{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T19:21:41Z","timestamp":1769109701310,"version":"3.49.0"},"reference-count":96,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T00:00:00Z","timestamp":1715385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003981","name":"ASI-CNR","doi-asserted-by":"publisher","award":["20195HH0"],"award-info":[{"award-number":["20195HH0"]}],"id":[{"id":"10.13039\/501100003981","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003981","name":"ASI-CNR","doi-asserted-by":"publisher","award":["2022-15-U.0"],"award-info":[{"award-number":["2022-15-U.0"]}],"id":[{"id":"10.13039\/501100003981","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003981","name":"ASI-CNR","doi-asserted-by":"publisher","award":["CN00000033"],"award-info":[{"award-number":["CN00000033"]}],"id":[{"id":"10.13039\/501100003981","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Italian Ministry of University and Research, PNRR, Missione 4 Componente 2, \u201cDalla ricerca all\u2019impresa\u201d, Investimento 1.4","award":["20195HH0"],"award-info":[{"award-number":["20195HH0"]}]},{"name":"Italian Ministry of University and Research, PNRR, Missione 4 Componente 2, \u201cDalla ricerca all\u2019impresa\u201d, Investimento 1.4","award":["2022-15-U.0"],"award-info":[{"award-number":["2022-15-U.0"]}]},{"name":"Italian Ministry of University and Research, PNRR, Missione 4 Componente 2, \u201cDalla ricerca all\u2019impresa\u201d, Investimento 1.4","award":["CN00000033"],"award-info":[{"award-number":["CN00000033"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This work aims to show the potential of imaging spectroscopy in assessing water quality and aquatic vegetation in Lake Trasimeno, Italy. Hyperspectral reflectance data from the PRISMA, DESIS and EnMAP missions (2019\u20132022, summer periods) were compared with in situ measurements from WISPStation and used as inputs for water quality product generation algorithms. The bio-optical model BOMBER was run to simultaneously retrieve water quality parameters (Chlorophyll-a (Chl-a) and Total Suspended Matter, (TSM)) and the coverage of submerged and emergent macrophytes (SM, EM); value-added products, such as Phycocyanin concentration maps, were generated through a machine learning approach. The results showed radiometric agreement between satellite and in situ data, with R2 &gt; 0.9, a Spectral Angle &lt; 10\u00b0 and water quality mapping errors &lt; 30%. Both SM and EM coverage varied significantly from 2019 (135 ha, 0 ha, respectively) to 2022 (2672 ha, 343 ha), likely influenced by changes in rainfall and lake levels. The areas of greatest variability in Chl-a and TSM were identified in the littoral zones in the western side of the lake, while the highest variation in the fractional cover of SM and density of EM were observed in the south-eastern region; this information could support the water authorities\u2019 monitoring activities. To this end, further developments to improve the reference field data for the validation of water quality products are recommended.<\/jats:p>","DOI":"10.3390\/rs16101704","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T08:33:03Z","timestamp":1715589183000},"page":"1704","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8025-9179","authenticated-orcid":false,"given":"Alice","family":"Fabbretto","sequence":"first","affiliation":[{"name":"National Research Council, Institute of Electromagnetic Sensing of the Environment, 20133 Milan, Italy"},{"name":"Tartu Observatory, Department of Remote Sensing, University of Tartu, 61602 T\u00f5ravere, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7185-8464","authenticated-orcid":false,"given":"Mariano","family":"Bresciani","sequence":"additional","affiliation":[{"name":"National Research Council, Institute of Electromagnetic Sensing of the Environment, 20133 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4152-3409","authenticated-orcid":false,"given":"Andrea","family":"Pellegrino","sequence":"additional","affiliation":[{"name":"National Research Council, Institute of Electromagnetic Sensing of the Environment, 20133 Milan, Italy"},{"name":"University of Sapienza, Department of Engineering, 00185 