 295. [Google Scholar] [CrossRef]
- Hu, Y.; Dou, T.; Yang, B. A review of research on retrieving the concentration of suspended particulate matter and chlorophyll-a in lake based on GOCI images. J. Water Resour. Water Eng. 2017, 28, 26–39. [Google Scholar]
- Huang, X.; Zhu, J.; Han, B.; Jamet, C.; Tian, Z.; Zhao, Y.; Li, J.; Li, T. Evaluation of Four Atmospheric Correction Algorithms for GOCI Images over the Yellow Sea. Remote Sens. 2019, 11, 1631. [Google Scholar] [CrossRef]
- Ahn, J.-H.; Park, Y.-J. Estimating Water Reflectance at Near-Infrared Wavelengths for Turbid Water Atmospheric Correction: A Preliminary Study for GOCI-II. Remote Sens. 2020, 12, 3791. [Google Scholar] [CrossRef]
- Ahn, J.H.; Park, Y.J.; Kim, W.; Lee, B.; Oh, I.S. Vicarious calibration of the Geostationary Ocean Color Imager. Opt. Express 2015, 23, 23236–23258. [Google Scholar] [CrossRef] [PubMed]
- Goyens, C.; Jamet, C.; Ruddick, K.G. Spectral relationships for atmospheric correction. II. Improving NASA’s standard and MUMM near infra-red modeling schemes. Opt. Express 2013, 21, 21176–21187. [Google Scholar] [CrossRef] [PubMed]
- Pan, Y.; Shen, F.; Verhoef, W. An improved spectral optimization algorithm for atmospheric correction over turbid coastal waters: A case study from the Changjiang (Yangtze) estuary and the adjacent coast. Remote Sens. Environ. 2017, 191, 197–214. [Google Scholar] [CrossRef]
- He, X.; Bai, Y.; Pan, D.; Huang, N.; Dong, X.; Chen, J.; Chen, C.-T.A.; Cui, Q. Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters. Remote Sens. Environ. 2013, 133, 225–239. [Google Scholar] [CrossRef]
- Men, J.; Feng, L.; Chen, X.; Tian, L. Atmospheric correction under cloud edge effects for Geostationary Ocean Color Imager through deep learning. ISPRS J. Photogramm. Remote Sens. 2023, 201, 38–53. [Google Scholar] [CrossRef]
- Li, H.; He, X.; Bai, Y.; Shanmugam, P.; Park, Y.-J.; Liu, J.; Zhu, Q.; Gong, F.; Wang, D.; Huang, H. Atmospheric correction of geostationary satellite ocean color data under high solar zenith angles in open oceans. Remote Sens. Environ. 2020, 249, 112022. [Google Scholar] [CrossRef]
- An, D.; Yu, D.; Zheng, X.; Zhou, Y.; Meng, L.; Xing, Q. Monitoring the Dissipation of the Floating Green Macroalgae Blooms in the Yellow Sea (2007–2020) on the Basis of Satellite Remote Sensing. Remote Sens. 2021, 13, 3811. [Google Scholar] [CrossRef]
- Bing, Z.; Xiaoli, C.; Sen, W.; Xinxin, Y. Analysis of the Causes of Cyanobacteria Bloom: A Review. J. Resour. Ecol. 2020, 11, 405–413. [Google Scholar] [CrossRef]
- Blondeau-Patissier, D.; Gower, J.F.R.; Dekker, A.G.; Phinn, S.R.; Brando, V.E. A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans. Prog. Oceanogr. 2014, 123, 123–144. [Google Scholar] [CrossRef]
- Cannizzaro, J.P.; Barnes, B.B.; Hu, C.; Corcoran, A.A.; Hubbard, K.A.; Muhlbach, E.; Sharp, W.C.; Brand, L.E.; Kelble, C.R. Remote detection of cyanobacteria blooms in an optically shallow subtropical lagoonal estuary using MODIS data. Remote Sens. Environ. 2019, 231, 111227. [Google Scholar] [CrossRef]
- Huan, Y.; Sun, D.; Wang, S.; Zhang, H.; Qiu, Z.; Bilal, M.; He, Y. Remote sensing estimation of phytoplankton absorption associated with size classes in coastal waters. Ecol. Indic. 2021, 121, 107198. [Google Scholar] [CrossRef]
- Paerl, H.W.; Xu, H.; McCarthy, M.J.; Zhu, G.; Qin, B.; Li, Y.; Gardner, W.S. Controlling harmful cyanobacterial blooms in a hyper-eutrophic lake (Lake Taihu, China): The need for a dual nutrient (N & P) management strategy. Water Res. 2011, 45, 1973–1983. [Google Scholar] [PubMed]
- Sakib, M.H.; Rashid, A.H.A.; Yang, C.S. Comparing performance of inter-sensor NDVI for the detection of floating macroalgal blooms in the Yellow Sea. Indian J. Geo Mar. Sci. 2021, 50, 613–619. [Google Scholar]
- Sun, X.; Wu, M.; Xing, Q.; Song, X.; Zhao, D.; Han, Q.; Zhang, G. Spatio-temporal patterns of Ulva prolifera blooms and the corresponding influence on chlorophyll-a concentration in the Southern Yellow Sea, China. Sci. Total Environ. 2018, 640–641, 807–820. [Google Scholar] [CrossRef]
- Hong, T.T.M.; Park, Y.-G.; Choi, J.M. Divergence Observation in a Mesoscale Eddy during Chla Bloom Revealed in Submesoscale Satellite Currents. Remote Sens. 2023, 15, 995. [Google Scholar] [CrossRef]
- Noh, J.H.; Kim, W.; Son, S.H.; Ahn, J.H.; Park, Y.J. Remote quantification of Cochlodinium polykrikoides blooms occurring in the East Sea using geostationary ocean color imager (GOCI). Harmful Algae 2018, 73, 129–137. [Google Scholar] [CrossRef]
- Sun, D.; Huan, Y.; Qiu, Z.; Hu, C.; Wang, S.; He, Y. Remote-Sensing Estimation of Phytoplankton Size Classes from GOCI Satellite Measurements in Bohai Sea and Yellow Sea. J. Geophys. Res. Ocean. 2017, 122, 8309–8325. [Google Scholar] [CrossRef]
- Fan, C.; Zhang, Y.; Wang, X. Chlorophyll-a concentration inversion and distribution with GOCI images in the Changjiang Estuary. In Proceedings of the First International Conference on Spatial Atmospheric Marine Environmental Optics (SAME 2023), Shanghai, China, 7–9 April 2023; pp. 201–210. [Google Scholar]
- Choi, J.-K.; Min, J.-E.; Noh, J.H.; Han, T.-H.; Yoon, S.; Park, Y.J.; Moon, J.-E.; Ahn, J.-H.; Ahn, S.M.; Park, J.-H. Harmful algal bloom (HAB) in the East Sea identified by the Geostationary Ocean Color Imager (GOCI). Harmful Algae 2014, 39, 295–302. [Google Scholar] [CrossRef]
- Shen, F.; Tang, R.; Sun, X.; Liu, D. Simple methods for satellite identification of algal blooms and species using 10-year time series data from the East China Sea. Remote Sens. Environ. 2019, 235, 111484. [Google Scholar] [CrossRef]
- Xu, M.; Gao, Z.; Liu, C. Detecting harmful algal blooms using Geostationary Ocean Color Imager (GOCI) data in Bohai Sea, China. In Proceedings of the Remote Sensing and Modeling of Ecosystems for Sustainability XII, San Diego, CA, USA, 9–13 August 2015; pp. 203–208. [Google Scholar]
- Yimin, L.; Zhenyu, T.; Chen, Y.; Feng, H.; Di, M.; Juhua, L.; Hongtao, D. Extraction of Algal Blooms in Dianchi Lake Based on Multi-Source Satellites Using Machine Learning Algorithms. Adv. Earth Sci. 2022, 37, 1141. [Google Scholar]
