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Flood estimation using satellite observations with deep learning algorithms is effective in detecting flood patterns and environmental relationships that may be overlooked by conventional methods. Soil Moisture Active Passive (SMAP) fractional water (FW) was used as a reference to estimate flood areas in a long short-term memory (LSTM) model using a combination of soil moisture information, rainfall forecasts, and floodplain topography. To perform flood modeling in LSTM, datasets with different spatial resolutions were resampled to 30 m spatial resolution using bicubic interpolation. The model\u2019s efficacy was quantified by validating the LSTM-based flood inundation area with a water mask from Senti-nel-1 SAR images for regions with different topographic characteristics. The average area under the curve (AUC) value of the LSTM model was 0.93, indicating a high accuracy estimation of FW. The confusion matrix-derived metrics were used to validate the flood inundation area and had a high-performance accuracy of ~0.9. SMAP FW showed optimal performance in low-covered vegetation, seasonal water variations and flat regions. The estimates of flood inundation areas show the methodological promise of the proposed framework for improved disaster preparedness and resilience.<\/jats:p>","DOI":"10.3390\/s25082503","type":"journal-article","created":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T08:09:17Z","timestamp":1744790957000},"page":"2503","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Estimation of Flood Inundation Area Using Soil Moisture Active Passive Fractional Water Data with an LSTM Model"],"prefix":"10.3390","volume":"25","author":[{"given":"Rekzi D.","family":"Febrian","sequence":"first","affiliation":[{"name":"Department of Global Smart City, Sungkyunkwan University, Suwon 440-746, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanyub","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Global Smart City, Sungkyunkwan University, Suwon 440-746, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5251-6100","authenticated-orcid":false,"given":"Yangwon","family":"Lee","sequence":"additional","affiliation":[{"name":"Major of Geomatics Engineering, Division of Earth Environmental System Science, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinsoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Major of Geomatics Engineering, Division of Earth Environmental System Science, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minha","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Global Smart City, Sungkyunkwan University, Suwon 440-746, Republic of Korea"},{"name":"Department of Water Resources, Graduate School of Water Resources, Sungkyunkwan University, Suwon 440-746, Republic of Korea"},{"name":"School of Civil, Architectural Engineering & Landscape Architecture, Sungkyunkwan University, Suwon 440-746, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1080\/19475705.2014.945101","article-title":"Flood hazards in an urbanizing watershed in Riyadh, Saudi Arabia","volume":"7","author":"Sharif","year":"2014","journal-title":"Geomat. 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