{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T12:47:09Z","timestamp":1786538829433,"version":"3.56.0"},"reference-count":278,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,13]],"date-time":"2023-07-13T00:00:00Z","timestamp":1689206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study provides a comprehensive review of the efforts utilized in the measurement of water quality parameters (WQPs) with a focus on total dissolved solids (TDS) and total suspended solids (TSS). The current method used in the measurement of TDS and TSS includes conventional field and gravimetric approaches. These methods are limited due to the associated cost and labor, and limited spatial coverages. Remote Sensing (RS) applications have, however, been used over the past few decades as an alternative to overcome these limitations. Although they also present underlying atmospheric interferences in images, radiometric and spectral resolution issues. Studies of these WQPs with RS, therefore, require the knowledge and utilization of the best mechanisms. The use of RS for retrieval of TDS, TSS, and their forms has been explored in many studies using images from airborne sensors onboard unmanned aerial vehicles (UAVs) and satellite sensors such as those onboard the Landsat, Sentinel-2, Aqua, and Terra platforms. The images and their spectral properties serve as inputs for deep learning analysis and statistical, and machine learning models. Methods used to retrieve these WQP measurements are dependent on the optical properties of the inland water bodies. While TSS is an optically active parameter, TDS is optically inactive with a low signal\u2013noise ratio. The detection of TDS in the visible, near-infrared, and infrared bands is due to some process that (usually) co-occurs with changes in the TDS that is affecting a WQP that is optically active. This study revealed significant improvements in incorporating RS and conventional approaches in estimating WQPs. The findings reveal that improved spatiotemporal resolution has the potential to effectively detect changes in the WQPs. For effective monitoring of TDS and TSS using RS, we recommend employing atmospheric correction mechanisms to reduce image atmospheric interference, exploration of the fusion of optical and microwave bands, high-resolution hyperspectral images, utilization of ML and deep learning models, calibration and validation using observed data measured from conventional methods. Further studies could focus on the development of new technology and sensors using UAVs and satellite images to produce real-time in situ monitoring of TDS and TSS. The findings presented in this review aid in consolidating understanding and advancement of TDS and TSS measurements in a single repository thereby offering stakeholders, researchers, decision-makers, and regulatory bodies a go-to information resource to enhance their monitoring efforts and mitigation of water quality impairments.<\/jats:p>","DOI":"10.3390\/rs15143534","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:39:41Z","timestamp":1689295181000},"page":"3534","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":269,"title":["Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3169-6410","authenticated-orcid":false,"given":"Godson Ebenezer","family":"Adjovu","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas, NV 89154, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3116-1416","authenticated-orcid":false,"given":"Haroon","family":"Stephen","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas, NV 89154, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"James","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas, NV 89154, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9903-9321","authenticated-orcid":false,"given":"Sajjad","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas, NV 89154, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/s10661-018-6621-7","article-title":"Impact of City Effluents on Water Quality of Indus River: Assessment of Temporal and Spatial Variations in the Southern Region of Khyber Pakhtunkhwa, Pakistan","volume":"190","author":"Khan","year":"2018","journal-title":"Environ. Monit. Assess."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Adjovu, G.E., Stephen, H., James, D., and Ahmad, S. (2023). Overview of the Application of Remote Sensing in Effective Monitoring of Water Quality Parameters. Remote Sens., 15.","DOI":"10.3390\/rs15071938"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"461","DOI":"10.3390\/earth4030025","article-title":"Spatial and Temporal Dynamics of Key Water Quality Parameters in a Thermal Stratified Lake Ecosystem: The Case Study of Lake Mead","volume":"4","author":"Adjovu","year":"2023","journal-title":"Earth"},{"key":"ref_4","unstructured":"Baird, R.B., Eaton, A.D., and Rice, E.W. (2017). Standard Methods for the Examination of Water and Wastewater, Water Environment Federation."},{"key":"ref_5","unstructured":"Standard Methods Committee, Wilder, B.H., Costa, H.S., Kosmowski, C.M., and Purcell, W.E. (2023, July 02). Methods 2540. Available online: http:\/\/edgeanalytical.com\/wp-content\/uploads\/Waste_SM2540.pdf."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ogashawara, I., Mishra, D.R., and Gitelson, A.A. (2017). Remote Sensing of Inland Waters: Background and Current State-of-the-Art, Elsevier Inc.","DOI":"10.1016\/B978-0-12-804644-9.00001-X"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"41612","DOI":"10.1007\/s11356-021-14726-4","article-title":"A Critical and Intensive Review on Assessment of Water Quality Parameters through Geospatial Techniques","volume":"28","author":"Dey","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.ecolind.2015.12.009","article-title":"Remote Sensing for Lake Research and Monitoring\u2014Recent Advances","volume":"64","author":"Oppelt","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"56887","DOI":"10.1007\/s11356-022-21348-x","article-title":"What Is the Relationship between Land Use and Surface Water Quality? A Review and Prospects from Remote Sensing Perspective","volume":"29","author":"Cheng","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2989\/16085914.2015.1014994","article-title":"Water Quality Monitoring in Sub-Saharan African Lakes: A Review of Remote Sensing Applications","volume":"40","author":"Dube","year":"2015","journal-title":"Afr. J. Aquat. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Giao, N.T., Van Cong, N., and Nhien, H.T.H. (2021). Using Remote Sensing and Multivariate Statistics in Analyzing the Relationship between Land Use Pattern and Water Quality in Tien Giang Province, Vietnam. Water, 13.","DOI":"10.3390\/w13081093"},{"key":"ref_12","first-page":"31","article-title":"Evaluating the Potential of Sentinel-2 Satellite Images for Water Quality Characterization of Artificial Reservoirs: The Bin El Ouidane Reservoir Case Study (Morocco)","volume":"7","author":"Karaoui","year":"2019","journal-title":"Karaoui Ismail Arioua Abdelkrim Abdelghani Boudhar Hssaisoune Mohammed Sabri"},{"key":"ref_13","first-page":"353","article-title":"Water-Body Area Extraction From High Resolution Satellite Images-An Introduction, Review, and Comparison","volume":"3","author":"Nath","year":"2010","journal-title":"Int. J. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2014.09.021","article-title":"Remote Sensing of Inland Waters: Challenges, Progress and Future Directions","volume":"157","author":"Palmer","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"053506","DOI":"10.1117\/1.3559497","article-title":"Water Quality Monitoring Using Landsat Themate Mapper Data with Empirical Algorithms in Chagan Lake, China","volume":"5","author":"Song","year":"2011","journal-title":"J. Appl. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Dong, J., Xiao, X., Xiao, T., Yang, Z., Zhao, G., Zou, Z., and Qin, Y. (2017). Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors. Water, 9.","DOI":"10.3390\/w9040256"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lemessa, F., Simane, B., Seyoum, A., and Gebresenbet, G. (2023). Assessment of the Impact of Industrial Wastewater on the Water Quality of Rivers around the Bole Lemi Industrial Park (BLIP), Ethiopia. Sustainability, 15.","DOI":"10.3390\/su15054290"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Adjovu, G.E., Stephen, H., and Ahmad, S. (2023). Spatiotemporal Variability in Total Dissolved Solids and Total Suspended Solids along the Colorado River. Hydrology, 10.","DOI":"10.3390\/hydrology10060125"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gootman, K.S., and Hubbart, J.A. (2023). Characterization of Sub-Catchment Stream and Shallow Groundwater Nutrients and Suspended Sediment in a Mixed Land Use, Agro-Forested Watershed. Water, 15.","DOI":"10.3390\/w15020233"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Al-Mahasneh, M., Al Bsoul, A., Al-Ananzeh, N., Al-Khasawane, H.E., Al-Mahasneh, M., and Tashtoush, R. (2023). The Characterization of Groundwater Quality for Safe Drinking Water Wells via Disinfection and Sterilization in Jordan: A Case Study. Hydrology, 10.","DOI":"10.3390\/hydrology10060135"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Azzirgue, E.M., Cherif, E.K., El Azhari, H., Dakak, H., Yachou, H., Ghanimi, A., Nouayti, N., Esteves da Silva, J., and Salmoun, F. (2023). Interactions Evaluation between the Jouamaa Hakama Groundwater and Ouljat Echatt River in the North of Morocco, Using Hydrochemical Modeling, Multivariate Statistics and GIS. Water, 15.","DOI":"10.3390\/w15091752"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e14001","DOI":"10.1002\/hyp.14001","article-title":"Water Quality and Spatio-Temporal Hot Spots in an Effluent-Dominated Urban River","volume":"35","author":"Schliemann","year":"2021","journal-title":"Hydrol. Process."