- Kim, D.-W.; Kim, S.-H.; Baek, J.-Y.; Lee, J.-S.; Jo, Y.-H. GOCI-II based sea surface salinity estimation using machine learning for the first-year summer. Int. J. Remote Sens. 2022, 43, 6605–6623. [Google Scholar] [CrossRef]
- Cao, H.; Han, L. Hourly remote sensing monitoring of harmful algal blooms (HABs) in Taihu Lake based on GOCI images. Environ. Sci. Pollut. Res. Int. 2021, 28, 35958–35970. [Google Scholar] [CrossRef]
- Lee, M.-S.; Park, K.-A.; Micheli, F. Derivation of Red Tide Index and Density Using Geostationary Ocean Color Imager (GOCI) Data. Remote Sens. 2021, 13, 298. [Google Scholar] [CrossRef]
- Son, Y.B.; Choi, B.-J.; Kim, Y.H.; Park, Y.-G. Tracing floating green algae blooms in the Yellow Sea and the East China Sea using GOCI satellite data and Lagrangian transport simulations. Remote Sens. Environ. 2015, 156, 21–33. [Google Scholar] [CrossRef]
- Son, Y.B.; Min, J.-E.; Ryu, J.-H. Detecting massive green algae (Ulva prolifera) blooms in the Yellow Sea and East China Sea using geostationary ocean color imager (GOCI) data. Ocean Sci. J. 2012, 47, 359–375. [Google Scholar] [CrossRef]
- Feng, C.; Wang, S.; Li, Z. Long-term spatial variation of algal blooms extracted using the U-net model from 10 years of GOCI imagery in the East China Sea. J. Environ. Manag. 2022, 321, 115966. [Google Scholar] [CrossRef]
- Qiu, Z.; Li, Z.; Bilal, M.; Wang, S.; Sun, D.; Chen, Y. Automatic method to monitor floating macroalgae blooms based on multilayer perceptron: Case study of Yellow Sea using GOCI images. Opt. Express 2018, 26, 26810–26829. [Google Scholar] [CrossRef]
- Pan, B.; Shi, Z.; An, Z.; Jiang, Z.; Ma, Y. A novel spectral-unmixing-based green algae area estimation method for GOCI data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 10, 437–449. [Google Scholar] [CrossRef]
- Xue, K.; Ma, R.; Cao, Z.; Shen, M.; Hu, M.; Xiong, J. Monitoring fractional floating algae cover over eutrophic lakes using multisensor satellite images: MODIS, VIIRS, GOCI, and OLCI. IEEE Trans. Geosci. Remote Sens. 2022, 60, 4211715. [Google Scholar] [CrossRef]
- Qi, L.; Hu, C.; Visser, P.M.; Ma, R. Diurnal changes of cyanobacteria blooms in Taihu Lake as derived from GOCI observations. Limnol. Oceanogr. 2018, 63, 1711–1726. [Google Scholar] [CrossRef]
- Ai, Y.; Lee, S.; Lee, J. Drinking water treatment residuals from cyanobacteria bloom-affected areas: Investigation of potential impact on agricultural land application. Sci. Total Environ. 2020, 706, 135756. [Google Scholar] [CrossRef] [PubMed]
- Wang, S.; Zhang, X.; Chen, N.; Wang, W. Classifying diurnal changes of cyanobacterial blooms in Lake Taihu to identify hot patterns, seasons and hotspots based on hourly GOCI observations. J. Environ. Manag. 2022, 310, 114782. [Google Scholar] [CrossRef] [PubMed]
- Li, J.; Li, Y.; Dong, X.; Wang, H.; Cai, X.; Zhu, Y.; Lyu, H.; Zeng, S.; Bi, S.; Wang, G. Contributions of meteorology and nutrient to the surface cyanobacterial blooms at different timescales in the shallow eutrophic Lake Taihu. Sci. Total Environ. 2023, 894, 165064. [Google Scholar] [CrossRef] [PubMed]
- Du, C.; Li, Y.; Wang, Q.; Liu, G.; Zheng, Z.; Mu, M.; Li, Y. Tempo-spatial dynamics of water quality and its response to river flow in estuary of Taihu Lake based on GOCI imagery. Environ. Sci. Pollut. Res. Int. 2017, 24, 28079–28101. [Google Scholar] [CrossRef]
- Wang, M.; Son, S.; Harding, L.W. Retrieval of diffuse attenuation coefficient in the Chesapeake Bay and turbid ocean regions for satellite ocean color applications. J. Geophys. Res. Ocean. 2009, 114, C10011. [Google Scholar] [CrossRef]
- Li, J.; Yu, Q.; Tian, Y.Q.; Becker, B.L. Remote sensing estimation of colored dissolved organic matter (CDOM) in optically shallow waters. ISPRS J. Photogramm. Remote Sens. 2017, 128, 98–110. [Google Scholar] [CrossRef]
- Lin, S.; Jing, C.; Rui, L. A method of water quality analysis: Chlorophyll a concentration estimation of Dongping Lake based on GOCI image. Environ. Prot. 2017, 45, 60–63. [Google Scholar]
- Lyu, H.; Zhang, J.; Zha, G.; Wang, Q.; Li, Y. Developing a two-step retrieval method for estimating total suspended solid concentration in Chinese turbid inland lakes using Geostationary Ocean Colour Imager (GOCI) imagery. Int. J. Remote Sens. 2015, 36, 1385–1405. [Google Scholar] [CrossRef]
- Sagan, V.; Peterson, K.T.; Maimaitijiang, M.; Sidike, P.; Sloan, J.; Greeling, B.A.; Maalouf, S.; Adams, C. Monitoring inland water quality using remote sensing: Potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing. Earth-Sci. Rev. 2020, 205, 103187. [Google Scholar] [CrossRef]
- Qing, S.; Cui, T.; Lai, Q.; Bao, Y.; Diao, R.; Yue, Y.; Hao, Y. Improving remote sensing retrieval of water clarity in complex coastal and inland waters with modified absorption estimation and optical water classification using Sentinel-2 MSI. Int. J. Appl. Earth Obs. Geoinf. 2021, 102, 102377. [Google Scholar] [CrossRef]
- Bao, Y.; Tian, Q.; Chen, M.; Lü, C. Analysis on Diurnal Variation of Chlorophyll-a Concentration of Taihu Lake Based on Optical Classification with GOCI Data. Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu 2016, 36, 2562–2567. [Google Scholar] [PubMed]
- Cui, T.W.; Zhang, J.; Wang, K.; Wei, J.W.; Mu, B.; Ma, Y.; Zhu, J.H.; Liu, R.J.; Chen, X.Y. Remote sensing of chlorophyll a concentration in turbid coastal waters based on a global optical water classification system. ISPRS J. Photogramm. Remote Sens. 2020, 163, 187–201. [Google Scholar] [CrossRef]
- Hu, M.; Zhang, Y.; Ma, R.; Xue, K.; Cao, Z.; Chu, Q.; Jing, Y. Optimized remote sensing estimation of the lake algal biomass by considering the vertically heterogeneous chlorophyll distribution: Study case in Lake Chaohu of China. Sci. Total Environ. 2021, 771, 144811. [Google Scholar] [CrossRef] [PubMed]
- Kim, W.; Moon, J.-E.; Park, Y.-J.; Ishizaka, J. Evaluation of chlorophyll retrievals from Geostationary Ocean Color Imager (GOCI) for the North-East Asian region. Remote Sens. Environ. 2016, 184, 482–495. [Google Scholar] [CrossRef]
- Neil, C.; Spyrakos, E.; Hunter, P.D.; Tyler, A.N. A global approach for chlorophyll-a retrieval across optically complex inland waters based on optical water types. Remote Sens. Environ. 2019, 229, 159–178. [Google Scholar] [CrossRef]
