Abstract
In spring 2021, mucilage, also known as “sea snot” or “sea saliva” has been intensely observed in the Sea of Marmara and has reached a threatening level. Due to the declining water quality, many marine organisms have perished, the fishing industry and tourism have been adversely affected. In this paper, a detailed investigation was carried out to assess the effects of mucilage phenomenon on the seawater quality, sea surface temperature, and backscattered radar signal power in two separate mucilage-covered areas in the Sea of Marmara. The quality of the mucilage-covered seawater was investigated by calculating physico-chemical parameters such as sea surface temperature, electrical conductivity, the potential of hydrogen, suspended solids, dissolved oxygen concentration, and chlorophyll-a in the water samples taken. With in-situ measurements, the spectral responses of intense and middle-intense mucilage were determined by a full-range spectroradiometer and compared with the spectral signature of clean seawater. Furthermore, utilizing space-borne synthetic aperture radar (SAR) and optical images of Sentinel-1, Sentinel-2 and Sentinel-3, the effects of mucilage on spectral reflectance, radar signal backscattering, and sea surface temperature were investigated depending on its intensity. The results of in-situ measurements and laboratory analyses showed considerable effects of mucilage on water quality. The space-borne analyses demonstrated that middle-intense and intense mucilage cause approximately 0.5 and 1-decibel decrease in backscattered radar signal power against clean water. In terms of sea surface temperature, the differences between clean seawater and middle-intense and intense mucilage areas were estimated as 1.05–2.25 °C, respectively.
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Sentinel-1, -2, -3 space-borne data used in this study is available on European Space Agency Copernicus Sentinel Data Hub.
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References
Acar U, Yilmaz OS, Celen M et al (2021) Determination of mucilage in the sea of marmara using remote sensing techniques with Google Earth Engine. Int J Environ Geoinform 8:423–434. https://doi.org/10.30897/ijegeo.957284
Aktan Y, Dede A, Ciftci Turetken PS (2008) Mucilage event associated with diatoms and dinoflagellates in Sea of Marmara, Turkey. Harmful Algae News 36:1–3
Allan JD, Castillo MM, Capps KA (2021) Stream ecology: structure and function of running waters. Springer International Publishing, Cham
Altiok Η, Kayisoglu M (2015) Seasonal and interannual variability of water exchange in the Strait of Istanbul. Mediterr Mar Sci 16:644–655. https://doi.org/10.12681/mms.1225
Amitrano D, Di Martino G, Iodice A et al (2018) Unsupervised rapid flood mapping using Sentinel-1 GRD SAR images. IEEE Trans Geosci Remote Sens 56:3290–3299. https://doi.org/10.1109/TGRS.2018.2797536
Angelliaume S, Dubois-Fernandez PC, Jones CE et al (2018) SAR imagery for detecting sea surface slicks: performance assessment of polarization-dependent parameters. IEEE Trans Geosci Remote Sens 56(8):4237–4257
Artuz ML, Artuz OB, Gulen D et al (2010) Monitoring the changing oceanographic conditions of the Marmara Sea Project: 2009 Tekirdag Region study data
Azam F, Fonda Umani S, Funari E (1999) Significance of bacteria in the mucilage phenomenon in the northern Adriatic Sea. Ann Ist Super Sanita 35:411–419
Balci M, Balkis N (2017) Assessment of phytoplankton and environmental variables for water quality and trophic state classification in the Gemlik Gulf, Marmara Sea (Turkey). Mar Pollut Bull 115:172–189. https://doi.org/10.1016/j.marpolbul.2016.12.007
Balkis N, Atabay H, Türetgen I et al (2011) Role of single-celled organisms in mucilage formation on the shores of Büyükada Island (the Marmara Sea). J Mar Biol Assoc UK 91:771–781. https://doi.org/10.1017/S0025315410000081