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Buma, W.G., and Lee, S.I. (2020). Evaluation of Sentinel-2 and Landsat 8 Images for Estimating Chlorophyll-a Concentrations in Lake Chad, Africa. Remote Sens., 12.","DOI":"10.3390\/rs12152437"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"119134","DOI":"10.1016\/j.jclepro.2019.119134","article-title":"Determination of Optically Inactive Water Quality Variables Using Landsat 8 Data: A Case Study in Geshlagh Reservoir Affected by Agricultural Land Use","volume":"247","author":"Vakili","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111682","DOI":"10.1016\/j.rse.2020.111682","article-title":"Determining the Drivers of Suspended Sediment Dynamics in Tidal Marsh-Influenced Estuaries Using High-Resolution Ocean Color Remote Sensing","volume":"240","author":"Zhang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Leggesse, E.S., Zimale, F.A., Sultan, D., Enku, T., Srinivasan, R., and Tilahun, S.A. (2023). Predicting Optical Water Quality Indicators from Remote Sensing Using Machine Learning Algorithms in Tropical Highlands of Ethiopia. Hydrology, 10.","DOI":"10.3390\/hydrology10050110"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shaikh, T.A., Adjovu, G.E., Stephen, H., and Ahmad, S. (2023, January 21\u201325). Impacts of Urbanization on Watershed Hydrology and Runoff Water Quality of a Watershed: A Review. Proceedings of the World Environmental and Water Resources Congress 2023, Henderson, NV, USA.","DOI":"10.1061\/9780784484852.116"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Leigh, C., Kandanaarachchi, S., McGree, J.M., Hyndman, R.J., Alsibai, O., Mengersen, K., and Peterson, E.E. (2019). Predicting Sediment and Nutrient Concentrations from High-Frequency Water-Quality Data. PLoS ONE, 14.","DOI":"10.1101\/599712"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dritsas, E., and Trigka, M. (2023). Efficient Data-Driven Machine Learning Models for Water Quality Prediction. Computation, 11.","DOI":"10.3390\/computation11020016"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1016\/j.jenvman.2017.11.049","article-title":"Evaluating Statistical Model Performance in Water Quality Prediction","volume":"206","author":"Avila","year":"2018","journal-title":"J. Environ. Manag."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fant, C., Srinivasan, R., Boehlert, B., Rennels, L., Chapra, S.C., Strzepek, K.M., Corona, J., Allen, A., and Martinich, J. (2017). Climate Change Impacts on US Water Quality Using Two Models: HAWQS and US Basins. Water, 9.","DOI":"10.3390\/w9020118"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"57","DOI":"10.2166\/wst.2004.0488","article-title":"Monitoring of COD as an Organic Indicator in Waste Water and Treated Effluent by Fluorescence Excitation-Emission (FEEM) Matrix Characterization","volume":"50","author":"Lee","year":"2004","journal-title":"Water Sci. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Serafy, G.Y.H.E., Schaeffer, B.A., Neely, M., Spinosa, A., Odermatt, D., Weathers, K.C., Baracchini, T., Bouffard, D., Carvalho, L., and Conmy, R.N. (2021). Integrating Inland and Coastal Water Quality Data for Actionable Knowledge. Remote Sens., 13.","DOI":"10.3390\/rs13152899"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhang, F., Chen, S., Wang, C., Chen, J., Zhou, H., and Xue, Y. (2020). Remote Sensing Evaluation of Total Suspended Solids Dynamic with Markov Model: A Case Study of Inland Reservoir across Administrative Boundary in South China. Sensors, 20.","DOI":"10.3390\/s20236911"},{"key":"ref_35","unstructured":"U.S. Environmental Protection Agency (2017). NPDES Compliance Inspection Manual\u2014Chapter 5\u2014Sampling."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gholizadeh, M.H., Melesse, A.M., and Reddi, L. (2016). A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques. Sensors, 16.","DOI":"10.3390\/s16081298"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1061\/9780784484852.105","article-title":"Utilization of Machine Learning Models and Satellite Data for the Estimation of Total Dissolved Solids in the Colorado River System","volume":"Volume 1","author":"Adjovu","year":"2023","journal-title":"Proceedings of the World Environmental and Water Resources Congress 2023"},{"key":"ref_38","unstructured":"(2023, June 08). Hach Solids (Total & Dissolved). Available online: https:\/\/www.hach.com\/parameters\/solids."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1061\/(ASCE)0733-9372(1989)115:6(1213)","article-title":"Seasonal and Long-Term Variations of Dissolved Solids in Lakes and Reservoirs","volume":"115","year":"1989","journal-title":"J. Environ. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S0034-4257(01)00341-8","article-title":"Spectral Signature of Highly Turbid Waters","volume":"81","author":"Doxaran","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_41","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_42","unstructured":"Crotts, A. (1996). An Experimental Technique in Lowering Total Dissolved Solids in Wastewater. [Bachelor\u2019s Thesis, University of Nevada Las Vegas]."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"105684","DOI":"10.1016\/j.apgeochem.2023.105684","article-title":"Salinity and Total Dissolved Solids Measurements for Natural Waters: An Overview and a New Salinity Method Based on Specific Conductance and Water Type","volume":"154","author":"McCleskey","year":"2023","journal-title":"Appl. Geochem."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hossain, A.K.M.A., Mathias, C., and Blanton, R. (2021). Remote Sensing of Turbidity in the Tennessee River Using Landsat 8 Satellite. Remote Sens., 13.","DOI":"10.3390\/rs13183785"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4954","DOI":"10.1029\/2018WR024054","article-title":"Salinity Yield Modeling of the Upper Colorado River Basin Using 30-m Resolution Soil Maps and Random Forests","volume":"55","author":"Nauman","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_46","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_47","unstructured":"Fondriest Environmental Inc (2023, July 12). Conductivity, Salinity & Total Dissolved Solids-Environmental Measurement Systems. Available online: https:\/\/www.fondriest.com\/environmental-measurements\/parameters\/water-quality\/conductivity-salinity-tds\/."},{"key":"ref_48","unstructured":"Hayden, L., Wood, J., Hassell, S., Jones, A., and Deese, A. (2023, July 12). Water-Quality Assessment of the Pasquotank River Watershed; Analysis of Dissolved Oxygen, PH, Salt, Total Dissolved Solids, and Conductivity. Available online: http:\/\/nia.ecsu.edu\/ureomps2011\/teams\/ptank\/PQT%20IEEE%20PAPER.pdf."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1007\/s10230-017-0484-y","article-title":"Evaluating Relationships between Total Dissolved Solids (TDS) and Total Suspended Solids (TSS) in a Mining-Influenced Watershed","volume":"31","author":"Butler","year":"2018","journal-title":"Mine Water Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3996\/112015-JFWM-111","article-title":"Effects of Temperature, Total Dissolved Solids, and Total Suspended Solids on Survival and Development Rate of Larval Arkansas River Shiner","volume":"8","author":"Mueller","year":"2017","journal-title":"J. Fish Wildl. Manag."},{"key":"ref_51","first-page":"497","article-title":"Distribution of PH Values and Dissolved Trace-Metal Concentrations in Streams Mining in the Animas River Watershed, San Juan County, Colorado Professional Paper 1651","volume":"1651","author":"Wright","year":"2007","journal-title":"Integr. Investig. Environ. Eff. Hist. Min. Animas River Watershed San Juan Cty. Color"},{"key":"ref_52","unstructured":"U.S. EPA (2023, June 29). 2018 Edition of the Drinking Water Standards and Health Advisories Tables, Available online: https:\/\/www.epa.gov\/system\/files\/documents\/2022-01\/dwtable2018.pdf."},{"key":"ref_53","unstructured":"(2009). U.S. EPA National Primary Drinking Water Guidelines (Standard No. EPA 816-F-09-004)."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2156","DOI":"10.1080\/01431161.2022.2057205","article-title":"Spectral Indices for Estimating Total Dissolved Solids in Freshwater Wetlands Using Semi-Empirical Models. A Case Study of Guartinaja and Momil Wetlands","volume":"43","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Shareef, M.A., Toumi, A., and Khenchaf, A. (2016, January 21\u201323). Estimating of Water Quality Parameters Using SAR and Thermal Microwave Remote Sensing Data. Proceedings of the 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Monastir, Tunisia.","DOI":"10.1109\/ATSIP.2016.7523149"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Shevah, Y. (2013). 1.4 Adaptation to Water Scarcity and Regional Cooperation in the Middle East, Elsevier Ltd.","DOI":"10.1016\/B978-0-12-382182-9.00004-9"},{"key":"ref_57","unstructured":"United States Geology Survey (USGS) (2023, July 02). Definition of \u201cBrackish\u201d, Available online: https:\/\/ne.water.usgs.gov\/ogw\/review\/brackish.html."},{"key":"ref_58","first-page":"296","article-title":"Ocean Salinity","volume":"8","author":"Nasreen","year":"2022","journal-title":"Int. J. Mod. Trends Sci. Technol."},{"key":"ref_59","unstructured":"Antonov, J.I., and Levitus, S. (2006). World Ocean Atlas 2005."},{"key":"ref_60","unstructured":"Water Resources Mission Area (2023, July 02). Brackish Groundwater Assessment, Available online: https:\/\/www.usgs.gov\/mission-areas\/water-resources\/science\/brackish-groundwater-assessment."},{"key":"ref_61","unstructured":"Godsey, W.E. (2023, July 02). Fresh, Brackish or Saline Water for Hydraulic Fracs: What Are the Options?, Available online: https:\/\/www.epa.gov\/sites\/default\/files\/documents\/02_Godsey_-_Source_Options_508.pdf."