A machine learning-based strategy for estimating non-optically active water quality parameters using Sentinel-2 imagery

References

• Beaulant, A. L., G. Perron, J. Kleinpeter, C. Weber, T. Ranchin, and L. Wald. 2008. “Adding Virtual Measuring Stations to a Network for Urban Air Pollution Mapping.” Environment International 34 (5): 599–605. doi:10.1016/j.envint.2007.12.004.
   PubMed Web of Science ®Google Scholar
• Brando, V. E., and A. G. Dekker. 2003. “Satellite Hyperspectral Remote Sensing for Estimating Estuarine and Coastal Water Quality.” IEEE Transactions on Geoscience and Remote Sensing 41: 1378–1387. doi:10.1109/TGRS.2003.812907.
   Web of Science ®Google Scholar
• Brezonik, P. L., L. G. Olmanson, J. C. Finlay, and M. E. Bauer. 2015. “Factors Affecting the Measurement of CDOM by Remote Sensing of Optically Complex Inland Waters.” Remote Sensing of Environment. doi:10.1016/j.rse.2014.04.033.
   PubMed Web of Science ®Google Scholar
• Brönmark, C., and L. A. Hansson. 2002. “Environmental Issues in Lakes and Ponds: Current State and Perspectives.” Environmental Conservation 29: 290–307. doi:10.1017/S0376892902000218.
   Web of Science ®Google Scholar
• Bugnot, A. B., M. B. Lyons, P. Scanes, G. F. Clark, S. K. Fyfe, A. Lewis, and E. L. Johnston. 2018. “A Novel Framework for the Use of Remote Sensing for Monitoring Catchments at Continental Scales.” Journal of Environmental Management 217: 939–950. Elsevier Ltd. doi:10.1016/j.jenvman.2018.03.058.
   PubMed Web of Science ®Google Scholar
• Cao, Z., R. Ma, H. Duan, N. Pahlevan, J. Melack, M. Shen, and K. Xue. 2020. “A Machine Learning Approach to Estimate Chlorophyll-A from Landsat-8 Measurements in Inland Lakes.” Remote Sensing of Environment 248: 111974. doi:10.1016/j.rse.2020.111974.
   Web of Science ®Google Scholar
• Carletti, R., M. Picci, and D. Romano. 2000. “Kriging and Bilinear Methods for Estimating Spatial Pattern of Atmospheric Pollutants.” Environmental Monitoring and Assessment 63 (2): 341–359. doi:10.1023/A:1006293110652.
   Web of Science ®Google Scholar
• Carlson, R. E. 1977. “A Trophic State Index for Lakes.” Limnology and Oceanography 22: 361–369. doi:10.4319/lo.1977.22.2.0361.
   Web of Science ®Google Scholar
• Chang, N. B., K. Bai, and C. F. Chen. 2017. “Integrating Multisensor Satellite Data Merging and Image Reconstruction in Support of Machine Learning for Better Water Quality Management.” Journal of Environmental Management 201: 227–240. doi:10.1016/j.jenvman.2017.06.045.
   PubMed Web of Science ®Google Scholar
• Chang, N. B., Z. Xuan, and Y. J. Yang. 2013. “Exploring Spatiotemporal Patterns of Phosphorus Concentrations in a Coastal Bay with MODIS Images and Machine Learning Models.” Remote Sensing of Environment 134: 100–110. doi:10.1016/j.rse.2013.03.002.
   Web of Science ®Google Scholar
• Chen, J., K. de Hoogh, J. Gulliver, B. Hoffmann, O. Hertel, M. Ketzel, M. Bauwelinck, et al. 2019. “A Comparison of Linear Regression, Regularization, and Machine Learning Algorithms to Develop Europe-Wide Spatial Models of Fine Particles and Nitrogen Dioxide.” Environment International 130: 104934. February. doi:10.1016/j.envint.2019.104934.
   Web of Science ®Google Scholar
• Chen, J., and W. Quan. 2012. “Using Landsat/TM Imagery to Estimate Nitrogen and Phosphorus Concentration in Taihu Lake, China.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5: 273–280. doi:10.1109/JSTARS.2011.2174339.
   Web of Science ®Google Scholar
• Deng, C., L. Zhang, and Y. Cen. 2019. “Retrieval of Chemical Oxygen Demand through Modified Capsule Network Based on Hyperspectral Data.” Applied Sciences (Switzerland). doi:10.3390/app9214620.