Balkis-Ozdelice N, Durmus T, Balci M (2021) A preliminary study on the intense pelagic and benthic mucilage phenomenon observed in the Sea of Marmara. Int J Environ Geoinform 8:414–422. https://doi.org/10.30897/ijegeo.954787
Beken C, Tolun L, Atabay H, Tan I (2017) Integrated pollution monitoring work in the sea, black sea sea final report. TÜBİTAK MAM Printing House, Gebze/Kocaeli
Best MA, Wither AW, Coates S (2007) Dissolved oxygen as a physico-chemical supporting element in the water framework directive. Mar Pollut Bull 55:53–64. https://doi.org/10.1016/j.marpolbul.2006.08.037
Bharda SK, Desai AY, TandelRutvikkumar P et al (2020) Correlation of limpet diversity with physico-chemical parameter at three different habitats along Saurashtra coast of Gujarat, India. J Entomol Zool Stud 8:771–777
Boyd CE (2020) Water quality: an introduction, Third. Springer, Cham, Amsterdam
Camoglu G, Aşik S, Genc L (2018) Farklı su stresi düzeylerinde yer tabanli spektral ölçümler ile tatli misirin verim tahmini. Çanakkale Onsekiz Mart Üniversitesi Fen Bilim Enstitüsü Derg 4:186–199. https://doi.org/10.28979/comufbed.478089
Cazzaniga I, Bresciani M, Colombo R et al (2019) A comparison of Sentinel-3-OLCI and Sentinel-2-MSI-derived Chlorophyll- a maps for two large Italian lakes. Remote Sens Lett 10:978–987. https://doi.org/10.1080/2150704X.2019.1634298
Chaturvedi SK, Banerjee S, Lele S (2020) An assessment of oil spill detection using Sentinel 1 SAR-C images. J Ocean Eng Sci 5:116–135. https://doi.org/10.1016/j.joes.2019.09.004
Colkesen I, Kavzoglu T, Sefercik UG et al (2023) Automated mucilage extraction index (AMEI): a novel spectral water index for identifying marine mucilage formations from Sentinel-2 imagery. Int J Remote Sens 44(1):105–141
Cozzi S, Ivančić I, Catalano G et al (2004) Dynamics of the oceanographic properties during mucilage appearance in the Northern Adriatic Sea: analysis of the 1997 event in comparison to earlier events. J Mar Syst 50:223–241. https://doi.org/10.1016/j.jmarsys.2004.01.007
Danovaro R, Fonda Umani S, Pusceddu A (2009) Climate change and the potential spreading of marine mucilage and microbial pathogens in the Mediterranean Sea. PLoS ONE 4:e7006. https://doi.org/10.1371/journal.pone.0007006
Devanthéry N, Crosetto M, Monserrat O et al (2018) Deformation monitoring using Sentinel-1 SAR data. Proceedings 2:344. https://doi.org/10.3390/ecrs-2-05157
Ding Y, Li M, Li S, An D (2010) Predicting chlorophyll content of greenhouse tomato with ground-based remote sensing. In: Larar AM, Chung H-S, Suzuki M (eds) Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications III. p 15
Donlon C, Berruti B, Buongiorno A et al (2012) The global monitoring for environment and security (GMES) Sentinel-3 mission. Remote Sens Environ 120:37–57. https://doi.org/10.1016/j.rse.2011.07.024
Duan M, Du X, Peng W et al (2019) Necessity of acknowledging background pollutants in management and assessment of unique basins. Water 11:1103. https://doi.org/10.3390/w11051103
Ediger D, Tuğrul S, Yılmaz A (2005) Vertical profiles of particulate organic matter and its relationship with chlorophyll-a in the upper layer of the NE Mediterranean Sea. J Mar Syst 55:311–326. https://doi.org/10.1016/j.jmarsys.2004.09.003
Ergul HA (2016) Evaluation of seasonal physicochemical conditions and chlorophyll-a concentrations in Izmit Bay, Marmara Sea. J Black Sea/mediterr Environ 22:201–217
Ergul HA, Balkis-Ozdelice N, Koral M et al (2021) The early stage of mucilage formation in the Marmara Sea during spring 2021. J Black Sea/mediterr Environ 27:232–257
Ertürk A, Erten E (2023) Unmixing of pollution-associated sea snot in the near surface after its outbreak in the Sea of Marmara using hyperspectral PRISMA data. IEEE Geosci Remote Sens Lett 20:1–5
Fatema K, Wan Maznah WO, Isa MM (2014) Spatial and temporal variation of physico-chemical parameters in the Merbok Estuary, Kedah, Malaysia. Trop Life Sci Res 25:1–19
