{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T21:06:28Z","timestamp":1775595988345,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T00:00:00Z","timestamp":1681862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003981","name":"Italian Space Agency","doi-asserted-by":"publisher","award":["2019-5- HH.0"],"award-info":[{"award-number":["2019-5- HH.0"]}],"id":[{"id":"10.13039\/501100003981","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003981","name":"Italian Space Agency","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":"Italian Space Agency","doi-asserted-by":"publisher","award":["870497"],"award-info":[{"award-number":["870497"]}],"id":[{"id":"10.13039\/501100003981","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003981","name":"Italian Space Agency","doi-asserted-by":"publisher","award":["#80NSSC21K0499"],"award-info":[{"award-number":["#80NSSC21K0499"]}],"id":[{"id":"10.13039\/501100003981","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003981","name":"Italian Space Agency","doi-asserted-by":"publisher","award":["#2021-00050"],"award-info":[{"award-number":["#2021-00050"]}],"id":[{"id":"10.13039\/501100003981","id-type":"DOI","asserted-by":"publisher"}]},{"name":"EU Horizon 2020 programme","award":["2019-5- HH.0"],"award-info":[{"award-number":["2019-5- HH.0"]}]},{"name":"EU Horizon 2020 programme","award":["2022-15-U.0"],"award-info":[{"award-number":["2022-15-U.0"]}]},{"name":"EU Horizon 2020 programme","award":["870497"],"award-info":[{"award-number":["870497"]}]},{"name":"EU Horizon 2020 programme","award":["#80NSSC21K0499"],"award-info":[{"award-number":["#80NSSC21K0499"]}]},{"name":"EU Horizon 2020 programme","award":["#2021-00050"],"award-info":[{"award-number":["#2021-00050"]}]},{"name":"NASA Ocean Biology and Biogeochemistry (OBB)","award":["2019-5- HH.0"],"award-info":[{"award-number":["2019-5- HH.0"]}]},{"name":"NASA Ocean Biology and Biogeochemistry (OBB)","award":["2022-15-U.0"],"award-info":[{"award-number":["2022-15-U.0"]}]},{"name":"NASA Ocean Biology and Biogeochemistry (OBB)","award":["870497"],"award-info":[{"award-number":["870497"]}]},{"name":"NASA Ocean Biology and Biogeochemistry (OBB)","award":["#80NSSC21K0499"],"award-info":[{"award-number":["#80NSSC21K0499"]}]},{"name":"NASA Ocean Biology and Biogeochemistry (OBB)","award":["#2021-00050"],"award-info":[{"award-number":["#2021-00050"]}]},{"name":"Swedish National Space Agency infrastructure","award":["2019-5- HH.0"],"award-info":[{"award-number":["2019-5- HH.0"]}]},{"name":"Swedish National Space Agency infrastructure","award":["2022-15-U.0"],"award-info":[{"award-number":["2022-15-U.0"]}]},{"name":"Swedish National Space Agency infrastructure","award":["870497"],"award-info":[{"award-number":["870497"]}]},{"name":"Swedish National Space Agency infrastructure","award":["#80NSSC21K0499"],"award-info":[{"award-number":["#80NSSC21K0499"]}]},{"name":"Swedish National Space Agency infrastructure","award":["#2021-00050"],"award-info":[{"award-number":["#2021-00050"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>PRISMA is the Italian Space Agency\u2019s first proof-of-concept hyperspectral mission launched in March 2019. The present work aims to evaluate the accuracy of PRISMA\u2019s standard Level 2d (L2d) products in visible and near-infrared (NIR) spectral regions over water bodies. For this assessment, an analytical comparison was performed with in situ water reflectance available through the ocean color component of the Aerosol Robotic Network (AERONET-OC). In total, 109 cloud-free images over 20 inland and coastal water sites worldwide were available for the match-up analysis, covering a period of three years. The quality of L2d products was further evaluated as a function of ancillary parameters, such as the trophic state of the water, aerosol optical depth (AOD), observation and illumination geometry, and the distance from the coastline (DC). The results showed significant levels of uncertainty in the L2d reflectance products, with median symmetric accuracies (MdSA) varying from 33% in the green to more than 100% in the blue and NIR bands, with higher median uncertainties in oligotrophic waters (MdSA of 85% for the entire spectral range) than in meso-eutrophic (MdSA of 46%) where spectral shapes were retained adequately. Slight variations in the statistical agreement were then noted depending on AOD values, observation and illumination geometry, and DC. Overall, the results indicate that water-specific atmospheric correction algorithms should be developed and tested to fully exploit PRISMA data as a precursor for future operational hyperspectral missions as the standard L2d products are mostly intended for terrestrial applications.