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1080\/09712119.2010.10539504","article-title":"Performance and Physiological Responses of Dairy Cattle to Water Total Dissolved Solids (TDS) under Heat Stress","volume":"38","author":"Shapasand","year":"2010","journal-title":"J. Appl. Anim. Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"228","DOI":"10.5539\/jsd.v3n3p228","article-title":"Use of Remote Sensing and GIS in Monitoring Water Quality","volume":"3","author":"Usali","year":"2010","journal-title":"J. Sustain. Dev."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3629","DOI":"10.1016\/j.watres.2005.06.007","article-title":"Suspended Solids in Missouri Reservoirs in Relation to Catchment Features and Internal Processes","volume":"39","author":"Jones","year":"2005","journal-title":"Water Res."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"He, C., Yao, Y., Lu, X., Chen, M., Ma, W., and Zhou, L. (2019). Exploring the Influence Mechanism of Meteorological Conditions on the Concentration of Suspended Solids and Chlorophyll-a in Large Estuaries Based on MODIS Imagery. Water, 11.","DOI":"10.3390\/w11020375"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"118655","DOI":"10.1016\/j.watres.2022.118655","article-title":"Freshwater Suspended Particulate Matter\u2014Key Components and Processes in Floc Formation and Dynamics","volume":"220","author":"Walch","year":"2022","journal-title":"Water Res."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"695","DOI":"10.14358\/PERS.69.6.695","article-title":"Remote Sensing Techniques to Assess Water Quality","volume":"69","author":"Ritchie","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Azzam, A., Uddin, H., and Mannan, U. (2022, January 13\u201314). Estimation of Suspended Sediment Concentration of Keenjhar Lake through Remote Sensing. Proceedings of the 12th International Civil Engineering Conference (ICEC-2022), Karachi, Pakistan.","DOI":"10.3390\/engproc2022022020"},{"key":"ref_69","unstructured":"US EPA (2019). Developing Water Quality Criteria for Suspended and Bedded Sediments (SABS)."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.desal.2007.02.071","article-title":"Water Desalination Cost Literature: Review and Assessment","volume":"223","author":"Karagiannis","year":"2008","journal-title":"Desalination"},{"key":"ref_71","unstructured":"Texas Water Development Board (2023, July 12). Seawater FAQs Answers to Frequently Asked Questions, Available online: https:\/\/www.twdb.texas.gov\/innovativewater\/desal\/faqseawater.asp#:~:text=1."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1111\/fwb.13286","article-title":"A Review of the Species, Community, and Ecosystem Impacts of Road Salt Salinisation in Fresh Waters","volume":"64","author":"Hintz","year":"2019","journal-title":"Freshw. Biol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40645-019-0311-0","article-title":"Soil Salinity Assessment by Using Near-Infrared Channel and Vegetation Soil Salinity Index Derived from Landsat 8 OLI Data: A Case Study in the Tra Vinh Province, Mekong Delta, Vietnam","volume":"7","author":"Nguyen","year":"2020","journal-title":"Prog. Earth Planet. Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1442","DOI":"10.1002\/saj2.20154","article-title":"Salinity: Electrical Conductivity and Total Dissolved Solids","volume":"84","author":"Corwin","year":"2020","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1002\/lol2.10215","article-title":"Impact of Salinization on Lake Stratification and Spring Mixing","volume":"8","author":"Ladwig","year":"2021","journal-title":"Limnol. Oceanogr. Lett."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1007\/s10661-016-5631-6","article-title":"Spectral Reflectance Characteristics of Soils in Northeastern Brazil as Influenced by Salinity Levels","volume":"188","author":"Pessoa","year":"2016","journal-title":"Environ. Monit. Assess."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Figler, A., B-B\u00e9res, V., Dobronoki, D., M\u00e1rton, K., Nagy, S.A., and B\u00e1csi, I. (2019). Salt Tolerance and Desalination Abilities of Nine Common Green Microalgae Isolates. Water, 11.","DOI":"10.3390\/w11122527"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s10661-015-5045-x","article-title":"Soil Salinity Detection from Satellite Image Analysis: An Integrated Approach of Salinity Indices and Field Data","volume":"188","author":"Morshed","year":"2016","journal-title":"Environ. Monit. Assess."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.desal.2011.01.007","article-title":"Salinity Reduction and Energy Conservation in Direct and Indirect Potable Water Reuse","volume":"272","author":"Venkatesan","year":"2011","journal-title":"Desalination"},{"key":"ref_80","first-page":"10","article-title":"Incomplete Spring Turnover in Small Deep Lakes in SE Michigan","volume":"2","author":"Denys","year":"2010","journal-title":"McNair Sch. Res. J."},{"key":"ref_81","unstructured":"(2023, June 08). Bureau of Reclamation Quality of Water Progress Report No. 24, Available online: https:\/\/www.usbr.gov\/uc\/progact\/salinity\/pdfs\/PR24final.pdf."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Tillman, F.D., Day, N.K., Miller, M.P., Miller, O.L., Rumsey, C.A., Wise, D.R., Longley, P.C., and McDonnell, M.C. (2022). A Review of Current Capabilities and Science Gaps in Water Supply Data, Modeling, and Trends for Water Availability Assessments in the Upper Colorado River Basin. Water, 14.","DOI":"10.3390\/w14233813"},{"key":"ref_83","unstructured":"Shope, C.L., and Gerner, S.J. (2016). Assessment of Dissolved-Solids Loading to the Colorado River in the Paradox Basin between the Dolores River and Gypsum Canyon Utah."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"2616","DOI":"10.1016\/j.scitotenv.2011.03.018","article-title":"Systems Dynamic Model to Forecast Salinity Load to the Colorado River Due to Urbanization within the Las Vegas Valley","volume":"409","author":"Venkatesan","year":"2011","journal-title":"Sci. Total Environ."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.envsci.2020.05.001","article-title":"The 1944 Water Treaty and the Incorporation of Environmental Values in U.S.-Mexico Transboundary Water Governance","volume":"112","author":"Mumme","year":"2020","journal-title":"Environ. Sci. Policy"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.1016\/j.jhydrol.2014.08.020","article-title":"A Data Reconnaissance on the Effect of Suspended-Sediment Concentrations on Dissolved-Solids Concentrations in Rivers and Tributaries in the Upper Colorado River Basin","volume":"519","author":"Tillman","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Venkatesan, A.K., Ahmad, S., Batista, J.R., and Johnson, W.S. (2010, January 16\u201320). Total Dissolved Solids Contribution to the Colorado River Associated with the Growth of Las Vegas Valley. Proceedings of the World Environmental and Water Resources Congress 2010: Challenges of Change, Providence, RI, USA.","DOI":"10.1061\/41114(371)348"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Glysson, G.D., Gray, J.R., and Schwarz, G.E. (2001, January 20\u201324). A Comparison of Load Estimates Using Total Suspended Solids and Suspended-Sediment Concentration Data. Proceedings of the World Water and Environmental Resources Congress 2001, Orlando, FL, USA.","DOI":"10.1061\/40569(2001)123"},{"key":"ref_89","unstructured":"US EPA (2017). National Water Quality Inventory: Report to Congress."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Wang, M., Chen, T., and Wang, X. (2023). Rapid Correction of Turbidity and CDOM Interference on Three-Dimensional Fluorescence Spectra of Live Algae Based on Deep Learning. Photonics, 10.","DOI":"10.3390\/photonics10060627"},{"key":"ref_91","first-page":"82","article-title":"Effectiveness of Chitosan To Reduce the Color Value, Turbidity, and Total Dissolved Solids in Shrimp-Washing Wastewater","volume":"115","author":"Hidayati","year":"2021","journal-title":"Russ. J. Agric. Socio-Econ. Sci."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"5315","DOI":"10.1109\/JSTARS.2016.2585142","article-title":"Remote Sensing of Phytoplankton Blooms in the Continental Shelf and Shelf-Break of Argentina: Spatio-Temporal Changes and Phenology","volume":"9","author":"Andreo","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Sent, G., Biguino, B., Favareto, L., Cruz, J., S\u00e1, C., Dogliotti, A.I., Palma, C., Brotas, V., and Brito, A.C. (2021). Deriving Water Quality Parameters Using Sentinel-2 Imagery: A Case Study in the Sado Estuary, Portugal. Remote Sens., 13.","DOI":"10.3390\/rs13051043"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.rse.2014.09.020","article-title":"A Single Algorithm to Retrieve Turbidity from Remotely-Sensed Data in All Coastal and Estuarine Waters","volume":"156","author":"Dogliotti","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_95","first-page":"119","article-title":"Calibration and Validation of an Algorithm for Remote Sensing of Turbidity Over La Plata River Estuary, Argentina","volume":"10","author":"Dogliotti","year":"2011","journal-title":"EARSeL eProceedings"},{"key":"ref_96","unstructured":"Ruddick, K., Vanhellemont, Q., Dogliotti, A.I., Nechad, B., Pringle, N., and Van der Zande, D. (2016, January 23\u201328). New Opportunities and Challenges for High Resolution Remote Sensing of Water Colour. Proceedings of the Ocean Optics XXIII, Victoria, BC, Canada."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.csr.2014.05.013","article-title":"Modelling the Inherent Optical Properties and Estimating the Constituents\u2019 Concentrations in Turbid and Eutrophic Waters","volume":"84","author":"Gokul","year":"2014","journal-title":"Cont. Shelf Res."