An application of 1D convolution and deep learning to remote sensing modelling of Secchi depth in the northern Adriatic Sea

References

• Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., & others. (2016). Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16. 265–283. https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi
   Google Scholar
• Agarwal, A., & Saxena, M. (2011). Assessment of pollution by physicochemical water param- eters using regression analysis: A case study of Gagan river at Moradabad-India. Advances in Applied Science Research, 2(2), 185–189.
   Google Scholar
• Ahmed, M., Mumtaz, R., Anwar, Z., Shaukat, A., Arif, O., & Shafait, F. (2022). A multi–step approach for optically active and inactive water quality parameter estimation using deep learning and remote sensing. Water, 14(13), 2112. Retrieved from. https://www.mdpi.com/2073-4441/14/13/2112
   Web of Science ®Google Scholar
• Anassontzis, E., Ball, A., Belias, A., Fotiou, A., Grammatikakis, G., Kontogiannis, H., Zhukov, V. (2010). Water transparency measurements in the deep Ionian Sea. Astroparticle Physics, 34(4), 187–197. https://doi.org/10.1016/j.astropartphys.2010.06.008
   Web of Science ®Google Scholar
• Andres, L., Boateng, K., Borja-Vega, C., & Thomas, E. (2018). A review of in-situ and remote sensing technologies to monitor water and sanitation interventions. Water, 10(6), 756. https://doi.org/10.3390/w10060756
   Web of Science ®Google Scholar
• Arias-Rodriguez, L. F., Tüzün, U. F., Duan, Z., Huang, J., Tuo, Y., & Disse, M. (2023). Global water quality of inland waters with harmonized landsat-8 and sentinel-2 using cloud-computed machine learning. Remote Sensing, 15(5), 1390. Retrieved from. https://www.mdpi.com/2072-4292/15/5/1390
   Web of Science ®Google Scholar
• Bandyopadhyay, M. (2021). Multi-stack hybrid CNN with non-monotonic activation functions for hyperspectral satellite image classification. Neural Computing & Applications, 33(21), 14809–14822. https://doi.org/10.1007/s00521-021-06120-5
   Web of Science ®Google Scholar
• Baughman, C. A., Jones, B. M., Bartz, K. K., Young, D. B., & Zimmerman, C. E. (2015). Reconstructing turbidity in a glacially influenced lake using the landsat TM and ETM+ surface reflectance climate data record archive, lake Clark, Alaska. Remote Sensing, 7(10), 13692–13710. https://doi.org/10.3390/rs71013692
   Web of Science ®Google Scholar
• Blake, G. H., & Topalović, D. (1996). The maritime boundaries of the Adriatic Sea. Ibru.
   Google Scholar
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Brewin, R. J. W., Brewin, T. G., Phillips, J., Rose, S., Abdulaziz, A., Wimmer, W., Platt, T. (2019). A printable device for measuring clarity and colour in lake and nearshore waters. Sensors, 19(4), 936. https://doi.org/10.3390/s19040936
   PubMed Web of Science ®Google Scholar
• Brockmann, C., Doerffer, R., Peters, M., Kerstin, S., Embacher, S., & Ruescas, A. (2016). 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. In Living planet symposium (Vol. 740, p. 54).
   Google Scholar
• Budhiman, S., Hobma, T. W., & Vekerdy, Z. Remote sensing for mapping TSM concentration in Mahakam Delta: An analytical approach. In Proceedings of the 13th OMISAR Workshop on Validation and Application of Satellite Data for Marine Resources Conservation, Bali, Indonézia, 5–9 October. 2004.
   Google Scholar
• Canfield, D., Brown, C., Bachmann, R., & Hoyer, M. (2002). Volunteer lake monitoring: Testing the reliability of data collected by the florida lakewatch program. Lake and Reservoir Management, 18(1), 1–9. https://doi.org/10.1080/07438140209353924
   Web of Science ®Google Scholar
• Chapman, D. V. (1996). Water quality assessments: A guide to the use of biota, sediments and water in environmental monitoring, chapter 3 - selection of water quality variables. CRC Press.