<\/jats:p>","DOI":"10.3390\/rs15082163","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T01:42:39Z","timestamp":1681954959000},"page":"2163","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Assessing the Accuracy of PRISMA Standard Reflectance Products in Globally Distributed Aquatic Sites"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4152-3409","authenticated-orcid":false,"given":"Andrea","family":"Pellegrino","sequence":"first","affiliation":[{"name":"Institute for Electromagnetic Sensing of the Environment, National Research Council (CNR-IREA), 20133 Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8025-9179","authenticated-orcid":false,"given":"Alice","family":"Fabbretto","sequence":"additional","affiliation":[{"name":"Institute for Electromagnetic Sensing of the Environment, National Research Council (CNR-IREA), 20133 Milano, Italy"},{"name":"Department of Remote Sensing, Tartu University, Tartu Observatory, Observatooriumi 1, 61602 Tartu, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7185-8464","authenticated-orcid":false,"given":"Mariano","family":"Bresciani","sequence":"additional","affiliation":[{"name":"Institute for Electromagnetic Sensing of the Environment, National Research Council (CNR-IREA), 20133 Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6492-0330","authenticated-orcid":false,"given":"Thainara Munhoz Alexandre","family":"de Lima","sequence":"additional","affiliation":[{"name":"National Institute for Space Research (INPE), S\u00e3o Jos\u00e9 dos Campos 12227-010, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4131-9080","authenticated-orcid":false,"given":"Federica","family":"Braga","sequence":"additional","affiliation":[{"name":"Institute of Marine Sciences, National Research Council (CNR-ISMAR), Castello, 30122 Venice, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5454-5212","authenticated-orcid":false,"given":"Nima","family":"Pahlevan","sequence":"additional","affiliation":[{"name":"NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA"},{"name":"Science Systems and Applications Inc., Lanham, MD 20706, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2193-5695","authenticated-orcid":false,"given":"Vittorio Ernesto","family":"Brando","sequence":"additional","affiliation":[{"name":"Institute of Marine Sciences, National Research Council (CNR-ISMAR), 00133 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0992-7203","authenticated-orcid":false,"given":"Susanne","family":"Kratzer","sequence":"additional","affiliation":[{"name":"Department of Ecology, Environment and Plant Sciences (DEEP), Stockholm University, SE-106 91 Stockholm, Sweden"}]},{"given":"Marco","family":"Gianinetto","sequence":"additional","affiliation":[{"name":"Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, 20133 Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3937-4988","authenticated-orcid":false,"given":"Claudia","family":"Giardino","sequence":"additional","affiliation":[{"name":"Institute for Electromagnetic Sensing of the Environment, National Research Council (CNR-IREA), 20133 Milano, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,19]]},"reference":[{"key":"ref_1","unstructured":"Bopp, L., Boyd, P., Donner, D., Kiessling, W., Martinetto, P., Ojea, E., Racault, M., Rost, B., Skern-Mauritzen, M., and Ghebrehiwet, M. (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability, Cambridge University Press. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.rse.2012.11.023","article-title":"Airborne hyperspectral remote sensing to assess spatial distribution of water quality characteristics in large rivers: The Mississippi River and its tributaries in Minnesota","volume":"130","author":"Olmanson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111604","DOI":"10.1016\/j.rse.2019.111604","article-title":"Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach","volume":"240","author":"Pahlevan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.jenvman.2005.11.019","article-title":"Water quality assessment using remote sensing techniques: Medrano Creek, Argentina","volume":"81","author":"Vignolo","year":"2006","journal-title":"J. Environ. Manag."},{"key":"ref_5","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_6","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_7","doi-asserted-by":"crossref","unstructured":"Stuart, M.B., McGonigle, A.J.S., and Willmott, J.R. (2019). Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems. Sensors, 19.","DOI":"10.3390\/s19143071"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s10712-018-9476-0","article-title":"Imaging Spectrometry of Inland and Coastal Waters: State of the Art, Achievements and Perspectives","volume":"40","author":"Giardino","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.rse.2013.03.009","article-title":"Using Hyperion imagery to monitor the spatial and temporal distribution of colored dissolved organic matter in estuarine and coastal regions","volume":"134","author":"Zhu","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1080\/15481603.2014.895577","article-title":"Evaluating Hyperspectral Imager for the Coastal Ocean (HICO) data for seagrass mapping in Indian River Lagoon, FL","volume":"51","author":"Cho","year":"2014","journal-title":"GIScience Remote Sens."