Fonda Umani S, Ghirardelli E, Specchi M (1989) Gli episodi di “mare sporco” nell’Adriatico dal 1729 ai giorni nostri. Ufficio stampa e pubbliche relazioni della Regione Friuli-Venezia Giulia, Trieste
Fukao T, Kimoto K, Yamatogi T et al (2009) Marine mucilage in Ariake Sound, Japan, is composed of transparent exopolymer particles produced by the diatom Coscinodiscus granii. Fish Sci 75:1007–1014. https://doi.org/10.1007/s12562-009-0122-0
Funari E, Ade P (1999) Human health implications associated with mucilage in the northern Adriatic Sea. Ann Ist Super Sanita 35:421–425
Gao J-X, Chen Y-M, Lü S-H et al (2012) A ground spectral model for estimating biomass at the peak of the growing season in Hulunbeier grassland, Inner Mongolia, China. Int J Remote Sens 33:4029–4043. https://doi.org/10.1080/01431161.2011.639401
Gazette O (2004) Turkish water pollution control regulation (WPCR). Repub. Turkey, Off. Gaz. 26786
Giani M, Savelli F, Berto D et al (2005) Temporal dynamics of dissolved and particulate organic carbon in the northern Adriatic Sea in relation to the mucilage events. Sci Total Environ 353:126–138. https://doi.org/10.1016/j.scitotenv.2005.09.062
Goffi A, Stroppiana D, Brivio PA et al (2020) Towards an automated approach to map flooded areas from Sentinel-2 MSI data and soft integration of water spectral features. Int J Appl Earth Obs Geoinf 84:101951. https://doi.org/10.1016/j.jag.2019.101951
Gotsis-Skretas O (1995) Mucilage appearances in Greek waters during 1982–1994. Sci Total Environ 165:229–230. https://doi.org/10.1016/0048-9697(95)04665-N
Guler C, Cobanoglu Z (1997) Su kalitesi (Water quality). Republic of Turkey Ministry of Health. Çevre Sağlığı Temel Kaynak Dizisi (Environmental Health Core Resource Series), p 43
Hafeez F, Zafar N, Nazir R et al (2019) Assessment of flood-induced changes in soil heavy metal and nutrient status in Rajanpur, Pakistan. Environ Monit Assess 191:234. https://doi.org/10.1007/s10661-019-7371-x
Hallegraeff GM, Anderson DM, Cembella AD, Enevoldsen HO (2004) Manual on harmful marine microalgae, 2nd revise. UNESCO, Paris
Hanna SHS, Rethwisch MD (2003) Characteristics of AVIRIS bands measurements in agricultural crops at Blythe area, California: II. Studies on Kenaf, Hibiscus canabinus. Remote Sens Agric Ecosyst Hydrol III 4542:9–21. https://doi.org/10.1117/12.454190
Hounslow AW (2018) Water quality data. CRC Press
Hu C (2022) Sea snots in the Marmara Sea as observed from medium-resolution satellites. IEEE Geosci Remote Sens Lett 19:1–5. https://doi.org/10.1109/LGRS.2022.3173997
Hu G, Dong Z, Wei Z, Lu J (2010) Land use and land cover change monitoring in the Zoige Wetland by remote sensing. In: Sixth International Symposium on Digital Earth: Data Processing and Applications, pp 268–276
Ileri S, Karaer F, Katip A, Onur S (2014) Evaluation of water quality in shallow lakes, case study of Lake Uluabat. Uludağ Univ J Fac Eng 19:47. https://doi.org/10.17482/uujfe.58132
Jamshidi S, Bin Abu Bakar N (2011) A study on distribution of chlorophyll-$\vec{a}$ in the coastal waters of Anzali Port, south Caspian Sea. Ocean Sci Discuss 8:435–451. https://doi.org/10.5194/osd-8-435-2011
Karadurmus U, Sari M (2022) Marine mucilage in the Sea of Marmara and its effects on the marine ecosystem: mass deaths. Turk J Zool 46:93–102. https://doi.org/10.3906/zoo-2108-14
Kavzoglu T (2008) Determination of environmental degradation due to urbanization and industrialization in Gebze, Turkey. Environ Eng Sci 25:429–438. https://doi.org/10.1089/ees.2006.0271
Kavzoglu T, Cetin M (2005) Gebze bölgesindeki sanayileşmenin zamansal gelişiminin ve çevresel etkilerinin uydu görüntüleri ile incelenmesi. In: TMMOB Harita ve Kadastro Mühendisleri Odası 10. Türkiye Harita Bilimsel ve Teknik Kurultayı. Ankara, Turkey