   Google Scholar
• Chen, J., Chen, S., Fu, R., Li, D., Jiang, H., Wang, C., Hicks, B. J. (2022). Remote sensing big data for water environment monitoring: Current status, challenges, and future prospects. Earth’s Future, 10(2), e2021EF002289. https://doi.org/10.1029/2021EF002289
   Web of Science ®Google Scholar
• Chollet, F. (2015). Keras. GitHub. Retrieved from https://github.com/fchollet/keras
   Google Scholar
• Cui, Y., Yan, Z., Wang, J., Hao, S., & Liu, Y. (2022). Deep learning–based remote sensing estimation of water transparency in shallow lakes by combining landsat 8 and sentinel 2 images. Environmental Science and Pollution Research, 29(3), 4401–4413. https://doi.org/10.1007/s11356-021-16004-9
   PubMed Web of Science ®Google Scholar
• De Marchis, M., Freni, G., & Napoli, E. (2013 09). Modelling of E. coli distribution in coastal areas subjected to combined sewer overflows. Water Science & Technology, 68(5), 1123–1136. https://doi.org/10.2166/wst.2013.353
   PubMed Web of Science ®Google Scholar
• Deutsch, E. S., Cardille, J. A., Koll-Egyed, T., & Fortin, M.-J. (2021). Landsat 8 lake water clarity empirical algorithms: Large-scale calibration and validation using government and citizen science data from across canada. Remote Sensing, 13(7), 1257. https://doi.org/10.3390/rs13071257
   Web of Science ®Google Scholar
• Diez, P., Neittaanmäki, P., Periaux, J., Tuovinen, T., & Pons-Prats, J. (2020). Computation and big data for transport: Digital Innovations in surface and Air transport Systems (Vol. 54). Springer Nature.
   Google Scholar
• Dogliotti, A., Ruddick, K., Nechad, B., Doxaran, D., & Knaeps, E. (2015). A single algorithm to retrieve turbidity from remotely-sensed data in all coastal and estuarine waters. Remote Sensing of Environment, 156, 157–168. https://doi.org/10.1016/j.rse.2014.09.020
   Web of Science ®Google Scholar
• Eker-Develi, E., Berthon, J.-F., & Free, G. (2022). Impact of environmental factors on phyto- plankton composition and their marker pigments in the northern Adriatic Sea. Oceanologia, 64(4), 615–630. https://doi.org/10.1016/j.oceano.2022.05.002
   Web of Science ®Google Scholar
• EO Browser,Sinergise Ltd. (n.d.). Ac- cessed: 2022-09-20 https://apps.sentinel-hub.com/eo-browser/.
   Google Scholar
• Fernandez-Moran, R., Gómez-Chova, L., Alonso, L., Mateo-García, G., & López-Puigdollers, D. (2021). Towards a novel approach for Sentinel-3 synergistic OLCI/SLSTR cloud and cloud shadow detection based on stereo cloud-top height estimation. ISPRS Journal of Photogrammetry & Remote Sensing, 181, 238–253. https://doi.org/10.1016/j.isprsjprs.2021.09.013
   Web of Science ®Google Scholar
• Ficek, D., & Zapadka, T. (2010). Variability of bio-optical parameters in lake jasień północny and lake jasień południowy. Limnological Review, 10(2), 67–76. https://doi.org/10.2478/v10194-011-0008-2
   Google Scholar
• Gholizadeh, M. H., Melesse, A. M., & Reddi, L. (2016). A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors, 16(8), 1298. https://doi.org/10.3390/s16081298
   PubMed Web of Science ®Google Scholar
• Gordon, H. R., & Wang, M. (1994, January). Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with seawifs: A preliminary algorithm. Applied Optics, 33(3), 443–452. Retrieved from https://opg.optica.org/ao/abstract.cfm?URI=ao-33-3-443
   PubMed Web of Science ®Google Scholar
