Analysis of Sentinel-3 SAR altimetry waveform retracking algorithms for deriving temporally consistent water levels over ice-covered lakes

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Highlights

  • The performances of Sentinel-3 SRAL SAR retrackers over inland lakes are evaluated.

  • The mechanism of how lake ice affects SRAL SAR water level estimates is revealed.

  • We develop a bimodal algorithm to derive consistent water levels for ice-covered lake.

Abstract

Satellite radar altimetry has been widely used in the monitoring of water levels of lakes, rivers and wetlands in the past decades. The conventional pulse-limited radar altimeters have a relatively large ground footprint, which limits their capability to retrieve surface elevation information over small and medium-sized water bodies. A new generation of satellite radar altimeter system, a dual-frequency SAR radar altimeter (SRAL) onboard the Copernicus Sentinel-3 satellite, has produced densely sampled elevation measurements with a smaller footprint for the Earth's surfaces since June 2016, owing to the Delay-Doppler processing technique. Four standard SRAL SAR altimetry waveform retracking algorithms (known as retrackers) have been designed to retrieve elevation measurements for different types of surfaces: Ice-Sheet retracker for polar ice sheets, SAMOSA-3 retracker for open ocean and coastal zones, OCOG retracker for sea-ice margins, and Sea-Ice retracker for sea ice. In this research, we evaluated the performances of the Sentinel-3 SRAL SAR altimetry retrackers over lakes, particularly over seasonally ice-covered lakes in one hydrological cycle. For 15 lakes and reservoirs with different sizes and at varying latitudes in the northern hemisphere, we compared the lake water levels estimated by each of standard SRAL SAR retrackers against in-situ water level measurements for different seasons (a full hydrologic cycle) during 2016–2017. Our evaluation shows that Sea-Ice retracker was unable to provide continuous estimates of lake water levels, as a result of the high rate of missing data. Although the precision and relative accuracy of lake water level estimates from these three standard SRAL SAR retrackers are similar, the SAMOSA-3 retracker has the least bias in comparison with ground-based gauge measurements. When the lakes in the mid- and high-latitude regions were covered by ice in the winter season, these three standard SAR retrackers generated erroneous lake water level measurements, significantly lower than the true lake water levels recorded by in-situ gauge stations. The measurement errors of these three standard retrackers increase with the growth of the lake ice thickness. To address the negative effect of the seasonal ice cover, we developed a new bimodal correction algorithm. We demonstrate that our bimodal correction algorithm can retrieve the ice thickness and reliably estimate water levels for the ice-covered lakes in winter, hence enabling the generation of temporally consistent lake water level measurements throughout all seasons for lake hydrological analysis.

Introduction

About 4% of the Earth's non-glaciated land surface is covered by about 117 million lakes (Verpoorter et al., 2014). These lakes provide habitats for numerous species (Schindler and Scheuerell, 2002) and the most accessible freshwater resources for human domestic, agricultural, and industrial activities (Postel et al., 1996). Monitoring lake water dynamics is important for water resource management and aquatic ecosystem service assessment. Lake water levels, particularly for high-latitude lakes, are very sensitive to regional and global climate changes (Ghanbari and Bravo, 2008; Gibson et al., 2006). To understand the impact of climate changes and anthropogenic activities on fresh water resources, it is necessary to obtain information about the seasonal and inter-annual variability of lake water levels.

Traditionally, lake water levels are measured at gauge stations established near lake shores. However, most of the lakes remain ungauged and many of the basic hydrologic parameters, e.g. the magnitude of seasonal cycle and long-term water level variation, are largely unknown. In particular, there is a widespread decline in the networks of gauge stations in most parts of the world due to the cost of installation and maintenance as well as management issues (Global Runoff Data Center, 2009; Fekete and Vörösmarty, 2002; Hannah et al., 2011; Shiklomanov et al., 2002).