   Google Scholar
• Doña, C., N. B. Chang, J. M. Vicente Caselles, A. C. Sánchez, J. Delegido, and B. W. Vannah. 2015. “Integrated Satellite Data Fusion and Mining for Monitoring Lake Water Quality Status of the Albufera de Valencia in Spain.” Journal of Environmental Management 151: 416–426. doi:10.1016/j.jenvman.2014.12.003.
   PubMed Web of Science ®Google Scholar
• Duan, H., R. Ma, and C. Hu. 2012. “Evaluation of Remote Sensing Algorithms for Cyanobacterial Pigment Retrievals during Spring Bloom Formation in Several Lakes of East China.” Remote Sensing of Environment 126: 126–135. doi:10.1016/j.rse.2012.08.011.
   Web of Science ®Google Scholar
• Duan, H., R. Ma, S. A. Loiselle, Q. Shen, H. Yin, and Y. Zhang. 2014. “Optical Characterization of Black Water Blooms in Eutrophic Waters.” Science of the Total Environment 482–483: 174–183. doi:10.1016/j.scitotenv.2014.02.113.
   PubMed Web of Science ®Google Scholar
• Ferdous, J., M. Tauhid, and U. Rahman. 2020. “Developing an Empirical Model from Landsat Data Series for Monitoring Water Salinity in Coastal Bangladesh.” Journal of Environmental Management 255: 109861. November 2019. doi:10.1016/j.jenvman.2019.109861.
   PubMed Web of Science ®Google Scholar
• Gao, Y., J. Gao, H. Yin, C. Liu, T. Xia, J. Wang, and Q. Huang. 2015. “Remote Sensing Estimation of the Total Phosphorus Concentration in a Large Lake Using Band Combinations and Regional Multivariate Statistical Modeling Techniques.” Journal of Environmental Management 151: 33–43. doi:10.1016/j.jenvman.2014.11.036.
   PubMed Web of Science ®Google Scholar
• Gholizadeh, M. H., and A. M. Melesse. 2017. “Study on Spatiotemporal Variability of Water Quality Parameters in Florida Bay Using Remote Sensing.” Journal of Remote Sensing & GIS 6 (3). doi:10.4172/2469-4134.1000207.
   Google Scholar
• Gitelson, A. A., B. C. Gao, R. R. Li, S. Berdnikov, and V. Saprygin. 2011. “Estimation of Chlorophyll-a Concentration in Productive Turbid Waters Using a Hyperspectral Imager for the Coastal Ocean - The Azov Sea Case Study.” Environmental Research Letters. doi:10.1088/1748-9326/6/2/024023.
   Web of Science ®Google Scholar
• Halme, E., P. Pellikka, and M. Mõttus. 2019. “Utility of Hyperspectral Compared to Multispectral Remote Sensing Data in Estimating Forest Biomass and Structure Variables in Finnish Boreal Forest.” International Journal of Applied Earth Observation and Geoinformation 83: 101942. doi:10.1016/j.jag.2019.101942.
   PubMed Web of Science ®Google Scholar
• Hoekstra, A. Y., J. Buurman, and K. C. H. Van Ginkel. 2018. “Urban Water Security: A Review.” Environmental Research Letters 13: 053002. doi:10.1088/1748-9326/aaba52.
   Web of Science ®Google Scholar
• Holyer, R. J. 1978. “Toward Universal Multispectral Suspended Sediment Algorithms.” Remote Sensing of Environment 7: 323–338. doi:10.1016/0034-4257(78)90023-8.
   Web of Science ®Google Scholar
• Hou, X., L. Feng, H. Duan, X. Chen, D. Sun, and K. Shi. 2017. “Fifteen-Year Monitoring of the Turbidity Dynamics in Large Lakes and Reservoirs in the Middle and Lower Basin of the Yangtze River, China.” Remote Sensing of Environment 190: 107–121. doi:10.1016/j.rse.2016.12.006.
   Web of Science ®Google Scholar
• Keith, D., J. Rover, J. Green, B. Zalewsky, M. Charpentier, G. Thursby, and J. Bishop. 2018. “Monitoring Algal Blooms in Drinking Water Reservoirs Using the Landsat-8 Operational Land Imager.” International Journal of Remote Sensing 39 (9): 2818–2846. doi:10.1080/01431161.2018.1430912.
