Evaluation of Sentinel-3 SRAL SAR altimetry over Chinese rivers

Abstract Satellite radar altimetry observations of water surface elevation (WSE) have become an important data source to supplement river gauge records. Sentinel-3 is the first radar altimetry mission operating with a synthetic aperture radar (SAR) altimeter at global scale and with a new on-board tracking system (i.e. open-loop), which has great potential in terms of delivering reliable observations of inland water bodies for the next two decades (several future missions include an open-loop tracking mode). In this context, it is very important to investigate the data quality at an early stage. In this study, a comprehensive evaluation of Sentinel-3A (S3A) is conducted at 50 virtual stations (VS) located on a wide range of rivers in China. The evaluation of Level 1 data shows that, over mountain rivers, a good prior surface elevation estimate on-board is vital to deliver useful datasets using the S3A open-loop tracking system. The Open-Loop Tracking Command version 5 (OLTC V5) has significantly improved the placement of the range window, which was misplaced and resulted in lack of data over many mountain rivers prior to OLTC V5 (March 2019). However, application of S3A over mountain rivers still requires careful evaluation, especially before March 2019. Four retrackers are evaluated including a physical SAR Altimetry Mode Studies and Applications retracker (SAMOSA+), a traditional Offset Center Of Gravity (OCOG), a Primary Peak Center Of Gravity (PPCOG), and a modified Multiple Waveform Persistent Peak (MWaPP+) retracker. For 26 VSs in plain areas, retracked WSE data achieved a root mean square error (RMSE) ranging from 0.12 m to 0.9 m. The comparison of retracking methods reveals that SAMOSA+, OCOG, and PPCOG are unable to handle multi-peak waveforms. But the MWaPP+ can significantly improve the accuracy of the estimated WSE over large rivers, especially when the waveforms are contaminated. Moreover, our result shows no considerable difference between medium (ca. 300 m wide) and large (wider than 500 m) rivers. Instead, surrounding topography and homogeneity of surroundings are very important factors influencing the shape of a waveform. For rivers surrounded by lakes, man-made channels etc., special care must be taken when processing altimetry data. Dedicated retracking methods, such as MWaPP+, and sophisticated methods for outlier detection are needed to improve precision over such rivers, as demonstrated here for the Yangtze River.

[1]  C. Kuo,et al.  Retracked Jason-2 Altimetry over Small Water Bodies: Case Study of Bajhang River, Taiwan , 2011 .

[2]  Alejandro Egido,et al.  Fully Focused SAR Altimetry: Theory and Applications , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[3]  L. Gottschalk,et al.  River flow regimes in a changing climate , 2002 .

[4]  Pavel Ditmar,et al.  Monitoring of lake level changes on the Tibetan Plateau and Tian Shan by retracking Cryosat SARIn waveforms , 2015 .

[5]  Peter Nygaard Godiksen,et al.  A data assimilation system combining CryoSat-2 data and hydrodynamic river models , 2018 .

[6]  Frédéric Frappart,et al.  Satellite radar altimetry water elevations performance over a 200 m wide river: Evaluation over the Garonne River , 2017 .

[7]  Ole Baltazar Andersen,et al.  Coastal sea level from inland CryoSat‐2 interferometric SAR altimetry , 2015 .

[8]  Ole Baltazar Andersen,et al.  The performance and potentials of the CryoSat-2 SAR and SARIn modes for lake level estimation , 2017 .

[9]  Ole Baltazar Andersen,et al.  Sea surface height determination in the Arctic using Cryosat-2 SAR data from primary peak empirical retrackers , 2015 .

[10]  Malcolm Davidson,et al.  Using the Interferometric Capabilities of the ESA CryoSat-2 Mission to Improve the Accuracy of Sea Ice Freeboard Retrievals , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Lei Wang,et al.  Estimating continental river basin discharges using multiple remote sensing data sets , 2016 .

[12]  C. Tøttrup,et al.  Informing a hydrological model of the Ogooué with multi-mission remote sensing data , 2017 .

[13]  G. Carayon,et al.  Poseidon-3 Radar Altimeter: New Modes and In-Flight Performances , 2010 .

