{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T12:53:29Z","timestamp":1781787209104,"version":"3.54.5"},"reference-count":54,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T00:00:00Z","timestamp":1695686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Florida Department of Environmental Protection","award":["NNX17AH01G"],"award-info":[{"award-number":["NNX17AH01G"]}]},{"name":"Florida Department of Environmental Protection","award":["OCE-163504"],"award-info":[{"award-number":["OCE-163504"]}]},{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["NNX17AH01G"],"award-info":[{"award-number":["NNX17AH01G"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["OCE-163504"],"award-info":[{"award-number":["OCE-163504"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NNX17AH01G"],"award-info":[{"award-number":["NNX17AH01G"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["OCE-163504"],"award-info":[{"award-number":["OCE-163504"]}],"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>Mapping the seagrass distribution and density in the underwater landscape can improve global Blue Carbon estimates. However, atmospheric absorption and scattering introduce errors in space-based sensors\u2019 retrieval of sea surface reflectance, affecting seagrass presence, density, and above-ground carbon (AGCseagrass) estimates. This study assessed atmospheric correction\u2019s impact on mapping seagrass using WorldView-2 satellite imagery from Saint Joseph Bay, Saint George Sound, and Keaton Beach in Florida, USA. Coincident in situ measurements of water-leaving radiance (Lw), optical properties, and seagrass leaf area index (LAI) were collected. Seagrass classification and the retrieval of LAI were compared after empirical line height (ELH) and dark-object subtraction (DOS) methods were used for atmospheric correction. DOS left residual brightness in the blue and green bands but had minimal impact on the seagrass classification accuracy. However, the brighter reflectance values reduced LAI retrievals by up to 50% compared to ELH-corrected images and ground-based observations. This study offers a potential correction for LAI underestimation due to incomplete atmospheric correction, enhancing the retrieval of seagrass density and above-ground Blue Carbon from WorldView-2 imagery without in situ observations for accurate atmospheric interference correction.<\/jats:p>","DOI":"10.3390\/rs15194715","type":"journal-article","created":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T03:49:14Z","timestamp":1695786554000},"page":"4715","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Impact of Atmospheric Correction on Classification and Quantification of Seagrass Density from WorldView-2 Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8337-7441","authenticated-orcid":false,"given":"Victoria J.","family":"Hill","sequence":"first","affiliation":[{"name":"Department of Ocean and Earth Sciences, Old Dominion University, Norfolk, VA 23529, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9399-4264","authenticated-orcid":false,"given":"Richard C.","family":"Zimmerman","sequence":"additional","affiliation":[{"name":"Department of Ocean and Earth Sciences, Old Dominion University, Norfolk, VA 23529, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Bissett","sequence":"additional","affiliation":[{"name":"Eathon Intelligence LLC, 2210 US Hwy 301 S, Suite 100, Tampa, FL 33619, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Kohler","sequence":"additional","affiliation":[{"name":"Trimble, Inc., 10368 Westmoor Drive, Westminster, CO 80021, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Blake","family":"Schaeffer","sequence":"additional","affiliation":[{"name":"Office of Research and Development, U.S. Environmental Protection Agency, Durham, NC 27709, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Megan","family":"Coffer","sequence":"additional","affiliation":[{"name":"Global Science & Technology, Inc., Greenbelt, MD 20770, USA"},{"name":"NOAA\/NESDIS Center for Satellite Applications and Research, College Park, MD 20740, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0091-6986","authenticated-orcid":false,"given":"Jiang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9320-0858","authenticated-orcid":false,"given":"Kazi Aminul","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,26]]},"reference":[{"key":"ref_1","unstructured":"Chmura, G., Short, F., Torio, D., Arroyo-Mora, P., Fajardo, P., Hatvany, M., and van Ardenne, L. (2016). North America\u2019s Blue Carbon: Assessing Seagrass, Salt Marsh and Mangrove Distribution and Carbon Sinks: Project Report, Commission for Environmental Cooperation."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"074041","DOI":"10.1088\/1748-9326\/ab7d06","article-title":"The global distribution of seagrass meadows","volume":"15","author":"McKenzie","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"112036","DOI":"10.1016\/j.rse.2020.112036","article-title":"Performance across WorldView-2 and RapidEye for reproducible seagrass mapping","volume":"250","author":"Coffer","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Koedsin, W., Intararuang, W., Ritchie, R.J., and Huete, A. (2016). An Integrated Field and Remote Sensing Method for Mapping Seagrass Species, Cover, and Biomass in Southern Thailand. Remote Sens., 8.","DOI":"10.3390\/rs8040292"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2082","DOI":"10.1007\/s12237-022-01050-4","article-title":"Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: A Semi-automated Remote Sensing Analysis","volume":"45","author":"Lebrasse","year":"2022","journal-title":"Estuaries