{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T03:33:25Z","timestamp":1776483205541,"version":"3.51.2"},"reference-count":27,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"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 article focuses on developing models that estimate suspended sediment concentrations (SSCs) for the Lower Brazos River, Texas, U.S. Historical samples of SSCs from gauge stations and satellite imagery from Landsat Missions and Sentinel Mission 2 were utilized to develop models to estimate SSCs for the Lower Brazos River. The models used in this study to accomplish this goal include support vector machines (SVMs), artificial neural networks (ANNs), extreme learning machines (ELMs), and exponential relationships. In addition, flow measurements were used to develop rating curves to estimate SSCs for the Brazos River as a baseline comparison of the models that used satellite imagery to estimate SSCs. The models were evaluated using a Taylor Diagram analysis on the test data set developed for the Brazos River data. Fifteen of the models developed using satellite imagery as inputs performed with a coefficient of determination R2 above 0.69, with the three best performing models having an R2 of 0.83 to 0.85. One of the best performing models was then utilized to estimate the SSCs before, during, and after Hurricane Harvey to evaluate the impact of this storm on the sediment dynamics along the Lower Brazos River and the model\u2019s ability to estimate SSCs.<\/jats:p>","DOI":"10.3390\/rs16101727","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T11:18:17Z","timestamp":1715599097000},"page":"1727","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Estimation of Suspended Sediment Concentration along the Lower Brazos River Using Satellite Imagery and Machine Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Trevor","family":"Stull","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, University of Texas at Arlington, Arlington, TX 76019, USA"}]},{"given":"Habib","family":"Ahmari","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, University of Texas at Arlington, Arlington, TX 76019, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s10661-020-08291-5","article-title":"Modeling water quality impacts from hurricanes and extreme weather events in urban coastal systems using Sentinel-2 spectral data","volume":"192","author":"Sobel","year":"2020","journal-title":"Environ. Monit. Assess."},{"key":"ref_2","unstructured":"Coonrod, J.E.A. (1998). Suspended Sediment Yield in Texas Watersheds, The University of Texas at Austin."},{"key":"ref_3","unstructured":"Walling, D.E., and Moorehead, P.W. (1989). Sediment\/Water Interactions: Proceedings of the Fourth International Symposium, Springer."},{"key":"ref_4","first-page":"251","article-title":"Contribution of gully erosion to sediment production on cultivated lands and rangelands","volume":"236","author":"Poesen","year":"1996","journal-title":"IAHS Publ.-Ser. Proc. Rep.-Intern. Assoc. Hydrol. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.1002\/hyp.6509","article-title":"Suspended sediment and discharge relationships to identify bank degradation as a main sediment source on small agricultural catchments","volume":"21","author":"Grimaldi","year":"2007","journal-title":"Hydrol. Process. Int. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.catena.2004.11.002","article-title":"Can flocs and water stable soil aggregates be differentiated within fluvial systems?","volume":"60","author":"Droppo","year":"2005","journal-title":"Catena"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"233","DOI":"10.3389\/fmars.2017.00233","article-title":"Estuarine suspended sediment dynamics: Observations derived from over a decade of satellite data","volume":"4","author":"Reisinger","year":"2017","journal-title":"Front. Mar. Sci."},{"key":"ref_9","first-page":"51","article-title":"The impact of particle size controls on stream turbidity measurement; some implications for suspended sediment yield estimation","volume":"210","author":"Foster","year":"1992","journal-title":"Eros. Sediment Transp. Monit. Programmes River Basins"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.2134\/jeq2009.0280","article-title":"Increasing precision of turbidity-based suspended sediment concentration and load estimates","volume":"39","author":"Jastram","year":"2010","journal-title":"J. Environ. Qual."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1002\/hyp.6763","article-title":"Estimating suspended sediment concentrations from turbidity measurements and the calibration problem","volume":"22","author":"Minella","year":"2008","journal-title":"Hydrol. Process. Int. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4909","DOI":"10.1007\/s11269-017-1785-4","article-title":"Estimate of suspended sediment concentration from monitored data of turbidity and water level using artificial neural networks","volume":"31","author":"Sari","year":"2017","journal-title":"Water Resour. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1016\/j.jhydrol.2016.07.048","article-title":"Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction","volume":"541","author":"Afan","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"125011","DOI":"10.1016\/j.jhydrol.2020.125011","article-title":"Two decades on the artificial intelligence models advancement for modeling river sediment concentration: State-of-the-art","volume":"588","author":"Rajaee","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_15","first-page":"W09531","article-title":"Evaluating the potential for remote bathymetric mapping of a turbid, sand-bed river: 1. Field spectroscopy and radiative transfer modeling","volume":"47","author":"Legleiter","year":"2011","journal-title":"Water Resour. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.isprsjprs.2005.09.003","article-title":"Characterizing the vertical diffuse attenuation coefficient for downwelling irradiance in coastal waters: Implications for water penetration by high resolution satellite data","volume":"60","author":"Mishra","year":"2005","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2251","DOI":"10.1109\/TGRS.2006.872909","article-title":"Multispectral bathymetry using a simple physically based algorithm","volume":"44","author":"Lyzenga","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","unstructured":"Phillips, J.D. (2006). Geomorphic Context, Constraints, and Change in the Lower Brazos and Navasota Rivers, Texas, Copperhead Road Geoscience."},{"key":"ref_19","unstructured":"Strom, K., and Hosseiny, H. (2015). Suspended Sediment Sampling and Annual Sediment Yield on the Middle Trinity River, University of Houston, Department of Civil and Environmental Engineering."},{"key":"ref_20","unstructured":"Texas Commission on Environmental Quality (2008). Monitoring Operations Division. Surface Water Quality Monitoring Procedures, Volume 1: Physical and Chemical Monitoring Methods."},{"key":"ref_21","unstructured":"U.S. Geological Survey (2023, June 28). USGS EROS Archive\u2014Sentinel-2\u2014Comparison of Sentinel-2 and Landsat, Available online: https:\/\/www.usgs.gov\/centers\/eros\/science\/usgs-eros-archive-sentinel-2-comparison-sentinel-2-and-landsat."},{"key":"ref_22","unstructured":"Burkov, A. (2023). The Hundred-Page Machine Learning Book, Andriy Burkov."},{"key":"ref_23","unstructured":"Burkov, A. (2020). Machine Learning Engineering, True Positive Incorporated."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7183","DOI":"10.1029\/2000JD900719","article-title":"Summarizing multiple aspects of model performance in a single diagram","volume":"106","author":"Taylor","year":"2001","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.isprsjprs.2014.10.006","article-title":"Estimating wide range Total Suspended Solids concentrations from MODIS 250-m imageries: An improved method","volume":"99","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1111\/1752-1688.12616","article-title":"Measuring suspended-sediment concentration and turbidity in the middle Mississippi and lower Missouri rivers using landsat data","volume":"54","author":"Pereira","year":"2018","journal-title":"JAWRA J. Am. Water Resour. Assoc."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1727\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:41:41Z","timestamp":1760107301000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1727"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":27,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16101727"],"URL":"https:\/\/doi.org\/10.3390\/rs16101727","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,13]]}}}