[1] H R Gordon. Removal of atmospheric effects from satellite imagery of the oceans. Applied Optics, 17, 1631-1636(1978).

[2] K G Ruddick, F Ovidio, M Rijkeboer. Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters. Applied Optics, 39, 897-912(2000).

[3] D Z Liu, D Y Fu. Atmospheric correction of Hyperion imagery over estuarine waters: A case study of the Pearl River Estuary in southern China. International Journal of Remote Sensing, 38, 199-210(2017).

[4] S Groom, S Sathyendranath, Y Ban et al. Satellite Ocean colour: Current status and future perspective. Frontiers in Marine Science, 6, 485(2019).

[5] D G Hadjimitsis, C R I Clayton, V S Hope. An assessment of the effectiveness of atmospheric correction algorithms through the remote sensing of some reservoirs. International Journal of Remote Sensing, 25, 3651-3674(2004).

[6] M Bilal, Z F Qiu, Y Wang et al. Comparison between SREM and 6SV atmospheric correction methods, 1434-1436(2021).

[7] V S Martins, C C F Barbosa, L A S de Carvalho et al. Assessment of atmospheric correction methods for Sentinel-2 MSI images applied to Amazon floodplain lakes. Remote Sensing, 9, 322(2017).

[8] M W Matthew, S M Adler-Golden, A Berk et al. Status of atmospheric correction using a MODTRAN4-based algorithm, 4049, 199-207(2000).

[9] A Berk, G P Anderson, P K Acharya et al. MODTRAN5: 2006 update, 62331F, 1-8(2006).

[10] S Sterckx, E Knaeps, S Adriaensen et al. Opera: An atmospheric correction for land and water, 1, 3-6(2015).

[11] Q Vanhellemont, K Ruddick. Acolite for Sentinel-2: Aquatic applications of MSI imagery, 9-13(2016).

[12] U Müller-Wilm, J Louis, R Richter et al. Sentinel-2 level 2A prototype processor: Architecture, algorithms and first results, 9-13(2013).

[13] M Pereira-Sandoval, A Ruescas, P Urrego et al. Evaluation of atmospheric correction algorithms over Spanish inland waters for Sentinel-2 multi spectral imagery data. Remote Sensing, 11, 1469(2019).

[14] M A Warren, S G H Simis, V Martinez-Vicente et al. Assessment of atmospheric correction algorithms for the Sentinel-2A MultiSpectral Imager over coastal and inland waters. Remote Sensing of Environment, 225, 267-289(2019).

[15] M König, M Hieronymi, N Oppelt. Application of Sentinel-2 MSI in Arctic research: Evaluating the performance of atmospheric correction approaches over Arctic Sea ice. Frontiers in Earth Science, 7, 22(2019).

[16] F Giannini, B P V Hunt, D Jacoby et al. Performance of OLCI Sentinel-3A satellite in the Northeast Pacific coastal waters. Remote Sensing of Environment, 256, 112317(2021).

[17] L De Keukelaere, S Sterckx, S Adriaensen et al. Atmospheric correction of Landsat-8/OLI and Sentinel-2/MSI data using iCOR algorithm: Validation for coastal and inland waters. European Journal of Remote Sensing, 51, 525-542(2018).

[18] N Pahlevan, A Mangin, S V Balasubramanian et al. ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters. Remote Sensing of Environment, 258, 112366(2021).

[19] Q Vanhellemont, K Ruddick. Atmospheric correction of Sentinel-3/OLCI data for mapping of suspended particulate matter and chlorophyll-a concentration in Belgian turbid coastal waters. Remote Sensing of Environment, 256, 112284(2021).

[20] S Novoa, D Doxaran, A Ody et al. Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sensing, 9, 61(2017).

[21] C Normandin, B Lubac, A Sottolichio et al. Analysis of suspended sediment variability in a large highly turbid estuary using a 5-year-long remotely sensed data archive at high resolution. Journal of Geophysical Research: Oceans, 124, 7661-7682(2019).

[22] P R Renosh, D Doxaran, L De Keukelaere et al. Evaluation of atmospheric correction algorithms for Sentinel-2-MSI and Sentinel-3-OLCI in highly turbid estuarine waters. Remote Sensing, 12, 1285(2020).

[23] Y F Du, H Y Lin, S Y He et al. Tide-induced variability and mechanisms of surface suspended sediment in the Zhoushan Archipelago along the southeastern coast of China based on GOCI data. Remote Sensing, 13, 929(2021).

[24] W Luo, F Shen, Q He et al. Changes in suspended sediments in the Yangtze River Estuary from 1984 to 2020: Responses to basin and estuarine engineering constructions. Science of the Total Environment, 805, 150381(2022).

[25] Z F Qiu, C Xiao, W Perrie et al. Using Landsat 8 data to estimate suspended particulate matter in the Yellow River Estuary. Journal of Geophysical Research: Oceans, 122, 276-290(2017).

[26] P Li, Y H Ke, J H Bai et al. Spatiotemporal dynamics of suspended particulate matter in the Yellow River Estuary, China during the past two decades based on time-series Landsat and Sentinel-2 data. Marine Pollution Bulletin, 149, 110518(2019).