• Guo, H., Zhu, X., Jeanne Huang, J., Zhang, Z., Tian, S., & Chen, Y. (2023). An en- hanced deep learning approach to assessing inland lake water quality and its response to climate and anthropogenic factors. Canadian Journal of Fisheries and Aquatic Sciences, 620, 129466. Retrieved from https://www.sciencedirect.com/science/article/pii/S0022169423004080 https://doi.org/10.1016/j.jhydrol.2023.129466
   Google Scholar
• Hafeez, S., Wong, M. S., Ho, H. C., Nazeer, M., Nichol, J., Abbas, S., Pun, L. (2019). Comparison of machine learning algorithms for retrieval of water quality indicators in case-ii waters: A case study of hong kong. Remote Sensing, 11(6), 617. Retrieved from. https://www.mdpi.com/2072-4292/11/6/617
   Web of Science ®Google Scholar
• Harvey, E. T., Walve, J., Andersson, A., Karlson, B., & Kratzer, S. (2019). The effect of optical properties on secchi depth and implications for eutrophication management. Frontiers in Marine Science, 5, 496. https://doi.org/10.3389/fmars.2018.00496
   Web of Science ®Google Scholar
• He, Y., Gong, Z., Zheng, Y., & Zhang, Y. (2021). Inland reservoir water quality inversion and eutrophication evaluation using BP neural network and remote sensing imagery: A case study of dashahe reservoir. Water, 13(20), 2844. https://doi.org/10.3390/w13202844
   Web of Science ®Google Scholar
• He, Y., Lu, Z., Wang, W., Zhang, D., Zhang, Y., Qin, B., Yang, X. (2022). Water clarity mapping of global lakes using a novel hybrid deep-learning-based recurrent model with landsat oli images. Water Research, 215, 118241. Retrieved from https://www.sciencedirect.com/science/article/pii/S0043135422002044 https://doi.org/10.1016/j.watres.2022.118241
   PubMed Web of Science ®Google Scholar
• Hoyer, M. V., & Canfield, D. E., Jr. (2021, August). Volunteer-collected water quality data can be used for science and management. Lake and Reservoir Management, 37(3), 235–245. https://doi.org/10.1080/10402381.2021.1876190
   Web of Science ®Google Scholar
• Huang, G., Falconer, R. A., & Lin, B. (2017). Integrated hydro-bacterial modelling for pre- dicting bathing water quality. Estuarine, Coastal and Shelf Science, 188, 145–155. https://doi.org/10.1016/j.ecss.2017.01.018
   Web of Science ®Google Scholar
• Hu, W., Huang, Y., Wei, L., Zhang, F., & Li, H. (2015). Deep convolutional neural networks for hyperspectral image classification. Journal of Sensors, 2015, 1–12. https://doi.org/10.1155/2015/258619
   Web of Science ®Google Scholar
• Hu, S., Yan, H., Tao, B., Yu, J., & Chen, W. (2019). Classification of sea and land waveforms based on deep learning for airborne laser bathymetry. Infrared and Laser Engineering, 48(11), 1113004–1113004.
   Google Scholar
• Ivanda, A., Šerić, L., Bugarić, M., & Braović, M. (2021). Mapping chlorophyll-a concentrations in the Kaštela Bay and Brač Channel using ridge regression and Sentinel-2 satellite images. Electronics, 10(23), 3004. https://doi.org/10.3390/electronics10233004
   Web of Science ®Google Scholar
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013a). An introduction to statistical learning, chapter 3: Linear regression (Vol. 112). Springer.
   Google Scholar
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013b). An introduction to statistical learning, chapter 6: Linear model selection and regularization (Vol. 112). Springer.
