{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:41:38Z","timestamp":1780591298878,"version":"3.54.1"},"reference-count":38,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,6,19]],"date-time":"2019-06-19T00:00:00Z","timestamp":1560902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFB0504202"],"award-info":[{"award-number":["2017YFB0504202"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41622107"],"award-info":[{"award-number":["41622107"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special projects for technological innovation in Hubei","award":["2018ABA078"],"award-info":[{"award-number":["2018ABA078"]}]},{"name":"Open fund of state laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University","award":["18R02"],"award-info":[{"award-number":["18R02"]}]},{"name":"Open fund of State Key Laboratory of Geo-information Engineering","award":["SKLGIE2018-M-3-3"],"award-info":[{"award-number":["SKLGIE2018-M-3-3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Suspended solids concentration (SSC) is an important indicator of the degree of water pollution. However, when using an empirical or semi-empirical model adapted to some of the inland waters to estimate SSC on unmanned aerial vehicle (UAV)-borne hyperspectral images, the accuracy is often not sufficient. Thus, in this study, we attempted to use the particle swarm optimization (PSO) algorithm to find the optimal parameters of the least-squares support vector machine (LSSVM) model for the quantitative inversion of SSC. A reservoir and a polluted riverway were selected as the study areas. The spectral data of the 36-point and 29-point 400\u2013900 nm wavelength range on the UAV-borne images were extracted. Compared with the semi-empirical model, the random forest (RF) algorithm and the competitive adaptive reweighted sampling (CARS) algorithm combined with partial least squares (PLS), the accuracy of the PSO-LSSVM algorithm in predicting the SSC was significantly improved. The training samples had a coefficient of determination (     R 2     ) of 0.98, a root mean square error (RMSE) of 0.68 mg\/L, and a mean absolute percentage error (MAPE) of 12.66% at the reservoir. For the polluted riverway, PSO-LSSVM also performed well. Finally, the established SSC inversion model was applied to UAV-borne hyperspectral remote sensing (HRS) images. The results confirmed that the distribution of the predicted SSC was consistent with the observed results in the field, which proves that PSO-LSSVM is a feasible approach for the SSC inversion of UAV-borne HRS images.<\/jats:p>","DOI":"10.3390\/rs11121455","type":"journal-article","created":{"date-parts":[[2019,6,19]],"date-time":"2019-06-19T10:43:32Z","timestamp":1560941012000},"page":"1455","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Inland Waters Suspended Solids Concentration Retrieval Based on PSO-LSSVM for UAV-Borne Hyperspectral Remote Sensing Imagery"],"prefix":"10.3390","volume":"11","author":[{"given":"Lifei","family":"Wei","sequence":"first","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Can","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9446-5850","authenticated-orcid":false,"given":"Yanfei","family":"Zhong","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhou","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Hu","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liqun","family":"Lin","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,19]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1007\/s13131-016-0802-4","article-title":"Parameter selection and model research on remote sensing evaluation for nearshore water quality","volume":"35","author":"Lei","year":"2016","journal-title":"Acta Oceanol. Sin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.rse.2013.06.020","article-title":"Retrieval of the seawater reflectance for suspended solids monitoring in the East China Sea using MODIS, MERIS and GOCI satellite data","volume":"146","author":"Doxaran","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.rse.2010.07.013","article-title":"Remote sensing retrieval of suspended sediment concentration in shallow waters","volume":"115","author":"Volpe","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1659\/MRD-JOURNAL-D-09-00042.1","article-title":"Remote Sensing of Suspended Particulate Matter in Himalayan Lakes: A Case Study of Alpine Lakes in the Mount Everest Region","volume":"30","author":"Giardino","year":"2010","journal-title":"Mt. Res. Dev."},{"key":"ref_6","first-page":"505","article-title":"Research Advance and Application Prospect of Unmanned Aerial Vehicle Remote Sensing System","volume":"39","author":"Li","year":"2014","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MGRS.2018.2867592","article-title":"Mini-UAV-Borne Hyperspectral Remote Sensing: From Observation and Processing to Applications","volume":"6","author":"Zhong","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_8","unstructured":"Ma, R., Duan, H., Tang, J., and Chen, Z. (2010). Remote Sensing of Lake Water Environment, Science Press. [1st ed.]."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.rse.2006.12.017","article-title":"Assessment of water quality in Lake Garda (Italy) using Hyperion","volume":"109","author":"Giardino","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/S0048-9697(00)00688-4","article-title":"Detection of water quality using simulated satellite data and semi-empirical algorithms in Finland","volume":"268","author":"Hannonen","year":"2001","journal-title":"Sci. Total Environ."