{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T04:01:55Z","timestamp":1790827315215,"version":"4.1.0"},"reference-count":52,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T00:00:00Z","timestamp":1663113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"H2020 Water-ForCE project","award":["101004186"],"award-info":[{"award-number":["101004186"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Landsat series has marked the history of Earth observation by performing the longest continuous imaging program from space. The recent Landsat-9 carrying Operational Land Imager 2 (OLI-2) captures a higher dynamic range than sensors aboard Landsat-8 or Sentinel-2 (14-bit vs. 12-bit) that can potentially push forward the frontiers of aquatic remote sensing. This potential stems from the enhanced radiometric resolution of OLI-2, providing higher sensitivity over water bodies that are usually low-reflective. This study performs an initial assessment on retrieving water quality parameters from Landsat-9 imagery based on both physics-based and machine learning modeling. The concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM) are retrieved based on physics-based inversion in four Italian lakes encompassing oligo to eutrophic conditions. A neural network-based regression model is also employed to derive Chl-a concentration in San Francisco Bay. We perform a consistency analysis between the constituents derived from Landsat-9 and near-simultaneous Sentinel-2 imagery. The Chl-a and TSM retrievals are validated using in situ matchups. The results indicate relatively high consistency among the water quality products derived from Landsat-9 and Sentinel-2. However, the Landsat-9 constituent maps show less grainy noise, and the matchup validation indicates relatively higher accuracies obtained from Landsat-9 (e.g., TSM R2 of 0.89) compared to Sentinel-2 (R2 = 0.71). The improved constituent retrieval from Landsat-9 can be attributed to the higher signal-to-noise (SNR) enabled by the wider dynamic range of OLI-2. We performed an image-based SNR estimation that confirms this assumption.<\/jats:p>","DOI":"10.3390\/rs14184596","type":"journal-article","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T23:16:36Z","timestamp":1663197396000},"page":"4596","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Water Quality Retrieval from Landsat-9 (OLI-2) Imagery and Comparison to Sentinel-2"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9432-3032","authenticated-orcid":false,"given":"Milad","family":"Niroumand-Jadidi","sequence":"first","affiliation":[{"name":"Digital Society Center, Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy"},{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, M\u00fcnchner Str. 20, Oberpfaffenhofen, D-82234 We\u00dfling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3104-7656","authenticated-orcid":false,"given":"Francesca","family":"Bovolo","sequence":"additional","affiliation":[{"name":"Digital Society Center, Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7185-8464","authenticated-orcid":false,"given":"Mariano","family":"Bresciani","sequence":"additional","affiliation":[{"name":"Institute of Electromagnetic Sensing of the Environment, National Research Council of Italy (CNR-IREA), Via Corti 12, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0939-5267","authenticated-orcid":false,"given":"Peter","family":"Gege","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, M\u00fcnchner Str. 20, Oberpfaffenhofen, D-82234 We\u00dfling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3937-4988","authenticated-orcid":false,"given":"Claudia","family":"Giardino","sequence":"additional","affiliation":[{"name":"Institute of Electromagnetic Sensing of the Environment, National Research Council of Italy (CNR-IREA), Via Corti 12, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/0034-4257(83)90035-4","article-title":"Modeling inland water quality using Landsat data","volume":"13","author":"Carpenter","year":"1983","journal-title":"Remote Sens. 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