{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:49:14Z","timestamp":1767340154063,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2016,1,23]],"date-time":"2016-01-23T00:00:00Z","timestamp":1453507200000},"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>This study investigates the potential of satellite altimetry for water level time series estimation of smaller inland waters where only very few measurements above the water surface are available. A new method was developed using off-nadir measurements to estimate the parabola generated by the hooking effect. For this purpose, a new waveform retracker was used as well as an adopted version of the RANdom SAmple Consensus (RANSAC) algorithm. The method is applied to compute time series of the water levels height of the Mekong River and some of its tributaries from Envisat high-frequency data. Reliable time series can be obtained from river crossings with widths of less than 500 m and without direct nadir measurements over the water. The expected annual variations are clearly depicted and the time series well agree with available in situ gauging data. The mean RMS value is 1.22 m between the resulting time series and in situ data, the best result is 0.34 m, the worst 2.26 m, and 80% of the time series have an RMS below 1.5 m.<\/jats:p>","DOI":"10.3390\/rs8020091","type":"journal-article","created":{"date-parts":[[2016,1,25]],"date-time":"2016-01-25T10:03:06Z","timestamp":1453716186000},"page":"91","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Treating the Hooking Effect in Satellite Altimetry Data: A Case Study along the Mekong River and Its Tributaries"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6178-9402","authenticated-orcid":false,"given":"Eva","family":"Boergens","sequence":"first","affiliation":[{"name":"Deutsches Geod\u00e4tisches Forschungsinstitut, Technische Universit\u00e4t M\u00fcnchen, Arcisstra\u00dfe 21, 80333 Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8940-4639","authenticated-orcid":false,"given":"Denise","family":"Dettmering","sequence":"additional","affiliation":[{"name":"Deutsches Geod\u00e4tisches Forschungsinstitut, Technische Universit\u00e4t M\u00fcnchen, Arcisstra\u00dfe 21, 80333 Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4741-3449","authenticated-orcid":false,"given":"Christian","family":"Schwatke","sequence":"additional","affiliation":[{"name":"Deutsches Geod\u00e4tisches Forschungsinstitut, Technische Universit\u00e4t M\u00fcnchen, Arcisstra\u00dfe 21, 80333 Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0718-6069","authenticated-orcid":false,"given":"Florian","family":"Seitz","sequence":"additional","affiliation":[{"name":"Deutsches Geod\u00e4tisches Forschungsinstitut, Technische Universit\u00e4t M\u00fcnchen, Arcisstra\u00dfe 21, 80333 Munich, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2016,1,23]]},"reference":[{"key":"ref_1","unstructured":"Global Runoff Data Center (2013). 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