- Soomets, T.; Toming, K.; Paavel, B.; Kutser, T. Evaluation of remote sensing and modeled chlorophyll-a products of the Baltic Sea. J. Appl. Remote Sens. 2022, 16, 046516. [Google Scholar] [CrossRef]
- Cao, Z.; Wang, M.; Ma, R.; Zhang, Y.; Duan, H.; Jiang, L.; Xue, K.; Xiong, J.; Hu, M. A decade-long chlorophyll-a data record in lakes across China from VIIRS observations. Remote Sens. Environ. 2024, 301, 113953. [Google Scholar] [CrossRef]
- Park, J.-E.; Park, K.-A. Application of Deep Learning for Speckle Removal in GOCI Chlorophyll-a Concentration Images (2012–2017). Remote Sens. 2021, 13, 585. [Google Scholar] [CrossRef]
- Shi, K.; Li, Y.; Li, L.; Lu, H.; Song, K.; Liu, Z.; Xu, Y.; Li, Z. Remote chlorophyll-a estimates for inland waters based on a cluster-based classification. Sci. Total Environ. 2013, 444, 1–15. [Google Scholar] [CrossRef] [PubMed]
- Li, H.; He, X.; Bai, Y.; Chen, X.; Gong, F.; Zhu, Q.; Hu, Z. Assessment of satellite-based chlorophyll-a retrieval algorithms for high solar zenith angle conditions. J. Appl. Remote Sens. 2017, 11, 012004. [Google Scholar] [CrossRef]
- Zhao, D.; Feng, L. Assessment of the Number of Valid Observations and Diurnal Changes in Chl-a for GOCI: Highlights for Geostationary Ocean Color Missions. Sensors 2020, 20, 3377. [Google Scholar] [CrossRef] [PubMed]
- Sakuno, Y.; Makio, K.; Koike, K. Chlorophyll-a Estimation in Tachibana Bay by Data Fusion of GOCI and MODIS Using Linear Combination Index Algorithm. Adv. Remote Sens. 2013, 2013, 40465. [Google Scholar] [CrossRef]
- Zhao, M.; Bai, Y.; Li, H.; He, X.; Gong, F.; Li, T. Fluorescence Line Height Extraction Algorithm for the Geostationary Ocean Color Imager. Remote Sens. 2022, 14, 2511. [Google Scholar] [CrossRef]
- Yang, Y.; He, S.; Gu, Y.; Zhu, C.; Wang, L.; Ma, X.; Li, P. Retrieval of Chlorophyll a Concentration Using GOCI Data in Sediment-Laden Turbid Waters of Hangzhou Bay and Adjacent Coastal Waters. J. Mar. Sci. Eng. 2023, 11, 1098. [Google Scholar] [CrossRef]
- Bao, Y.; Tian, Q.; Chen, M. A Weighted Algorithm Based on Normalized Mutual Information for Estimating the Chlorophyll-a Concentration in Inland Waters Using Geostationary Ocean Color Imager (GOCI) Data. Remote Sens. 2015, 7, 11731–11752. [Google Scholar] [CrossRef]
- Huang, C.; Shi, K.; Yang, H.; Li, Y.; Zhu, A.-X.; Sun, D.; Xu, L.; Zou, J.; Chen, X. Satellite observation of hourly dynamic characteristics of algae with Geostationary Ocean Color Imager (GOCI) data in Lake Taihu. Remote Sens. Environ. 2015, 159, 278–287. [Google Scholar] [CrossRef]
- Duan, H.; Ma, R.; Zhang, Y.; Loiselle, S.A.; Xu, J.; Zhao, C.; Zhou, L.; Shang, L. A new three-band algorithm for estimating chlorophyll concentrations in turbid inland lakes. Environ. Res. Lett. 2010, 5, 044009. [Google Scholar] [CrossRef]
- Guo, Y.; Huang, C.; Li, Y.; Du, C.; Li, Y.; Chen, W.; Shi, L.; Ji, G. an expanded three band model to monitor inland optically complex water using Geostationary Ocean Color Imager (GOCI). Front. Remote Sens. 2022, 3, 803884. [Google Scholar] [CrossRef]
- Yulong, G.; Changchun, H.; Yunmei, L.; Chenggong, D.; Lingfei, S.; Yuan, L.; Weiqiang, C.; Hejie, W.; Enxiang, C.; Guangxing, J. Hyperspectral reconstruction method for optically complex inland waters based on bio-optical model and sparse representing. Remote Sens. Environ. 2022, 276, 113045. [Google Scholar] [CrossRef]
- Guo, Y.; Wei, X.; Huang, Z.; Li, H.; Ma, R.; Cao, Z.; Shen, M.; Xue, K. Retrievals of Chlorophyll-a from GOCI and GOCI-II Data in Optically Complex Lakes. Remote Sens. 2023, 15, 4886. [Google Scholar] [CrossRef]
- Binding, C.E.; Bowers, D.G.; Mitchelson-Jacob, E.G. Estimating suspended sediment concentrations from ocean colour measurements in moderately turbid waters; the impact of variable particle scattering properties. Remote Sens. Environ. 2005, 94, 373–383. [Google Scholar] [CrossRef]
- Cao, Z.; Ma, R.; Duan, H.; Xue, K.; Shen, M. Effect of Satellite Temporal Resolution on Long-Term Suspended Particulate Matter in Inland Lakes. Remote Sens. 2019, 11, 2785. [Google Scholar] [CrossRef]
- Kang, Y.; Dong, C. Spatio-temporal Analysis of suspended sediment Concentration in the Yongjiang Estuary Based on GOCI. IOP Conf. Ser. Earth Environ. Sci. 2018, 108, 032017. [Google Scholar] [CrossRef]
- Xu, Y.; Qin, B.; Zhu, G.; Zhang, Y.; Shi, K.; Li, Y.; Shi, Y.; Chen, L. High Temporal Resolution Monitoring of Suspended Matter Changes from GOCI Measurements in Lake Taihu. Remote Sens. 2019, 11, 985. [Google Scholar] [CrossRef]
- Jiang, D.; Zhang, H.; Zou, T.; Li, Y.; Tang, C.; Li, R. Suspended particle size retrieval based on geostationary ocean color imager (GOCI) in the Bohai Sea. J. Coast. Res. 2016, 74, 117–125. [Google Scholar] [CrossRef]
- Lei, S.; Xu, J.; Li, Y.; Du, C.; Liu, G.; Zheng, Z.; Xu, Y.; Lyu, H.; Mu, M.; Miao, S.; et al. An approach for retrieval of horizontal and vertical distribution of total suspended matter concentration from GOCI data over Lake Hongze. Sci. Total Environ. 2020, 700, 134524. [Google Scholar] [CrossRef]
- Liu, J.; Liu, J.; He, X.; Chen, T.; Zhu, F.; Wang, Y.; Hao, Z.; Chen, P. Retrieval of total suspended particulate matter in highly turbid Hangzhou Bay waters based on geostationary ocean color imager. In Proceedings of the Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2017, Warsaw, Poland, 11–14 September 2017; pp. 105–111. [Google Scholar]
- Padial, A.A.; Thomaz, S.M. Prediction of the light attenuation coefficient through the Secchi disk depth: Empirical modeling in two large Neotropical ecosystems. Limnology 2008, 9, 143–151. [Google Scholar] [CrossRef]
- Ahn, Y.-H.; Moon, J.-E.; Gallegos, S. Development of suspended particulate matter algorithms for ocean color remote sensing. Korean J. Remote Sens. 2001, 17, 285–295. [Google Scholar]
- Moon, J.-E.; Ahn, Y.-H.; Ryu, J.-H.; Shanmugam, P. Development of ocean environmental algorithms for Geostationary Ocean Color Imager (GOCI). Korean J. Remote Sens. 2010, 26, 189–207. [Google Scholar]
- Ruddick, K.; Vanhellemont, Q.; Yan, J.; Neukermans, G.; Wei, G.; Shang, S. Variability of suspended particulate matter in the Bohai Sea from the geostationary Ocean Color Imager (GOCI). Ocean Sci. J. 2012, 47, 331–345. [Google Scholar] [CrossRef]