Rome, Italy"}]},{"given":"Krista","family":"Alikas","sequence":"additional","affiliation":[{"name":"Tartu Observatory, Department of Remote Sensing, University of Tartu, 61602 T\u00f5ravere, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5289-8842","authenticated-orcid":false,"given":"Monica","family":"Pinardi","sequence":"additional","affiliation":[{"name":"National Research Council, Institute of Electromagnetic Sensing of the Environment, 20133 Milan, Italy"}]},{"given":"Salvatore","family":"Mangano","sequence":"additional","affiliation":[{"name":"National Research Council, Institute of Electromagnetic Sensing of the Environment, 20133 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1756-0947","authenticated-orcid":false,"given":"Rosalba","family":"Padula","sequence":"additional","affiliation":[{"name":"ARPA Umbria, Regional Environmental Protection Agency, 06132 Perugia, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3937-4988","authenticated-orcid":false,"given":"Claudia","family":"Giardino","sequence":"additional","affiliation":[{"name":"National Research Council, Institute of Electromagnetic Sensing of the Environment, 20133 Milan, Italy"},{"name":"NBFC, National Biodiversity Future Center, 90133 Palermo, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.4319\/lo.2009.54.6_part_2.2283","article-title":"Lakes as Sentinels of Climate Change","volume":"54","author":"Adrian","year":"2009","journal-title":"Limnol. Oceanogr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s10584-022-03457-1","article-title":"Indicators of the Effects of Climate Change on Freshwater Ecosystems","volume":"176","author":"Rose","year":"2023","journal-title":"Clim. Chang."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.oneear.2022.01.008","article-title":"The Role of Water Quality Monitoring in the Sustainable Use of Ambient Waters","volume":"5","author":"Chapman","year":"2022","journal-title":"One Earth"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1016\/j.scitotenv.2018.12.255","article-title":"Protecting and Restoring Europe\u2019s Waters: An Analysis of the Future Development Needs of the Water Framework Directive","volume":"658","author":"Carvalho","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4446","DOI":"10.1080\/01431161.2015.1083630","article-title":"Satellite-Based Products for Monitoring Optically Complex Inland Waters in Support of EU Water Framework Directive","volume":"36","author":"Alikas","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","unstructured":"(2024, March 05). European Commission Water Framework Directive. Available online: https:\/\/environment.ec.europa.eu\/topics\/water\/water-framework-directive_en."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"866712","DOI":"10.3389\/frsen.2022.866712","article-title":"International Intercomparison of In Situ Chlorophyll-a Measurements for Data Quality Assurance of the Swedish Monitoring Program","volume":"3","author":"Kratzer","year":"2022","journal-title":"Front. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1132346","DOI":"10.3389\/fenvs.2023.1132346","article-title":"Retrieve of Total Suspended Matter in Typical Lakes in China Based on Broad Bandwidth Satellite Data: Random Forest Model with Forel-Ule Index","volume":"11","author":"Zhai","year":"2023","journal-title":"Front. Environ. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.iswcr.2023.07.002","article-title":"Remote Estimates of Suspended Particulate Matter in Global Lakes Using Machine Learning Models","volume":"12","author":"Wen","year":"2024","journal-title":"Int. Soil Water Conserv. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"166363","DOI":"10.1016\/j.scitotenv.2023.166363","article-title":"Remote Estimation of Phycocyanin Concentration in Inland Waters Based on Optical Classification","volume":"899","author":"Lyu","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"113464","DOI":"10.1016\/j.rse.2023.113464","article-title":"Spectral Features of Ocean Colour Radiometric Products in the Presence of Cyanobacteria Blooms in the Baltic Sea","volume":"287","author":"Cazzaniga","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_12","unstructured":"Roelfsema, C., Dennison, B., Phinn, S., Dekker, A., and Brando, V. (2001, January 9\u201313). Remote Sensing of a Cyanobacterial Bloom (Lyngbya majuscula) in Moreton Bay, Australia. Proceedings of the IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217), Sydney, Ausralia."