},{"key":"ref_12","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_13","unstructured":"Van Mol, B., and Ruddick, K. (2004, January 8). The Compact High Resolution Imaging Spectrometer (CHRIS): The future of hyperspectral satellite sensors. Imagery of Oostende coastal and inland waters. Proceedings of the Airborne Imaging Spectroscopy Workshop, Brugge, Belgium."},{"key":"ref_14","unstructured":"Wang, Q., Zhang, Z., Hao, Z., Liu, B., and Xiong, J. (2020, January 11\u201313). Optical Classification of Coastal Water Body in China using Hyperspectral Imagery CHRIS\/PROBA. Proceedings of the IOP Conference Series: Earth and Environmental Science, Surakarta, Indonesia."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6888","DOI":"10.1364\/AO.389485","article-title":"Leonardo Spaceborne Infrared Payloads for Earth Observation: SLSTRs for Copernicus Sentinel 3 and PRISMA Hyperspectral Camera for PRISMA Satellite","volume":"59","author":"Coppo","year":"2020","journal-title":"Appl. Opt."},{"key":"ref_16","unstructured":"Lopinto, E., and Ananasso, C. (2013, January 3\u20137). The Prisma hyperspectral mission. Proceedings of the 33rd EARSeL Symposium, Towards Horizon, Matera, Italy."},{"key":"ref_17","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_18","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_19","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_20","doi-asserted-by":"crossref","unstructured":"Borfecchia, F., Micheli, C., De Cecco, L., Sannino, G., Struglia, M.V., Di Sarra, A.G., Gomez, C., and Mattiazzo, G. (2021). Satellite multi\/hyper spectral HR sensors for mapping the Posidonia oceanica in south mediterranean islands. Sustainability, 13.","DOI":"10.20944\/preprints202110.0248.v1"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lima, T.M.A.D., Giardino, C., Bresciani, M., Barbosa, C.C.F., Fabbretto, A., Pellegrino, A., and Begliomini, F.N. (2023). Assessment of Estimated Phycocyanin and Chlorophyll-a Concentration from PRISMA and OLCI in Brazilian Inland Waters: A Comparison between Semi-Analytical and Machine Learning Algorithms. Remote Sens., 15.","DOI":"10.3390\/rs15051299"},{"key":"ref_22","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_23","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_24","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1080\/2150704X.2013.830203","article-title":"Assessing water quality in the northern Adriatic Sea from HICO\u2122 data","volume":"4","author":"Braga","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.3390\/w7051921","article-title":"Assessing Potential Algal Blooms in a Shallow Fluvial Lake by Combining Hydrodynamic Modelling and Remote-Sensed Images","volume":"7","author":"Pinardi","year":"2015","journal-title":"Water"},{"key":"ref_26","unstructured":"Wang, M. (2023, February 26). Atmospheric Correction for Remotely-Sensed Ocean-Colour Products. Available online: http:\/\/dx.doi.org\/10.25607\/OBP-101."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.1364\/AO.26.002111","article-title":"Coastal Zone Color Scanner atmospheric correction algorithm: Multiple scattering effects","volume":"26","author":"Gordon","year":"1987","journal-title":"Appl. Opt."},{"key":"ref_28","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_29","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_30","doi-asserted-by":"crossref","first-page":"3383","DOI":"10.1080\/014311600750020000","article-title":"Developments in the\u2019validation\u2019of satellite sensor products for the study of the land surface","volume":"21","author":"Justice","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2006.01.015","article-title":"A multi-sensor approach for the on-orbit validation of ocean color satellite data products","volume":"102","author":"Bailey","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Pahlevan, N., Balasubramanian, S.V., Sarkar, S., and Franz, B.A. (2018). Toward Long-Term Aquatic Science Products from Heritage Landsat Missions. Remote Sens., 10.","DOI":"10.3390\/rs10091337"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.rse.2019.03.010","article-title":"Adaptation of the dark spectrum fitting atmospheric correction for aquatic applications of the Landsat and Sentinel-2 archives","volume":"225","author":"Vanhellemont","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ilori, C.O., Pahlevan, N., and Knudby, A. (2019). Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing. Remote Sens., 11.","DOI":"10.3390\/rs11040469"},{"key":"ref_36","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_37","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_38","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.1016\/j.rse.2011.03.018","article-title":"Comparison of three SeaWiFS atmospheric correction algorithms for turbid waters using AERONET-OC measurements","volume":"115","author":"Jamet","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1364\/AO.51.000220","article-title":"Assessment of a bidirectional reflectance distribution correction of above-water and satellite water-leaving radiance in coastal waters","volume":"51","author":"Hlaing","year":"2012","journal-title":"Appl. Opt."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.rse.2009.09.003","article-title":"Validation of SeaWiFS and MODIS aerosol products with globally distributed AERONET data","volume":"114","author":"Clerici","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","unstructured":"Guarini, R., Loizzo, R., Longo, F., Mari, S., Scopa, T., and Varacalli, G. (2017, January 23\u201328). Overview of the prisma space and ground segment and its hyperspectral products. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8126986"},{"key":"ref_43","unstructured":"(2022, May 07). ASI\u2014Italian Space Agency, 2021. PRISMA Algorithm Theoretical Basis Document (ATBD), Issue 1, Date 14\/12\/2021. Available online: http:\/\/prisma.asi.it\/missionselect\/docs.php."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/S0034-4257(99)00054-1","article-title":"Operational Atmospheric Correction of Landsat TM Data","volume":"70","author":"Ouaidrari","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.rse.2019.03.018","article-title":"Assessment of atmospheric correction algorithms for the Sentinel-2A MultiSpectral Imager over coastal and inland waters","volume":"225","author":"Warren","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.5194\/essd-11-1037-2019","article-title":"A compilation of global bio-optical in situ data for ocean-colour satellite applications\u2013version two","volume":"11","author":"Valente","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.1175\/JTECH-D-22-0029.1","article-title":"Automated Quality Control of AERONET-OC L WN Data","volume":"39","author":"Zibordi","year":"2022","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1175\/JTECH-D-22-0061.1","article-title":"AERONET-OC L WN Uncertainties: Revisited","volume":"40","author":"Cazzaniga","year":"2023","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1175\/JTECH-D-20-0085.1","article-title":"Advances in the ocean color component of the aerosol robotic network (AERONET-OC)","volume":"38","author":"Zibordi","year":"2021","journal-title":"J. Atmos. Ocean."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2006GL025778","article-title":"Comparison of SeaWiFS, MODIS and MERIS radiometric products at a coastal site","volume":"33","author":"Zibordi","year":"2006","journal-title":"Geophys. Res. Lett."},{"key":"ref_51","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."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.rse.2017.07.016","article-title":"Atmospheric correction over coastal waters using multilayer neural networks","volume":"199","author":"Fan","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Tan, J., Frouin, R., Ramon, D., and Steinmetz, F. (2019). On the adequacy of representing water reflectance by semi-analytical models in ocean color remote sensing. Remote Sens., 11.","DOI":"10.3390\/rs11232820"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1023\/A:1024048429145","article-title":"The solar spectral irradiance from 200 to 2400 nm as measured by the SOLSPEC spectrometer from the ATLAS and EURECA missions","volume":"214","author":"Thuillier","year":"2003","journal-title":"Sol. Phys."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"169","DOI":"10.5194\/amt-12-169-2019","article-title":"Advancements in the Aerosol Robotic Network (AERONET) Version 3 database\u2014Algoritmo di controllo della qualit\u00e0 quasi in tempo reale automatizzato con screening delle nuvole migliorato per le misurazioni della profondit\u00e0 ottica dell\u2019aerosol (AOD) del fotometro solare","volume":"12","author":"Giles","year":"2019","journal-title":"Atmos. Mis. Tech."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.rse.2017.12.021","article-title":"On the detectability of adjacency effects in ocean color remote sensing of mid-latitude coastal en-vironments by SeaWiFS, MODIS-A, MERIS, OLCI, OLI and MSI","volume":"209","author":"Bulgarelli","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_57","unstructured":"Van der Zande, D., Vanhellemont, Q., De Keukelaere, L., Knaeps, E., and Ruddick, K. (2016, January 23\u201328). Validation of Landsat-8\/OLI for ocean colour applications with AERONET-OC sites in Belgian coastal waters. Proceedings of the Ocean Optics Conference, Victoria, BC, Canada."