Kavzoglu T, Goral M (2022) Google earth engine for monitoring marine mucilage: Izmit Bay in Spring 2021. Hydrology 9:135
Kavzoglu T, Reis S (2008) Performance analysis of maximum likelihood and artificial neural network classifiers for training sets with mixed pixels. Giscience Remote Sens 45:330–342. https://doi.org/10.2747/1548-1603.45.3.330
Kavzoglu T, Colkesen I, Sefercik UG, Ozturk MY (2021) Marmara Denizi’ndeki müsilaj oluşumlarının çok zamanlı optik ve termal uydu görüntülerinden makine öğrenme algo-ritması ile tespiti ve analizi (Detection and analysis of mucilage formations in the Sea of Marmara from multi-temporal optical and thermal s. Harit Derg (mapp j) 166:1–9
Kim D, Choi M, Kim J, Kim U (2019) Advances in remote sensing to understand extreme hydrological events. Adv Meteorol 2019:1–2. https://doi.org/10.1155/2019/8235037
Koppel K, Zalite K, Voormansik K, Jagdhuber T (2017) Sensitivity of Sentinel-1 backscatter to characteristics of buildings. Int J Remote Sens 38:6298–6318. https://doi.org/10.1080/01431161.2017.1353160
Kunwar P, Kachhwaha TS, Kumar A et al (2010) Use of high-resolution IKONOS data and GIS technique for transformation of landuse/landcover for sustainable development. Curr Sci 98:204–212
Li M, Zhang X, Zhang Y et al (2005) Investigation of crop growth condition with hyperspectral reflectance based on ground-based remote sensing. Multispectral and hyperspectral remote sensing instruments and applications II. SPIE, pp 301–308
Li X, Zhou Y, Gong P et al (2020) Developing a method to estimate building height from Sentinel-1 data. Remote Sens Environ 240:111705. https://doi.org/10.1016/j.rse.2020.111705
Liu X, Zhu W, Yang X, Pan Y (2006) Modeling of population density based on GIS and RS. In: 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, pp 1431–1434
MacKenzie L, Sims I, Beuzenberg V, Gillespie P (2002) Mass accumulation of mucilage caused by dinoflagellate polysaccharide exudates in Tasman Bay, New Zealand. Harmful Algae 1:69–83. https://doi.org/10.1016/S1568-9883(02)00006-9
Maraslioglu F, Bektas S, Ozen A (2020) Comparative performance of physicochemical and diatom-based metrics in assessing the water quality of Mert Stream, Turkey. J Ecol Eng 21:18–31. https://doi.org/10.12911/22998993/127392
Mecozzi M, Acquistucci R, Di Noto V et al (2001) Characterization of mucilage aggregates in Adriatic and Tyrrhenian Sea: structure similarities between mucilage samples and the insoluble fractions of marine humic substance. Chemosphere 44:709–720. https://doi.org/10.1016/S0045-6535(00)00375-1
MGM (2021) Statistical analysis report of the water temperature of the sea surroundings in Turkey (1970–2021). Republic of Turkey, Ministry of Environment, Urbanization and Climate Change, General Directorate of Meteorology, p 4. https://mgm.gov.tr/FILES/resmi-istatistikler/yayinlar/denizler-2021.pdf
Morales M, Marti P, Llopis A et al (1999) An environmental study by factor analysis of surface seawaters in the Gulf of Valencia (Western Mediterranean). Anal Chim Acta 394:109–117. https://doi.org/10.1016/S0003-2670(99)00198-1
Mukherjee J, Gebru G, Sood A et al (2010) Wheat yield and acreage prediction using LISS-III and AWiFS sensors data of indian remote sensing satellite of Rupnager district of Punjab, India. Ital J Remote Sens 42:115–127. https://doi.org/10.5721/ItJRS20104239
Nagler T, Rott H, Hetzenecker M et al (2015) The Sentinel-1 mission: new opportunities for ice sheet observations. Remote Sens 7:9371–9389. https://doi.org/10.3390/rs70709371
Nalbant C (1998) İstanbul Boğazı’ndaki su akıntılarının deniz suyu kirlenmesine etkileri (The effects of water currents in the Bosphorus on seawater pollution). Department of Environmental Engineering
Navalgund RR, Jayaraman V, Roy PS (2007) Remote sensing applications: an overview. Curr Sci 93:1747–1766
