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

Boergens, Eva; Dettmering, D; Schwatke, C; Seitz, Florian

doi:10.3390/rs8020091

Cited by 39 publications

(32 citation statements)

References 32 publications

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“…[6] validated water levels obtained from the different retrackers available from Envisat over the Amazon basin with in situ data, and Ref. [7] demonstrated that reliable water level estimates can be obtained from Envisat over narrow branches of the Mekong River by accounting for the hooking…”

Section: Introductionmentioning

confidence: 99%

The Performance and Potentials of the CryoSat-2 SAR and SARIn Modes for Lake Level Estimation

Nielsen

Stenseng

Andersen

et al. 2017

Water

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Abstract:Over the last few decades, satellite altimetry has proven to be valuable for monitoring lake levels. With the new generation of altimetry missions, CryoSat-2 and Sentinel-3, which operate in Synthetic Aperture Radar (SAR) and SAR Interferometric (SARIn) modes, the footprint size is reduced to approximately 300 m in the along-track direction. Here, the performance of these new modes is investigated in terms of uncertainty of the estimated water level from CryoSat-2 data and the agreement with in situ data. The data quality is compared to conventional low resolution mode (LRM) altimetry products from Envisat, and the performance as a function of the lake area is tested. Based on a sample of 145 lakes with areas ranging from a few to several thousand km 2 , the CryoSat-2 results show an overall superior performance. For lakes with an area below 100 km 2 , the uncertainty of the lake levels is only half of that of the Envisat results. Generally, the CryoSat-2 lake levels also show a better agreement with the in situ data. The lower uncertainty of the CryoSat-2 results entails a more detailed description of water level variations.

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Section: Introductionmentioning

confidence: 99%

The Performance and Potentials of the CryoSat-2 SAR and SARIn Modes for Lake Level Estimation

Nielsen

Stenseng

Andersen

et al. 2017

Water

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“…This will add an additional error contribution to the uncertainties of water level measurements of altimeter and gauging station. Keeping this in mind, an accuracy of 30 cm is very good, since it is in the same order of magnitude with results for dedicated river time series [10,14].…”

Section: Comparison With In Situ Gauging Stationsmentioning

confidence: 83%

“…Within the last decade, various investigations on new retracking algorithms have been made in order to enhance the accuracy of coastal and inland water level estimation. In particular, progress has been made regarding lakes and reservoirs (e.g., [6,7]) and large rivers (e.g., [8][9][10]). Actually, at least four global databases exist providing inland water level time series for a variety of water bodies based on several altimeter missions [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Potential of ENVISAT Radar Altimetry for Water Level Monitoring in the Pantanal Wetland

Dettmering¹,

Schwatke²,

Boergens³

et al. 2016

Remote Sensing

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Wetlands are important ecosystems playing an essential role for continental water regulation and the hydrologic cycle. Moreover, they are sensitive to climate changes as well as anthropogenic influences, such as land-use or dams. However, the monitoring of these regions is challenging as they are normally located in remote areas without in situ measurement stations. Radar altimetry provides important measurements for monitoring and analyzing water level variations in wetlands and flooded areas. Using the example of the Pantanal region in South America, this study demonstrates the capability and limitations of ENVISAT radar altimeter for monitoring water levels in inundation areas. By applying an innovative processing method consisting of a rigorous data screening by means of radar echo classification as well as an optimized waveform retracking, water level time series with respect to a global reference and with a temporal resolution of about one month are derived. A comparison between altimetry-derived height variations and six in situ time series reveals accuracies of 30 to 50 cm RMS. The derived water level time series document seasonal height variations of up to 1.5 m amplitude with maximum water levels between January and June. Large scale geographical pattern of water heights are visible within the wetland. However, some regions of the Pantanal show water level variations less than a few decimeter, which is below the accuracies of the method. These areas cannot be reliably monitored by ENVISAT.

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“…The Envisat water level time series in the Mekong Basin has a temporal resolution of 35 days, even though data gaps may occur for some cycles. For small tributaries, a correction for the Hooking effect is applied additionally in [5].…”

Section: Envisat Datamentioning

confidence: 99%

“…In recent years many studies have been published that apply satellite pulse-limited altimetry over rivers of various sizes (e.g., [2][3][4][5][6][7]). CryoSat-2, launched in 2010, is the first satellite carrying a delay-Doppler altimeter [8] that operates in three measuring modes: the classical pulse-limited low resolution (LR) mode, the delay-Doppler synthetic-aperture radar (SAR) mode, and the SAR interferometric (SARin) mode.…”

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

Cited by 39 publications

References 32 publications

The Performance and Potentials of the CryoSat-2 SAR and SARIn Modes for Lake Level Estimation

The Performance and Potentials of the CryoSat-2 SAR and SARIn Modes for Lake Level Estimation

Potential of ENVISAT Radar Altimetry for Water Level Monitoring in the Pantanal Wetland

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

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