},{"key":"ref_98","unstructured":"Nechad, B., Ruddick, K.G., and Neukermans, G. (2009). Proceedings of Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2009, SPIE."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rse.2011.07.025","article-title":"In Situ Evidence of Non-Zero Reflectance in the OLCI 1020nm Band for a Turbid Estuary","volume":"120","author":"Knaeps","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"53","DOI":"10.3989\/scimar.04847.22A","article-title":"An Empirical Remote Sensing Algorithm for Retrieving Total Suspended Matter in a Large Estuarine Region","volume":"83","author":"Camiolo","year":"2019","journal-title":"Sci. Mar."},{"key":"ref_101","unstructured":"Dogliotti, A.I., Ruddick, K., Nechad, B., and Lasta, C. (2011, January 6\u201310). Estimating Turbidity in the La Plata River from MODIS Imagery. Proceedings of the VI International Conference Current Problems in Optics of Natural Waters, St. Petersburg, Russia."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.jenvman.2015.06.003","article-title":"Developing the Remote Sensing-Based Early Warning System for Monitoring TSS Concentrations in Lake Mead","volume":"160","author":"Imen","year":"2015","journal-title":"J. Environ. Manag."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Hajigholizadeh, M., Moncada, A., Kent, S., and Melesse, A.M. (2021). Land\u2013lake Linkage and Remote Sensing Application in Water Quality Monitoring in Lake Okeechobee, Florida, USA. Land, 10.","DOI":"10.3390\/land10020147"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"012053","DOI":"10.1088\/1755-1315\/561\/1\/012053","article-title":"Study of Total Suspended Solid Concentration Based on Doxaran Algorithm Using Landsat 8 Image in Coastal Water between Bodri River Estuary up to East Flood Canal Semarang City","volume":"561","author":"Sanjoto","year":"2020","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1080\/02626667.2019.1593988","article-title":"Socio-Economic Drought Assessment in Lake Mead, USA, Based on a Multivariate Standardized Water-Scarcity Index","volume":"64","author":"Edalat","year":"2019","journal-title":"Hydrol. Sci. J."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1016\/j.watres.2010.10.020","article-title":"Ultraviolet Absorption Properties of Suspended Particulate Matter in Untreated Surface Waters","volume":"45","author":"Cantwell","year":"2011","journal-title":"Water Res."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s10661-009-1220-2","article-title":"Assessment of Water Quality Parameters of the Harike Wetland in India, a Ramsar Site, Using IRS LISS IV Satellite Data","volume":"170","author":"Mabwoga","year":"2010","journal-title":"Environ. Monit. Assess."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1007\/s40808-019-00609-8","article-title":"Identification of Seasonal Variation of Water Turbidity Using NDTI Method in Panchet Hill Dam, India","volume":"5","author":"Bid","year":"2019","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"012019","DOI":"10.1088\/1755-1315\/118\/1\/012019","article-title":"Correlation between Conductivity and Total Dissolved Solid in Various Type of Water: A Review","volume":"118","author":"Rusydi","year":"2018","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"5773","DOI":"10.1109\/JSTARS.2021.3085411","article-title":"An Assessment of Water Color for Inland Water in China Using a Landsat 8-Derived Forel-Ule Index and the Google Earth Engine Platform","volume":"14","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"7534","DOI":"10.1080\/01431161.2013.823524","article-title":"Barriers to Adopting Satellite Remote Sensing for Water Quality Management","volume":"34","author":"Schaeffer","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.jenvman.2006.09.024","article-title":"Characterization and Prediction of Highway Runoff Constituent Event Mean Concentration","volume":"85","author":"Kayhanian","year":"2007","journal-title":"J. Environ. Manag."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Fallatah, O., and Khattab, M.R. (2023). Evaluation of Groundwater Quality and Suitability for Irrigation Purposes and Human Consumption in Saudi Arabia. Water, 15.","DOI":"10.3390\/w15132352"},{"key":"ref_114","first-page":"96420U","article-title":"Estimation and Characterization of Physical and Inorganic Chemical Indicators of Water Quality by Using SAR Images","volume":"9642","author":"Shareef","year":"2015","journal-title":"SAR Image Anal. Model. Technol. XV"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.2166\/wst.2018.092","article-title":"Relationship between Total Dissolved Solids and Electrical Conductivity in Marcellus Hydraulic Fracturing Fluids","volume":"77","author":"Taylor","year":"2018","journal-title":"Water Sci. Technol."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2008WR007424","article-title":"Remote Sensing of Suspended Sediment Concentration, Flow Velocity, and Lake Recharge in the Peace-Athabasca Delta, Canada","volume":"45","author":"Pavelsky","year":"2009","journal-title":"Water Resour. Res."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s11270-020-04531-z","article-title":"Evaluation of Water Quality Parameters in Marshes Zone Southern of Iraq Based on Remote Sensing and GIS Techniques","volume":"231","author":"Hasab","year":"2020","journal-title":"Water. Air. Soil Pollut."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Giardino, C., Bresciani, M., Braga, F., Cazzaniga, I., De Keukelaere, L., Knaeps, E., and Brando, V.E. (2017). Bio-Optical Modeling of Total Suspended Solids, Elsevier Inc.","DOI":"10.1016\/B978-0-12-804644-9.00005-7"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"5269","DOI":"10.1080\/01431160500191704","article-title":"Applications of Landsat-5 TM Imagery in Assessing and Mapping Water Quality in Reelfoot Lake, Tennessee","volume":"27","author":"Wang","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"115159","DOI":"10.1016\/j.watres.2019.115159","article-title":"Assessing the Transition Effects in a Drinking Water Distribution System Caused by Changing Supply Water Quality: An Indirect Approach by Characterizing Suspended Solids","volume":"168","author":"Chen","year":"2020","journal-title":"Water Res."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"143","DOI":"10.2166\/wst.2004.0682","article-title":"Spectral In-Situ Analysis of NO2, NO3, COD, DOC and TSS in the Effluent of a WWTP","volume":"50","author":"Rieger","year":"2004","journal-title":"Water Sci. Technol."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Moeini, M., Shojaeizadeh, A., and Geza, M. (2021). Supervised Machine Learning for Estimation of Total Suspended Solids in Urban Watersheds. Water, 13.","DOI":"10.3390\/w13020147"},{"key":"ref_123","unstructured":"Hach (2023, June 25). Solids, Total Method 8271. Available online: https:\/\/images.hach.com\/asset-get.download.jsa?id=7639984016."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Yang, H., Kong, J., Hu, H., Du, Y., Gao, M., and Chen, F. (2022). A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges. Remote Sens., 14.","DOI":"10.3390\/rs14081770"},{"key":"ref_125","unstructured":"US EPA (1983). Methods for Chemical Analysis of Water and Wastes."},{"key":"ref_126","unstructured":"Woodside, J. (2023, June 08). What Is the Difference among Turbidity, TDS, and TSS?. Available online: https:\/\/www.ysi.com\/ysi-blog\/water-blogged-blog\/2022\/05\/understanding-turbidity-tds-and-tss."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Adjovu, G.E., Stephen, H., and Ahmad, S. (2022, January 5\u20138). Monitoring of Total Dissolved Solids Using Remote Sensing Band Reflectance and Salinity Indices: A Case Study of the Imperial County Section, AZ-CA, of the Colorado River. Proceedings of the World Environmental and Water Resources Congress 2022, Atlanta, GA, USA.","DOI":"10.1061\/9780784484258.106"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Pereira, O.J.R., Merino, E.R., Montes, C.R., Barbiero, L., Rezende-Filho, A.T., Lucas, Y., and Melfi, A.J. (2020). Estimating Water PH Using Cloud-Based Landsat Images for a New Classification of the Nhecol\u00e2Ndia Lakes (Brazilian Pantanal). Remote Sens., 12.","DOI":"10.3390\/rs12071090"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"DeLuca, N.M., Zaitchik, B.F., and Curriero, F.C. (2018). Can Multispectral Information Improve Remotely Sensed Estimates of Total Suspended Solids? A Statistical Study in Chesapeake Bay. Remote Sens., 10.","DOI":"10.3390\/rs10091393"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1080\/02626668009491950","article-title":"Satellite Remote Sensing of Water Turbidity","volume":"25","author":"Moore","year":"1980","journal-title":"Hydrol. Sci. Bull."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Zhang, C., Liu, Y., Chen, X., and Gao, Y. (2022). Estimation of Suspended Sediment Concentration in the Yangtze Main Stream Based on Sentinel-2 MSI Data. Remote Sens., 14.","DOI":"10.3390\/rs14184446"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"105701","DOI":"10.1016\/j.marenvres.2022.105701","article-title":"Ocean Water Quality Monitoring Using Remote Sensing Techniques: A Review","volume":"180","author":"Mohseni","year":"2022","journal-title":"Mar. Environ. Res."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1080\/02626669609491524","article-title":"Remote Sensing, Ecological Water Quality Modelling and in Situ Measurements: A Case Study in Shallow Lakes","volume":"41","author":"Dekker","year":"1996","journal-title":"Hydrol. Sci. J."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1016\/j.scitotenv.2009.11.057","article-title":"Estimation of Suspended Sediment Concentrations Using Terra MODIS: An Example from the Lower Yangtze River, China","volume":"408","author":"Wang","year":"2010","journal-title":"Sci. Total Environ."