   PubMed Web of Science ®Google Scholar
• Kishino, M., A. Tanaka, and J. Ishizaka. 2005. “Retrieval of Chlorophyll A, Suspended Solids, and Colored Dissolved Organic Matter in Tokyo Bay Using ASTER Data.” Remote Sensing of Environment 99 (1–2): 66–74. doi:10.1016/j.rse.2005.05.016.
   Web of Science ®Google Scholar
• Kurt, A., B. Gulbagci, F. Karaca, and O. Alagha. 2008. “An Online Air Pollution Forecasting System Using Neural Networks.” Environment International 34 (5): 592–598. doi:10.1016/j.envint.2007.12.020.
   PubMed Web of Science ®Google Scholar
• Le, C., Y. Li, Y. Zha, D. Sun, C. Huang, and H. Zhang. 2011. “Remote Estimation of Chlorophyll a in Optically Complex Waters Based on Optical Classification.” Remote Sensing of Environment 115 (2): 725–737. doi:10.1016/j.rse.2010.10.014.
   PubMed Web of Science ®Google Scholar
• Li, J., C. Hu, Q. Shen, B. B. Barnes, B. Murch, L. Feng, M. Zhang, and B. Zhang. 2017a. “Recovering Low Quality MODIS-Terra Data over Highly Turbid Waters through Noise Reduction and Regional Vicarious Calibration Adjustment: A Case Study in Taihu Lake.” Remote Sensing of Environment. doi:10.1016/j.rse.2017.05.027.
   Web of Science ®Google Scholar
• Li, Y., Y. Zhang, K. Shi, G. Zhu, Y. Zhou, Y. Zhang, and Y. Guo. 2017b. “Monitoring Spatiotemporal Variations in Nutrients in a Large Drinking Water Reservoir and Their Relationships with Hydrological and Meteorological Conditions Based on Landsat 8 Imagery.” Science of the Total Environment. doi:10.1016/j.scitotenv.2017.05.075.
   Web of Science ®Google Scholar
• Lunetta, R. S., J. F. Knight, H. W. Paerl, J. J. Streicher, B. L. Peierls, T. Gallo, J. G. Lyon, T. H. Mace, and C. P. Buzzelli. 2009. “Measurement of Water Colour Using AVIRIS Imagery to Assess the Potential for an Operational Monitoring Capability in the Pamlico Sound Estuary, USA.” International Journal of Remote Sensing. doi:10.1080/01431160802552801.
   PubMed Web of Science ®Google Scholar
• Ma, R., D. Pan, H. Duan, and Q. Song. 2009. “Absorption and Scattering Properties of Water Body in Taihu Lake, China: Backscattering.” International Journal of Remote Sensing 30 (9): 2321–2335. doi:10.1080/01431160802549385.
   PubMed Web of Science ®Google Scholar
• Ma, R., J. Tang, J. Dai, Y. Zhang, and Q. Song. 2006. “Absorption and Scattering Properties of Water Body in Taihu Lake, China: Absorption.” International Journal of Remote Sensing 27 (19): 4277–4304. doi:10.1080/01431160600851835.
   PubMed Web of Science ®Google Scholar
• Mannino, A., M. G. Novak, S. B. Hooker, K. Hyde, and D. Aurin. 2014. “Algorithm Development and Validation of CDOM Properties for Estuarine and Continental Shelf Waters along the Northeastern U.S. Coast.” Remote Sensing of Environment 152: 576–602. doi:10.1016/j.rse.2014.06.027.
   Web of Science ®Google Scholar
• Mathew, M. M., N. Srinivasa Rao, and V. R. Mandla. 2017. “Development of Regression Equation to Study the Total Nitrogen, Total Phosphorus and Suspended Sediment Using Remote Sensing Data in Gujarat and Maharashtra Coast of India.” Journal of Coastal Conservation 21: 917–927. doi:10.1007/s11852-017-0561-1.
   Web of Science ®Google Scholar
• Maxwell, A. E., T. A. Warner, and F. Fang. 2018. “Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review.” International Journal of Remote Sensing 39: 2784–2817. doi:10.1080/01431161.2018.1433343.
   Web of Science ®Google Scholar
• McFeeters, S. K. 1996. “The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features.” International Journal of Remote Sensing 17 (7): 1425–1432. doi:10.1080/01431169608948714.
   Web of Science ®Google Scholar
• MEE (Ministry of ecology and environment of the people’s Republic of China). 2002. Environmental Quality Standard for Surface Water (GB3838-2002). Beijing: China Environmental Science Press.