Coasts"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Misbari, S., and Hashim, M. (2016). Change Detection of Submerged Seagrass Biomass in Shallow Coastal Water. Remote Sens., 8.","DOI":"10.3390\/rs8030200"},{"key":"ref_7","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_8","first-page":"145","article-title":"Mapping seagrass coverage and spatial patterns with high spatial resolution IKONOS imagery","volume":"54","author":"Pu","year":"2017","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.rse.2014.05.001","article-title":"Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object based image analysis approach","volume":"150","author":"Roelfsema","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2915","DOI":"10.1080\/01431161.2022.2074809","article-title":"Assessment of WorldView-2 images for aboveground seagrass carbon stock mapping in patchy and continuous seagrass meadows","volume":"43","author":"Wicaksono","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112020","DOI":"10.1016\/j.rse.2020.112020","article-title":"Sentinel-2 remote sensing of Zostera noltei-dominated intertidal seagrass meadows","volume":"251","author":"Zoffoli","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106560","DOI":"10.1016\/j.ecolind.2020.106560","article-title":"Opportunities for seagrass research derived from remote sensing: A review of current methods","volume":"117","author":"Veettil","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_13","unstructured":"National Research Council (2004). Climate Data Records from Environmental Satellites: Interim Report, National Academies Press."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1007\/s12237-020-00773-6","article-title":"The Use of Imagery and GIS Techniques to Evaluate and Compare Seagrass Dynamics across Multiple Spatial and Temporal Scales","volume":"45","author":"Kaufman","year":"2020","journal-title":"Estuaries Coasts"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1007\/BF02693927","article-title":"Detecting trends in seagrass abundance using aerial photograph interpretation: Problems arising with the evolution of mapping methods","volume":"28","author":"Meehan","year":"2005","journal-title":"Estuaries"},{"key":"ref_16","unstructured":"Bell, S.S., Fonseca, M.S., and Stafford, N.B. (2007). Seagrasses: Biology, Ecology and Conservation, Springer."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s41019-020-00126-0","article-title":"Semi-supervised Adversarial Domain Adaptation for Seagrass Detection Using Multispectral Images in Coastal Areas","volume":"5","author":"Islam","year":"2020","journal-title":"Data Sci. Eng."},{"key":"ref_18","unstructured":"Islam, K.A., P\u00e9rez, D., Hill, V., Schaeffer, B.A., Zimmerman, R.C., and Li, J. Seagrass detection in coastal water through deep capsule networks. Proceedings of the Chinese Conference on Pattern Recognition and Computer Vision (PRCV)."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Simpson, J., Bruce, E., Davies, K.P., and Barber, P. (2022). A Blueprint for the Estimation of Seagrass Carbon Stock Using Remote Sensing-Enabled Proxies. Remote Sens., 14.","DOI":"10.3390\/rs14153572"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1038\/387253a0","article-title":"The value of the world\u2019s ecosystem services and natural capital","volume":"387","author":"Costanza","year":"1997","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108097","DOI":"10.1016\/j.ecolind.2021.108097","article-title":"Seagrass valuation from fish abundance, biomass and recreational catch","volume":"130","author":"Carnell","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"444","DOI":"10.4319\/lo.2003.48.1_part_2.0444","article-title":"Ocean color remote sensing of seagrass and bathymetry in the Bahamas Banks by high-resolution airborne imagery","volume":"48","author":"Dierssen","year":"2003","journal-title":"Limnol. Oceanogr."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1007\/s12237-013-9764-3","article-title":"Evaluating Light Availability, Seagrass Biomass, and Productivity Using Hyperspectral Airborne Remote Sensing in Saint Joseph\u2019s Bay, Florida","volume":"37","author":"Hill","year":"2014","journal-title":"Estuaries Coasts"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hemminga, M.A., and Duarte, C.M. (2000). Seagrass Ecology Seagrass, Cambridge University Press.","DOI":"10.1017\/CBO9780511525551"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/S0304-3770(98)00058-8","article-title":"Above- and below-ground biomass and production by Thalassia tesudinum in a tropical reef","volume":"61","year":"1998","journal-title":"Aquat. Bot."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/S0304-3770(98)00064-3","article-title":"Seasonal variation in biomass, morphometric parameters and production of seagrasses in the lagoon of Venice","volume":"61","author":"Sfriso","year":"1998","journal-title":"Aquat. Bot."