- Nechad, B.; Ruddick, K.G.; Park, Y. Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters. Remote Sens. Environ. 2010, 114, 854–866. [Google Scholar] [CrossRef]
- Hu, Z.; Pan, D.; He, X.; Bai, Y. Diurnal Variability of Turbidity Fronts Observed by Geostationary Satellite Ocean Color Remote Sensing. Remote Sens. 2016, 8, 147. [Google Scholar] [CrossRef]
- Moon, J.-E.; Park, Y.-J.; Ryu, J.-H.; Choi, J.-K.; Ahn, J.-H.; Min, J.-E.; Son, Y.-B.; Lee, S.-J.; Han, H.-J.; Ahn, Y.-H. Initial validation of GOCI water products against in situ data collected around Korean peninsula for 2010–2011. Ocean Sci. J. 2012, 47, 261–277. [Google Scholar] [CrossRef]
- Siswanto, E.; Tang, J.; Yamaguchi, H.; Ahn, Y.-H.; Ishizaka, J.; Yoo, S.; Kim, S.-W.; Kiyomoto, Y.; Yamada, K.; Chiang, C. Empirical ocean-color algorithms to retrieve chlorophyll-a, total suspended matter, and colored dissolved organic matter absorption coefficient in the Yellow and East China Seas. J. Oceanogr. 2011, 67, 627–650. [Google Scholar] [CrossRef]
- Yu, X. Retrieval of Suspended Matter Concentration and Reconstruction of Missing Data Based on GOCI in Bohai and Yellow Sea; Ocean University of China: Qingdao, China, 2013. [Google Scholar]
- Cheng, Z.; Wang, X.H.; Paull, D.; Gao, J. Application of the geostationary ocean color imager to mapping the diurnal and seasonal variability of surface suspended matter in a macro-tidal estuary. Remote Sens. 2016, 8, 244. [Google Scholar] [CrossRef]
- Choi, J.-K.; Park, Y.J.; Lee, B.R.; Eom, J.; Moon, J.-E.; Ryu, J.-H. Application of the Geostationary Ocean Color Imager (GOCI) to mapping the temporal dynamics of coastal water turbidity. Remote Sens. Environ. 2014, 146, 24–35. [Google Scholar] [CrossRef]
- Meng, Q.; Xu, J.; Wang, L.; Chen, Y.; Wang, X. Diurnal Changes Monitoring and Analysis of the Total Suspended Matters in Bohai Sea Using Geostationary Ocean Color Imager. IOP Conf. Ser. Earth Environ. Sci. 2019, 234, 012036. [Google Scholar] [CrossRef]
- Zhang, M.; Tang, J.; Dong, Q.; Song, Q.; Ding, J. Retrieval of total suspended matter concentration in the Yellow and East China Seas from MODIS imagery. Remote Sens. Environ. 2010, 114, 392–403. [Google Scholar] [CrossRef]
- He, A.; He, X.; Bai, Y.; Zhu, Q.; Gong, F.; Huang, H.; Pan, D. Simulation of Sedimentation in Lake Taihu with Geostationary Satellite Ocean Color Data. Remote Sens. 2019, 11, 379. [Google Scholar] [CrossRef]
- Amin, R.; Shulman, I. Hourly turbidity monitoring using Geostationary Ocean Color Imager fluorescence bands. J. Appl. Remote Sens. 2015, 9, 096024. [Google Scholar] [CrossRef]
- Kukushkin, A.S. Long-term seasonal variability of water transparency in the surface layer of the deep part of the Black Sea. Russ. Meteorol. Hydrol. 2014, 39, 178–186. [Google Scholar] [CrossRef]
- Wang, S.; Mao, Y.; Zheng, L.; Qiu, Z.; Bilal, M.; Sun, D. Remote sensing of water turbidity in the eastern China seas from geostationary ocean colour imager. Int. J. Remote Sens. 2020, 41, 4080–4101. [Google Scholar] [CrossRef]
- Yan, Z.; Dingfeng, Y.; Xiaoyan, L.; Qian, Y.; Yingying, G. Research on remote sensing retrieval and diurnal variation of Secchi disk, depth of Jiaozhou Bay based on GOCI. Remote Sens. Nat. Resour. 2021, 33, 108. [Google Scholar]
- Bai, S.; Gao, J.; Sun, D.; Tian, M. Monitoring Water Transparency in Shallow and Eutrophic Lake Waters Based on GOCI Observations. Remote Sens. 2020, 12, 163. [Google Scholar] [CrossRef]
- Mao, Y.; Wang, S.; Qiu, Z.; Sun, D.; Bilal, M. Variations of transparency derived from GOCI in the Bohai Sea and the Yellow Sea. Opt. Express 2018, 26, 12191–12209. [Google Scholar] [CrossRef] [PubMed]
- Swift, T.J.; Perez-Losada, J.; Schladow, S.G.; Reuter, J.E.; Jassby, A.D.; Goldman, C.R. Water clarity modeling in Lake Tahoe: Linking suspended matter characteristics to Secchi depth. Aquat. Sci. 2006, 68, 1–15. [Google Scholar] [CrossRef]
- Swan, B.K.; Reifel, K.M.; Tiffany, M.A.; Watts, J.M.; Hurlbert, S.H. Spatial and temporal patterns of transparency and light attenuation in the Salton Sea, California, 1997–1999. Lake Reserv. Manag. 2007, 23, 653–662. [Google Scholar] [CrossRef]
- Testa, J.M.; Lyubchich, V.; Zhang, Q. Patterns and Trends in Secchi Disk Depth over Three Decades in the Chesapeake Bay Estuarine Complex. Estuaries Coasts 2019, 42, 927–943. [Google Scholar] [CrossRef]
- Zhou, Y.; Yu, D.; Cheng, W.; Gai, Y.; Yao, H.; Yang, L.; Pan, S. Monitoring multi-temporal and spatial variations of water transparency in the Jiaozhou Bay using GOCI data. Mar. Pollut. Bull. 2022, 180, 113815. [Google Scholar] [CrossRef]
- Lee, Z.; Shang, S.; Hu, C.; Du, K.; Weidemann, A.; Hou, W.; Lin, J.; Lin, G. Secchi disk depth: A new theory and mechanistic model for underwater visibility. Remote Sens. Environ. 2015, 169, 139–149. [Google Scholar] [CrossRef]
- Liu, X.-Y.; Hu, J.-W.; Tian, L.; Yu, D.-F.; Gao, H.; Yang, L.; An, D.-Y. Comparative study on transparency retrieved from GOCI under four different atmospheric correction algorithms in Jiaozhou Bay and Qingdao Coastal area. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2023, 17, 2077–2089. [Google Scholar] [CrossRef]
- Liu, X.; Wang, M. Global daily gap-free ocean color products from multi-satellite measurements. Int. J. Appl. Earth Obs. Geoinf. 2022, 108, 102714. [Google Scholar] [CrossRef]
- Mao, Y.; Qiu, Z.; Sun, D.; Wang, S.; Lu, Y.; Wu, C.; Yue, X.; Ye, Z. A Novel Remote Sensing Algorithm for Estimating Diffuse Attenuation Coefficient in the BohaiSea and Yellow Sea. Guangxi Sci. 2016, 23, 513–519. [Google Scholar]
- Ding, X.; Gong, F.; Zhu, Q.; Li, J.; Wang, X.; Bai, R.; Xu, Y. Using geostationary satellite ocean color data and superpixel to map the diurnal dynamics of water transparency in the eastern China seas. Ecol. Indic. 2022, 142, 109219. [Google Scholar] [CrossRef]
- He, X.; Pan, D.; Mao, Z. Water-transparency (Secchi Depth) monitoring in the China Sea with the SeaWiFS satellite sensor. In Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology VI, Maspalomas, Spain, 13–16 September 2004; pp. 112–122. [Google Scholar]