   Google Scholar
• Moses, W. J., A. A. Gitelson, R. L. Perk, D. Gurlin, D. C. Rundquist, B. C. Leavitt, T. M. Barrow, and P. Brakhage. 2012. “Estimation of Chlorophyll-a Concentration in Turbid Productive Waters Using Airborne Hyperspectral Data.” Water Research 46: 993–1004. doi:10.1016/j.watres.2011.11.068.
   PubMed Web of Science ®Google Scholar
• Moses, W. J., A. A. Gitelson, S. Berdnikov, and V. Povazhnyy. 2009. “Satellite Estimation of Chlorophyll-a Concentration Using the Red and NIR Bands of MERISThe Azov Sea Case Study.” IEEE Geoscience and Remote Sensing Letters. doi:10.1109/LGRS.2009.2026657.
   Web of Science ®Google Scholar
• Mountrakis, G., J. Im, and C. Ogole. 2011. “Support Vector Machines in Remote Sensing: A Review.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (3): 247–259. doi:10.1016/j.isprsjprs.2010.11.001.
   PubMed Web of Science ®Google Scholar
• O’Reilly, J. E., S. Maritorena, B. G. Mitchell, D. A. Siegel, K. L. Carder, S. A. Garver, M. Kahru, and C. McClain. 1998. “Ocean Color Chlorophyll Algorithms for SeaWiFS.” Journal of Geophysical Research: Oceans 103: 24937–24953. doi:10.1029/98JC02160.
   Web of Science ®Google Scholar
• Olmanson, L. G., P. L. Brezonik, and M. E. Bauer. 2013. “Airborne Hyperspectral Remote Sensing to Assess Spatial Distribution of Water Quality Characteristics in Large Rivers: The Mississippi River and Its Tributaries in Minnesota.” Remote Sensing of Environment 130: 254–265. doi:10.1016/j.rse.2012.11.023.
   Web of Science ®Google Scholar
• Pahlevan, N., B. Smith, J. Schalles, C. Binding, Z. Cao, R. Ma, K. Alikas, et al. 2020. “Seamless Retrievals of Chlorophyll-A from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in Inland and Coastal Waters: A Machine-Learning Approach.” Remote Sensing of Environment 240:111604. doi:10.1016/j.rse.2019.111604.
   Web of Science ®Google Scholar
• Rapinel, S., C. Mony, L. Lecoq, B. Clément, A. Thomas, and L. Hubert-Moy. 2019. “Evaluation of Sentinel-2 Time-Series for Mapping Floodplain Grassland Plant Communities.” Remote Sensing of Environment 223: 115–129. January. Elsevier. doi:10.1016/j.rse.2019.01.018.
   Web of Science ®Google Scholar
• Ritchie, J. C., F. R. Schiebe, and J. R. McHenry. 1976. “Remote Sensing of Suspended Sediments in Surface Waters.” Photogrammetric Engineering & Remote Sensing. 42 (12): 1539–1545.
   Web of Science ®Google Scholar
• Ritchie, J. C., P. V. Zimba, and J. H. Everitt. 2003. “Remote Sensing Techniques to Assess Water Quality.” Photogrammetric Engineering and Remote Sensing 69: 695–704. doi:10.14358/PERS.69.6.695.
   Web of Science ®Google Scholar
• Shahzad, M. I., M. Meraj, M. Nazeer, I. Zia, A. Inam, K. Mehmood, and H. Zafar. 2018. “Empirical Estimation of Suspended Solids Concentration in the Indus Delta Region Using Landsat-7 ETM+ Imagery.” Journal of Environmental Management 209: 254–261. doi:10.1016/j.jenvman.2017.12.070.
   PubMed Web of Science ®Google Scholar
• Shao, M., X. Tang, Y. Zhang, and W. Li. 2006. “City Clusters in China: Air and Surface Water Pollution.” Frontiers in Ecology and the Environment 4 (7): 353–361.
   PubMed Web of Science ®Google Scholar
• Shenglei, W., L. Junsheng, Z. Bing, S. Qian, Z. Fangfang, and L. Zhaoyi. 2016. “A Simple Correction Method for the MODIS Surface Reflectance Product over Typical Inland Waters in China.” International Journal of Remote Sensing 37 (24): 6076–6096. doi:10.1080/01431161.2016.1256508.