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"5373","DOI":"10.3390\/rs70505373","article-title":"A Semi-Analytical Model for Remote Sensing Retrieval of Suspended Sediment Concentration in the Gulf of Bohai, China","volume":"7","author":"Kong","year":"2015","journal-title":"Remote Sens."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1007\/s12665-021-09581-y","article-title":"Retrieval of Suspended Sediment Concentration of the Chilika Lake, India Using Landsat-8 OLI Satellite Data","volume":"80","author":"Jally","year":"2021","journal-title":"Environ. Earth Sci."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Marinho, R.R., Harmel, T., Martinez, J.M., and Junior, N.P.F. (2021). Spatiotemporal Dynamics of Suspended Sediments in the Negro River, Amazon Basin, from in Situ and Sentinel-2 Remote Sensing Data. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10020086"},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Novoa, S., Doxaran, D., Ody, A., Vanhellemont, Q., Lafon, V., Lubac, B., and Gernez, P. (2017). Atmospheric Corrections and Multi-Conditional Algorithm for Multi-Sensor Remote Sensing of Suspended Particulate Matter in Low-to-High Turbidity Levels Coastal Waters. Remote Sens., 9.","DOI":"10.3390\/rs9010061"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1016\/j.rse.2012.06.014","article-title":"A Regional Remote Sensing Algorithm for Total Suspended Matter in the East China Sea","volume":"124","author":"Mao","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.csr.2009.12.007","article-title":"Estimating Turbidity and Total Suspended Matter in the Adour River Plume (South Bay of Biscay) Using MODIS 250-m Imagery","volume":"30","author":"Petus","year":"2010","journal-title":"Cont. Shelf Res."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Kupssinsk\u00fc, L.S., Guimar\u00e3es, T.T., De Souza, E.M., Zanotta, D.C., Veronez, M.R., Gonzaga, L., and Mauad, F.F. (2020). A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning. Sensors, 20.","DOI":"10.3390\/s20072125"},{"key":"ref_142","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_143","doi-asserted-by":"crossref","unstructured":"Najafzadeh, M., and Basirian, S. (2023). Evaluation of River Water Quality Index Using Remote Sensing and Artificial Intelligence Models. Remote Sens., 15.","DOI":"10.3390\/rs15092359"},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Alshehri, F., and Rahman, A. (2023). Coupling Machine and Deep Learning with Explainable Artificial Intelligence for Improving Prediction of Groundwater Quality and Decision-Making in Arid Region, Saudi Arabia. Water, 15.","DOI":"10.3390\/w15122298"},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Adjovu, G.E., Stephen, H., and Ahmad, S. (2023). A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature. Water, 15.","DOI":"10.3390\/w15132439"},{"key":"ref_146","first-page":"163","article-title":"Remote Sensing Applications in Water Resources","volume":"93","author":"Kumar","year":"2013","journal-title":"J. Indian Inst. Sci."},{"key":"ref_147","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_148","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.rse.2011.12.018","article-title":"The Development of a New Optical Total Suspended Matter Algorithm for the Chesapeake Bay","volume":"119","author":"Ondrusek","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_149","unstructured":"Allan, M.G., Hicks, B.J., and Brabyn, L. (2007). Remote Sensing of Water Quality in the Rotorua Lakes, Centre for Biodiversity and Ecology Research, Department of Biological Sciences, School of Science and Engineering, The University of Waikato."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1080\/01431161003645840","article-title":"Landsat Remote Sensing of Chlorophyll a Concentrations in Central North Island Lakes of New Zealand","volume":"32","author":"Allan","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Uudeberg, K., Aavaste, A., K\u00f5ks, K.-L., Ansper, A., Uus\u00f5ue, M., Kangro, K., Ansko, I., Ligi, M., Toming, K., and Reinart, A. (2020). Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters. Remote Sens., 12.","DOI":"10.3390\/rs12060931"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"3258","DOI":"10.1109\/JSTARS.2022.3166398","article-title":"The Impact of Glacial Suspension Color on the Relationship between Its Properties and Marine Water Spectral Reflectance","volume":"15","author":"Osinska","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1007\/s12665-019-8126-2","article-title":"Development of Water Turbidity Index (WTI) and Seasonal Characteristics of Total Suspended Matter (TSM) Spatial Distribution in Ichkeul Lake, a Shallow Brackish Wetland, Northern-East Tunisia","volume":"78","author":"Ouni","year":"2019","journal-title":"Environ. Earth Sci."},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Dogliotti, A.I., Gossn, J.I., Vanhellemont, Q., and Ruddick, K.G. (2018). Detecting and Quantifying a Massive Invasion of Floating Aquatic Plants in the R\u00edo de La Plata Turbid Waters Using High Spatial Resolution Ocean Color Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10071140"},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2015.06.022","article-title":"A SWIR Based Algorithm to Retrieve Total Suspended Matter in Extremely Turbid Waters","volume":"168","author":"Knaeps","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_156","unstructured":"Dewidar, K., Ezaby, K.E., Daym, H.A., and Ibrahim, M. (2008, January 1\u20133). Mapping Some Water Quality Parameters by Using Landsat-7 ETM + for Manzala Lagoon, Egypt. Proceedings of the Environmental Sciences & Technology, Cairo, Egypt."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"113","DOI":"10.3126\/tj.v1i1.27709","article-title":"Surface Water Quality Assessment Using Remote Sensing, Gis and Artificial Intelligence","volume":"1","author":"KC","year":"2019","journal-title":"Technol. J."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2166\/nh.1987.0001","article-title":"Microwave Remote Sensing of Snowpack Properties: Potential and Limitations","volume":"18","author":"Bernier","year":"1987","journal-title":"Nord. Hydrol."},{"key":"ref_159","unstructured":"Government of Canada (2023, July 12). Microwave Remote Sensing Introduction. Available online: https:\/\/www.nrcan.gc.ca\/maps-tools-and-publications\/satellite-imagery-and-air-photos\/tutorial-fundamentals-remote-sensing\/microwave-remote-sensing\/9371."},{"key":"ref_160","unstructured":"Herndon, K., Meyer, F., Flores, A., Cherrington, E., and Kucera, L. (2023, July 12). What Is Synthetic Aperture Radar?|Earthdata, Available online: https:\/\/www.earthdata.nasa.gov\/learn\/backgrounders\/what-is-sar."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"69481","DOI":"10.1109\/ACCESS.2019.2918996","article-title":"Microwave Sensing of Water Quality","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_162","unstructured":"Carter, W.D., and Engman, E.T. (1984). Remote Sensing from Satellites, Elsevier Inc."},{"key":"ref_163","first-page":"165","article-title":"Remote Sensing in Hydrology","volume":"108","author":"Engman","year":"1998","journal-title":"Geophys. Monogr. Ser."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/S0034-4257(01)00238-3","article-title":"Lake Water Quality Classification with Airborne Hyperspectral Spectrometer and Simulated MERIS Data","volume":"79","author":"Koponen","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1080\/01431160600821127","article-title":"The MERIS Case 2 Water Algorithm","volume":"28","author":"Doerffer","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"3229","DOI":"10.1080\/01431160110104700","article-title":"Quantification of Surface Suspended Sediments along a River Dominated Coast with NOAA AVHRR and Sea WiFS Measurements: Louisiana, USA","volume":"23","author":"Myint","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"3143","DOI":"10.1080\/01431161.2016.1190477","article-title":"Spaceborne and Airborne Sensors in Water Quality Assessment","volume":"37","author":"Gholizadeh","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"373","DOI":"10.4236\/ars.2013.24040","article-title":"Soil Salinity Mapping and Monitoring in Arid and Semi-Arid Regions Using Remote Sensing Technology: A Review","volume":"2","author":"Allbed","year":"2013","journal-title":"Adv. Remote Sens."},{"key":"ref_169","first-page":"19","article-title":"Spectral Analysis of Water Reflectance for Hyperspectral Remote Sensing of Water Quailty in Estuarine Water","volume":"2","author":"Fan","year":"2014","journal-title":"J. Geosci. Environ. Prot."},{"key":"ref_170","doi-asserted-by":"crossref","unstructured":"Terentev, A., Dolzhenko, V., Fedotov, A., and Eremenko, D. (2022). Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review. Sensors, 22.","DOI":"10.3390\/s22030757"},{"key":"ref_171","doi-asserted-by":"crossref","unstructured":"Topp, S.N., Pavelsky, T.M., Jensen, D., Simard, M., and Ross, M.R.V. (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_172","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/s00267-017-0880-x","article-title":"Satellite Remote Sensing for Coastal Management: A Review of Successful Applications","volume":"60","author":"McCarthy","year":"2017","journal-title":"Environ. Manag."},{"key":"ref_173","unstructured":"European Space Agency (2023, July 12). SAR (ERS) Overview. Available online: http:\/\/earth.esa.int\/eogateway\/instruments\/sar-ers\/description."