},{"key":"ref_13","unstructured":"Jeppesen, E., Peder Jensen, J., S\u00f8ndergaard, M., Lauridsen, T., Junge Pedersen, L., and Jensen, L. (1997). Shallow Lakes \u201995, Springer."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5935","DOI":"10.1109\/JSTARS.2023.3283773","article-title":"Impact of Radiometric Variability on Ultra-High Resolution Hyperspectral Imagery Over Aquatic Vegetation: Preliminary Results","volume":"16","author":"Piaser","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liang, S., Gong, Z., Wang, Y., Zhao, J., and Zhao, W. (2022). Accurate Monitoring of Submerged Aquatic Vegetation in a Macrophytic Lake Using Time-Series Sentinel-2 Images. Remote Sens., 14.","DOI":"10.3390\/rs14030640"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Carr, J., D\u2019Odorico, P., McGlathery, K., and Wiberg, P. (2010). Stability and Bistability of Seagrass Ecosystems in Shallow Coastal Lagoons: Role of Feedbacks with Sediment Resuspension and Light Attenuation. J. Geophys. Res. Biogeosci., 115.","DOI":"10.1029\/2009JG001103"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s10661-022-10690-9","article-title":"Application and Recent Progress of Inland Water Monitoring Using Remote Sensing Techniques","volume":"195","author":"Cao","year":"2023","journal-title":"Environ. Monit. Assess."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.rse.2011.11.013","article-title":"Review of Constituent Retrieval in Optically Deep and Complex Waters from Satellite Imagery","volume":"118","author":"Odermatt","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1016\/j.scitotenv.2016.01.020","article-title":"Developments in Earth Observation for the Assessment and Monitoring of Inland, Transitional, Coastal and Shelf-Sea Waters","volume":"572","author":"Tyler","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.ecolind.2014.06.035","article-title":"The Potential of Remote Sensing in Ecological Status Assessment of Coloured Lakes Using Aquatic Plants","volume":"46","author":"Birk","year":"2014","journal-title":"Ecol. Indic."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3413","DOI":"10.1016\/j.rse.2007.09.017","article-title":"Mapping Seagrass Species, Cover and Biomass in Shallow Waters: An Assessment of Satellite Multi-Spectral and Airborne Hyper-Spectral Imaging Systems in Moreton Bay (Australia)","volume":"112","author":"Phinn","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Samarinas, N., Spiliotopoulos, M., Tziolas, N., and Loukas, A. (2023). Synergistic Use of Earth Observation Driven Techniques to Support the Implementation of Water Framework Directive in Europe: A Review. Remote Sens., 15.","DOI":"10.3390\/rs15081983"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s10712-019-09517-z","article-title":"Earth Observation Imaging Spectroscopy for Terrestrial Systems: An Overview of Its History, Techniques, and Applications of Its Missions","volume":"40","author":"Rast","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"6423","DOI":"10.3390\/rs6076423","article-title":"Mapping Coral Reef Benthos, Substrates, and Bathymetry, Using Compact Airborne Spectrographic Imager (CASI) Data","volume":"6","author":"Leiper","year":"2014","journal-title":"Remote Sens."},{"key":"ref_28","first-page":"1","article-title":"How Much Benthic Information Can Be Retrieved with Hyperspectral Sensor from the Optically Complex Coastal Waters?","volume":"14","author":"Paavel","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.rse.2015.05.008","article-title":"Prospective HyspIRI Global Observations of Tidal Wetlands","volume":"167","author":"Turpie","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"829","DOI":"10.5194\/isprs-archives-XLII-5-829-2018","article-title":"GROWTH OF INVASIVE AQUATIC MACROPHYTES OVER TAPI RIVER","volume":"XLII\u20135","author":"Chander","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.rse.2014.04.034","article-title":"Airborne Hyperspectral Data to Assess Suspended Particulate Matter and Aquatic Vegetation in a Shallow and Turbid