Satellite radar altimetry is a space technique developed to determine the marine geoid and estimate the changes of sea surface topography (Stammer and Cazenave, 2017). This technique is now commonly used for the monitoring of inland water bodies (Cretaux et al., 2017). A satellite radar altimeter emits a series of radar pulses towards the Earth's surface and accurately measure the range between satellite and the reflecting surface by tracking the time delay between the emission and reception of the radar pulse. The elevation measurement is then jointly determined by the range and the satellite position above the reference ellipsoid. The emitted and the returned radar pulses are recorded as pulse power over time, which is referred to as “waveform”. The time elapsed between the emission and the reception is precisely calculated by identifying the reference points on the emitted and the returned altimeter waveforms (Chelton et al., 2001; Frappart et al., 2017; Stammer and Cazenave, 2017).

There have been thirteen satellite radar altimetry missions since 1985, including Geosat (1985–1989), ERS-1 (1991–2000), ERS-2 (1995–2011), Geosat Follow-on (1998–2008), Topex/Poseidon (1992–2005), Envisat (2002–2012), Jason-1 (2002–2013), Jason-2 (2008–present), Jason-3 (2016–present), Cryosat-2 (2010–present), HY-2A (2011–present), Saral/Altika (2013–present) and Sentinel-3 (2016–present). Most of these satellite missions utilized the conventional pulse-limited radar altimeter to make elevation measurements of the Earth's surface. The effective footprint diameter of conventional pulse-limited radar altimeters ranges from 1.6 km to 13.4 km, depending on the radar pulse duration, the altitude of the satellite orbit, and the roughness of the reflecting surface (Chelton et al., 1989). A great number of studies have evaluated the performances of these conventional pulse-limited altimeters in the retrieval of lake/river level variations (Asadzadeh Jarihani et al., 2013; Birkett, 1995; Birkett and Beckley, 2010; Frappart et al., 2006b; Frappart et al., 2015; Morris and Gill, 1994a, Morris and Gill, 1994b; Ričko et al., 2012; Schwatke et al., 2015). The accuracy of altimetry-derived lake/river levels is largely affected by the target size, the topographic undulation of the reflecting surface, and the environment (e.g. land cover) surrounding the target (Baup et al., 2014; Birkett and Beckley, 2010; Maillard et al., 2015). The accuracy of the altimetry-derived lake levels as compared to gauge data can degrade from several centimeters for large lakes (e.g. with diameter larger than 20 km) to a couple of decimeters for small lakes (e.g. with diameter less than 10 km) (Birkett et al., 2011; Birkett, 1995; Birkett and Beckley, 2010; Sulistioadi et al., 2015), owing partly to a reduced number of measurements over the small lakes and partly to the radar pulse contamination by the variable terrain surfaces within the relatively large altimetry footprint.

The advent of ESA's Cryosat-2 mission has marked a new era of satellite radar altimetry. The Cryosat-2 satellite measures the Earth surface elevation with a synthetic aperture interferometric radar altimeter. It employs the along-track beam formation to generate a much smaller footprint strip (about 300 m along track and 1 km cross track), compared with the circular footprint (~10 km diameter) of the conventional pulse-limited altimeters (Wingham et al., 2006). The reduced size of the footprint strip enables the water level retrieval for inland water bodies with a relatively small size and increases the accuracy of water level estimates (Jiang et al., 2017; Nielsen et al., 2017; Villadsen et al., 2016).

