{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:29:35Z","timestamp":1774916975247,"version":"3.50.1"},"reference-count":77,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T00:00:00Z","timestamp":1706486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key R&amp;D Program of Zhejiang","award":["2022C03078"],"award-info":[{"award-number":["2022C03078"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Water parameter estimation based on remote sensing is one of the common water quality evaluation methods. However, it is difficult to describe the relationship between the reflectance and the concentration of non-optically active substances due to their weak optical characteristics, and machine learning has become a viable solution for this problem. Therefore, based on machine learning methods, this study estimated four non-optically active water quality parameters including the permanganate index (CODMn), dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). Specifically, four machine learning models including Support Vector Machine Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) were constructed for each parameter and their performances were assessed. The results showed that the optimal models of CODMn, DO, TN, and TP were RF (R2 = 0.52), SVR (R2 = 0.36), XGBoost (R2 = 0.45), and RF (R2 = 0.39), respectively. The seasonal 10 m water quality over the Zhejiang Province was measured using these optimal models based on Sentinel-2 images, and the spatiotemporal distribution was analyzed. The results indicated that the annual mean values of CODMn, DO, TN, and TP in 2022 were 2.3 mg\/L, 6.6 mg\/L, 1.85 mg\/L, and 0.063 mg\/L, respectively, and the water quality in the western Zhejiang region was better than that in the northeastern Zhejiang region. The seasonal variations in water quality and possible causes were further discussed with some regions as examples. It was found that DO would decrease and CODMn would increase in summer due to the higher temperature and other factors. The results of this study helped understand the water quality in Zhejiang Province and can also be applied to the integrated management of the water environment. The models constructed in this study can also provide references for related research.<\/jats:p>","DOI":"10.3390\/rs16030514","type":"journal-article","created":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T05:14:32Z","timestamp":1706591672000},"page":"514","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Estimation of Non-Optically Active Water Quality Parameters in Zhejiang Province Based on Machine Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Lingfang","family":"Gao","sequence":"first","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Yulin","family":"Shangguan","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Zhong","family":"Sun","sequence":"additional","affiliation":[{"name":"Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China"}]},{"given":"Qiaohui","family":"Shen","sequence":"additional","affiliation":[{"name":"Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3914-5402","authenticated-orcid":false,"given":"Zhou","family":"Shi","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"133875","DOI":"10.1016\/j.chemosphere.2022.133875","article-title":"Twenty years of China\u2019s water pollution control: Experiences and challenges","volume":"295","author":"Tang","year":"2022","journal-title":"Chemosphere"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"154146","DOI":"10.1016\/j.scitotenv.2022.154146","article-title":"A review of non-point source water pollution modeling for the urban\u2013rural transitional areas of China: Research status and prospect","volume":"826","author":"Xue","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"124826","DOI":"10.1016\/j.jhydrol.2020.124826","article-title":"A review of remote sensing applications for water security: Quantity, quality, and extremes","volume":"585","author":"Chawla","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"163389","DOI":"10.1016\/j.scitotenv.2023.163389","article-title":"An advanced remote sensing retrieval method for urban non-optically active water quality parameters: An example from Shanghai","volume":"880","author":"Li","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103187","DOI":"10.1016\/j.earscirev.2020.103187","article-title":"Monitoring inland water quality using remote sensing: Potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing","volume":"205","author":"Sagan","year":"2020","journal-title":"Earth-Sci. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1038\/250213a0","article-title":"Remote sensing and lake eutrophication","volume":"250","author":"Wrigley","year":"1974","journal-title":"Nature"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"113104","DOI":"10.1016\/j.marpolbul.2021.113104","article-title":"Interference of CDOM in remote sensing of suspended particulate matter (SPM) based on MODIS in the Persian Gulf and Oman Sea","volume":"173","author":"Mohammadpour","year":"2021","journal-title":"Mar. Pollut. Bull."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"113724","DOI":"10.1016\/j.rse.2023.113724","article-title":"MODIS observations reveal decrease in lake suspended particulate matter across China over the past two decades","volume":"295","author":"Cao","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.rse.2018.09.014","article-title":"Spatio-temporal variations of CDOM in shallow inland waters from a semi-analytical inversion of Landsat-8","volume":"218","author":"Li","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"164862","DOI":"10.1016\/j.scitotenv.2023.164862","article-title":"Monitoring and spatial traceability of river water quality using Sentinel-2 satellite images","volume":"894","author":"Zhang","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yang, H., Du, Y., Zhao, H., and Chen, F. (2022). Water quality Chl-a inversion based on spatio-temporal fusion and convolutional neural network. Remote Sens., 14.","DOI":"10.3390\/rs14051267"},{"key":"ref_12","unstructured":"Greb, S., Dekker, A., and Binding, C. (2018). Earth Observations in Support of Global Water Quality Monitoring, International Ocean Colour Coordinating Group. Available online: https:\/\/ioccg.org\/what-we-do\/ioccg-publications\/ioccg-reports\/."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5755","DOI":"10.1364\/AO.41.005755","article-title":"Deriving inherent optical properties from water color: A multiband quasi-analytical algorithm for optically deep waters","volume":"41","author":"Lee","year":"2002","journal-title":"Appl. Opt."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6855","DOI":"10.1080\/01431161.2010.512947","article-title":"A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters","volume":"32","author":"Matthews","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111648","DOI":"10.1016\/j.rse.2020.111648","article-title":"An OLCI-based algorithm for semi-empirically partitioning absorption coefficient and estimating chlorophyll a concentration in various turbid case-2 waters","volume":"239","author":"Liu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Topp, S.N., Pavelsky, T.M., Jensen, D., Simard, M., and Ross, M.R. (2020). Research trends in the use of remote sensing for inland water quality science: Moving towards multidisciplinary applications. Water, 12.","DOI":"10.3390\/w12010169"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2309","DOI":"10.1080\/01431160902973873","article-title":"Empirical estimation of total phosphorus concentration in the mainstream of the Qiantang River in China using Landsat TM data","volume":"31","author":"Wu","year":"2010","journal-title":"Int. J Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.jenvman.2014.11.036","article-title":"Remote sensing estimation of the total phosphorus concentration in a large lake using band combinations and regional multivariate statistical modeling techniques","volume":"151","author":"Gao","year":"2015","journal-title":"J. Environ. Manag."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhu, X., Wen, Y., Li, X., Yan, F., and Zhao, S. (2023). Remote Sensing Inversion of Typical Water Quality Parameters of a Complex River Network: A Case Study of Qidong\u2019s Rivers. Sustainability, 15.","DOI":"10.3390\/su15086948"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Guo, Y., Yin, G., Zhang, X., Shi, Y., Hao, F., and Fu, Y. (2022). UAV multispectral image-based urban river water quality monitoring using stacked ensemble machine learning algorithms\u2014A case study of the Zhanghe river, China. Remote Sens., 14.","DOI":"10.3390\/rs14143272"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Padilla-Mendoza, C., Torres-Bejarano, F., Campo-Daza, G., and Gonz\u00e1lez-M\u00e1rquez, L.C. (2023). Potential of Sentinel Images to Evaluate Physicochemical Parameters Concentrations in Water Bodies\u2014Application in a Wetlands System in Northern Colombia. Water, 15.","DOI":"10.3390\/w15040789"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3205","DOI":"10.1007\/s13762-022-04029-7","article-title":"Evaluation of surface water quality of Ukkadam lake in Coimbatore using UAV and Sentinel-2 multispectral data","volume":"20","author":"Rahul","year":"2023","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.2166\/wpt.2022.061","article-title":"Water quality monitoring using remote sensing, Lower Manyame Sub-catchment, Zimbabwe","volume":"17","author":"Muhoyi","year":"2022","journal-title":"Water Pract. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1080\/15481603.2022.2116078","article-title":"Developing remote sensing methods for monitoring water quality of alpine rivers on the Tibetan Plateau","volume":"59","author":"Wang","year":"2022","journal-title":"GIScience Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"269","DOI":"10.14358\/PERS.85.4.269","article-title":"Machine learning based ensemble prediction of water quality variables using featurelevel 1 and decision-level fusion with proximal remote sensing","volume":"85","author":"Peterson","year":"2019","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Peterson, K.T., Sagan, V., Sidike, P., Cox, A.L., and Martinez, M. (2018). Suspended sediment concentration estimation from landsat imagery along the lower missouri and middle Mississippi Rivers using an extreme learning machine. Remote Sens., 10.","DOI":"10.3390\/rs10101503"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.eehl.2022.06.001","article-title":"A review of the application of machine learning in water quality evaluation","volume":"1","author":"Zhu","year":"2022","journal-title":"Eco-Environ. Health"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Deng, C., Zhang, L., and Cen, Y. (2019). Retrieval of chemical oxygen demand through modified capsule network based on hyperspectral data. Appl. Sci., 9.","DOI":"10.3390\/app9214620"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, Y., Gong, Z., Zheng, Y., and 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.","DOI":"10.3390\/w13202844"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"149805","DOI":"10.1016\/j.scitotenv.2021.149805","article-title":"Monitoring water quality using proximal remote sensing technology","volume":"803","author":"Sun","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_31","first-page":"100865","article-title":"Dissolved oxygen estimation in aquaculture sites using remote sensing and machine learning","volume":"28","author":"Chatziantoniou","year":"2022","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ding, L., Qi, C., Li, G., and Zhang, W. (2023). TP Concentration Inversion and Pollution Sources in Nanyi Lake Based on Landsat 8 Data and InVEST Model. Sustainability, 15.","DOI":"10.3390\/su15129678"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tan, Z., Ren, J., Li, S., Li, W., Zhang, R., and Sun, T. (2023). Inversion of Nutrient Concentrations Using Machine Learning and Influencing Factors in Minjiang River. Water, 15.","DOI":"10.3390\/w15071398"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"155993","DOI":"10.1016\/j.scitotenv.2022.155993","article-title":"Simulated net ecosystem productivity of subtropical forests and its response to climate change in Zhejiang Province, China","volume":"838","author":"Mao","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"9132","DOI":"10.1109\/JSTARS.2021.3109292","article-title":"Remote sensing of turbidity for lakes in Northeast China using sentinel-2 images with machine learning algorithms","volume":"14","author":"Ma","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.1080\/01431161.2020.1846222","article-title":"A machine learning-based strategy for estimating non-optically active water quality parameters using Sentinel-2 imagery","volume":"42","author":"Guo","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2101","DOI":"10.1007\/s10653-019-00466-5","article-title":"Integration of remote sensing data and in situ measurements to monitor the water quality of the Ismailia Canal, Nile Delta, Egypt","volume":"42","author":"Fathi","year":"2020","journal-title":"Environ. Geochem. Health"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yang, Z., Gong, C., Ji, T., Hu, Y., and Li, L. (2022). Water quality retrieval from ZY1-02D hyperspectral imagery in urban water bodies and comparison with sentinel-2. Remote Sens., 14.","DOI":"10.3390\/rs14195029"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"101783","DOI":"10.1016\/j.ecoinf.2022.101783","article-title":"Estimation algorithm for chlorophyll-a concentrations in water from hyperspectral images based on feature derivation and ensemble learning","volume":"71","author":"Zhang","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_40","first-page":"29","article-title":"Estimation of total phosphorus concentration using a water classification method in inland water","volume":"71","author":"Du","year":"2018","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Hafeez, S., Wong, M.S., Ho, H.C., Nazeer, M., Nichol, J., Abbas, S., Tang, D., Lee, K.H., and 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 Sens., 11.","DOI":"10.3390\/rs11060617"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Valera, M., Walter, R.K., Bailey, B.A., and Castillo, J.E. (2020). Machine learning based predictions of dissolved oxygen in a small coastal embayment. J. Marine Sci. Eng., 8.","DOI":"10.3390\/jmse8121007"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogram."},{"key":"ref_44","first-page":"399","article-title":"High density biomass estimation for wetland vegetation using world view-2 imagery and random forest regression algorithm","volume":"18","author":"Mutanga","year":"2012","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1016\/j.ecolind.2018.08.041","article-title":"Machine learning predictions of trophic status indicators and plankton dynamic in coastal lagoons","volume":"95","author":"Ottaviani","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"9412","DOI":"10.1080\/01431161.2019.1633696","article-title":"Classification of algal bloom species from remote sensing data using an extreme gradient boosted decision tree model","volume":"40","author":"Ghatkar","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.foreco.2011.06.039","article-title":"Estimating forest attribute parameters for small areas using nearest neighbors techniques","volume":"272","author":"McRoberts","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3008","DOI":"10.1016\/j.ibiod.2015.02.013","article-title":"Method to predict key factors affecting lake eutrophication\u2014A new approach based on support vector regression model","volume":"102","author":"Xu","year":"2015","journal-title":"Int. Biodeterior. Biodegrad."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1970","DOI":"10.2175\/106143015X14362865226716","article-title":"Determination of trophic state changes with Diel dissolved oxygen: A case study in a shallow lake","volume":"87","author":"Xu","year":"2015","journal-title":"Water Environ. Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"874","DOI":"10.18307\/2023.0330","article-title":"Spatio-temporal dynamics of dissolved oxygen and its influencing factors in Lake Xiannv Jiangxi, China","volume":"35","author":"Xia","year":"2023","journal-title":"J. Lake Sci."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Dong, L., Gong, C., Huai, H., Wu, E., Lu, Z., Hu, Y., Li, L., and Yang, Z. (2023). Retrieval of water quality parameters in Dianshan Lake based on Sentinel-2 MSI imagery and machine learning: Algorithm evaluation and spatiotemporal change research. Remote Sens., 15.","DOI":"10.3390\/rs15205001"},{"key":"ref_53","first-page":"445","article-title":"Distribution and its influence factors of dissolved oxygen in Tianmuhu Lake","volume":"19","author":"Zeng","year":"2010","journal-title":"Resour. Environ. Yangtze Basin"},{"key":"ref_54","first-page":"5381","article-title":"Seasonal variations in nitrogen and phosphorus concentration and stoichiometry of Hanfeng Lake in the Three Gorges Reservoir Area","volume":"41","author":"Qian","year":"2020","journal-title":"Environ. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"109675","DOI":"10.1016\/j.ecolind.2022.109675","article-title":"Evaluating optically and non-optically active water quality and its response relationship to hydro-meteorology using multi-source data in Poyang Lake, China","volume":"145","author":"Fu","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"162599","DOI":"10.1016\/j.scitotenv.2023.162599","article-title":"The air temperature change effect on water quality in the Kvarken Archipelago area","volume":"874","author":"Girgibo","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1016\/j.jhydrol.2018.12.033","article-title":"Spatio-temporal analysis of urban changes and surface water quality","volume":"569","author":"Carstens","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1007\/s13201-019-1043-4","article-title":"Local determinants influencing stream water quality","volume":"10","author":"Hamid","year":"2020","journal-title":"Appl. Water Sci."},{"key":"ref_59","first-page":"94","article-title":"Research progress on release mechanisms of nitrogen and phosphorus of sediments in water bodies and their influencing factors","volume":"20","author":"Li","year":"2022","journal-title":"Wetland Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"577","DOI":"10.4319\/lo.1992.37.3.0577","article-title":"Importance of temperature, nitrate, and pH for phosphate release from aerobic sediments of four shallow, eutrophic lakes","volume":"37","author":"Jensen","year":"1992","journal-title":"Limnol. Oceanogr."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/S1001-0742(08)62071-9","article-title":"Effects of bacteria on nitrogen and phosphorus release from river sediment","volume":"20","author":"Wu","year":"2008","journal-title":"J. Environ. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/s12205-014-0192-0","article-title":"Phosphorus release from lake sediments: Effects of pH, temperature and dissolved oxygen","volume":"18","author":"Wu","year":"2014","journal-title":"KSCE J. Civ. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.18307\/2019.0514","article-title":"Advances and prospect in sediment-water interface of lakes: A review","volume":"31","author":"Fan","year":"2019","journal-title":"J. Lake Sci."},{"key":"ref_64","first-page":"1","article-title":"Effects of sediment physical properties on the phosphorus release in aquatic environment","volume":"58","author":"Zhu","year":"2015","journal-title":"Sci. China Phys. Mech. Astron."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.18307\/2017.0508","article-title":"Coupling between iron and phosphorus in sediments of shallow lakes in the middle and lower reaches of Yangtze River using diffusive gradients in thin films (DGT)","volume":"29","author":"Gong","year":"2017","journal-title":"J. Lake Sci."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.5194\/bg-12-1765-2015","article-title":"Organic N and P in eutrophic fjord sediments\u2013rates of mineralization and consequences for internal nutrient loading","volume":"12","author":"Valdemarsen","year":"2015","journal-title":"Biogeosciences"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1007\/s42452-021-04823-x","article-title":"Lake water quality observed after extreme rainfall events: Implications for water quality affected by stormy runoff","volume":"3","author":"Fukushima","year":"2021","journal-title":"SN Appl. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.scitotenv.2015.03.062","article-title":"Effects of rainfall patterns on water quality in a stratified reservoir subject to eutrophication: Implications for management","volume":"521","author":"Li","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"113514","DOI":"10.1016\/j.jenvman.2021.113514","article-title":"Water quality responses to rainfall and surrounding land uses in urban lakes","volume":"298","author":"Jia","year":"2021","journal-title":"J. Environ. Manag."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1007\/s11368-022-03392-9","article-title":"Spatiotemporal distribution of phosphorus fractions and the potential release risks in sediments in a Yangtze River connected lake: New insights into the influence of water-level fluctuation","volume":"23","author":"Ma","year":"2023","journal-title":"J. Soils Sediments"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"161902","DOI":"10.1016\/j.scitotenv.2023.161902","article-title":"Linking downstream river water quality to urbanization signatures in subtropical climate","volume":"870","author":"Pang","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"134242","DOI":"10.1016\/j.scitotenv.2019.134242","article-title":"Response of phosphorus fractionation in lake sediments to anthropogenic activities in China","volume":"699","author":"Ni","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1002\/wer.1240","article-title":"Landsat 8-observed water quality and its coupled environmental factors for urban scenery lakes: A case study of West Lake","volume":"92","author":"Zhu","year":"2020","journal-title":"Water Environ. Res."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.1515\/geo-2020-0119","article-title":"Impact of tourism activities on water pollution in the West Lake Basin (Hangzhou, China)","volume":"12","author":"Sun","year":"2020","journal-title":"Open Geosci."},{"key":"ref_75","first-page":"012018","article-title":"Optimization and Effect of Inner Water Diversion and Distribution in the West Lake of Hangzhou","volume":"Volume 264","author":"You","year":"2019","journal-title":"IOP Conference Series: Earth and Environmental Science"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Wang, X., Jiang, Y., Jiang, M., Cao, Z., Li, X., Ma, R., Xu, L., and Xiong, J. (2023). Estimation of total phosphorus concentration in lakes in the Yangtze-Huaihe region based on Sentinel-3\/OLCI images. Remote Sens., 15.","DOI":"10.3390\/rs15184487"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"111974","DOI":"10.1016\/j.rse.2020.111974","article-title":"A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes","volume":"248","author":"Cao","year":"2020","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/3\/514\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:51:07Z","timestamp":1760104267000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/3\/514"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,29]]},"references-count":77,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["rs16030514"],"URL":"https:\/\/doi.org\/10.3390\/rs16030514","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,29]]}}}