Lake","volume":"157","author":"Giardino","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s11273-009-9169-z","article-title":"Multispectral and Hyperspectral Remote Sensing for Identification and Mapping of Wetland Vegetation: A Review","volume":"18","author":"Adam","year":"2010","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bresciani, M., Giardino, C., Fabbretto, A., Pellegrino, A., Mangano, S., Free, G., and Pinardi, M. (2022). Application of New Hyperspectral Sensors in the Remote Sensing of Aquatic Ecosystem Health: Exploiting PRISMA and DESIS for Four Italian Lakes. Resources, 11.","DOI":"10.3390\/resources11020008"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Niroumand-Jadidi, M., Bovolo, F., and Bruzzone, L. (2020). Water Quality Retrieval from PRISMA Hyperspectral Images: First Experience in a Turbid Lake and Comparison with Sentinel-2. Remote Sens., 12.","DOI":"10.3390\/rs12233984"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"69","DOI":"10.5194\/isprs-archives-XLVI-1-W1-2021-69-2022","article-title":"INTERCOMPARISON OF DESIS, SENTINEL-2 (MSI) AND SENTINEL-3 (OLCI) DATA FOR WATER COLOUR APPLICATIONS","volume":"XLVI-1\/W1-2021","author":"Soppa","year":"2022","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Katlane, R., Doxaran, D., ElKilani, B., and Trabelsi, C. (2023). Remote Sensing of Turbidity in Optically Shallow Waters Using Sentinel-2 MSI and PRISMA Satellite Data. PFG\u2014J. Photogramm. Remote Sens. Geoinf. Sci., 1\u201317.","DOI":"10.1007\/s41064-023-00257-9"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"112693","DOI":"10.1016\/j.rse.2021.112693","article-title":"Advancing Cyanobacteria Biomass Estimation from Hyperspectral Observations: Demonstrations with HICO and PRISMA Imagery","volume":"266","author":"Pahlevan","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Taggio, N., Aiello, A., Ceriola, G., Kremezi, M., Kristollari, V., Kolokoussis, P., Karathanassi, V., and Barbone, E. (2022). A Combination of Machine Learning Algorithms for Marine Plastic Litter Detection Exploiting Hyperspectral PRISMA Data. Remote Sens., 14.","DOI":"10.3390\/rs14153606"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3383","DOI":"10.1080\/014311600750020000","article-title":"Developments in the \u201cvalidation\u201d of Satellite Sensor Products for the Study of the Land Surface","volume":"21","author":"Justice","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"112415","DOI":"10.1016\/j.rse.2021.112415","article-title":"Assessing the Influence of Different Validation Protocols on Ocean Colour Match-up Analyses","volume":"259","author":"Concha","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4496","DOI":"10.1080\/01431161.2020.1718240","article-title":"Towards a European Cal\/Val Service for Earth Observation","volume":"41","author":"Sterckx","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Giardino, C., Bresciani, M., Braga, F., Fabbretto, A., Ghirardi, N., Pepe, M., Gianinetto, M., Colombo, R., Cogliati, S., and Ghebrehiwot, S. (2020). First Evaluation of PRISMA Level 1 Data for Water Applications. Sensors, 20.","DOI":"10.3390\/s20164553"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.isprsjprs.2022.08.009","article-title":"Assessment of PRISMA Water Reflectance Using Autonomous Hyperspectral Radiometry","volume":"192","author":"Braga","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Pellegrino, A., Fabbretto, A., Bresciani, M., de Lima, T.M.A., Braga, F., Pahlevan, N., Brando, V.E., Kratzer, S., Gianinetto, M., and Giardino, C. (2023). Assessing the Accuracy of PRISMA Standard Reflectance Products in Globally Distributed Aquatic Sites. Remote Sens., 15.","DOI":"10.3390\/rs15082163"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"113153","DOI":"10.1016\/j.rse.2022.113153","article-title":"Validation of Ocean Color Remote Sensing Reflectance Data: Analysis of Results at European Coastal Sites","volume":"280","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"567","DOI":"10.5194\/os-8-567-2012","article-title":"In Situ Determination of the Remote Sensing Reflectance: An Inter-Comparison","volume":"8","author":"Zibordi","year":"2012","journal-title":"Ocean Sci."