   Google Scholar
• Katlane, R., Dupouy, C., El Kilani, B., Berges, J. C. (2020). Estimation of chlorophyll and turbidity using sentinel 2A and EO1 data in Kneiss Archipelago Gulf of Gabes, Tunisia. International Journal of Geosciences, 11(10), 708. https://doi.org/10.4236/ijg.2020.1110035
   Google Scholar
• Knudby, A. (2021). 7. Accuracy assessment. Remote sensing. eCampusOntario Open Library. https://ecampusontario.pressbooks.pub/remotesensing/
   Google Scholar
• Kulshreshtha, A., & Shanmugam, P. (2017). Estimation of underwater visibility in coastal and inland waters using remote sensing data. Environmental Monitoring and Assessment, 189(4), 1–18. https://doi.org/10.1007/s10661-017-5905-7
   Web of Science ®Google Scholar
• Kyryliuk, D., & Kratzer, S. (2019). Evaluation of Sentinel-3A OLCI products derived using the Case-2 regional CoastColour processor over the Baltic Sea. Sensors, 19(16), 3609. https://doi.org/10.3390/s19163609
   PubMed Web of Science ®Google Scholar
• Lateef, R. A., & Abbas, A. R. (2022). Tuning the hyperparameters of the 1D CNN model to improve the performance of human activity recognition. Engineering and Technology Journal, 40(4), 547–554. https://doi.org/10.30684/etj.v40i4.2054
   Google Scholar
• Lednicka, B., & Kubacka, M. (2022). Semi-empirical model of remote-sensing reflectance for chosen areas of the southern Baltic. Sensors, 22(3), 1105. https://doi.org/10.3390/s22031105
   PubMed Web of Science ®Google Scholar
• Lee, Z., Shang, S., Du, K., Lin, G., Liu, T., & Zoffoli, L. (2019). Estimating the transmittance of visible solar radiation in the upper ocean using Secchi disk observations. Journal of Geophysical Research: Oceans, 124(3), 1434–1444. https://doi.org/10.1029/2018JC014543
   Web of Science ®Google Scholar
• Lee, Z., Shang, S., Du, K., & Wei, J. (2018). Resolving the long-standing puzzles about the observed Secchi depth relationships. Limnology & Oceanography, 63(6), 2321–2336. https://doi.org/10.1002/lno.10940
   Web of Science ®Google Scholar
• Lee, Z., Shang, S., Hu, C., Du, K., Weidemann, A., Hou, W., Lin, G. (2015). Secchi disk depth: A new theory and mechanistic model for underwater visibility. Remote Sensing of Environment, 169, 139–149. https://doi.org/10.1016/j.rse.2015.08.002
   Web of Science ®Google Scholar
• Lee, Z., & Tang, J. (2022). The two faces of “case-1” water. Journal of Remote Sensing, 2022. https://doi.org/10.34133/2022/9767452
   Google Scholar
• Lešek, A., Novak, G., Četina, M., & Žagar, D. (2020). Modelling transport and decease of Escherichia coli in the coastal sea by using the particle tracking method. 633–634.
   Google Scholar
• Lešek, A., & Žagar, D. (2018). Use of the particle tracking method for modelling the transport and deceasing of Escherichia coli in the sea. Acta Hydrotechnica, 31(55), 119–142. https://doi.org/10.15292/acta.hydro.2018.08
   Google Scholar
• Li, J., & Heap, A. D. (2011). A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecological Informatics, 6(3), 228–241. Retrieved from. https://www.sciencedirect.com/science/article/pii/S1574954110001147
   Web of Science ®Google Scholar
• Li, Y., Shi, K., Zhang, Y., Zhu, G., Zhang, Y., Wu, Z., Li, N. (2020). Analysis of water clarity decrease in Xin’anjiang reservoir, China, from 30-year landsat tm, ETM+, and OLI observations. Canadian Journal of Fisheries and Aquatic Sciences, 590, 125476. Retrieved from https://www.sciencedirect.com/science/article/pii/S0022169420309367 https://doi.org/10.1016/j.jhydrol.2020.125476
   Google Scholar
• Li, Y., Zhang, H., Xue, X., Jiang, Y., & Shen, Q. (2018). Deep learning for remote sensing image classification: A survey. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, 8(6), e1264. https://doi.org/10.1002/widm.1264
   Web of Science ®Google Scholar
• Loh, W.-Y. (2011). Classification and regression trees. Wiley interdisciplinary reviews: Data mining and knowledge discovery. WIREs Data Mining and Knowledge Discovery, 1(1), 14–23. https://doi.org/10.1002/widm.8
   Web of Science ®Google Scholar
• Luis, K. M. A., Rheuban, J. E., Kavanaugh, M. T., Glover, D. M., Wei, J., Lee, Z., & Doney, S. C. (2019, August). Capturing coastal water clarity variability with Landsat 8. Marine Pollution Bulletin, 145, 96–104. https://doi.org/10.1016/j.marpolbul.2019.04.078
   PubMed Web of Science ®Google Scholar
• Maciel, D. A., Barbosa, C. C. F., de Moraes Novo, E. M. L., Flores Júnior, R., & Begliomini, F. N. (2021). Water clarity in brazilian water assessed using sentinel-2 and machine learning methods. Isprs Journal of Photogrammetry & Remote Sensing, 182, 134–152. Retrieved from https://www.sciencedirect.com/science/article/pii/S092427162100277X https://doi.org/10.1016/j.isprsjprs.2021.10.009
   Web of Science ®Google Scholar
• Maier, P. M., Keller, S., & Hinz, S. (2021). Deep learning with WASI simulation data for estimating chlorophyll a concentration of inland water bodies. Remote Sensing, 13(4), 718. https://doi.org/10.3390/rs13040718
   Web of Science ®Google Scholar
• Mancini, J. L. (1978). Numerical estimates of coliform mortality rates under various conditions. Journal (Water Pollution Control Federation), 50(11) , 2477–2484.