- He, X.; Pan, D.; Bai, Y.; Wang, T.; Chen, C.-T.A.; Zhu, Q.; Hao, Z.; Gong, F. Recent changes of global ocean transparency observed by SeaWiFS. Cont. Shelf Res. 2017, 143, 159–166. [Google Scholar] [CrossRef]
- Feng, L.; Hou, X.; Zheng, Y. Monitoring and understanding the water transparency changes of fifty large lakes on the Yangtze Plain based on long-term MODIS observations. Remote Sens. Environ. 2019, 221, 675–686. [Google Scholar] [CrossRef]
- Zeng, S.; Lei, S.; Li, Y.; Lyu, H.; Xu, J.; Dong, X.; Wang, R.; Yang, Z.; Li, J. Retrieval of Secchi Disk Depth in Turbid Lakes from GOCI Based on a New Semi-Analytical Algorithm. Remote Sens. 2020, 12, 1516. [Google Scholar] [CrossRef]
- Battin, T.J.; Luyssaert, S.; Kaplan, L.A.; Aufdenkampe, A.K.; Richter, A.; Tranvik, L.J. The boundless carbon cycle. Nat. Geosci. 2009, 2, 598–600. [Google Scholar] [CrossRef]
- Berggren, M.; Laudon, H.; Jansson, M. Landscape regulation of bacterial growth efficiency in boreal freshwaters. Glob. Biogeochem. Cycles 2007, 21, GB4002. [Google Scholar] [CrossRef]
- Min, S.-H.; Park, M.-O.; Kim, S.-W.; Han, I.-S.; Kim, W.; Park, Y.-J. Correlation between SST and CDOM during Summer Coastal Upwelling along the Southeast Coast of Korea. J. Coast. Res. 2018, 85, 1471–1475. [Google Scholar] [CrossRef]
- Wang, Y.; Shen, F.; Sokoletsky, L.; Sun, X. Validation and Calibration of QAA Algorithm for CDOM Absorption Retrieval in the Changjiang (Yangtze) Estuarine and Coastal Waters. Remote Sens. 2017, 9, 1192. [Google Scholar] [CrossRef]
- Ling, Z.; Sun, D.; Wang, S.; Qiu, Z.; Huan, Y.; Mao, Z.; He, Y. Remote sensing estimation of colored dissolved organic matter (CDOM) from GOCI measurements in the Bohai Sea and Yellow Sea. Environ. Sci. Pollut. Res. Int. 2020, 27, 6872–6885. [Google Scholar] [CrossRef] [PubMed]
- Bai, Y.; Cai, W.J.; He, X.; Zhai, W.; Pan, D.; Dai, M.; Yu, P. A mechanistic semi-analytical method for remotely sensing sea surface pCO2 in river-dominated coastal oceans: A case study from the East China Sea. J. Geophys. Res. Ocean. 2015, 120, 2331–2349. [Google Scholar] [CrossRef]
- Bauer, J.E.; Cai, W.-J.; Raymond, P.A.; Bianchi, T.S.; Hopkinson, C.S.; Regnier, P.A. The changing carbon cycle of the coastal ocean. Nature 2013, 504, 61–70. [Google Scholar] [CrossRef] [PubMed]
- Liu, D.; Bai, Y.; He, X.; Tao, B.; Pan, D.; Chen, C.-T.A.; Zhang, L.; Xu, Y.; Gong, C. Satellite estimation of particulate organic carbon flux from Changjiang River to the estuary. Remote Sens. Environ. 2019, 223, 307–319. [Google Scholar] [CrossRef]
- Regnier, P.; Friedlingstein, P.; Ciais, P.; Mackenzie, F.T.; Gruber, N.; Janssens, I.A.; Laruelle, G.G.; Lauerwald, R.; Luyssaert, S.; Andersson, A.J. Anthropogenic perturbation of the carbon fluxes from land to ocean. Nat. Geosci. 2013, 6, 597–607. [Google Scholar] [CrossRef]
- Xu, J.; Lei, S.; Bi, S.; Li, Y.; Lyu, H.; Xu, J.; Xu, X.; Mu, M.; Miao, S.; Zeng, S.; et al. Tracking spatio-temporal dynamics of POC sources in eutrophic lakes by remote sensing. Water Res. 2020, 168, 115162. [Google Scholar] [CrossRef]
- Wei, X.; Shen, F.; Pan, Y.; Chen, S.; Sun, X.; Wang, Y. Satellite Observations of the Diurnal Dynamics of Particulate Organic Carbon in Optically Complex Coastal Oceans: The Continental Shelf Seas of China. J. Geophys. Res. Ocean. 2019, 124, 4710–4726. [Google Scholar] [CrossRef]
- Catalán, N.; Obrador, B.; Alomar, C.; Pretus, J.L. Seasonality and landscape factors drive dissolved organic matter properties in Mediterranean ephemeral washes. Biogeochemistry 2012, 112, 261–274. [Google Scholar] [CrossRef]
- Fichot, C.G.; Tzortziou, M.; Mannino, A. Remote sensing of dissolved organic carbon (DOC) stocks, fluxes and transformations along the land-ocean aquatic continuum: Advances, challenges, and opportunities. Earth-Sci. Rev. 2023, 242, 104446. [Google Scholar] [CrossRef]
- Huang, C.; Yunmei, L.; Liu, G.; Guo, Y.; Yang, H.; Zhu, A.X.; Song, T.; Huang, T.; Zhang, M.; Shi, K. Tracing high time-resolution fluctuations in dissolved organic carbon using satellite and buoy observations: Case study in Lake Taihu, China. Int. J. Appl. Earth Obs. Geoinf. 2017, 62, 174–182. [Google Scholar] [CrossRef]
- Gomes, H.d.R.; Xu, Q.; Ishizaka, J.; Carpenter, E.J.; Yager, P.L.; Goes, J.I. The influence of riverine nutrients in niche partitioning of phytoplankton communities—A contrast between the Amazon River Plume and the ChangJiang (Yangtze) River diluted water of the East China Sea. Front. Mar. Sci. 2018, 5, 343. [Google Scholar] [CrossRef]
- Reid, P.C.; Fischer, A.C.; Lewis-Brown, E.; Meredith, M.P.; Sparrow, M.; Andersson, A.J.; Antia, A.; Bates, N.R.; Bathmann, U.; Beaugrand, G. Impacts of the oceans on climate change. Adv. Mar. Biol. 2009, 56, 1–150. [Google Scholar] [PubMed]
- Wu, J.; Goes, J.I.; do Rosario Gomes, H.; Lee, Z.; Noh, J.-H.; Wei, J.; Shang, Z.; Salisbury, J.; Mannino, A.; Kim, W. Estimates of diurnal and daily net primary productivity using the Geostationary Ocean Color Imager (GOCI) data. Remote Sens. Environ. 2022, 280, 113183. [Google Scholar] [CrossRef]
- Cui, H.; Chen, J.; Cao, Z.; Huang, H.; Gong, F. A Novel Multi-Candidate Multi-Correlation Coefficient Algorithm for GOCI-Derived Sea-Surface Current Vector with OSU Tidal Model. Remote Sens. 2022, 14, 4625. [Google Scholar] [CrossRef]
- Sun, D.; Su, X.; Qiu, Z.; Wang, S.; Mao, Z.; He, Y. Remote sensing estimation of sea surface salinity from GOCI measurements in the southern Yellow Sea. Remote Sens. 2019, 11, 775. [Google Scholar] [CrossRef]
- Lee, H.-S.; Lee, K.-S. Capability of geostationary satellite imagery for sea ice monitoring in the Bohai and Yellow seas. J. Mar. Sci. Technol. 2016, 24, 10. [Google Scholar]
- Gu, F.; Zhang, R.; Tian-Kunze, X.; Han, B.; Zhu, L.; Cui, T.; Yang, Q. Sea Ice Thickness Retrieval Based on GOCI Remote Sensing Data: A Case Study. Remote Sens. 2021, 13, 936. [Google Scholar] [CrossRef]
- Yan, Y.; Huang, K.; Shao, D.; Xu, Y.; Gu, W. Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data. Sustainability 2019, 11, 777. [Google Scholar] [CrossRef]
- Zhou, Y.; Chen, K.; Li, X. Dual-branch neural network for sea fog detection in geostationary ocean color imager. IEEE Trans. Geosci. Remote Sens. 2022, 60, 4208617. [Google Scholar] [CrossRef]