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Bresciani, M., Pinardi, M., Free, G., Luciani, G., Ghebrehiwot, S., Laanen, M., Peters, S., Della Bella, V., Padula, R., and Giardino, C. (2020). The Use of Multisource Optical Sensors to Study Phytoplankton Spatio-Temporal Variation in a Shallow Turbid Lake. Water, 12.","DOI":"10.3390\/w12010284"},{"key":"ref_48","first-page":"3","article-title":"An Application of the Cocktail Method for the Classiication of the Hydrophytic Vegetation at Lake Trasimeno (Central Italy)","volume":"48","author":"Landucci","year":"2011","journal-title":"Fitosociologia"},{"key":"ref_49","unstructured":"Bolpagni, R. (2013). Macrophyte Richness and Aquatic Vegetation Complexity of the Lake Idro (Northern Italy). Ann. Bot., 35\u201343."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"180","DOI":"10.4081\/jlimnol.2012.e19","article-title":"Retrospective Assessment of Macrophytic Communities in Southern Lake Garda (Italy) from in Situ and MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) Data","volume":"71","author":"Bresciani","year":"2012","journal-title":"J. Limnol."},{"key":"ref_51","unstructured":"Melelli, A., and Fatichenti, F. (2013). L\u2019acqua in Umbria. Disponibilit\u00e0, Consumo e Salute. Le Rappresentazioni e gli Atteggiamenti dei Cittadini, ARPA Umbria."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.landusepol.2017.08.028","article-title":"Anthropogenic Nutrients and Eutrophication in Multiple Land Use Watersheds: Best Management Practices and Policies for the Protection of Water Resources","volume":"69","author":"Valero","year":"2017","journal-title":"Land. Use Policy"},{"key":"ref_53","unstructured":"(2024, March 13). Regione Umbria Servizio Idrografico. Available online: https:\/\/annali.regione.umbria.it\/#."},{"key":"ref_54","unstructured":"Peters, S., Laanen, M., Groetsch, P., Ghezehegn, S., Poser, K., Hommersom, A., De Reus, E., and Spaias, L. (2018, January 7). WISPstation: A New Autonomous above Water Radiometer System. Proceedings of the Ocean Optics XXIV Conference, Dubrovnik, Croatia."},{"key":"ref_55","unstructured":"Riddick, C., Tyler, A., Hommersom, A., Alikas, K., Kangro, K., Ligi, M., Bresciani, M., Antilla, S., Vaiciute, D., and Bucas, M. (2024, May 09). EOMORES D5.3: Final Validation Report. Available online: https:\/\/zenodo.org\/records\/4057057."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1093\/plankt\/fbh151","article-title":"Effect of a Waveband Shift on Chlorophyll Retrieval from MERIS Imagery of Inland and Coastal Waters","volume":"27","author":"Gons","year":"2004","journal-title":"J. Plankton Res."},{"key":"ref_57","unstructured":"Rijkeboer, M. (2000). Algoritmen Voor Het Bepalen van de Concentratie Chlorofyl-a En Zwevend Stof Met de Optische Teledetectie Methode in Verschillende Optische Watertypen, Dept. of Spatial Analysis and Decision Support. No. O-00\/08."},{"key":"ref_58","unstructured":"Simis, S.G.H. (2006). Blue-Green Catastrophe: Remote Sensing of Mass Viral Lysis of Cyanobacteria. [Ph.D. Thesis, Vrije Universiteit Amsterdam]."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Giardino, C., Bresciani, M., Fava, F., Matta, E., Brando, V., and Colombo, R. (2015). Mapping Submerged Habitats and Mangroves of Lampi Island Marine National Park (Myanmar) from in Situ and Satellite Observations. Remote Sens., 8.","DOI":"10.3390\/rs8010002"},{"key":"ref_60","unstructured":"(2024, March 04). Regione Umbria Osservatorio Faunistico Regionale. Available online: https:\/\/www.regione.umbria.it\/turismo-attivita-sportive\/osservatorio-faunistico."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"112499","DOI":"10.1016\/j.rse.2021.112499","article-title":"The PRISMA Imaging Spectroscopy Mission: Overview and First Performance Analysis","volume":"262","author":"Cogliati","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"61955","DOI":"10.1109\/ACCESS.2021.3073903","article-title":"Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic Indexes","volume":"9","author":"Kremezi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Candela, L., Formaro, R., Guarini, R., Loizzo, R., Longo, F., and Varacalli, G. (2016, January 10\u201315). The PRISMA Mission. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729057"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Alonso, K., Bachmann, M., Burch, K., Carmona, E., Cerra, D., de los Reyes, R., Dietrich, D., Heiden, U., H\u00f6lderlin, A., and Ickes, J. (2019). Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS). Sensors, 19.","DOI":"10.3390\/s19204471"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Krutz, D., M\u00fcller, R., Knodt, U., G\u00fcnther, B., Walter, I., Sebastian, I., S\u00e4uberlich, T., Reulke, R., Carmona, E., and Eckardt, A. (2019). The Instrument Design of the DLR Earth Sensing Imaging Spectrometer (DESIS). Sensors, 19.","DOI":"10.3390\/s19071622"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"de los Reyes, R., Langheinrich, M., Schwind, P., Richter, R., Pflug, B., Bachmann, M., M\u00fcller, R., Carmona, E., Zekoll, V., and Reinartz, P. (2020). PACO: Python-Based Atmospheric Correction. Sensors, 20.","DOI":"10.3390\/s20051428"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Habermeyer, M., Pinnel, N., Storch, T., Honold, H.P., Tucker, P., Guanter, L., Segl, K., and Fischer, S. (August, January 28). The EnMAP Mission: From Observation Request to Data Delivery. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8897821"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"8830","DOI":"10.3390\/rs70708830","article-title":"The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation","volume":"7","author":"Guanter","year":"2015","journal-title":"Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.rse.2014.07.025","article-title":"Sensor Independent Adjacency Correction Algorithm for Coastal and Inland Water Systems","volume":"157","author":"Kiselev","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"113632","DOI":"10.1016\/j.rse.2023.113632","article-title":"The EnMAP Imaging Spectroscopy Mission towards Operations","volume":"294","author":"Storch","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_71","unstructured":"(2024, January 18). ASI\u2014Italian Space Agency PRISMA Algorithm Theoretical Basis Document (ATBD). Available online: http:\/\/prisma.asi.it\/missionselect\/docs.php."},{"key":"ref_72","unstructured":"(2024, March 04). DLR\u2014German Space Agency DESIS Instrument. Available online: https:\/\/www.dlr.de\/eoc\/desktopdefault.aspx\/tabid-13622\/23667_read-54280\/."},{"key":"ref_73","unstructured":"(2024, March 04). DLR\u2014German Space Agency EnMAP Specification. Available online: https:\/\/www.enmap.org\/mission\/."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.1080\/01431160500034086","article-title":"Technical Note: Simple and Robust Removal of Sun Glint for Mapping Shallow-water Benthos","volume":"26","author":"Hedley","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_75","first-page":"16","article-title":"Phinn An Assessment of the Accuracy and Precision of Water Quality Parameters Retrieved with the Matrix Inversion Method","volume":"8","author":"Campbell","year":"2010","journal-title":"Limnol. Ocean. Methods"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Soomets, T., Uudeberg, K., Jakovels, D., Brauns, A., Zagars, M., and Kutser, T. (2020). Validation and Comparison of Water Quality Products in Baltic Lakes Using Sentinel-2 MSI and Sentinel-3 OLCI Data. Sensors, 20.","DOI":"10.3390\/s20030742"},{"key":"ref_77","unstructured":"Vanhellemont, Q., and Ruddick, K. (2016, January 9). Acolite for Sentinel-2: Aquatic Applications of MSI Imagery. Proceedings of the 2016 ESA Living Planet Symposium, Prague, Czech Republic."},{"key":"ref_78","unstructured":"Sagayam, K.M., Bruntha, P.M., Sridevi, M., Renith Sam, M., Kose, U., and Deperlioglu, O. (2021). Advanced Machine Vision Paradigms for Medical Image Analysis, Elsevier."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"109","DOI":"10.4081\/jlimnol.2017.1629","article-title":"Remote Sensing of Macrophyte Morphological Traits: Implications for the Management of Shallow Lakes","volume":"76","author":"Villa","year":"2017","journal-title":"J. Limnol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"6329","DOI":"10.1364\/AO.37.006329","article-title":"Hyperspectral Remote Sensing for Shallow Waters I A Semianalytical Model","volume":"37","author":"Lee","year":"1998","journal-title":"Appl. Opt."