   Web of Science ®Google Scholar
• Mansouri, E., Feizi, F., Jafari Rad, A., & Arian, M. (2018). Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: A case study in the Sarvian area, central Iran. Advances in Fission-Track Geochronology, 9(2), 373–384.
   Google Scholar
• Mao, Z., Chen, J., Pan, D., Tao, B., & Zhu, Q. (2012). A regional remote sensing algorithm for total suspended matter in the East China Sea. Remote Sensing of Environment, 124, 819–831. https://doi.org/10.1016/j.rse.2012.06.014
   Web of Science ®Google Scholar
• Menon, N., George, G., Ranith, R., Sajin, V., Murali, S., Abdulaziz, A., Sathyendranath, S. (2021, MAY). Citizen science tools reveal changes in estuarine water quality following demolition of buildings. Remote Sensing, 13(9), 1683. https://doi.org/10.3390/rs13091683
   Web of Science ®Google Scholar
• Mobley, C. D., Stramski, D., Paul Bissett, W., & Boss, E. (2004). Optical modeling of ocean waters: Is the case 1-case 2 classification still useful? Oceanography, 17(SPL. ISS. 2), 60. https://doi.org/10.5670/oceanog.2004.48
   Google Scholar
• Morel, A. Y., & Gordon, H. R. (1980). Report of the working group on water color. Boundary- Layer Meteorology, 18(3), 343–355. https://doi.org/10.1007/BF00122030
   Web of Science ®Google Scholar
• Morel, A., & Prieur, L. (1977). Analysis of variations in ocean color 1. Limnology & Oceanography, 22(4), 709–722. https://doi.org/10.4319/lo.1977.22.4.0709
   Web of Science ®Google Scholar
• Nasir, N., Kansal, A., Alshaltone, O., Barneih, F., Shanableh, A., Al-Shabi, M., & Al Sham- Maa, A. (2023). Deep learning detection of types of water-bodies using optical variables and ensembling. Intelligent Systems with Applications, 18, 200222. Retrieved from https://www.sciencedirect.com/science/article/pii/S2667305323000479 https://doi.org/10.1016/j.iswa.2023.200222
   Google Scholar
• Padial, A. A., & Thomaz, S. M. (2008). Prediction of the light attenuation coefficient through the secchi disk depth: Empirical modeling in two large neotropical ecosystems. Limnology, 9(2), 143–151. https://doi.org/10.1007/s10201-008-0246-4
   Web of Science ®Google Scholar
• Paulson, C. A., & Simpson, J. J. (1977). Irradiance measurements in the Upper ocean. Journal of Physical Oceanography, 7(6), 952–956. https://doi.org/10.1175/1520-0485(1977)007<0952:IMITUO>2.0.CO;2
   Web of Science ®Google Scholar
• Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Duches- Nay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
   Web of Science ®Google Scholar
• Prabhat, A., & Khullar, V. (2017). Sentiment classification on big data using naïve bayes and logistic regression. In 2017 International Conference on Computer Communication and Informatics (ICCCI) (p. 1–5).
   Google Scholar
• Pyo, J., Hong, S. M., Jang, J., Park, S., Park, J., Noh, J. H., & Cho, K. H. (2022). Drone-borne sensing of major and accessory pigments in algae using deep learning modeling. GIScience & Remote Sensing, 59(1), 310–332. https://doi.org/10.1080/15481603.2022.2027120
   Web of Science ®Google Scholar
• Qazi, E. U. H., Almorjan, A., & Zia, T. (2022). A one-dimensional convolutional neural network (1D-CNN) based deep learning System for network intrusion detection. Applied Sciences, 12(16), 7986.