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1002\/2017SW001669","article-title":"Measures of model performance based on the log accuracy ratio","volume":"16","author":"Morley","year":"2018","journal-title":"Space Weather"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"7404","DOI":"10.1364\/OE.26.007404","article-title":"Performance metrics for the assessment of satellite data products: An ocean color case study","volume":"26","author":"Seegers","year":"2018","journal-title":"Opt. Express"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1016\/j.marpolbul.2018.08.008","article-title":"Environmental changes in Ariake Sea of Japan and their relationships with Isahaya Bay reclamation","volume":"135","author":"Jia","year":"2018","journal-title":"Mar. Pollut. Bull."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.csr.2012.11.010","article-title":"Phytoplankton summer bloom dynamics in the Bah\u00eda Blanca Estuary in relation to changing environmental conditions","volume":"52","author":"Guinder","year":"2013","journal-title":"Cont. Shelf Res."},{"key":"ref_62","unstructured":"Qualls, T., Harris, H.J., and Harris, V. (2023, February 26). The state of the bay: The condition of the bay of Green Bay\/Lake Michigan, NOAA Repository, Available online: https:\/\/repository.library.noaa.gov\/view\/noaa\/34653\/noaa_34653_DS1.pdf."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Eleveld, M.A., Ruescas, A.B., Hommersom, A., Moore, T.S., Peters, S.W.M., and Brockmann, C. (2017). An Optical Classification Tool for Global Lake Waters. Remote Sens., 9.","DOI":"10.3390\/rs9050420"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3938","DOI":"10.1080\/01431161.2016.1204480","article-title":"Satellite-based water quality monitoring in Lake V\u00e4nern, Sweden","volume":"37","author":"Philipson","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.jglr.2015.01.001","article-title":"Challenges in tracking harmful algal blooms: A synthesis of evidence from Lake Erie","volume":"41","author":"Ho","year":"2015","journal-title":"J. Great Lakes Res."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Ogashawara, I. (2020). Determination of Phycocyanin from Space\u2014A Bibliometric Analysis. Remote Sens., 12.","DOI":"10.3390\/rs12030567"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"106420","DOI":"10.1016\/j.ecoleng.2021.106420","article-title":"Research on narrow and generalized water environment carrying capacity, economic benefit of Lake Okeechobee, USA","volume":"173","author":"Song","year":"2021","journal-title":"Ecol. Eng."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Cui, A., Zhang, J., Ma, Y., and Zhang, X. (2022). A Noise De-Correlation Based Sun Glint Correction Method and Its Effect on Shallow Bathymetry Inversion. Remote Sens., 14.","DOI":"10.3390\/rs14235981"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.rse.2007.02.026","article-title":"Assessment of satellite ocean color products at a coastal site","volume":"110","author":"Zibordi","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1364\/AO.17.001631","article-title":"Removal of atmospheric effects from satellite imagery of the oceans","volume":"17","author":"Gordon","year":"1978","journal-title":"Appl. Opt."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.rse.2015.12.048","article-title":"Dependence of satellite ocean color data products on viewing angles: A comparison between SeaWiFS, MODIS, and VIIRS","volume":"175","author":"Barnes","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/S0034-4257(00)00177-2","article-title":"A semianalytical approach to the calibration of AVIRIS data to reflectance over water: Application in a temperate estuary","volume":"75","author":"Mustard","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.rse.2018.07.015","article-title":"Atmospheric correction of metre-scale optical satellite data for inland and coastal water applications","volume":"216","author":"Vanhellemont","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"113412","DOI":"10.1016\/j.rse.2022.113412","article-title":"Atmospheric Correction Inter-comparison eXercise, ACIX-II Land: An assessment of atmospheric correction processors for Landsat 8 and Sentinel-2 over land","volume":"285","author":"Doxani","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"112366","DOI":"10.1016\/j.rse.2021.112366","article-title":"ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters","volume":"258","author":"Pahlevan","year":"2021","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2163\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:19:16Z","timestamp":1760123956000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2163"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,19]]},"references-count":75,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15082163"],"URL":"https:\/\/doi.org\/10.3390\/rs15082163","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,19]]}}}