{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T07:01:53Z","timestamp":1781247713485,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,9,1]],"date-time":"2018-09-01T00:00:00Z","timestamp":1535760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["1R01AI123931-01A1"],"award-info":[{"award-number":["1R01AI123931-01A1"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1069213"],"award-info":[{"award-number":["1069213"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Total suspended solids (TSS) is an important environmental parameter to monitor in the Chesapeake Bay due to its effects on submerged aquatic vegetation, pathogen abundance, and habitat damage for other aquatic life. Chesapeake Bay is home to an extensive and continuous network of in situ water quality monitoring stations that include TSS measurements. Satellite remote sensing can address the limited spatial and temporal extent of in situ sampling and has proven to be a valuable tool for monitoring water quality in estuarine systems. Most algorithms that derive TSS concentration in estuarine environments from satellite ocean color sensors utilize only the red and near-infrared bands due to the observed correlation with TSS concentration. In this study, we investigate whether utilizing additional wavelengths from the Moderate Resolution Imaging Spectroradiometer (MODIS) as inputs to various statistical and machine learning models can improve satellite-derived TSS estimates in the Chesapeake Bay. After optimizing the best performing multispectral model, a Random Forest regression, we compare its results to those from a widely used single-band algorithm for the Chesapeake Bay. We find that the Random Forest model modestly outperforms the single-band algorithm on a holdout cross-validation dataset and offers particular advantages under high TSS conditions. We also find that both methods are similarly generalizable throughout various partitions of space and time. The multispectral Random Forest model is, however, more data intensive than the single band algorithm, so the objectives of the application will ultimately determine which method is more appropriate.<\/jats:p>","DOI":"10.3390\/rs10091393","type":"journal-article","created":{"date-parts":[[2018,9,3]],"date-time":"2018-09-03T10:50:51Z","timestamp":1535971851000},"page":"1393","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Can Multispectral Information Improve Remotely Sensed Estimates of Total Suspended Solids? A Statistical Study in Chesapeake Bay"],"prefix":"10.3390","volume":"10","author":[{"given":"Nicole M.","family":"DeLuca","sequence":"first","affiliation":[{"name":"Department of Earth and Planetary Sciences, Johns Hopkins University, 3400 N. Charles Street, Olin Hall, Baltimore, MD 21218, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin F.","family":"Zaitchik","sequence":"additional","affiliation":[{"name":"Department of Earth and Planetary Sciences, Johns Hopkins University, 3400 N. Charles Street, Olin Hall, Baltimore, MD 21218, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank C.","family":"Curriero","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolf Street, Baltimore, MD 21205, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1111\/j.1758-2229.2009.00123.x","article-title":"Serodiversity and ecological distribution of Vibrio parahaemolyticus in the Venetian Lagoon, Northeast Italy","volume":"2","author":"Caburlotto","year":"2010","journal-title":"Environ. Microbiol. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7249","DOI":"10.1128\/AEM.01296-12","article-title":"Ecology of Vibrio parahaemolyticus and Vibrio vulnificus in the Coastal and Estuarine Waters of Louisiana, Maryland, Mississippi, and Washington (United States)","volume":"78","author":"Johnson","year":"2012","journal-title":"Appl. Environ. Microbiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e01147-17","DOI":"10.1128\/AEM.01147-17","article-title":"Environmental determinants of Vibrio parahaemolyticus in the Chesapeake Bay","volume":"83","author":"Davis","year":"2017","journal-title":"Appl. Environ. Microbiol."},{"key":"ref_4","first-page":"1","article-title":"Sediment transport in Chesapeake Bay during floods: Analysis using satellite and surface observations","volume":"4","author":"Stumpf","year":"1988","journal-title":"J. Coastal Res."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"3213","DOI":"10.5194\/bg-9-3213-2012","article-title":"Optical characterisation of suspended particles in the Mackenzie River plume (Canadian Arctic Ocean) and implications for ocean colour remote sensing","volume":"9","author":"Doxaran","year":"2012","journal-title":"Biogeosciences"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1080\/01431160902893485","article-title":"Detecting the spatial and temporal variability of chlorophyll-a concentration and total suspended solids in Apalachicola Bay, Florida using MODIS imagery","volume":"31","author":"Wang","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.marenvres.2011.09.014","article-title":"An enhanced MODIS remote sensing model for detecting rainfall effects on sediment plume in the coastal waters of Apalachicola Bay","volume":"72","author":"Chen","year":"2011","journal-title":"Mar. Environ. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6653","DOI":"10.1080\/01431161.2010.512938","article-title":"A study of sediment transport in a shallow estuary using MODIS imagery and particle tracking simulation","volume":"32","author":"Zhao","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","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 material algorithm for the Chesapeake Bay","volume":"119","author":"Ondrusek","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1016\/j.rse.2013.10.002","article-title":"Influence of the three gorges dam on total suspended matters in the Yangtze estuary and its adjacent coastal waters: Observations from MODIS","volume":"140","author":"Feng","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4173","DOI":"10.1080\/01431161.2014.916053","article-title":"Satellite multi-sensor mapping of suspended particulate matter in turbid estuarine and coastal ocean, China","volume":"35","author":"Shen","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Han, B., Loisel, H., Vantrepotte, V., M\u00e9riaux, X., Bry\u00e9re, P., Ouillon, S., Dessailly, D., Xing, Q., and Zhu, J. (2016). Development of a semi-analytical algorithm for the retrieval of suspended particulate matter from remote sensing over clear to very turbid waters. Remote Sens., 8.","DOI":"10.3390\/rs8030211"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.ecss.2017.04.002","article-title":"Resiliency of the western Chesapeake Bay to total suspended solid concentrations following storms and accounting for land-cover","volume":"191","author":"Hasan","year":"2017","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"13018","DOI":"10.1364\/OE.21.013018","article-title":"A semi-analytical total suspended sediment retrieval model in turbid coastal waters: A case study in Changjiang River Estuary","volume":"21","author":"Chen","year":"2013","journal-title":"Opt. Express"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"14363","DOI":"10.1029\/JC094iC10p14363","article-title":"Calibration of a general optical equation for remote sensing of suspended sediments in a moderately turbid estuary","volume":"94","author":"Stumpf","year":"1989","journal-title":"J. Geophys. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.ecss.2006.02.016","article-title":"Bio-optics of the Chesapeake Bay from measurements and radiative transfer closure","volume":"68","author":"Tzortziou","year":"2006","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8707","DOI":"10.1002\/2017JC013146","article-title":"Estimating particulate inorganic carbon concentrations of the global ocean from ocean color measurements using a reflectance difference approach","volume":"122","author":"Mitchell","year":"2017","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"27891","DOI":"10.1364\/OE.21.027891","article-title":"A simple optical model to estimate suspended particulate matter in Yellow River Estuary","volume":"21","author":"Qiu","year":"2013","journal-title":"Opt. Express"},{"key":"ref_21","first-page":"1","article-title":"Evaluation of Empirical and Semianalytical Spectral Reflectance Models for Surface Suspended Sediment Concentration in the Highly Variable Estuarine and Coastal Waters of East China","volume":"9","author":"Sokoletsky","year":"2016","journal-title":"IEEE J. Sel. Top. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"260","DOI":"10.4319\/lo.1978.23.2.0260","article-title":"Optical classification of natural waters","volume":"23","author":"Smith","year":"1978","journal-title":"Limnol. Oceanogr."},{"key":"ref_23","unstructured":"Bukata, R.P., Jerome, J.H., Kondratyev, K.Y., and Pozdnyakov, D.V. (1995). Optical Properties and Remote Sensing of Inland and Coastal Waters, CRC Press."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3354\/meps303001","article-title":"Eutrophication of Chesapeake Bay: Historical trends and ecological interactions","volume":"303","author":"Kemp","year":"2005","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.4319\/lo.1989.34.7.1235","article-title":"Rates and patterns of estuarine sediment accumulation","volume":"34","author":"Brush","year":"1989","journal-title":"Limnol. Oceanogr."},{"key":"ref_26","unstructured":"(2017, December 12). Clean Water Act Section 303(d): Notice for the Establishment of the Total Maximum Daily Load (TMDL) for the Chesapeake Bay (EPA), Available online: Https:\/\/www.gpo.gov\/fdsys\/pkg\/FR-2011-01-05\/ pdf\/2010-33280.pdf."},{"key":"ref_27","unstructured":"(2017, August 16). NASA\u2019s OceanColor Web, Available online: Https:\/\/oceancolor.gsfc.nasa.gov."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7521","DOI":"10.1364\/OE.18.007521","article-title":"Estimation of near-infrared water-leaving reflectance for satellite ocean color data processing","volume":"18","author":"Bailey","year":"2010","journal-title":"Opt. Express"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.rse.2013.06.018","article-title":"Spatially resolving ocean color and sediment dispersion in river plumes, coastal systems, and continental shelf waters","volume":"137","author":"Aurin","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_30","unstructured":"(2017, August 16). Chesapeake Bay Program Water Quality Database (1984-present). Available online: http:\/\/www.chesapeakebay.net\/what\/downloads\/cbp_water_quality_database_1984_present."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/j.rse.2012.04.008","article-title":"Remotely sensed estimates of surface salinity in the Chesapeake Bay: A statistical approach","volume":"123","author":"Urquhart","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_32","unstructured":"Nychka, D., Furrer, R., Paige, J., and Sain, S. (2017, August 16). Fields: Tools for Spatial Data. Available online: https:\/\/cran.r-project.org\/web\/packages\/fields\/fields.pdf."},{"key":"ref_33","unstructured":"R Core Team (2017, August 16). R: A Language and Environment for Statistical Computing. Available online: http:\/\/www.R-project.org."},{"key":"ref_34","unstructured":"Breiman, L., Freidman, J., Olshen, R., and Stone, C. (1984). Classification and Regression Trees, Chapman and Hall\/CRC."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1214\/09-AOAS285","article-title":"BART: Bayesian additive regression trees","volume":"4","author":"Chipman","year":"2010","journal-title":"Ann. Appl. Stat."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"370","DOI":"10.2307\/2344614","article-title":"Generalized linear models","volume":"135","author":"Nelder","year":"1972","journal-title":"J. R. Stat. Soc. Ser. A."},{"key":"ref_38","first-page":"297","article-title":"Generalized additive models","volume":"1","author":"Hastie","year":"1986","journal-title":"Stat. Sci."},{"key":"ref_39","first-page":"1","article-title":"Multivariate adaptive regression spline","volume":"19","author":"Friedman","year":"1991","journal-title":"Ann. Stat."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1109\/59.141695","article-title":"Short-term load forecasting using an artificial neural network","volume":"7","author":"Lee","year":"1992","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_42","first-page":"65","article-title":"A simple sequentially rejective multiple test procedure","volume":"6","author":"Holm","year":"1979","journal-title":"Scand. J. Stat."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Dorji, P., and Fearns, P. (2016). A quantitative comparison of total suspended sediment algorithms: A case study of the last decade for MODIS and Landsat-based sensors. Remote Sens., 8.","DOI":"10.3390\/rs8100810"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/9\/1393\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:18:18Z","timestamp":1760195898000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/9\/1393"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,1]]},"references-count":44,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2018,9]]}},"alternative-id":["rs10091393"],"URL":"https:\/\/doi.org\/10.3390\/rs10091393","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,1]]}}}