   Google Scholar
• QGIS Development Team. (n.d.). QGIS geographic information System [Computer Software Manual]. Retrieved from https://www.qgis.org
   Google Scholar
• Qin, P., Simis, S., & Tilstone, G. (2017, 10). Radiometric validation of atmospheric correction for MERIS in the Baltic Sea based on continuous observations from ships and AERONET- OC. Remote Sensing of Environment, 200, 263–280. https://doi.org/10.1016/j.rse.2017.08.024
   Web of Science ®Google Scholar
• Radiometric Resolution–21 bands in VIS/SWIR. (n.d.). Accessed: 2022-12-01 https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci/resolutions/radiomet
   Google Scholar
• Rodrigues, G., Potes, M., Penha, A. M., Costa, M. J., & Morais, M. M. (2022). The use of Sentinel-3/OLCI for monitoring the water quality and optical water types in the largest Portuguese reservoir. Remote Sensing, 14(9), 2172. https://doi.org/10.3390/rs14092172
   Web of Science ®Google Scholar
• Rubin, H. J., Lutz, D. A., Steele, B. G., Cottingham, K. L., Weathers, K. C., Ducey, M. J., Chipman, J. W. (2021). Remote sensing of lake water clarity: Performance and transfer- ability of both historical algorithms and machine learning. Remote Sensing, 13(8), 1434. https://doi.org/10.3390/rs13081434
   Web of Science ®Google Scholar
• Sagan, V., Peterson, K. T., Maimaitijiang, M., Sidike, P., Sloan, J., Greeling, B. A., Adams, C. (2020). Monitoring inland water quality using remote sensing: Potential and limitations of spectral indices, bio-optical simulations, machine learn- ing, and cloud computing. Earth Science Review, 205, 103187. Retrieved from https://www.sciencedirect.com/science/article/pii/S0012825220302336 https://doi.org/10.1016/j.earscirev.2020.103187
   Web of Science ®Google Scholar
• Sara, U., Akter, M., & Uddin, M. S. (2019). Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study. Journal of Computer and Communications, 7(3), 8–18. https://doi.org/10.4236/jcc.2019.73002
   Google Scholar
• Secchi Disk Project. (2013). Accessed: 2022-09-30 https://www.playingwithdata.com/secchi-disk-project/.
   Google Scholar
• Sentinel-3 OLCI L1B. (n.d.). Invalid Date https://docs.sentinel-hub.com/api/latest/data/sentinel-3-olci-l1b/.
   Google Scholar
• Sentinel-3 OLCI Land User Handbook. (n.d.). Accessed: 2023-04-27 https://sentinel.esa.int/documents/247904/4598066/Sentinel
   Google Scholar
• Shah, M., Raval, M. S., & Divakaran, S. (2022). A deep learning perspective to atmospheric correction of satellite images. In IGARSS 2022—2022 IEEE International Geoscience and Remote Sensing Symposium (p. 346–349).
   Google Scholar
• Shen, M., Duan, H., Cao, Z., Xue, K., Qi, T., Ma, J., Song, X. (2020). Sentinel-3 OLCI observations of water clarity in large lakes in eastern China: Implications for SDG 6.3.2 evaluation. Remote Sensing of Environment, 247, 111950. Retrieved from. https://www.sciencedirect.com/science/article/pii/S0034425720303205
   Web of Science ®Google Scholar
• Slonecker, E. T., Jones, D. K., & Pellerin, B. A. (2016). The new landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM). Marine Pollution Bulletin, 107(2), 518–527. Resetting the Bar: Establishing Baselines for Persistent Contaminants after Hurricane Sandy in the Coastal Environments of New Jersey and New York, USA. https://doi.org/10.1016/j.marpolbul.2016.02.076
   PubMed Web of Science ®Google Scholar
• Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14, 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
   Web of Science ®Google Scholar
• Soriano-González, J., Urrego, E. P., Sòria-Perpinyà, X., Angelats, E., Alcaraz, C., Delegido, J., Moreno, J. (2022). Towards the combination of c2rcc processors for improving water quality retrieval in inland and coastal areas. Remote Sensing, 14(5), 1124. https://doi.org/10.3390/rs14051124
   Web of Science ®Google Scholar
• Stehman, S. V., & Czaplewski, R. L. (1998). Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sensing of Environment, 64(3), 331–344. https://doi.org/10.1016/S0034-4257(98)00010-8
   Web of Science ®Google Scholar
• Steinmetz, F., Deschamps, P.-Y., & Ramon, D. (2011, May). Atmospheric correction in presence of sun glint: Application to meris. Optics Express, 19(10), 9783–9800. Retrieved from https://opg.optica.org/oe/abstract.cfm?URI=oe-19-10-9783
   PubMed Web of Science ®Google Scholar
• Stravisi, F. (1999). Optical seawater properties in the Gulf of Trieste. Bollettino della Società Adriatica di Scienze, 79, 61–75.
