{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T15:58:44Z","timestamp":1777996724560,"version":"3.51.4"},"reference-count":65,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T00:00:00Z","timestamp":1644969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A long-duration, multi-mission altimeter dataset is analyzed to determine its accuracy in determining long-term trends in significant wave height. Two calibration methods are investigated: \u201caltimeter\u2013buoy\u201d calibration and \u201caltimeter\u2013altimeter\u201d calibration. The \u201caltimeter\u2013altimeter\u201d approach shows larger positive trends globally, but both approaches are subject to temporal non-homogeneity between altimeter missions. This limits the accuracy of such datasets to approximately \u00b10.2 cm\/year in determining trends in significant wave height. The sampling pattern of the altimeters is also investigated to determine if under-sampling impacts the ability of altimeters to measure trends for higher percentiles. It is concluded that, at the 99th percentile level, sampling issues result in a positive bias in values of trend. At lower percentiles (90th and mean), the sampling issues do not bias the trend estimates significantly.<\/jats:p>","DOI":"10.3390\/rs14040974","type":"journal-article","created":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T21:36:24Z","timestamp":1645047384000},"page":"974","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Can Multi-Mission Altimeter Datasets Accurately Measure Long-Term Trends in Wave Height?"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2233-9227","authenticated-orcid":false,"given":"Ian R.","family":"Young","sequence":"first","affiliation":[{"name":"Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3286-5565","authenticated-orcid":false,"given":"Agustinus","family":"Ribal","sequence":"additional","affiliation":[{"name":"Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia"},{"name":"Department of Mathematics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Makassar 90245, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102517","DOI":"10.1016\/j.jeem.2021.102517","article-title":"Carbon pricing of international transport fuels: Impacts on carbon emissions and trade activity","volume":"110","author":"Mundaca","year":"2021","journal-title":"J. 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