[27] P Li, S L Chen, H Y Ji et al. Combining Landsat-8 and Sentinel-2 to investigate seasonal changes of suspended particulate matter off the abandoned distributary mouths of Yellow River Delta. Marine Geology, 441, 106622(2021).

[28] R H A, S Qing, Y H Bao. The inspection and application of atmospheric correction algorithm in Landsat-8 OLI data. Marine Sciences, 42, 107-115(2018).

[29] Q Vanhellemont, K Ruddick. Advantages of high quality SWIR bands for ocean colour processing: Examples from Landsat-8. Remote Sensing of Environment, 161, 89-106(2015).

[30] Y F Luo, D Doxaran, Q Vanhellemont. Retrieval and validation of water turbidity at metre-scale using Pléiades satellite data: A case study in the Gironde Estuary. Remote Sensing, 12, 946(2020).

[31] Z Y Liu, T W Cui, S H Zhang et al. Piecewise linear retrieval suspended particulate matter for the Yellow River Estuary based on Landsat8 OLI. Spectroscopy and Spectral Analysis, 38, 2536-2541(2018).

[32] L Chen, L S Yang, Y L Hao et al. Optical characteristics of Yellow River Estuary waters and suspended matters concentration retrieval model. Journal of Inner Mongolia Normal University (Natural Science Edition), 41, 258-261, 268(2012).

[33] Z Han, C X Yun, X Z Jiang. Experimental study on reflected spectrum of suspended sediment. Journal of Hydraulic Engineering, 34, 118-122(2003).

[34] Y F Luo, D Doxaran, K Ruddick et al. Saturation of water reflectance in extremely turbid media based on field measurements, satellite data and bio-optical modelling. Optics Express, 26, 10435(2018).

[35] K Ruddick. Ocean colour remote sensing in turbid coastal waters(2014).

[36] S Y Kotchenova, E F Vermote. Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part II. Homogeneous Lambertian and anisotropic surfaces. Applied Optics, 46, 4455-4464(2007).

[37] E F Vermote, D Tanre, J L Deuze et al. Second simulation of the satellite signal in the solar spectrum, 6S: An overview. IEEE Transactions on Geoscience and Remote Sensing, 35, 675-686(1997).

[38] Q Vanhellemont. Adaptation of the dark spectrum fitting atmospheric correction for aquatic applications of the Landsat and Sentinel-2 archives. Remote Sensing of Environment, 225, 175-192(2019).

[39] S Sterckx, E Knaeps, S Kratzer et al. SIMilarity Environment Correction (SIMEC) applied to MERIS data over inland and coastal waters. Remote Sensing of Environment, 157, 96-110(2015).

[40] E Wolters, C Toté, S Sterckx et al. iCOR atmospheric correction on Sentinel-3/OLCI over land: Intercomparison with AERONET, RadCalNet, and SYN level-2. Remote Sensing, 13, 654(2021).

[41] C Brockmann, R Doerffer, M Peters et al. Evolution of the C2RCC neural network for Sentinel 2 and 3 for the retrieval of ocean colour products in normal and extreme optically complex waters, 740, 54(2016).

[42] R Doerffer, H Schiller. The MERIS Case 2 water algorithm. International Journal of Remote Sensing, 28, 517-535(2007).

[43] X J Shen, H L Zhang, R Cheng et al. Classification of aerosols types over the Yellow and Bohai Sea. Acta Scientiae Circumstantiae, 41, 1649-1655(2021).

[44] M Main-Knorn, B Pflug, V Debaecker et al. Calibration and validation plan for the L2A processor and products of the Sentinel-2 mission. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W3, 1249-1255(2015).

[45] B Mayer, A Kylling. Technical note: The libRadtran software package for radiative transfer calculations―description and examples of use. Atmospheric Chemistry and Physics, 5, 1855-1877(2005).

[46] K Toming, T Kutser, R Uiboupin et al. Mapping water quality parameters with Sentinel-3 ocean and land colour instrument imagery in the Baltic Sea. Remote Sensing, 9, 1070(2017).

[47] Y Li, Y L Guo, C M Cheng et al. Remote estimation of total suspended matter concentration in the Hangzhou Bay based on OLCI and its water color product applicability analysis. Acta Oceanologica Sinica, 41, 156-169(2019).

[48] T Cooley, G P Anderson, G W Felde et al. FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation, 1414-1418(2002).

[49] M Main-Knorn, B Pflug, J Louis et al. Sen2Cor for Sentinel-2, 1042704(2017).

[50] B Bulgarelli, G Zibordi. On the detectability of adjacency effects in ocean color remote sensing of mid-latitude coastal environments by SeaWiFS, MODIS-A, MERIS, OLCI, OLI and MSI. Remote Sensing of Environment, 209, 423-438(2018).

[51] J W Zhang, Z F Qiu. Evaluation of data quality of FY-3D satellite sensor MERSI Ⅱ over marine waters. Acta Optica Sinica, 41, 1201002(2021).