Retracking Altimeter Waveforms Near the Coasts

Gommenginger, Christine; Thibaut, P.; Fenoglio-Marc, Luciana; Quartly, G.; Deng, Xiaoli; Gómez-Enri, Jesús; Challenor, Peter; Gao, Yueming

doi:10.1007/978-3-642-12796-0_4

Cited by 135 publications

(111 citation statements)

References 33 publications

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“…The method determines the sub-waveforms in Improved Threshold Retracker (ITR) data [8]. As an initial step, we consider here all ITR sub-waveforms, although only the first one is used for determining the water height.…”

Section: Multi-subwaveform Retracker Msrmentioning

confidence: 99%

Treating the Hooking Effect in Satellite Altimetry Data: A Case Study along the Mekong River and Its Tributaries

et al. 2016

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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.

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Section: Multi-subwaveform Retracker Msrmentioning

confidence: 99%

Treating the Hooking Effect in Satellite Altimetry Data: A Case Study along the Mekong River and Its Tributaries

et al. 2016

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“…In the standard approach no waveform classification is applied. The surface heights are computed using the Improved Threshold Retracker [11]. For larger lakes all available altimetry tracks (from one or more missions) are combined in a Kalman filter.…”

Section: Dahiti: Water Level Time Series From Multi-mission Satellitementioning

confidence: 99%

“…These parameters are defined by the Offset Center Of Gravity (OCOG) retracker [22]: amplitude A and width W of the waveform as well as the bin position of the waveform center of gravity (COG). The OCOG parameters are determined according to the following equations [11]:…”

Section: Waveform Classificationmentioning

confidence: 99%

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Lake Level Estimation Based on CryoSat-2 SAR Altimetry and Multi-Looked Waveform Classification

et al. 2016

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Abstract:In this study, reliable water levels for four lakes are estimated based on an innovative processing strategy using a semi-automatic CryoSat-2 Synthetic Aperture Radar (SAR) multi-looked waveform classification. The selection of valid water returns is an essential step in inland altimetry applications. In order to identify reliable observations allowing for an accurate retracking, an unsupervised classification method for CryoSat-2 SAR multi-looked waveforms has been developed based on the k-mean algorithm. With this approach, changes in the water surface extent or surrounding inundation areas can be taken into account. In addition, a modified version of the Improved Threshold Retracker is developed in order to obtain optimal results for the lake heights. The used method is based on the identification of the optimal sub-waveform by employing height thresholds. The validation of the derived CryoSat-2 SAR time series with in-situ gauging data yields root mean square (RMS) differences between 3 and 90 cm for the different lakes. Compared to modeled CryoSat-2 water heights derived according to the approach used in the AltWater database our water level time series are slightly improved in terms of RMS accuracy but they contain more gaps due to the lack of reliable observations. In comparison with classical radar altimeter missions such as Envisat or Jason-2, the SAR-based time series show smaller RMS differences for the small lakes but larger RMS differences for the large lakes covered by multiple repeat missions. The presented innovative processing strategy can be easily adopted to other satellite altimetry SAR data such as from the new Sentinel-3 mission.

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“…Waveform retracking was performed to fit a model or functional form to the measured waveforms, and retrieve geophysical parameters such as range, echo power, etc [4]. Four retracking methods are available to use; threshold [5], subwaveform threshold [6], improved threshold [7], and beta-5 parameter [8].…”

Section: Introductionmentioning

confidence: 99%

Estimation of marine gravity anomaly model from satellite altimetry data (Case Study : Kalimantan and Sulawesi Waters-Indonesia)

Hartanto

Huda

Putra

et al. 2018

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. Nowadays satellite altimetry has become an advanced instrument to observe many natural physical phenomena, such as sea-level rise, ocean circulation, water mass changes, and marine gravity anomaly. The use of satellite altimetry data to compute marine gravity anomaly provides good results and costs relatively low. Those advantages make geodesists utilize this method as an alternative in geoid determination, especially over the seas. Several sets of satellite altimetry data from Cryosat 2, Jason 1 phase C, Geosat and ERS1 were used to compute gravity anomaly over the surrounding waters of Kalimantan and Sulawesi Island in Indonesia. The study area spans between -7 0 -7 0 N and 108 0 -127 0 E with a spatial resolution of 1'x1'. In the preprocessing step, the altimetry data especially Geosat and ERS1, were retracked to reduce errors due to the land influence. The main computation step was done by using two different methods, least square collocation (LSC) and Inverse Vening-Meinesz (IVM). The computed gravity anomaly models then assessed with the in-situ marine gravity data from the National Geophysical Data Center (NGDC). The best model in term of RMS error is the 10 km Gaussian filtered LSC with an RMS error of 15.042 mgal. The least accurate model is the non-filtered IVM with an RMS of 16.704 mgal.

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Retracking Altimeter Waveforms Near the Coasts

Cited by 135 publications

References 33 publications

Treating the Hooking Effect in Satellite Altimetry Data: A Case Study along the Mekong River and Its Tributaries

Treating the Hooking Effect in Satellite Altimetry Data: A Case Study along the Mekong River and Its Tributaries

Lake Level Estimation Based on CryoSat-2 SAR Altimetry and Multi-Looked Waveform Classification

Estimation of marine gravity anomaly model from satellite altimetry data (Case Study : Kalimantan and Sulawesi Waters-Indonesia)

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