{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T17:09:54Z","timestamp":1784826594882,"version":"3.55.0"},"reference-count":60,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T00:00:00Z","timestamp":1728604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"OAA JPSS Program Office and the GOES-R Program Office"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Visible Infrared Imaging Radiometer Suite (VIIRS) and Advanced Baseline Imager (GOES-R ABI) flood products have been widely used by the National Weather Service (NWS) for river flood monitoring, and by the Federal Emergency Management Agency (FEMA) for rescue and relief efforts. Some water bodies, like wetlands, are detected as water but not marked as permanent or normal water, which may result in their misclassification as floodwaters by VIIRS and GOES-R flood products. These water bodies generally do not cause significant property damage or fatalities, but they can complicate the identification of truly hazardous floods. This study utilizes the severe Louisiana flood event caused by Hurricane Ida to demonstrate how to differentiate wetlands from real-hazard flooding. Since Hurricane Ida made landfall in 2021, and there was no major flood event in 2022, VIIRS and ABI flood data from 2021 and 2022 were selected. The difference in annual total flooding days between 2021 and 2022 was calculated and combined with long-time flood frequency to distinguish non-hazard floodwaters due to wetlands identified from real-hazard floods caused by the hurricane. The results were compared with the wetlands from the change detection analysis. The confusion matrix analysis indicated an accuracy of 91.58%, precision of 89.97%, and F1-score of 76.63% for the VIIRS flood products. For the GOES-R ABI flood products, the confusion matrix analysis yielded an accuracy of 86.88%, precision of 97.49%, and F1-score of 75.21%. The accuracy and F1-score values for the GOES-R ABI flood products are slightly lower than those for the VIIRS flood products, possibly due to their lower spatial resolution, but still within a feasible range.<\/jats:p>","DOI":"10.3390\/rs16203769","type":"journal-article","created":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T08:10:16Z","timestamp":1728634216000},"page":"3769","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Extracting Wetlands in Coastal Louisiana from the Operational VIIRS and GOES-R Flood Products"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0556-7486","authenticated-orcid":false,"given":"Tianshu","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1695-4303","authenticated-orcid":false,"given":"Donglian","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanmei","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satya","family":"Kalluri","sequence":"additional","affiliation":[{"name":"NOAA JPSS Program Office, Lanham, MD 20706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6232-2871","authenticated-orcid":false,"given":"Lihang","family":"Zhou","sequence":"additional","affiliation":[{"name":"NOAA JPSS Program Office, Lanham, MD 20706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sean","family":"Helfrich","sequence":"additional","affiliation":[{"name":"NOAA JPSS Program Office, Lanham, MD 20706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Earth System Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, MD 20742, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Straka","sequence":"additional","affiliation":[{"name":"Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison, 1225 W. Dayton St., Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0753-3179","authenticated-orcid":false,"given":"Viviana","family":"Maggioni","sequence":"additional","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5812-1014","authenticated-orcid":false,"given":"Fernando","family":"Miralles-Wilhelm","sequence":"additional","affiliation":[{"name":"Center for Environmental Sciences, University of Maryland, Cambridge, MD 21613, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1109\/JSTARS.2011.2125778","article-title":"Deriving water fraction and flood maps from MODIS images using a decision tree approach","volume":"4","author":"Sun","year":"2011","journal-title":"IEEE J. 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