- Jeon, H.-K.; Kim, S.; Edwin, J.; Yang, C.-S. Sea fog identification from GOCI images using CNN transfer learning models. Electronics 2020, 9, 311. [Google Scholar] [CrossRef]
- Arp, C.D.; Cherry, J.E.; Brown, D.; Bondurant, A.C.; Endres, K.L. Observation-derived ice growth curves show patterns and trends in maximum ice thickness and safe travel duration of Alaskan lakes and rivers. Cryosphere 2020, 14, 3595–3609. [Google Scholar] [CrossRef]
- Pearce, D.A. Antarctic subglacial lake exploration: A new frontier in microbial ecology. ISME J. 2009, 3, 877–880. [Google Scholar] [CrossRef] [PubMed]
- Hampton, S.E.; Galloway, A.W.; Powers, S.M.; Ozersky, T.; Woo, K.H.; Batt, R.D.; Labou, S.G.; O’Reilly, C.M.; Sharma, S.; Lottig, N.R. Ecology under lake ice. Ecol. Lett. 2017, 20, 98–111. [Google Scholar] [CrossRef] [PubMed]
- Yang, H.; Kong, J.; Hu, H.; Du, Y.; Gao, M.; Chen, F. A review of remote sensing for water quality retrieval: Progress and challenges. Remote Sens. 2022, 14, 1770. [Google Scholar] [CrossRef]
- Kirk, J. Dependence of relationship between inherent and apparent optical properties of water on solar altitude. Limnol. Oceanogr. 1984, 29, 350–356. [Google Scholar] [CrossRef]
- Kirk, J.T. Volume scattering function, average cosines, and the underwater light field. Limnol. Oceanogr. 1991, 36, 455–467. [Google Scholar] [CrossRef]
- McKee, D.; Cunningham, A.; Dudek, A. Optical water type discrimination and tuning remote sensing band-ratio algorithms: Application to retrieval of chlorophyll and Kd (490) in the Irish and Celtic Seas. Estuar. Coast. Shelf Sci. 2007, 73, 827–834. [Google Scholar] [CrossRef]
- Zhang, Y.; Xu, Z.; Yang, Y.; Wang, G.; Zhou, W.; Cao, W.; Li, Y.; Zheng, W.; Deng, L.; Zeng, K. Diurnal variation of the diffuse attenuation coefficient for downwelling irradiance at 490 nm in coastal East China Sea. Remote Sens. 2021, 13, 1676. [Google Scholar] [CrossRef]
- Yu, X.; Salama, M.S.; Shen, F.; Verhoef, W. Retrieval of the diffuse attenuation coefficient from GOCI images using the 2SeaColor model: A case study in the Yangtze Estuary. Remote Sens. Environ. 2016, 175, 109–119. [Google Scholar] [CrossRef]
- Tang, R.; Shen, F.; Pan, Y.; Ruddick, K.; Shang, P. Multi-source high-resolution satellite products in Yangtze Estuary: Cross-comparisons and impacts of signal-to-noise ratio and spatial resolution. Opt. Express 2019, 27, 6426–6441. [Google Scholar] [CrossRef] [PubMed]
- Hu, C.; Feng, L.; Lee, Z. Evaluation of GOCI sensitivity for at-sensor radiance and GDPS-retrieved chlorophyll-a products. Ocean Sci. J. 2012, 47, 279–285. [Google Scholar] [CrossRef]
- Qi, L.; Lee, Z.; Hu, C.; Wang, M. Requirement of minimal signal-to-noise ratios of ocean color sensors and uncertainties of ocean color products. J. Geophys. Res. Ocean. 2017, 122, 2595–2611. [Google Scholar] [CrossRef]
- Bi, S.; Röttgers, R.; Hieronymi, M. Transfer model to determine the above-water remote-sensing reflectance from the underwater remote-sensing ratio. Opt. Express 2023, 31, 10512–10524. [Google Scholar] [CrossRef]
- Xu, F.; He, X.; Jin, X.; Cai, W.; Bai, Y.; Wang, D.; Gong, F.; Zhu, Q. Spherical vector radiative transfer model for satellite ocean color remote sensing. Opt. Express 2023, 31, 11192–11212. [Google Scholar] [CrossRef] [PubMed]
- Prakash, W.; Varma, A.; Bhandari, S. An algorithm for the precise location of the solar specular reflection point in the visible band images from geostationary meteorological satellites. Comput. Geosci. 1994, 20, 1467–1482. [Google Scholar] [CrossRef]
- Wu, X.; Lu, Y.; Jiao, J.; Ding, J.; Fu, W.; Qian, W. Using sea wave simulations to interpret the sunglint reflection variation with different spatial resolutions. IEEE Geosci. Remote Sens. Lett. 2020, 19, 1501304. [Google Scholar] [CrossRef]
- Park, Y.; Ahn, Y.; Han, H.; Yang, H.; Moon, J.; Ahn, J.; Lee, B.; Min, J.; Lee, S.; Kim, K. GOCI Level 2 Ocean Color Products (GDPS 1.3) Brief Algorithm Description; Korea Ocean Satellite Center (KOSC): Ansan, Republic of Korea, 2014; pp. 24–40. [Google Scholar]
- Kim, Y.J.; Kim, W.; Im, J.; Choi, J.; Lee, S. Atmospheric-correction-free red tide quantification algorithm for GOCI based on machine learning combined with a radiative transfer simulation. ISPRS J. Photogramm. Remote Sens. 2023, 199, 197–213. [Google Scholar] [CrossRef]
- Mao, K.; Yuan, Z.; Zuo, Z.; Xu, T.; Shen, X.; Gao, C. Changes in global cloud cover based on remote sensing data from 2003 to 2012. Chin. Geogr. Sci. 2019, 29, 306–315. [Google Scholar] [CrossRef]
- Colomina, I.; Molina, P. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2014, 92, 79–97. [Google Scholar] [CrossRef]
- Kwon, D.Y.; Kim, J.; Park, S.; Hong, S. Advancements of remote data acquisition and processing in unmanned vehicle technologies for water quality monitoring: An extensive review. Chemosphere 2023, 343, 140198. [Google Scholar] [CrossRef] [PubMed]
- Yuan, S.; Li, Y.; Bao, F.; Xu, H.; Yang, Y.; Yan, Q.; Zhong, S.; Yin, H.; Xu, J.; Huang, Z. Marine environmental monitoring with unmanned vehicle platforms: Present applications and future prospects. Sci. Total Environ. 2023, 858, 159741. [Google Scholar] [CrossRef]
- Zhao, H.; Zhou, Y.; Wu, H.; Kutser, T.; Han, Y.; Ma, R.; Yao, Z.; Zhao, H.; Xu, P.; Jiang, C. Potential of Mie–fluorescence–Raman lidar to profile chlorophyll a concentration in inland waters. Environ. Sci. Technol. 2023, 57, 14226–14236. [Google Scholar] [CrossRef] [PubMed]
- Wang, W.; Shi, K.; Zhang, Y.; Li, N.; Sun, X.; Zhang, D.; Zhang, Y.; Qin, B.; Zhu, G. A ground-based remote sensing system for high-frequency and real-time monitoring of phytoplankton blooms. J. Hazard. Mater. 2022, 439, 129623. [Google Scholar] [CrossRef] [PubMed]
- Wang, X.; Wang, W.; He, Y.; Zhang, S.; Huang, W.; Woolway, R.I.; Shi, K.; Yang, X. Numerical simulation of thermal stratification in Lake Qiandaohu using an improved WRF-Lake model. J. Hydrol. 2023, 618, 129184. [Google Scholar] [CrossRef]
- Li, Y.; Li, Y.-M.; Wang, Q.; Zhu, L.; Guo, Y.-L. An Observing System Simulation Experiments framework based on the ensemble square root Kalman Filter for evaluating the concentration of chlorophyll a by multi-source data: A case study in Taihu Lake. Aquat. Ecosyst. Health Manag. 2014, 17, 233–241. [Google Scholar] [CrossRef]