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.2166\/ws.2020.381","article-title":"Machine Learning Method for Quick Identification of Water Quality Index (WQI) Based on Sentinel-2 MSI Data: Ebinur Lake Case Study","volume":"21","author":"Li","year":"2021","journal-title":"Water Sci. Technol. Water Supply"},{"key":"ref_175","doi-asserted-by":"crossref","unstructured":"Runge, A., and Grosse, G. (2019). Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions. Remote Sens., 11.","DOI":"10.3390\/rs11141730"},{"key":"ref_176","unstructured":"Meijerink, A.M.J., Bannert, D., Batelaan, O., Lubczynski, M.W., and Pointet, T. (2007). Remote Sensing Applications to Groundwater, UNESCO."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s10661-008-0156-2","article-title":"Application of MODIS Satellite Data in Monitoring Water Quality Parameters of Chaohu Lake in China","volume":"148","author":"Wu","year":"2009","journal-title":"Environ. Monit. Assess."},{"key":"ref_178","unstructured":"National Aeronautics and Space Administration (2023, June 26). MODIS, Available online: https:\/\/aqua.nasa.gov\/modis#:~:text=ThefirstMODISinstrumentwas,onAquainMay2002.&text=SelectedforflightonTerra,%2Ccross-trackscanningradiometer."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"119082","DOI":"10.1016\/j.watres.2022.119082","article-title":"MODIS-Landsat Fusion-Based Single-Band Algorithms for TSS and Turbidity Estimation in an Urban-Waste-Dominated River Reach","volume":"224","author":"Sahoo","year":"2022","journal-title":"Water Res."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"111768","DOI":"10.1016\/j.rse.2020.111768","article-title":"Robust Algorithm for Estimating Total Suspended Solids (TSS) in Inland and Nearshore Coastal Waters","volume":"246","author":"Balasubramanian","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_181","doi-asserted-by":"crossref","unstructured":"Niroumand-Jadidi, M., Bovolo, F., Bresciani, M., Gege, P., and Giardino, C. (2022). Water Quality Retrieval from Landsat-9 (OLI-2) Imagery and Comparison to Sentinel-2. Remote Sens., 14.","DOI":"10.3390\/rs14184596"},{"key":"ref_182","doi-asserted-by":"crossref","unstructured":"Sa\u2019ad, F.N.A., Tahir, M.S., Jemily, N.H.B., Ahmad, A., and Amin, A.R.M. (2021). Monitoring Total Suspended Sediment Concentration in Spatiotemporal Domain over Teluk Lipat Utilizing Landsat 8 (OLI). Appl. Sci., 11.","DOI":"10.3390\/app11157082"},{"key":"ref_183","first-page":"1","article-title":"Assessing the Potential of Remotely Sensed Data for Water Quality Monitoring of Coastal and Inland Waters","volume":"5","author":"Bhatti","year":"2008","journal-title":"Soc. Soc. Manag. Syst."},{"key":"ref_184","unstructured":"The European Space Agency (2023, June 27). SPOT 6\u2014Earth Online. Available online: https:\/\/earth.esa.int\/eogateway\/missions\/spot-6."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1109\/TGRS.2003.812907","article-title":"Satellite Hyperspectral Remote Sensing for Estimating Estuarine and Coastal Water Quality","volume":"41","author":"Brando","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1080\/15481603.2014.895580","article-title":"Characterization of Spatial and Temporal Variation of Suspended Sediments in the Yellow and East China Seas Using Satellite Ocean Color Data","volume":"51","author":"Son","year":"2014","journal-title":"GIScience Remote Sens."},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1080\/014311600209913","article-title":"Detection of Total Suspended Sediments in the North Sea Using AVHRR and Ship Data","volume":"21","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_188","unstructured":"Payra, S., Sharma, A., and Verma, S. (2023). Atmospheric Remote Sensing, Elsevier."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1007\/s11270-020-04844-z","article-title":"Estimation of Total Dissolved Solids in Water Bodies by Spectral Indices Case Study: Shatt Al-Arab River","volume":"231","author":"Maliki","year":"2020","journal-title":"Water. Air. Soil Pollut."},{"key":"ref_190","unstructured":"Adjovu, G.E., Ahmad, S., and Stephen, H. (2021, January 7\u201311). Analysis of Suspended Material in Lake Mead Using Remote Sensing Indices. Proceedings of the World Environmental and Water Resources Congress 2021, Virtually."},{"key":"ref_191","first-page":"77","article-title":"Remote Sensing for Water Quality and Biological Measurements in Coastal Waters","volume":"46","author":"Johnson","year":"1980","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/S0034-4257(02)00009-3","article-title":"Application of an Empirical Neural Network to Surface Water Quality Estimation in the Gulf of Finland Using Combined Optical Data and Microwave Data","volume":"81","author":"Zhang","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1109\/36.921422","article-title":"Passive Active L- and S-Band (PALS) Microwave Sensor for Ocean Salinity and Soil Moisture Measurements","volume":"39","author":"Wilson","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"111769","DOI":"10.1016\/j.rse.2020.111769","article-title":"Sea Surface Salinity Estimates from Spaceborne L-Band Radiometers: An Overview of the First Decade of Observation (2010\u20132019)","volume":"242","author":"Reul","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.agwat.2004.09.038","article-title":"Assessment of Hydrosaline Land Degradation by Using a Simple Approach of Remote Sensing Indicators","volume":"77","author":"Khan","year":"2005","journal-title":"Agric. Water Manag."},{"key":"ref_196","doi-asserted-by":"crossref","unstructured":"Herrault, P.A., Gandois, L., Gascoin, S., Tananaev, N., Le Dantec, T., and Teisserenc, R. (2016). Using High Spatio-Temporal Optical Remote Sensing to Monitor Dissolved Organic Carbon in the Arctic River Yenisei. Remote Sens., 8.","DOI":"10.3390\/rs8100803"},{"key":"ref_197","unstructured":"Gallagher, L.C. (2004). Dissolved Organic Matter in Coastal and Inland Waters, University of Victoria."},{"key":"ref_198","unstructured":"Montalvo, L.G. (2010). Spectral Analysis of Suspended Material in Coastal Waters: A Comparison between Band Math Equations, Department of Geology, University of Puerto Rico."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1088\/1755-1315\/98\/1\/012058","article-title":"Remote Sensing Studies of Suspended Sediment Concentration Variation in Barito Delta","volume":"98","author":"Arisanty","year":"2017","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"062012","DOI":"10.1088\/1755-1315\/227\/6\/062012","article-title":"Spatio-Temporal Assessment of Inland Surface Water Quality Using Remote Sensing Data in the Wake of Changing Climate","volume":"227","author":"Malahlela","year":"2019","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_201","unstructured":"Abbas, A., and Khan, S. (2007). Using Remote Sensing Techniques for Appraisal of Irrigated Soil Salinity, Modelling and Simulation Society of Australia and New Zealand."},{"key":"ref_202","doi-asserted-by":"crossref","unstructured":"Veronez, M.R., Kupssinsk\u00fc, L.S., Guimar\u00e3es, T.T., Koste, E.C., Da Silva, J.M., De Souza, L.V., Oliverio, W.F.M., Jardim, R.S., Koch, I., and De Souza, J.G. (2018). Proposal of a Method to Determine the Correlation between Total Suspended Solids and Dissolved Organic Matter in Water Bodies from Spectral Imaging and Artificial Neural Networks. Sensors, 18.","DOI":"10.3390\/s18010159"},{"key":"ref_203","doi-asserted-by":"crossref","first-page":"117","DOI":"10.29121\/granthaalayah.v5.i10.2017.2289","article-title":"Using Water Indices (NDWI, MNDWI, NDMI, WRI and AWEI) To Detect Physical and Chemical Parameters By Apply Remote Sensing and Gis Techniques","volume":"5","author":"Mustafa","year":"2017","journal-title":"Int. J. Res.\u2014Granthaalayah"},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"25","DOI":"10.17656\/jzs.10630","article-title":"Water Quality Assessment Models for Dokan Lake Using Landsat 8 OLI Satellite Images","volume":"19","author":"Abdullah","year":"2017","journal-title":"J. Zankoy Sulaimani\u2014Part A"},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12403-008-0001-4","article-title":"Water Quality, Exposure and Health: Purpose and Goals","volume":"1","author":"Aral","year":"2009","journal-title":"Water Qual. Expo. Health"},{"key":"ref_206","first-page":"193","article-title":"Role of Statistical Remote Sensing for Inland Water Quality Parameters Prediction","volume":"21","author":"Abdelmalik","year":"2018","journal-title":"Egypt. J. Remote Sens. Sp. Sci."},{"key":"ref_207","unstructured":"United States Geology Survey (USGS) (2023, July 03). What Are the Band Designations for the Landsat Satellites?, Available online: https:\/\/www.usgs.gov\/faqs\/what-are-band-designations-landsat-satellites."},{"key":"ref_208","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1007\/s12517-016-2725-y","article-title":"Land Use\/Land Cover Change Impact on Groundwater Quantity and Quality: A Case Study of Ajman Emirate, the United Arab Emirates, Using Remote Sensing and GIS","volume":"9","author":"Elmahdy","year":"2016","journal-title":"Arab. J. Geosci."},{"key":"ref_209","doi-asserted-by":"crossref","first-page":"111284","DOI":"10.1016\/j.rse.2019.111284","article-title":"A Harmonized Image Processing Workflow Using Sentinel-2\/MSI and Landsat-8\/OLI for Mapping Water Clarity in Optically Variable Lake Systems","volume":"231","author":"Page","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.rse.2018.10.027","article-title":"Sentinel-2\/Landsat-8 Product Consistency and Implications for Monitoring Aquatic Systems","volume":"220","author":"Pahlevan","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_211","unstructured":"European Space Agency (2023, July 03). Sentinel-2 User Handbook. Available online: https:\/\/sentinel.esa.int\/documents\/247904\/685211\/sentinel-2_user_handbook."