[14]  Ole Baltazar Andersen,et al.  Validation of CryoSat-2 SAR mode based lake levels , 2015 .

[15]  Henrik Madsen,et al.  Evaluation of multi-mode CryoSat-2 altimetry data over the Po River against in situ data and a hydrodynamic model , 2018 .

[16]  Xiaoli Deng,et al.  Improved inland water levels from SAR altimetry using novel empirical and physical retrackers , 2016 .

[17]  Vernon B. Sauer,et al.  Stage measurement at gaging stations , 2010 .

[18]  Luca Brocca,et al.  River Discharge Estimation by Using Altimetry Data and Simplified Flood Routing Modeling , 2013, Remote. Sens..

[19]  Henrik Madsen,et al.  Simultaneous calibration of multiple hydrodynamic model parameters using satellite altimetry observations of water surface elevation in the Songhua River , 2019, Remote Sensing of Environment.

[20]  M. Bierkens,et al.  Climate Change Will Affect the Asian Water Towers , 2010, Science.

[21]  John C. Ries,et al.  Chapter 1 Satellite Altimetry , 2001 .

[22]  Remko Scharroo,et al.  Coastal SAR and PLRM altimetry in German Bight and West Baltic Sea , 2016, Advances in Space Research.

[23]  Peter Bauer-Gottwein,et al.  River monitoring from satellite radar altimetry in the Zambezi River basin , 2012 .

[24]  Francesco Soldovieri,et al.  Detection and Characterization of Ship Targets Using CryoSat-2 Altimeter Waveforms , 2016, Remote. Sens..

[25]  Frédérique Seyler,et al.  Rating curves and estimation of average water depth at the upper Negro River based on satellite altimeter data and modeled discharges , 2006 .

[26]  N. Arnell,et al.  The impacts of climate change on river flow regimes at the global scale , 2013 .

[27]  Emmanuel Trouvé,et al.  Evaluation of CryoSAT-2 for height retrieval over the Himalayan range , 2012 .

[28]  Peter Bauer-Gottwein,et al.  Influence of local geoid variation on water surface elevation estimates derived from multi-mission altimetry for Lake Namco , 2019, Remote Sensing of Environment.

[29]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

[30]  Ole Baltazar Andersen,et al.  Coastal sea level from CryoSat-2 SARIn altimetry in Norway , 2017, Advances in Space Research.

[31]  Di Long,et al.  Validation and application of water levels derived from Sentinel-3A for the Brahmaputra River , 2019, Science China Technological Sciences.

[32]  C. Shum,et al.  Satellite radar altimetry for monitoring small rivers and lakes in Indonesia , 2014 .

[33]  Peter Bauer-Gottwein,et al.  CryoSat-2 Altimetry Applications over Rivers and Lakes , 2017 .

[34]  P. Berry,et al.  Global inland water monitoring from multi‐mission altimetry , 2005 .

[35]  Fernando Niño,et al.  Validation of Jason-3 tracking modes over French rivers , 2018 .

[36]  Peter Bauer-Gottwein,et al.  CryoSat-2 radar altimetry for monitoring freshwater resources of China , 2017 .

[37]  Peter Bauer-Gottwein,et al.  Assimilation of radar altimetry to a routing model of the Brahmaputra River , 2013 .

[38]  Frédéric Frappart,et al.  Evolution of the Performances of Radar Altimetry Missions from ERS-2 to Sentinel-3A over the Inner Niger Delta , 2018, Remote. Sens..

[39]  F. Schwartz,et al.  Discharge and water‐depth estimates for ungauged rivers: Combining hydrologic, hydraulic, and inverse modeling with stage and water‐area measurements from satellites , 2015 .

[40]  L. Phalippou,et al.  CryoSat: A mission to determine the fluctuations in Earth’s land and marine ice fields ☆ , 2006 .

[41]  Philippe Maillard,et al.  New processing approaches on the retrieval of water levels in Envisat and SARAL radar altimetry over rivers: A case study of the São Francisco River, Brazil , 2015 .

[42]  Luca Brocca,et al.  The use of remote sensing-derived water surface data for hydraulic model calibration , 2014 .