The Sentinel-3 mission is a constellation of two identical satellites: the Sentinel-3A launched on February 16, 2016, and Sentinel-3B launched on April 25, 2018. Each satellite has a repeat cycle of 27 days with a sub-cycle of ~4 days (Donlon et al., 2012). With a high-inclination (98.65°) polar orbit, Sentinel-3A/B provides global altimetry coverage up to 81.35° latitude (Donlon et al., 2012). A Synthetic Aperture Radar Altimeter (SRAL) instrument is one of the three primary payloads onboard each satellite. The SRAL is a dual-frequency altimeter that employs a primary Ku-band (13.575 GHz) to measure the distance between the satellite and Earth's surface, and uses a secondary C-Band (5.41 GHz) to correct the range delays due to the ionosphere. The SRAL can operate at two modes: the Low Resolution Mode (LRM) and the Synthetic Aperture Radar (SAR) mode. The LRM is only a back-up mode (Sentinel-3-Team, 2017). In this mode, the SRAL works as a conventional pulse-limited radar altimeter. In the SAR mode, it employs SAR technology inherited from Cryosat-2 to increase the along-track sampling resolution (~300 m) and elevation measurement accuracy (Donlon et al., 2012). The shape of the SAR altimetry waveform echoed from a flat surface differs significantly from the classic shape of conventional pulse-limited radar altimetry waveform (Phalippou and Enjolras, 2007; Raney, 1998; Ray et al., 2015). After Doppler processing, the SAR waveform has an impulse-like shape, in contrast to the step-function shape of a conventional pulse-limited radar waveform (Raney, 1998).

Seven waveform retracking algorithms (“retrackers”) have been developed to determine the elevations for different types of surfaces and for different operating modes of Sentinel-3 altimeters (MSSL/CNES/CLS, 2019). The retrackers implemented for the LRM mode include the Ocean-3 retracker for ocean surfaces, the OCOG retracker for ice surfaces, the ICE retracker for the interior continental ice, and the MLE4 retracker for the ice sheets. In the SAR mode, the retrackers include the SAMOSA-3 retracker for ocean surfaces, the OCOG retracker for sea-ice margins, the Ice-Sheet retracker for ice sheets, and the Sea-Ice retracker for sea ice surfaces. The LRM retrackers were inherited from conventional pulse-limited satellite radar altimetry missions, e.g., Envisat mission (Frappart et al., 2006a). The SAR retrackers, however, were developed using completely new analytical waveform models (Dinardo et al., 2015; Jain et al., 2014) or by modifying the conventional retrackers to account for the differences between the conventional pulse-limited waveforms and the SAR altimetry waveforms (MSSL/CNES/CLS, 2019).

It should be noted that none of the SRAL SAR retrackers above were specially designed for inland waters. The performance of the LRM (or conventional) retrackers over inland waters have been evaluated in previous studies (Frappart et al., 2006a; Santos da Silva et al., 2010). However, until now no research effort has been reported to assess the performances of SRAL SAR retrackers in the retrieval of lake water levels. A large quantity of lakes is distributed between 45°N and 75°N latitudes (Verpoorter et al., 2014). Those high-latitude lakes are covered by ice in winter and the ice-cover duration and thickness vary depending on the latitudes (Surdu et al., 2014; Weyhenmeyer et al., 2004). It has been reported that the presence of lake ice could cause biases in the radar altimetry range and lead to unreliable estimates of lake water levels (Birkett, 1995; Birkett and Beckley, 2010; Kouraev et al., 2007). The common practice is to identify and exclude the observations acquired in the water-frozen season, leading to discontinuous lake level measurements. The derivation of temporally consistent and continuous water levels for high-latitude lakes in all seasons entails the development of a new robust retracker that can address the ice cover influences.

In this research, we aim to evaluate the performances of different SRAL SAR retrackers in retrieving lake water levels in a full hydrologic cycle, assess the effect of the lake ice cover on the returned SAR altimetry waveforms and associated measurement error in lake level retrieval in winter season, and develop a new bimodal correction algorithm capable of generating reliable water-equivalent lake level estimates for the ice-covered lakes in winter. The remainder of this paper is organized as follows. Section 2 describes the case study lakes and the in-situ datasets utilized in this study. Section 3 introduces the SRAL data products, the SAR altimetry waveform, the SRAL SAR retrackers and our new bimodal correction algorithm. Section 4 evaluates the performances of SRAL SAR retrackers by comparing their lake level estimates to the in-situ gauge measurements over 15 lakes with different ice cover conditions. The influences of lake ice on the echoed SRAL SAR altimetry waveform and the performance of our new bimodal correction algorithm over ice-covered lakes are examined in detail. In Section 5, we summarize our research findings and draw some conclusions.