},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"109861","DOI":"10.1016\/j.jenvman.2019.109861","article-title":"Developing an Empirical Model from Landsat Data Series for Monitoring Water Salinity in Coastal Bangladesh","volume":"255","author":"Ferdous","year":"2020","journal-title":"J. Environ. Manag."},{"key":"ref_213","doi-asserted-by":"crossref","unstructured":"Tran1, P.H., Nguyen, A.K., Liou, Y.-A., Hoang, P.P., and Thanh, H.N. (2020). Estimation of Salinity Intrusion by Using Landsat 8 OLI Data in The Mekong Delta, Vietnam. Prog. Earth Planet. Sci., 7, 1.","DOI":"10.1186\/s40645-019-0311-0"},{"key":"ref_214","unstructured":"Hossain, A.K.M.A., Chao, X., and Jia, Y. (2010, January 12). Development of Remote Sensing Based Index for Estimating\/Mapping Suspended Sediment Concentration in River and Lake Environments. Proceedings of the 8th International Symposium on ECOHYDRAULICS, Seoul, Republic of Korea."},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1016\/j.rse.2009.11.022","article-title":"Calibration and Validation of a Generic Multisensor Algorithm for Mapping of Total Suspended Matter in Turbid Waters","volume":"114","author":"Nechad","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s11270-008-9716-x","article-title":"Determination of Environmental Quality of a Drinking Water Reservoir by Remote Sensing, GIS and Regression Analysis","volume":"194","author":"Coskun","year":"2008","journal-title":"Water. Air. Soil Pollut."},{"key":"ref_217","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1080\/01431160310001592571","article-title":"Development and Applications of STARRS: A next Generation Airborne Salinity Imager","volume":"25","author":"Miller","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_218","doi-asserted-by":"crossref","unstructured":"Jin, X., He, X., Bai, Y., Shanmugam, P., Ying, J., Gong, F., and Zhu, Q. (2019). Assessment and Improvement of Sea Surface Microwave Emission Models for Salinity Retrieval in the East China Sea. Remote Sens., 11.","DOI":"10.3390\/rs11212486"},{"key":"ref_219","doi-asserted-by":"crossref","first-page":"2689","DOI":"10.1175\/JTECH-D-13-00110.1","article-title":"Small-Scale Variability in Sea Surface Salinity and Implications for Satellite-Derived Measurements","volume":"30","author":"Vinogradova","year":"2013","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"64","DOI":"10.5670\/oceanog.2009.39","article-title":"Global Ocean Prediction with the Hybrid Coordinate Ocean Model (HYCOM)","volume":"22","author":"Chassignet","year":"2009","journal-title":"Oceanography"},{"key":"ref_221","doi-asserted-by":"crossref","unstructured":"Kao, H.Y., Lagerloef, G.S.E., Lee, T., Melnichenko, O., Meissner, T., and Hacker, P. (2018). Assessment of Aquarius Sea Surface Salinity. Remote Sens., 10.","DOI":"10.3390\/rs10091341"},{"key":"ref_222","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1080\/014311600210605","article-title":"Monitoring Water Quality in Florida Bay with Remotely Sensed Salinity and in Situ Bio-Optical Observations","volume":"21","author":"Zaitzeff","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_223","first-page":"570","article-title":"Environmental Monitoring of Estuaries: Estimating and Mapping Various Environmental Indicators in Matla Estuarine Complex, Using Landsat TM Digital Data","volume":"3","author":"Ray","year":"2013","journal-title":"Int. J. Geomat. Geosci."},{"key":"ref_224","first-page":"64","article-title":"Numerical Modeling of Sediment Transport and Its Effect on Algal Biomass Distribution in Lake Pontchartrain Due to Flood Release from Bonnet Carr&#233; Spillway","volume":"4","author":"Chao","year":"2016","journal-title":"J. Geosci. Environ. Prot."},{"key":"ref_225","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.rse.2004.08.007","article-title":"Assessment of Estuarine Water-Quality Indicators Using MODIS Medium-Resolution Bands: Initial Results from Tampa Bay, FL","volume":"93","author":"Hu","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_226","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2004.07.012","article-title":"Using MODIS Terra 250 m Imagery to Map Concentrations of Total Suspended Matter in Coastal Waters","volume":"93","author":"Miller","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_227","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1080\/01431160512331314092","article-title":"Use of Reflectance Band Ratios to Estimate Suspended and Dissolved Matter Concentrations in Estuarine Waters","volume":"26","author":"Doxaran","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_228","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_229","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.1111\/j.1752-1688.2006.tb06029.x","article-title":"Lake Water Quality Assessment from Landsat Thematic Mapper Data Using Neural Network: An Approach to Optimal Band Combination Selection","volume":"42","author":"Sudheer","year":"2006","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_230","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1007\/s11270-011-0959-6","article-title":"Hyperspectral Remote Sensing of Total Phosphorus (TP) in Three Central Indiana Water Supply Reservoirs","volume":"223","author":"Song","year":"2012","journal-title":"Water Air Soil Pollut."},{"key":"ref_231","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s10661-015-4607-2","article-title":"Water Quality Monitoring of Al-Habbaniyah Lake Using Remote Sensing and in Situ Measurements","volume":"187","author":"Rabee","year":"2015","journal-title":"Environ. Monit. Assess."},{"key":"ref_232","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.isprsjprs.2015.05.009","article-title":"Improved Capabilities of the Chinese High-Resolution Remote Sensing Satellite GF-1 for Monitoring Suspended Particulate Matter (SPM) in Inland Waters: Radiometric and Spatial Considerations","volume":"106","author":"Li","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_233","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.pce.2017.02.013","article-title":"Remote Sensing of Surface Water Quality in Relation to Catchment Condition in Zimbabwe","volume":"100","author":"Masocha","year":"2017","journal-title":"Phys. Chem. Earth"},{"key":"ref_234","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1016\/j.scitotenv.2018.04.006","article-title":"Quantifying Suspended Solids in Small Rivers Using Satellite Data","volume":"634","author":"Isidro","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_235","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s10661-018-6506-9","article-title":"Monitoring Water Quality in a Hypereutrophic Reservoir Using Landsat ETM+ and OLI Sensors: How Transferable Are the Water Quality Algorithms?","volume":"190","author":"Deutsch","year":"2018","journal-title":"Environ. Monit. Assess."},{"key":"ref_236","doi-asserted-by":"crossref","first-page":"106236","DOI":"10.1016\/j.ecolind.2020.106236","article-title":"Chlorophyll-a and Total Suspended Solids Retrieval and Mapping Using Sentinel-2A and Machine Learning for Inland Waters","volume":"113","author":"Saberioon","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_237","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.48084\/etasr.2664","article-title":"A Satellite-Based Remote Sensing Technique for Surface Water Quality Estimation","volume":"9","author":"Japitana","year":"2019","journal-title":"Eng. Technol. Appl. Sci. Res."},{"key":"ref_238","doi-asserted-by":"crossref","first-page":"50","DOI":"10.21833\/ijaas.2019.05.009","article-title":"Landsat Data to Estimate a Model of Water Quality Parameters in Tigris and Euphrates Rivers\u2014Iraq","volume":"6","author":"Abbas","year":"2019","journal-title":"Int. J. Adv. Appl. Sci."},{"key":"ref_239","first-page":"1755","article-title":"Estimating Total Dissolved Solids and Total Suspended Solids in Mosul Dam Lake in Situ and Using Remote Sensing Technique","volume":"7","author":"Aljoborey","year":"2019","journal-title":"Period. Eng. Nat. Sci. USA"},{"key":"ref_240","doi-asserted-by":"crossref","first-page":"134","DOI":"10.3103\/S1063455X2002006X","article-title":"Water Quality of River Beas, India, and Its Correlation with Reflectance Data","volume":"42","author":"Kumar","year":"2020","journal-title":"J. Water Chem. Technol."},{"key":"ref_241","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s13201-020-01352-7","article-title":"Modelling of Total Dissolved Solids in Water Supply Systems Using Regression and Supervised Machine Learning Approaches","volume":"11","author":"Ewusi","year":"2021","journal-title":"Appl. Water Sci."},{"key":"ref_242","doi-asserted-by":"crossref","first-page":"126032","DOI":"10.1016\/j.jhydrol.2021.126032","article-title":"Mapping the Spatiotemporal Variability of Salinity in the Hypersaline Lake Urmia Using Sentinel-2 and Landsat-8 Imagery","volume":"595","author":"Bayati","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_243","doi-asserted-by":"crossref","first-page":"35","DOI":"10.17501\/2513258X.2019.3103","article-title":"Detection of Total Dissolved Solids from Landsat 8 OLI Image in Coastal Bangladesh","volume":"3","author":"Ferdous","year":"2019","journal-title":"Int. Conf. Clim. Chang."},{"key":"ref_244","doi-asserted-by":"crossref","unstructured":"Ali Shaikh, T., Ahmad, S., and Stephen, H. (2021, January 7\u201311). Assessing Spatiotemporal Change in Land Cover and Total Dissolved Solids Concentration Using Remote Sensing Data. Proceedings of the World Environmental and Water Resources Congress, Virtually.","DOI":"10.1061\/9780784483466.036"},{"key":"ref_245","doi-asserted-by":"crossref","first-page":"2983","DOI":"10.1109\/TGRS.2018.2879024","article-title":"Assessment of Surface Water Quality by Using Satellite Images Fusion Based on PCA Method in the Lake Gala, Turkey","volume":"57","author":"Batur","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_246","doi-asserted-by":"crossref","first-page":"1490","DOI":"10.1016\/j.asr.2019.12.007","article-title":"Mapping Water Salinity Using Landsat-8 OLI Satellite Images (Case Study: Karun Basin Located in Iran)","volume":"65","author":"Ansari","year":"2020","journal-title":"Adv. Space Res."