Improved inland water levels from SAR altimetry using novel empirical and physical retrackers

Villadsen, Heidi; Deng, Xiaoli; Andersen, Ole; Stenseng, Lars; Nielsen, Karina; Knudsen, Per

doi:10.1016/j.jhydrol.2016.03.051

Cited by 75 publications

(79 citation statements)

References 38 publications

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“…Therefore, the leading edge of the returned waveform deviates from the altimeter tracking position (nominal tracking point), causing an error in the measured range which is accounted for by retracking [39] (Figure 3). usually have a complicated shape with multiple peaks [32,38]. Therefore, the leading edge of the returned waveform deviates from the altimeter tracking position (nominal tracking point), causing an error in the measured range which is accounted for by retracking [39] (Figure 3).…”

Section: Basic Principles Of Radar Altimetrymentioning

confidence: 99%

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CryoSat-2 Altimetry Applications over Rivers and Lakes

Jiang

Schneider

Andersen

et al. 2017

Water

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Abstract:Monitoring the variation of rivers and lakes is of great importance. Satellite radar altimetry is a promising technology to do this on a regional to global scale. Satellite radar altimetry data has been used successfully to observe water levels in lakes and (large) rivers, and has also been combined with hydrologic/hydrodynamic models. Except CryoSat-2, all radar altimetry missions have been operated in conventional low resolution mode with a short repeat orbit (35 days or less). CryoSat-2, carrying a Synthetic Aperture Radar (SAR) altimeter, has a 369-day repeat and a drifting ground track pattern and provides new opportunities for hydrologic research. The narrow inter-track distance (7.5 km at the equator) makes it possible to monitor many lakes and rivers and SAR mode provides a finer along-track resolution, higher return power and speckle reduction through multi-looks. However, CryoSat-2 challenges conventional ways of dealing with satellite inland water altimetry data because virtual station time series cannot be directly derived for rivers. We review the CryoSat-2 mission characteristics, data products, and its use and perspectives for inland water applications. We discuss all the important steps in the workflow for hydrologic analysis with CryoSat-2, and conclude with a discussion of promising future research directions.

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Section: Basic Principles Of Radar Altimetrymentioning

confidence: 99%

“…Schneider et al [30] calibrated a hydrodynamic river model with CryoSat-2 observations. Published study results show that performance of CryoSat-2 achieves root mean square error (RMSE) of a few centimeters for lakes [18,31] and between about 30 cm down to less than 5 cm using special data handling strategies for the Amazon River [32].…”

Section: Introductionmentioning

confidence: 99%

CryoSat-2 Altimetry Applications over Rivers and Lakes

Jiang

Schneider

Andersen

et al. 2017

Water

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“…Also, CryoSat-2 SAR data has been classified based on the SAR waveform for lakes [11], lakes and rivers [17], or ice [18]. This study takes a step further and uses not only the waveform but also the range-integrated power (RIP) for a classification of the altimeter measurements in water and non-water returns over the Mekong River Basin in Southeast Asia.…”

Section: Introductionmentioning

confidence: 99%

River Levels Derived with CryoSat-2 SAR Data Classification—A Case Study in the Mekong River Basin

Boergens¹,

Nielsen²,

Andersen³

et al. 2017

Remote Sensing

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Abstract:In this study we use CryoSat-2 SAR (delay-Doppler synthetic-aperture radar) data in the Mekong River Basin to estimate water levels. Compared to classical pulse limited radar altimetry, medium-and small-sized inland waters can be observed with CryoSat-2 SAR data with a higher accuracy due to the smaller along track footprint. However, even with this SAR data the estimation of water levels over a medium-sized river (width less than 500 m) is still challenging with only very few consecutive observations over the water. The target identification with land-water masks tends to fail as the river becomes smaller. Therefore, we developed a classification approach to divide the observations into water and land returns based solely on the data. The classification is done with an unsupervised classification algorithm, and it is based on features derived from the SAR and range-integrated power (RIP) waveforms. After the classification, classes representing water and land are identified. Better results are obtained when the Mekong River Basin is divided into different geographical regions: upstream, middle stream, and downstream. The measurements classified as water are used in a next step to estimate water levels for each crossing over a river in the Mekong River network. The resulting water levels are validated and compared to gauge data, Envisat data, and CryoSat-2 water levels derived with a land-water mask. The CryoSat-2 water levels derived with the classification lead to more valid observations with fewer outliers in the upstream region than with a land-water mask (1700 with 2% outliers vs. 1500 with 7% outliers). The median of the annual differences that is used in the validation is in all test regions smaller for the CryoSat-2 classification results than for Envisat or CryoSat-2 land-water mask results (for the entire study area: 0.76 m vs. 0.96 m vs. 0.83 m, respectively). Overall, in the upstream region with small-and medium-sized rivers the classification approach is more effective for deriving reliable water level observations than in the middle stream region with wider rivers.

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“…Several studies exist related to the estimation of lake levels based on classical radar altimetry whereas only very few studies exist concerning the performance of CryoSat-2 SAR data over lakes [6,7]. This study is focused on the estimation of lake levels by using CryoSat-2 SAR measurements.…”

Section: Introductionmentioning

confidence: 99%

“…Usually, constant water masks-provided for instance by the World Wildlife Fund (WWF) website (https://www. worldwildlife.org/pages/global-lakes-and-wetlands-database) or extracted from remote sensing images-are applied ( [6,7]). However, these external information does not consider changes in the water surface extent or surrounding inundation areas.…”

Section: Introductionmentioning

confidence: 99%

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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Improved inland water levels from SAR altimetry using novel empirical and physical retrackers

Cited by 75 publications

References 38 publications

CryoSat-2 Altimetry Applications over Rivers and Lakes

CryoSat-2 Altimetry Applications over Rivers and Lakes

River Levels Derived with CryoSat-2 SAR Data Classification—A Case Study in the Mekong River Basin

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

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