Nikolaidis, Aligizaki K, Koukaras K, Moschandreou K (2006) Mucilage phenomena in North Aegean Sea, Greece: another harmful effect of dinoflagellates. In: 12th International Conference on Harmful Algae, pp 4–8
Nuthammachot N, Phairuang W, Stratoulias D (2017) Removing Speckle noise in Sentinel-1A radar satellite imagery using filtering techniques. J Remote Sens GIS Assoc ThailandRESGAT 18:80–92
Ozalp BH (2021) First massive mucilage event observed in deep waters of Çanakkale Strait (Dardanelles). Turk J Black Sea/mediterr Environ 27:49–66
Ozdogan N, Sefercik UG, Kilinc Y et al (2021) Su kalitesinin insansiz hava araci verileri ve fiziko-kimyasal parametrelerin analizi ile belirlenmesi: Aydınlar (Gülüç) Çayı örneği. Eur J Sci Technol. https://doi.org/10.31590/ejosat.887105
Ozsoy E, Cagatay MN, Balkis N et al (2016) The Sea of Marmara: marine biodiversity, fisheries, conservation and governance-eutrophication in the Sea of Marmara. Turkish Marine Research Foundation (TUDAV), pp 723–736
Park Y-J, Ruddick K, Lacroix G (2010) Detection of algal blooms in European waters based on satellite chlorophyll data from MERIS and MODIS. Int J Remote Sens 31:6567–6583. https://doi.org/10.1080/01431161003801369
Ren H-Y, Zhuang D-F, Pan J-J et al (2008) Hyper-spectral remote sensing to monitor vegetation stress. J Soils Sediments 8:323–326. https://doi.org/10.1007/s11368-008-0030-4
Rice EW, Baird RB, Eaton AD (2017) No Title, 23rd Editi. American Public Health Association, American Water Works Association, and Water Environment Federation, Washington, DC
Rinaldi A, Vollenweider RA, Montanari G et al (1995) Mucilages in Italian seas: the Adriatic and Tyrrhenian Seas, 1988–1991. Sci Total Environ 165:165–183. https://doi.org/10.1016/0048-9697(95)04550-K
Rokade V, Kundal P, Joshi A (2007) Groundwater potential modelling through remote sensing and GIS: a case study from Rajura Taluka, Chandrapur district, Maharashtra. J Geol Soc India 69:943–948
Royer PD, Cobb NS, Clifford MJ et al (2011) Extreme climatic event-triggered overstorey vegetation loss increases understorey solar input regionally: primary and secondary ecological implications. J Ecol 99:714–723. https://doi.org/10.1111/j.1365-2745.2011.01804.x
Sanver U, Yesildirek A (2023) An autonomous marine mucilage monitoring system. Sustainability 15(4):3340
Schmidt F, Persson A (2003) Comparison of DEM data capture and topographic wetness indices. Precis Agric 4:179–192. https://doi.org/10.1023/A:1024509322709
Scoullos M, Plavšić M, Karavoltsos S, Sakellari A (2006) Partitioning and distribution of dissolved copper, cadmium and organic matter in Mediterranean marine coastal areas: the case of a mucilage event. Estuar Coast Shelf Sci 67:484–490. https://doi.org/10.1016/j.ecss.2005.12.007
Sefercik UG, Atesoglu A (2017) Three-dimensional forest stand height map production utilizing airborne laser scanning dense point clouds and precise quality evaluation. iForest Biogeosci for 10:491–497. https://doi.org/10.3832/ifor2039-010
Sefercik UG, Buyuksalih G, Atalay C, Jacobsen K (2018) Validation of Sentinel-1A and AW3D30 DSMs for the metropolitan area of Istanbul, Turkey. PFG J Photogramm Remote Sens Geoinf Sci 86:141–155. https://doi.org/10.1007/s41064-018-0054-3
Sha J, Xiong H, Li C et al (2021) Harmful algal blooms and their eco-environmental indication. Chemosphere 274:129912. https://doi.org/10.1016/j.chemosphere.2021.129912
Soomets T, Uudeberg K, Jakovels D et al (2020) Validation and comparison of water quality products in Baltic lakes using Sentinel-2 msi and Sentinel-3 OLCI data. Sensors 20:742. https://doi.org/10.3390/s20030742
Sreenivasulu G, Jayaraju N, Sundara Raja Reddy BC, Lakshmi Prasad T (2015) Physico-chemical parameters of coastal water from Tupilipalem coast, Southeast coast of India. J Coast Sci 2:34–39. https://doi.org/10.6084/m9.figshare.1526099