},{"key":"ref_247","doi-asserted-by":"crossref","unstructured":"Sun, D., Su, X., Qiu, Z., Wang, S., Mao, Z., and He, Y. (2019). Remote Sensing Estimation of Sea Surface Salinity from GOCI Measurements in the Southern Yellow Sea. Remote Sens., 11.","DOI":"10.3390\/rs11070775"},{"key":"ref_248","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.ecss.2017.01.008","article-title":"Remotely Sensed Sea Surface Salinity in the Hyper-Saline Arabian Gulf: Application to Landsat 8 OLI Data","volume":"187","author":"Zhao","year":"2017","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_249","first-page":"62","article-title":"Integrated Remote Sensing and GIS Approach for Water Quality Analysis of Gomti River, Uttar Pradesh","volume":"3","author":"Somvanshi","year":"2012","journal-title":"Int. J. Environ. Sci."},{"key":"ref_250","first-page":"102301","article-title":"Lockdown Effects on Total Suspended Solids Concentrations in the Lower Min River (China) during COVID-19 Using Time-Series Remote Sensing Images","volume":"98","author":"Xu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_251","doi-asserted-by":"crossref","first-page":"112386","DOI":"10.1016\/j.rse.2021.112386","article-title":"Remotely Estimating Total Suspended Solids Concentration in Clear to Extremely Turbid Waters Using a Novel Semi-Analytical Method","volume":"258","author":"Jiang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_252","doi-asserted-by":"crossref","unstructured":"Di Trapani, A., Corbari, C., and Mancini, M. (2020). Effect of the Three Gorges Dam on Total Suspended Sediments from Modis and Landsat Satellite Data. Water, 12.","DOI":"10.3390\/w12113259"},{"key":"ref_253","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.ecss.2015.01.018","article-title":"Estimation of Total Suspended Matter Concentration from MODIS Data Using a Neural Network Model in the China Eastern Coastal Zone","volume":"155","author":"Chen","year":"2015","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_254","doi-asserted-by":"crossref","unstructured":"Kang, W., Lee, K., and Kim, S. (2023). Use of Underwater-Image Color to Determine Suspended-Sediment Concentrations Transported to Coastal Regions. Appl. Sci., 13.","DOI":"10.3390\/app13127219"},{"key":"ref_255","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1007\/s11852-017-0564-y","article-title":"Using MODIS Remote Sensing Data for Mapping the Spatio-Temporal Variability of Water Quality and River Turbid Plume","volume":"21","author":"Hamidi","year":"2017","journal-title":"J. Coast. Conserv."},{"key":"ref_256","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.crte.2017.08.004","article-title":"Retrieval of Suspended Sediment Concentrations Using Landsat-8 OLI Satellite Images in the Orinoco River (Venezuela)","volume":"350","author":"Yepez","year":"2018","journal-title":"Comptes Rendus\u2014Geosci."},{"key":"ref_257","doi-asserted-by":"crossref","first-page":"4347","DOI":"10.5194\/gmd-10-4347-2017","article-title":"A Landsat-Based Model for Retrieving Total Suspended Solids Concentration of Estuaries and Coasts in China","volume":"10","author":"Wang","year":"2017","journal-title":"Geosci. Model Dev."},{"key":"ref_258","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2014.02.011","article-title":"A Three-Band Semi-Analytical Model for Deriving Total Suspended Sediment Concentration from HJ-1A\/CCD Data in Turbid Coastal Waters","volume":"93","author":"Chen","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_259","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1080\/01431161.2016.1275056","article-title":"Mapping Concentrations of Surface Water Quality Parameters Using a Novel Remote Sensing and Artificial Intelligence Framework","volume":"38","author":"Zhang","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_260","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1080\/2150704X.2013.830203","article-title":"Assessing Water Quality in the Northern Adriatic Sea from Hicotm Data","volume":"4","author":"Braga","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_261","unstructured":"Normand, A.E. (2020). Landsat 9 and the Future of the Sustainable Land Imaging Program."},{"key":"ref_262","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2011WR011005","article-title":"Evaluation of Medium to Low Resolution Satellite Imagery for Regional Lake Water Quality Assessments","volume":"47","author":"Olmanson","year":"2011","journal-title":"Water Resour. Res."},{"key":"ref_263","doi-asserted-by":"crossref","unstructured":"Garcia, R.A., Lee, Z., and Hochberg, E.J. (2018). Hyperspectral Shallow-Water Remote Sensing with an Enhanced Benthic Classifier. Remote Sens., 10.","DOI":"10.3390\/rs10010147"},{"key":"ref_264","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1080\/01431168108948342","article-title":"Remote Sensing of Bottom Reflectance and Water Attenuation Parameters in Shallow Water Using Aircraft and Landsat Data","volume":"2","author":"Lyzenga","year":"1981","journal-title":"Int. J. Remote Sens."},{"key":"ref_265","unstructured":"Rahman, M.T. (2017). Remote Sensing Techniques and GIS Applications in Earth and Environmental Studies, IGI Global."},{"key":"ref_266","unstructured":"Chang, J., and Clay, E.D. (2016). iGrow Corn: Best Management Practices, South Dakota State University."},{"key":"ref_267","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1007\/s10661-011-2053-3","article-title":"Retrieval of Total Suspended Matter (TSM) and Chlorophyll-a (Chl-a) Concentration from Remote-Sensing Data for Drinking Water Resources","volume":"184","author":"Song","year":"2011","journal-title":"Environ. Monit. Assess."},{"key":"ref_268","doi-asserted-by":"crossref","first-page":"4643924","DOI":"10.1155\/2022\/4643924","article-title":"Assessment of Sentinel-2 and Landsat-8 OLI for Small-Scale Inland Water Quality Modeling and Monitoring Based on Handheld Hyperspectral Ground Truthing","volume":"2022","author":"Abdelal","year":"2022","journal-title":"J. Sens."},{"key":"ref_269","unstructured":"Dunn, A.M., Hofmann, O.S., Waters, B., and Witchel, E. (2011, January 10\u201312). Cloaking Malware with the Trusted Platform Module. Proceedings of the 20th USENIX Security Symposium (USENIX Security 11), San Francisco, CA, USA."},{"key":"ref_270","doi-asserted-by":"crossref","unstructured":"Bui, Q.T., Jamet, C., Vantrepotte, V., M\u00e9riaux, X., Cauvin, A., and Mograne, M.A. (2022). Evaluation of Sentinel-2\/MSI Atmospheric Correction Algorithms over Two Contrasted French Coastal Waters. Remote Sens., 14.","DOI":"10.3390\/rs14051099"},{"key":"ref_271","unstructured":"Adjovu, G.E. (2020). Evaluating the Performance of A GIS-Based Tool for Delineating Swales Along Two Highways in Tennessee. [Master\u2019s Thesis, Tennessee Technological University]."},{"key":"ref_272","doi-asserted-by":"crossref","first-page":"885","DOI":"10.13031\/2013.23153","article-title":"Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations","volume":"50","author":"Moriasi","year":"2007","journal-title":"Trans. ASABE"},{"key":"ref_273","doi-asserted-by":"crossref","first-page":"837","DOI":"10.4236\/jep.2015.68076","article-title":"Sensitivity Analysis and Calibration of Hydrological Modeling of the Watershed Northeast Brazil","volume":"6","author":"Almeida","year":"2015","journal-title":"J. Environ. Prot."},{"key":"ref_274","unstructured":"Adjovu, G.E., and Gamble, R. (2019, January 10\u201312). Development of HEC-HMS Model for the Cane Creek Watershed. Proceedings of the 28th Tennessee Water Resources Symposium Tennessee Section of the American Water Resources Association, Montgomery Bell State Park, Burns, TN, USA. Available online: https:\/\/img1.wsimg.com\/blobby\/go\/12ed7af3-57dc-468c-af58-da8360f35f16\/downloads\/Proceedings2019.pdf?ver=1618503482462."},{"key":"ref_275","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, X., Xu, C., and Tang, X. (2023). Dissolved Oxygen Prediction Model for the Yangtze River Estuary Basin Using IPSO-LSSVM. Water, 15.","DOI":"10.3390\/w15122206"},{"key":"ref_276","doi-asserted-by":"crossref","first-page":"12858","DOI":"10.1038\/s41598-017-12853-y","article-title":"Evaluation of Water Quality Based on a Machine Learning Algorithm and Water Quality Index for the Ebinur Lake Watershed, China","volume":"7","author":"Wang","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_277","doi-asserted-by":"crossref","first-page":"101414","DOI":"10.1016\/j.ecoinf.2021.101414","article-title":"Machine Learning Models Applied to TSS Estimation in a Reservoir Using Multispectral Sensor Onboard to RPA","volume":"65","author":"Dias","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_278","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.rse.2017.08.033","article-title":"Sentinel-2 MultiSpectral Instrument (MSI) Data Processing for Aquatic Science Applications: Demonstrations and Validations","volume":"201","author":"Pahlevan","year":"2017","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3534\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:11:33Z","timestamp":1760127093000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3534"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,13]]},"references-count":278,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15143534"],"URL":"https:\/\/doi.org\/10.3390\/rs15143534","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,13]]}}}