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"3831","DOI":"10.1364\/AO.38.003831","article-title":"Hyperspectral Remote Sensing for Shallow Waters: 2 Deriving Bottom Depths and Water Properties by Optimization","volume":"38","author":"Lee","year":"1999","journal-title":"Appl. Opt."},{"key":"ref_82","unstructured":"(2024, March 04). Github (MDN). Available online: https:\/\/github.com\/STREAM-RS\/STREAM-RS."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.ecss.2005.11.024","article-title":"Monitoring Cyanobacterial Blooms by Satellite Remote Sensing","volume":"67","author":"Kutser","year":"2006","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1634","DOI":"10.1175\/2009JTECHO654.1","article-title":"AERONET-OC: A Network for the Validation of Ocean Color Primary Products","volume":"26","author":"Zibordi","year":"2009","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_85","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_86","doi-asserted-by":"crossref","first-page":"3638","DOI":"10.3390\/rs4123638","article-title":"Impact of Aerosol Model Selection on Water-Leaving Radiance Retrievals from Satellite Ocean Color Imagery","volume":"4","author":"McCarthy","year":"2012","journal-title":"Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.rse.2014.11.014","article-title":"Advanced Radiometry Measurements and Earth Science Applications with the Airborne Prism Experiment (APEX)","volume":"158","author":"Schaepman","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1330317","DOI":"10.3389\/frsen.2024.1330317","article-title":"Validation of Satellite Water Products Based on HYPERNETS in Situ Data Using a Match-up Database (MDB) File Structure","volume":"5","author":"Brando","year":"2024","journal-title":"Front. Remote Sens."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1016\/j.jqsrt.2009.10.001","article-title":"Evaluation of Sun Glint Models Using MODIS Measurements","volume":"111","author":"Zhang","year":"2010","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1080\/02626667.2023.2185150","article-title":"An Integrated Water Resource Management Approach for Lake Trasimeno, Italy","volume":"68","author":"Venturi","year":"2023","journal-title":"Hydrol. Sci. J."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"4875","DOI":"10.1007\/s11270-012-1243-0","article-title":"Water Quality Monitoring Using Remote Sensing and an Artificial Neural Network","volume":"223","author":"Chebud","year":"2012","journal-title":"Water Air Soil Pollut."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"6321","DOI":"10.3390\/s8106321","article-title":"A Multivariate Model for Coastal Water Quality Mapping Using Satellite Remote Sensing Images","volume":"8","author":"Su","year":"2008","journal-title":"Sensors"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"3885","DOI":"10.1007\/s11269-010-9639-3","article-title":"Application of Remote Sensing in Water Resource Management: The Case Study of Lake Trasimeno, Italy","volume":"24","author":"Giardino","year":"2010","journal-title":"Water Resour. Manag."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1111\/fwb.13240","article-title":"Spatial Heterogeneity and Short-term Oxygen Dynamics in the Rhizosphere of Vallisneria Spiralis: Implications for Nutrient Cycling","volume":"64","author":"Marzocchi","year":"2019","journal-title":"Freshw. Biol."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s00027-019-0635-2","article-title":"The Role of Patch Size in Ecosystem Engineering Capacity: A Case Study of Aquatic Vegetation","volume":"81","author":"Licci","year":"2019","journal-title":"Aquat. Sci."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/fwb.13582","article-title":"Shifting States, Shifting Services: Linking Regime Shifts to Changes in Ecosystem Services of Shallow Lakes","volume":"66","author":"Janssen","year":"2021","journal-title":"Freshw. Biol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1704\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:44:03Z","timestamp":1760107443000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1704"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,11]]},"references-count":96,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16101704"],"URL":"https:\/\/doi.org\/10.3390\/rs16101704","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,11]]}}}