},{"key":"ref_11","first-page":"285","article-title":"Development of Suspended Particulate Matter Algorithms for Ocean Color Remote Sensing","volume":"17","author":"Ahn","year":"2001","journal-title":"Korean J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1080\/01431169308953956","article-title":"Quantitative remote sensing methods for real-time monitoring of inland waters quality","volume":"14","author":"Gitelson","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, Z., Kawamura, K., Sakuno, Y., Fan, X., Gong, Z., and Lim, J. (2017). Retrieval of Chlorophyll-a and Total Suspended Solids Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression Based on Field Hyperspectral Measurements in Irrigation Ponds in Higashihiroshima, Japan. Remote Sens., 9.","DOI":"10.3390\/rs9030264"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hafeez, S., Wong, M., Ho, H., Nazeer, M., Nichol, J., Abbas, S., Tang, D., Lee, K., 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_15","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.aca.2011.07.027","article-title":"Support vector machines in water quality management","volume":"703","author":"Singh","year":"2012","journal-title":"Anal. Chim. Acta"},{"key":"ref_16","unstructured":"Mueller, J.L., Fargion, G.S., McClain, C.R., Mueller, J.L., Morel, A., Frouin, R., Davis, C., Arnone, R., Carder, K., and Steward, R.G. (2003). Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Radiometric Measurements and Data Analysis Protocols."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10391","DOI":"10.3390\/ijerph120910391","article-title":"Estimation of Chlorophyll-a Concentration and the Trophic State of the Barra Bonita Hydroelectric Reservoir Using OLI\/Landsat-8 Images","volume":"12","author":"Watanabe","year":"2015","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_18","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_19","first-page":"910","article-title":"Correlations Between Water Quality Indexes and Reflectance Spectra of Huangpujiang River","volume":"10","author":"Gong","year":"2006","journal-title":"J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1016\/j.trac.2009.07.007","article-title":"Review of the most common pre-processing techniques for near-infrared spectra","volume":"28","author":"Rinnan","year":"2009","journal-title":"Trends Anal. Chem."},{"key":"ref_21","first-page":"195","article-title":"Remote Measurement of Algal Chlorophyll in Surface Waters: The Case for the First Derivative of Reflectance Near 690 nm","volume":"62","author":"Rundquist","year":"1996","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1080\/01431160903382892","article-title":"Spectral indices for estimating ecological indicators of karst rocky desertification","volume":"31","author":"Zhang","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/S0048-9697(00)00687-2","article-title":"A semi-operative approach to lake water quality retrieval from remote sensing data","volume":"268","author":"Pulliainen","year":"2001","journal-title":"Sci. Total Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least Squares Support Vector Machine Classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neural Process. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"10574","DOI":"10.1016\/j.eswa.2011.02.107","article-title":"A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting","volume":"38","author":"Ismail","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yan, Z.G. (2012, January 6). The PSO-LSSVM model for predicting the failure depth of coal seam floor. Proceedings of the Intelligent Control & Automation, Beijing, China.","DOI":"10.1109\/WCICA.2012.6357944"},{"key":"ref_28","first-page":"1941","article-title":"Particle swarm optimization","volume":"12","author":"Kennedy","year":"1995","journal-title":"Proc. IEEE Int. Conf. Neural. Netw. Piscataway. IEEE Serv. Cent."},{"key":"ref_29","unstructured":"Eberhart, R.C., and Shi, Y. (2002, January 10). Particle swarm optimization: Developments, applications and resources. Proceedings of the Congress on Evolutionary Computation, Wellington, New Zealand."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Eberhart, R.C., and Shi, Y. (,  1998). Comparison between genetic algorithms and particle swarm optimization. Proceedings of the International Conference on Evolutionary Programming, Berlin, Germany.","DOI":"10.1007\/BFb0040812"},{"key":"ref_31","first-page":"154","article-title":"A Particle Swarm Optimization Algorithm Based on Dynamic Intertia Weight","volume":"24","author":"Zhu","year":"2007","journal-title":"Comput. Simul."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1061\/(ASCE)0733-9496(2007)133:3(192)","article-title":"Multipurpose Reservoir Operation Using Particle Swarm Optimization","volume":"133","author":"Kumar","year":"2007","journal-title":"J. Water Resour. Plan. Manag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/j.aei.2012.03.007","article-title":"Hybrid particle swarm optimization and differential evolution for optimal design of water distribution systems","volume":"26","author":"Sedki","year":"2012","journal-title":"Adv. Eng. Inform."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.rse.2014.06.016","article-title":"Validation of MERIS spectral inversion processors using reflectance, IOP and water quality measurements in boreal lakes","volume":"157","author":"Kallio","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_35","first-page":"1463","article-title":"The response of both surface reflectance and the underwater light field to various levels of suspended sediments: Preliminary results","volume":"60","author":"Han","year":"1994","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1029\/2010GL043227","article-title":"Optically black waters in the northern Baltic Sea","volume":"37","author":"Berthon","year":"2010","journal-title":"Geophys. Res. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.scitotenv.2014.02.113","article-title":"Optical characterization of black water blooms in eutrophic waters","volume":"482\u2013483","author":"Duan","year":"2014","journal-title":"Sci. Total Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S0034-4257(01)00341-8","article-title":"Spectral signature of highly turbid waters: Application with SPOT data to quantify suspended particulate matter concentrations","volume":"81","author":"Doxaran","year":"2002","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/12\/1455\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:59:42Z","timestamp":1760187582000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/12\/1455"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,19]]},"references-count":38,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["rs11121455"],"URL":"https:\/\/doi.org\/10.3390\/rs11121455","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,19]]}}}