   Google Scholar
• Teubner, K., Teubner, I., Pall, K., Kabas, W., Tolotti, M., Ofenböck, T., & Dokulil, M. T. (2020). New emphasis on water transparency as socio-ecological indicator for urban water: Bridging ecosystem service supply and sustainable ecosystem health. Frontiers in Environmental Science, 8, 573724. https://doi.org/10.3389/fenvs.2020.573724
   Web of Science ®Google Scholar
• Umer, B., & Malačič, V. (2022). Biexponential decrease of PAR in coastal waters (northern Adriatic). Geofizika, 39(1), 1–29. https://doi.org/10.15233/gfz.2022.39.2
   Web of Science ®Google Scholar
• Upreti, A. (2022). Machine learning Application in G.I.S. and remote sensing: An overview. Preprints, 2022070302. https://doi.org/10.20944/preprints202207.0302.v1
   Google Scholar
• Vanhellemont, Q., & Ruddick, K. (2021). 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. Retrieved from. https://www.sciencedirect.com/science/article/pii/S003442572100002X
   Web of Science ®Google Scholar
• Van Rossum, G., & Drake, F. L., Jr. (1995). Python tutorial (Vol. 620). Centrum voor Wiskunde en Informatica Amsterdam.
   Google Scholar
• Yue, J., Feng, H., Yang, G., & Li, Z. (2018). A comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy. Remote Sensing, 10(1), 66. https://doi.org/10.3390/rs10010066
   Web of Science ®Google Scholar
• Zavatarelli, M., Raicich, F., Bregant, D., Russo, A., & Artegiani, A. (1998). Climatological biogeochemical characteristics of the Adriatic Sea. Journal of Marine Systems, 18(1), 227–263. https://doi.org/10.1016/S0924-7963(98)00014-1
   Web of Science ®Google Scholar
• Zebin, T., Scully, P. J., & Ozanyan, K. B. (2016). Human activity recognition with inertial sensors using a deep learning approach. In 2016 IEEE Sensors (p. 1–3).
   Google Scholar
• Zhang, Y., Liu, X., Yin, Y., Wang, M., & Qin, B. (2012). Predicting the light attenuation coefficient through secchi disk depth and beam attenuation coefficient in a large, shallow, freshwater lake. Hydrobiologia, 693, 29–37. https://doi.org/10.1007/s10750-012-1084-2
   Web of Science ®Google Scholar
• Zhang, Y., Shi, K., Sun, X., Zhang, Y., Li, N., Wang, W., & others. (2022). Improving remote sensing estimation of Secchi disk depth for global lakes and reservoirs using machine learning methods. GIScience & Remote Sensing, 59(1), 1367–1383. https://doi.org/10.1080/15481603.2022.2116102
   Web of Science ®Google Scholar
• Zhang, L., Zhang, L., & Du, B. (2016). Deep learning for remote sensing data: A technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4(2), 22–40. https://doi.org/10.1109/MGRS.2016.2540798
   Web of Science ®Google Scholar
• Zhao, C., Yu, D., Yang, L., Zhou, Y., Gao, H., Bian, X., & Li, Y. (2022). Remote sensing algorithms of seawater transparency: A review. In 2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) (p. 744–749).
   Google Scholar