- Guo, Y.; Li, Y.; Zhu, L.; Wang, Q.; Lv, H.; Huang, C.; Li, Y. An inversion-based fusion method for inland water remote monitoring. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 5599–5611. [Google Scholar] [CrossRef]
- Murakami, H. Ocean color estimation by Himawari-8/AHI. In Proceedings of the Remote Sensing of the Oceans and Inland Waters: Techniques, Applications, and Challenges, New Delhi, India, 4–7 April 2016; pp. 177–186. [Google Scholar]
- Guo, Y.; Huang, C.; Zhang, Y.; Li, Y.; Chen, W. A novel multitemporal image-fusion algorithm: Method and application to GOCI and himawari images for inland water remote sensing. IEEE Trans. Geosci. Remote Sens. 2020, 58, 4018–4032. [Google Scholar] [CrossRef]
- Chang, N.-B.; Vannah, B.; Yang, Y.J. Comparative sensor fusion between hyperspectral and multispectral satellite sensors for monitoring microcystin distribution in Lake Erie. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 2426–2442. [Google Scholar] [CrossRef]
- Mélin, F.; Zibordi, G. Optically based technique for producing merged spectra of water-leaving radiances from ocean color remote sensing. Appl. Opt. 2007, 46, 3856–3869. [Google Scholar] [CrossRef] [PubMed]
- Mélin, F.; Zibordi, G.; Djavidnia, S. Merged series of normalized water leaving radiances obtained from multiple satellite missions for the Mediterranean Sea. Adv. Space Res. 2009, 43, 423–437. [Google Scholar] [CrossRef]
- Mélin, F.; Vantrepotte, V.; Chuprin, A.; Grant, M.; Jackson, T.; Sathyendranath, S. Assessing the fitness-for-purpose of satellite multi-mission ocean color climate data records: A protocol applied to OC-CCI chlorophyll-a data. Remote Sens. Environ. 2017, 203, 139–151. [Google Scholar] [CrossRef]
- Cao, Z.; Duan, H.; Shen, M.; Ma, R.; Xue, K.; Liu, D.; Xiao, Q. Using VIIRS/NPP and MODIS/Aqua data to provide a continuous record of suspended particulate matter in a highly turbid inland lake. Int. J. Appl. Earth Obs. Geoinf. 2018, 64, 256–265. [Google Scholar] [CrossRef]
- Cao, F.; Miller, W.L. A new algorithm to retrieve chromophoric dissolved organic matter (CDOM) absorption spectra in the UV from ocean color. J. Geophys. Res. Ocean. 2015, 120, 496–516. [Google Scholar] [CrossRef]
- He, X.; Bai, Y.; Pan, D.; Tang, J.; Wang, D. Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters. Opt. Express 2012, 20, 20754–20770. [Google Scholar] [CrossRef] [PubMed]
- Werdell, P.J.; Bailey, S.W.; Franz, B.A.; Harding, L.W., Jr.; Feldman, G.C.; McClain, C.R. Regional and seasonal variability of chlorophyll-a in Chesapeake Bay as observed by SeaWiFS and MODIS-Aqua. Remote Sens. Environ. 2009, 113, 1319–1330. [Google Scholar] [CrossRef]
- Simis, S.G.; Peters, S.W.; Gons, H.J. Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water. Limnol. Oceanogr. 2005, 50, 237–245. [Google Scholar] [CrossRef]
- Liu, G.; Simis, S.G.; Li, L.; Wang, Q.; Li, Y.; Song, K.; Lyu, H.; Zheng, Z.; Shi, K. A four-band semi-analytical model for estimating phycocyanin in inland waters from simulated MERIS and OLCI data. IEEE Trans. Geosci. Remote Sens. 2017, 56, 1374–1385. [Google Scholar] [CrossRef]
- Lyu, L.; Song, K.; Wen, Z.; Liu, G.; Fang, C.; Shang, Y.; Li, S.; Tao, H.; Wang, X.; Li, Y. Remote estimation of phycocyanin concentration in inland waters based on optical classification. Sci. Total Environ. 2023, 899, 166363. [Google Scholar] [CrossRef] [PubMed]
- Kwon, Y.S.; Pyo, J.; Kwon, Y.-H.; Duan, H.; Cho, K.H.; Park, Y. Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir. Remote Sens. Environ. 2020, 236, 111517. [Google Scholar] [CrossRef]
- Pyo, J.; Duan, H.; Baek, S.; Kim, M.S.; Jeon, T.; Kwon, Y.S.; Lee, H.; Cho, K.H. A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery. Remote Sens. Environ. 2019, 233, 111350. [Google Scholar] [CrossRef]
- Shen, M.; Cao, Z.; Xie, L.; Zhao, Y.; Qi, T.; Song, K.; Lyu, L.; Wang, D.; Ma, J.; Duan, H. Microcystins risk assessment in lakes from space: Implications for SDG 6.1 evaluation. Water Res. 2023, 245, 120648. [Google Scholar] [CrossRef] [PubMed]
- Liu, G.; Li, L.; Song, K.; Li, Y.; Lyu, H.; Wen, Z.; Fang, C.; Bi, S.; Sun, X.; Wang, Z. An OLCI-based algorithm for semi-empirically partitioning absorption coefficient and estimating chlorophyll a concentration in various turbid case-2 waters. Remote Sens. Environ. 2020, 239, 111648. [Google Scholar] [CrossRef]
- Gitelson, A.A.; Dall’Olmo, G.; Moses, W.; Rundquist, D.C.; Barrow, T.; Fisher, T.R.; Gurlin, D.; Holz, J. A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation. Remote Sens. Environ. 2008, 112, 3582–3593. [Google Scholar] [CrossRef]
- Bai, R.; He, X.; Bai, Y.; Gong, F.; Zhu, Q.; Wang, D.; Li, T. Atmospheric correction algorithm based on the interpolation of ultraviolet and shortwave infrared bands. Opt. Express 2023, 31, 6805–6826. [Google Scholar] [CrossRef] [PubMed]
- Pi, X.; Luo, Q.; Feng, L.; Xu, Y.; Tang, J.; Liang, X.; Ma, E.; Cheng, R.; Fensholt, R.; Brandt, M. Mapping global lake dynamics reveals the emerging roles of small lakes. Nat. Commun. 2022, 13, 5777. [Google Scholar] [CrossRef]
- Tao, H.; Song, K.; Liu, G.; Wen, Z.; Wang, Q.; Du, Y.; Lyu, L.; Du, J.; Shang, Y. Songhua River basin’s improving water quality since 2005 based on Landsat observation of water clarity. Environ. Res. 2021, 199, 111299. [Google Scholar] [CrossRef]
- Ani, C.J.; Baird, M.; Robson, B. Modelling buoyancy-driven vertical movement of Trichodesmium application in the Great Barrier Reef. Ecol. Model. 2024, 487, 110567. [Google Scholar] [CrossRef]
- Wen, Z.; Shang, Y.; Lyu, L.; Tao, H.; Liu, G.; Fang, C.; Li, S.; Song, K. Re-estimating China’s lake CO2 flux considering spatiotemporal variability. Environ. Sci. Ecotechnology 2024, 19, 100337. [Google Scholar] [CrossRef] [PubMed]
- Sommerfield, C.K.; Wong, K.C. Mechanisms of sediment flux and turbidity maintenance in the Delaware Estuary. J. Geophys. Res. Ocean. 2011, 116, C01005. [Google Scholar] [CrossRef]
- Uncles, R.; Elliott, R.; Weston, S. Observed fluxes of water, salt and suspended sediment in a partly mixed estuary. Estuar. Coast. Shelf Sci. 1985, 20, 147–167. [Google Scholar] [CrossRef]