Stereńczak K, Kozak J (2011) Evaluation of digital terrain models generated in forest conditions from airborne laser scanning data acquired in two seasons. Scand J for Res 26:374–384. https://doi.org/10.1080/02827581.2011.570781
Tas S, Ergul H, Balkis-Ozdelice N (2016) Harmful algal blooms (HABs) and mucilage formations in the Sea of Marmara. Turkish Marine Research Foundation, Istanbul
Terzi O, Sunter AT (2019) Atakum Sahili’ndeki deniz suyu kalitesinin değerlendirilmesi, 2016 (Evaluation of seawater quality of Atakum Beach, 2016). Turkish Bull Hyg Exp Biol 76:275–284. https://doi.org/10.5505/TurkHijyen.2018.22230
Thompson JA, Bell JC, Butler CA (2001) Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape modeling. Geoderma 100:67–89. https://doi.org/10.1016/S0016-7061(00)00081-1
Toklu-Alicli B, Polat S, Balkis-Ozdelice N (2020) Temporal variations in the abundance of picoplanktonic Synechococcus (Cyanobacteria) during a mucilage event in the Gulfs of Bandırma and Erdek. Estuar Coast Shelf Sci 233:106513. https://doi.org/10.1016/j.ecss.2019.106513
Tomasino MG (1996) Is it feasible to predict “slime blooms” or “mucilage” in the northern Adriatic Sea? Ecol Modell 84:189–198. https://doi.org/10.1016/0304-3800(94)00108-1
Torres R, Snoeij P, Geudtner D et al (2012) GMES Sentinel-1 mission. Remote Sens Environ 120:9–24. https://doi.org/10.1016/j.rse.2011.05.028
Tufekci V, Balkis N, Polat Beken C et al (2010) Phytoplankton composition and environmental conditions of the mucilage event in the Sea of Marmara. Turk J Biol 34:199–210. https://doi.org/10.3906/biy-0812-1
Vassilopoulou S, Hurni L, Dietrich V et al (2002) Orthophoto generation using IKONOS imagery and high-resolution DEM: a case study on volcanic hazard monitoring of Nisyros Island (Greece). ISPRS J Photogramm Remote Sens 57:24–38. https://doi.org/10.1016/S0924-2716(02)00126-0
Wu ML, Wang YS, Sun CC et al (2010) Identification of coastal water quality by statistical analysis methods in Daya Bay, South China Sea. Mar Pollut Bull 60:852–860. https://doi.org/10.1016/j.marpolbul.2010.01.007
Xie C, Li J, Pan F et al (2018) Environmental factors influencing mucilage accumulation of the endangered Brasenia schreberi in China. Sci Rep 8(1):1–10. https://doi.org/10.1038/s41598-018-36448-3
Yagci AL, Colkesen I, Kavzoglu T, Sefercik UG (2022) Daily monitoring of marine mucilage using the MODIS products: a case study of 2021 mucilage bloom in the Sea of Marmara. Turk Environ Monit Assess 194:170. https://doi.org/10.1007/s10661-022-09831-x
Yilmaz TD, Coskun F, Celik S, Deniz S (2017) The Evaluation of microbiological analysis of sea water in Mersin withnin the context of blue flag implementations. Turk Bull Hyg Exp Biol 74:131–134. https://doi.org/10.5505/TurkHijyen.2017.60134
Zhang Q, Ge L, Zhang R et al (2021) Towards a deep-learning-based framework of Sentinel-2 Imagery for automated active fire detection. Remote Sens 13:4790. https://doi.org/10.3390/rs13234790
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The authors would like to thank European Space Agency for providing Sentinel-1, Sentinel-2, and Sentinel-3 satellite images as free of charge.
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All authors contributed to conception and design of the study. Materials were prepared by all authors. In-situ and remote sensing data were collected by UGS, IC, TK and MYO. Physico-chemical water quality analyses were performed by NO. Space-borne data analyses were performed by UGS, IC and TK. All authors have read and agreed to the published version of the manuscript.
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Sefercik, U.G., Colkesen, I., Kavzoglu, T. et al. Assessing the Physical and Chemical Characteristics of Marine Mucilage Utilizing In-Situ and Remote Sensing Data (Sentinel-1, -2, -3). PFG (2023). https://doi.org/10.1007/s41064-023-00254-y
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DOI: https://doi.org/10.1007/s41064-023-00254-y