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1080\/0143116021000026779","article-title":"The empirical line method for the atmospheric correction of IKONOS imagery","volume":"24","author":"cKarpouzli","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.3390\/rs4051425","article-title":"Towards Deeper Measurements of Tropical Reefscape Structure Using the WorldView-2 Spaceborne Sensor","volume":"4","author":"Collin","year":"2012","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1049\/iet-ipr.2017.0295","article-title":"Dark target effectiveness for dark-object subtraction atmospheric correction method on mangrove above-ground carbon stock mapping","volume":"12","author":"Wicasksono","year":"2018","journal-title":"IET Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Eugenio, F., Marcello, J., Martin, J., and Rodr\u00edguez-Esparrag\u00f3n, D. (2017). Benthic habitat mapping using multispectral high-resolution imagery: Evaluation of shallow water atmospheric correction techniques. Sensors, 17.","DOI":"10.3390\/s17112639"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2605","DOI":"10.3390\/rs3122605","article-title":"The importance of accounting for atmospheric effects in the application of NDVI and interpretation of satellite imagery supporting archaeological research: The case studies of Palaepaphos and Nea Paphos sites in Cyprus","volume":"3","author":"Agapiou","year":"2011","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1080\/22797254.2018.1457937","article-title":"Atmospheric correction of Landsat-8\/OLI and Sentinel-2\/MSI data using iCOR algorithm: Validation for coastal and inland waters","volume":"51","author":"Sterckx","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_33","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_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","first-page":"256","DOI":"10.1111\/j.1365-3091.1962.tb01150.x","article-title":"Recent Sedimentary History of St. Joseph Bay, Florida St. Joseph Bay","volume":"1","author":"Stewart","year":"1962","journal-title":"Sedimentology"},{"key":"ref_36","unstructured":"Big Bend Seagrasses Aquatic Preserve (2015). Big Bend Seagrasses Aquatic Preserve Management Plan, Big Bend Seagrasses Aquatic Preserve."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.ecss.2013.07.012","article-title":"Optical variability along a river plume gradient: Implications for management and remote sensing","volume":"131","author":"Cannizzaro","year":"2013","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_38","unstructured":"Mueller, J.L., Fargion, G.S., and McClain, C.R. (2003). Volume Absorption Coefficients: Instruments, Characterization, Field Measurements and Data Analysis Protocols, Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"8710","DOI":"10.1364\/AO.36.008710","article-title":"Absorption spectrum (380\u2013700 nm) of pure water. Part II: Integrating cavity measurements","volume":"36","author":"Pope","year":"1997","journal-title":"Appl. Opt."},{"key":"ref_40","unstructured":"Mobley, C.D. (1994). Light and Water: Radiative Transfer in Natural Waters, Academic Press."},{"key":"ref_41","unstructured":"Morel, A., and Mueller, J.L. (2003). Normalized Water-Leaving Radiance and Remote Sensing Reflectance: Bidirectional Refelctance and Other Factors."},{"key":"ref_42","unstructured":"Mobley, C.D. (2022). The Ocean Optics Book, International Ocean Colour Coordinating Group (IOCCG)."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.4319\/lo.1989.34.8.1473","article-title":"A Numerical-Model for the Computation of Radiance Distributions in Natural-Waters with Wind-Roughened Surfaces","volume":"34","author":"Mobley","year":"1989","journal-title":"Limnol. Oceanogr."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/0034-4257(88)90019-3","article-title":"An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data","volume":"24","author":"Chavez","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_45","unstructured":"Kuester, M. (2023, July 31). Absolute Radiometric Calibration. Available online: https:\/\/dg-cms-uploads-production.s3.amazonaws.com\/uploads\/document\/file\/209\/ABSRADCAL_FLEET_2016v0_Rel20170606.pdf."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1080\/2150704X.2020.1717013","article-title":"Resolvable estuaries for satellite derived water quality within the continental United States","volume":"11","author":"Schaeffer","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer. Available online: https:\/\/catalogue.library.cern\/literature\/yqnn7-y0x04."},{"key":"ref_49","unstructured":"CIRES (2014). Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado, B. Continuously Updated Digital Elevation Model (CUDEM)\u20141\/9 Arc-Second Resolution Bathymetric-Topographic Tiles, CIRES."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01621459.1952.10483441","article-title":"Use of ranks in one-criterion variance analysis","volume":"47","author":"Kruskal","year":"1952","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1214\/aoms\/1177730491","article-title":"On a test of whether one of two random variables is stochastically larger than the other","volume":"18","author":"Mann","year":"1947","journal-title":"Ann. Math. Stat."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental algorithms for scientific computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/0304-3770(91)90081-F","article-title":"Seagrass depth limits","volume":"40","author":"Duarte","year":"1991","journal-title":"Aquat. Bot."},{"key":"ref_54","unstructured":"Batiuk, R., Bergstrom, P., Kemp, W.M., Koch, E.W., Murry, L., Stevenson, J., Bartleson, R., Carter, V., Rybicki, N., and Landwehr, J. (2000). Chesapeake Bay Submerged Aquatic Vegetation Water Quality and Habitat-Based Requirements and Restoration Targets: A Second Synthesis, Chesapeake Bay Program Office."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4715\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:58:57Z","timestamp":1760129937000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4715"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,26]]},"references-count":54,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15194715"],"URL":"https:\/\/doi.org\/10.3390\/rs15194715","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,26]]}}}