- Li, Y.; Robinson, S.V.; Nguyen, L.H.; Liu, J. Satellite prediction of coastal hypoxia in the northern Gulf of Mexico. Remote Sens. Environ. 2023, 284, 113346. [Google Scholar] [CrossRef]
- Hu, C.; Lu, Y.; Sun, S.; Liu, Y. Optical remote sensing of oil spills in the ocean: What is really possible? J. Remote Sens. 2021, 2021, 9141902. [Google Scholar] [CrossRef]
- Sun, Z.; Sun, S.; Zhao, J.; Ai, B.; Yang, Q. Detection of Massive Oil Spills in Sun Glint Optical Imagery through Super-Pixel Segmentation. J. Mar. Sci. Eng. 2022, 10, 1630. [Google Scholar] [CrossRef]
- Jiao, J.; Lu, Y.; Liu, Y. Optical quantification of oil emulsions in multi-band coarse-resolution imagery using a lab-derived HSV model. Mar. Pollut. Bull. 2022, 178, 113640. [Google Scholar] [CrossRef]
- Raffaelli, D.G.; Hawkins, S.J. Intertidal Ecology; Springer Science & Business Media: Berlin/Heidelberg, Germany, 1996. [Google Scholar]
- Jia, M.; Wang, Z.; Mao, D.; Ren, C.; Wang, C.; Wang, Y. Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine. Remote Sens. Environ. 2021, 255, 112285. [Google Scholar] [CrossRef]
- Kennedy, V.S.; Newell, R.I.; Shumway, S. Natural environmental factors. In The Eastern Oyster Crassostrea virginica; Maryland Sea Grant: College Park, MD, USA, 1996; pp. 467–513. [Google Scholar]
Abbreviations or Symbols | Abbreviations or Symbols | ||
---|---|---|---|
ABs | Algal blooms | OSMI | Ocean Scanning Multispectral Imager |
AFAI | Alternative floating algae index | OE | Optics EXPRESS |
AC | Atmospheric correction | POC | Particulate organic carbon |
CNKI | China National Knowledge Infrastructure | PC | Phycocyanin |
Chla | Chlorophyll a | RF | Random forest |
CDOM | Colored dissolved organic matter | Rayleigh-corrected radiance | |
CBI | Cyanobacterial bloom intensity | RI | Red tide index |
kd | Diffuse attenuation coefficient | RS | Remote Sensing |
DOC | Dissolved organic carbon | RSE | Remote Sensing of Environment |
FAC | Floating algae cover | Remote sensing reflectance | |
FLH | Fluorescence line height | Sci Total Environ | Science of the Total Environment |
GABI | Generalized algal bloom index algorithm | SIA | Sea ice area |
GLI | Generation Global Imager | SIT | Sea ice thickness |
GLIMR | Geostationary Littoral Imaging and Monitoring Radiometer | SSCs | Sea surface currents |
HABs | Harmful algal blooms | SSS | Sea surface salinity |
IEEE T-GRS | IEEE Transactions on Geoscience and Remote Sensing | SDD | Secchi disk depth |
ICWs | Inland and coastal waters | SGLI | Second Generation Global Imager |
JAG | International Journal of Applied Earth Observation and Geoinformation | SWIR | Shortwave infrared |
Int J Remote Sens | International Journal of Remote Sensing | SZA | Solar zenith angle |
ISPRS | ISPRS Journal of Photogrammetry and Remote Sensing | SPM | Suspended particulate matter |
LCI | Linear Combination Index | SCI | Synthetic chlorophyll index |
MERIS | Medium-Resolution Imaging Spectrometer Instrument | GOCI | The Geostationary Ocean Color Imager |
MODIS | Moderate-Resolution Imaging Spectroradiometer | UV | Ultraviolet |
NASA | National Aeronautics and Space Administration | VIIRS | Visible Infrared Imaging Radiometer |
NIR | Near-infrared | WR | Water Research |
NPP | Net primary production | YRE | Yalu River estuary |
NDVI | Normalized difference vegetation index | YOC | Yellow and East China Sea Ocean Color |
NRTI | Normalized red tide index |
Number | Data | Spatial Resolution (m) | Temporal Resolution | Launched Time |
---|---|---|---|---|
1 | CZCS | 1000 | One day | 1978 |
2 | SeaWiFS | 1100, 4500 | One day | 1997 |
3 | MODIS_TERRA | 250, 500, 1000 | One day | 1999 |
4 | MODIS_AQUA | 250, 500, 1000 | One day | 2002 |
5 | VIIRS Suomi NPP | 375, 750 | One day | 2011 |
6 | VIIRS NOAA-20 | 375, 750 | One day | 2017 |
7 | VIIRS NOAA-21 | 375, 750 | One day | 2021 |
8 | MERIS | 300, 1200 | Three days | 2002 |
9 | Sentinel-3A OLCI | 300 | <Two days | 2016 |
10 | Sentinel-3B OLCI | 300 | <Two days | 2018 |
11 | ADEOS | 700 | Ten days | 1996 |
12 | ADEOS-II | 250, 1000 | Four days | 2002 |
13 | SGLI | 250 | One day | 2017 |
14 | HY-1A | 250 | Three days/Seven days | 2002 |
15 | HY-1B | 250 | One day/Seven days | 2007 |
16 | HY-1C | 250 | One day/Three days | 2018 |
17 | HY-1D | 250 | One day/Three days | 2022 |
18 | HY-1E | 100 | One day/Three days | 2023 |
19 | Oceansat-1 | 360 | Two days | 1996 |
20 | Oceansat-2 | 360 | Two days | 2009 |
21 | Oceansat-3 | 360 | One day/Three days | 2022 |
22 | OSMI | 1000 | Three days | 1999 |
23 | GOCI | 500 | One hour | 2010 |
24 | GOCI-II | 250 | One hour | 2020 |
Bands | Center Wavelength/nm | Band Width/nm | Spectrum Type | Signal-to-Noise Ratio |
---|---|---|---|---|
B1 | 412 | 20 | VIS | 1077 |
B2 | 443 | 20 | VIS | 1199 |
B3 | 490 | 20 | VIS | 1316 |
B4 | 555 | 20 | VIS | 1223 |
B5 | 660 | 20 | VIS | 1192 |
B6 | 680 | 10 | VIS | 1093 |
B7 | 745 | 20 | NIR | 1107 |
B8 | 865 | 40 | NIR | 1009 |
Band | Wavelength/nm | Bandwidth/nm | Primary Use |
---|---|---|---|
B1 | 380 | 20 | CDOM |
B2 | 412 | 20 | CDOM, Chla |
B3 | 443 | 20 | Chla absorption maximum |
B4 | 490 | 20 | Chla, other pigments |
B5 | 510 | 20 | Chla, absorbing aerosol in ocean waters |
B6 | 555 | 20 | Turbidity, SPM |
B7 | 620 | 20 | Detect phytoplankton species |
B8 | 660 | 20 | Baseline of fluorescence signal, Chla, SPM |
B9 | 680 | 10 | Fluorescence signal |
B10 | 709 | 10 | Fluorescence base signal, AC, SPM |
B11 | 745 | 20 | AC, vegetation index |
B12 | 865 | 40 | AC, aerosol optical depth |
B13 | PAN | 483 | / |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Shao, S.; Wang, Y.; Liu, G.; Song, K. A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters. Remote Sens. 2024, 16, 1623. https://doi.org/10.3390/rs16091623
Shao S, Wang Y, Liu G, Song K. A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters. Remote Sensing. 2024; 16(9):1623. https://doi.org/10.3390/rs16091623
Chicago/Turabian StyleShao, Shidi, Yu Wang, Ge Liu, and Kaishan Song. 2024. "A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters" Remote Sensing 16, no. 9: 1623. https://doi.org/10.3390/rs16091623