   Web of Science ®Google Scholar
• Shi, K., Y. Zhang, G. Zhu, X. Liu, Y. Zhou, H. Xu, B. Qin, G. Liu, and Y. Li. 2015. “Long-Term Remote Monitoring of Total Suspended Matter Concentration in Lake Taihu Using 250 M MODIS-Aqua Data.” Remote Sensing of Environment 164: 43–56. doi:10.1016/j.rse.2015.02.029.
   Web of Science ®Google Scholar
• Shuchman, R. A., M. Sayers, G. L. Fahnenstiel, and G. Leshkevich. 2013. “A Model for Determining Satellite-Derived Primary Productivity Estimates for Lake Michigan.” Journal of Great Lakes Research 39: 46–54. doi:10.1016/j.jglr.2013.05.001.
   Web of Science ®Google Scholar
• Tan, J., K. A. Cherkauer, and I. Chaubey. 2016. “Developing A Comprehensive Spectral-Biogeochemical Database of Midwestern Rivers for Water Quality Retrieval Using Remote Sensing Data: A Case Study of the Wabash River and Its Tributary, Indiana.” Remote Sensing 8 (6): 517. doi:10.3390/rs8060517.
   Web of Science ®Google Scholar
• Vapnik, V. N. 1995. “Adaptive and Learning Systems for Signal Processing, Communications and Control.” In The Nature of Statistical Learning Theory, edited by Michael Jordan, 138–167. New York: Springer-Verlag. doi:10.2307/1271368.
   Google Scholar
• Vignolo, A., A. Pochettino, and D. Cicerone. 2006. “Water Quality Assessment Using Remote Sensing Techniques: Medrano Creek, Argentina.” Journal of Environmental Management 81 (4): 429–433. doi:10.1016/j.jenvman.2005.11.019.
   PubMed Web of Science ®Google Scholar
• Wang, J., M. Hu, B. Gao, H. Fan, and J. Wang. 2019. “A Spatiotemporal Interpolation Method for the Assessment of Pollutant Concentrations in the Yangtze River Estuary and Adjacent Areas from 2004 to 2013.” Environmental Pollution. doi:10.1016/j.envpol.2019.05.132.
   Web of Science ®Google Scholar
• Wang, S., J. Li, B. Zhang, E. Spyrakos, A. N. Tyler, Q. Shen, F. Zhang, et al. 2018. “Trophic State Assessment of Global Inland Waters Using a MODIS-Derived Forel-Ule Index.” Remote Sensing of Environment. doi:10.1016/j.rse.2018.08.026.
   Web of Science ®Google Scholar
• Wang, S., J. Li, B. Zhang, Z. Lee, E. Spyrakos, L. Feng, C. Liu, et al. 2020. “Changes of Water Clarity in Large Lakes and Reservoirs across China Observed from Long-Term MODIS.” Remote Sensing of Environment. doi:10.1016/j.rse.2020.111949.
   Web of Science ®Google Scholar
• Wang, Y., H. Xia, J. Fu, and G. Sheng. 2004. “Water Quality Change in Reservoirs of Shenzhen, China: Detection Using LANDSAT/TM Data.” Science of the Total Environment. doi:10.1016/j.scitotenv.2004.02.020.
   Web of Science ®Google Scholar
• Wu, C., J. Wu, Q. Jiaguo, L. Zhang, H. Huang, L. Lou, and Y. Chen. 2010. “Empirical Estimation of Total Phosphorus Concentration in the Mainstream of the Qiantang River in China Using Landsat TM Data.” International Journal of Remote Sensing 31: 2309–2324. doi:10.1080/01431160902973873.
   Web of Science ®Google Scholar
• Wu, M., W. Zhang, X. Wang, and D. Luo. 2009. “Application of MODIS Satellite Data in Monitoring Water Quality Parameters of Chaohu Lake in China.” Environmental Monitoring and Assessment 148 (1–4): 255–264. doi:10.1007/s10661-008-0156-2.
   PubMed Web of Science ®Google Scholar
• Xiong, Y., Y. Ran, S. Zhao, H. Zhao, and Q. Tian. 2020. “Remotely Assessing and Monitoring Coastal and Inland Water Quality in China: Progress, Challenges and Outlook.” Critical Reviews in Environmental Science and Technology 50 (12): 1266–1302. doi:10.1080/10643389.2019.1656511.
   Web of Science ®Google Scholar