Section snippets

Case study lakes and their winter ice conditions

In this study, we selected 15 lakes and reservoirs (Fig. 1) from four countries (Finland, Canada, USA, Sweden) to assess the accuracy of SRAL SAR retrackers. These lakes are located in a wide range of latitudes and have significantly different ice cover conditions in winter season. Among these lakes, Reservoir Porttipahta in Finland is the smallest with a surface area of 205.6 km2, and Lake Superior in the North America is the largest with a surface area of 81,935.7 km2. Table 1 lists the

SRAL data products

In the SAR mode, the SRAL instrument is able to measure the surface elevations at a high along-track resolution (~300 m), which greatly improves the retrieval of elevations over more variable surfaces, e.g. inland waters, terrestrial land surfaces and coastal areas. The pulse repetition frequency (PRF) of Ku band at this mode is 18 KHz, much higher as compared to LRM mode on past and current altimetry missions. The SRAL is also supported by a dual-frequency passive microwave radiometer (MWR)

Lake surface profiles retrieved by different SAR retrackers

The Sentiel-3 satellite tracks and in-situ gauge stations for Lake Erie and Great Slave Lake are shown in Fig. 5. The performances of the SRAL retrackers along these tracks are evaluated in terms of the number of valid measurements, the consistency of the measurements and the difference of the measurements from in-situ gage observations. The effect of ice cover on the performances of the different retrackers is examined.

Fig. 6a and b shows the surface elevation profiles along Track 576 over

Conclusions

Satellite radar altimetry has been widely used for monitoring lake water levels over the past three decades. The SAR altimeter (SRAL) onboard the most recent Sentinel-3 satellite mission provides high resolution elevation measurements for the Earth's surfaces. The waveforms produced by the SAR altimeter are quite different from the conventional pulse-limited altimetry waveforms. Four standard SRAL SAR retracking algorithms have been developed, but their performances on the retrieval of lake

CRediT authorship contribution statement

Song Shu: Conceptualization, Methodology, Software, Formal analysis, Writing - original draft.Hongxing Liu: Methodology, Writing - review & editing, Funding acquisition.Richard A. Beck: Resources, Writing - review & editing, Supervision.Frédéric Frappart: Validation, Writing - review & editing.Johanna Korhonen: Validation, Writing - review & editing.Min Xu: Writing - review & editing.Bo Yang: Writing - review & editing.Kenneth M. Hinkel: Writing - review & editing.Yan Huang: Writing - review &

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 41771461).

References (83)

  • J. Santos da Silva et al.

    Water levels in the Amazon basin derived from the ERS 2 and ENVISAT radar altimetry missions

    Remote Sens. Environ.

    (2010)
  • J.B.T. Scott et al.

    A ground-based radar backscatter investigation in the percolation zone of the Greenland ice sheet

  • S. Shu et al.

    Estimation of snow accumulation over frozen Arctic lakes using repeat ICESat laser altimetry observations – a case study in northern Alaska

    Remote Sens. Environ.

    (2018)
  • H. Villadsen et al.

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

    J. Hydrol.

    (2016)
  • D.J. Wingham et al.

    CryoSat: a mission to determine the fluctuations in Earth’s land and marine ice fields

    Adv. Space Res.

    (2006)
  • ACRI-ST IPF Team

    Sentinel-3 Core PDGS Instrument Processing Facility (IPF) Implementation: Product Data Format Specification - Product Structures

    (2017)
  • R.A. Assel

    Great Lakes Ice Cover, First Ice, Last Ice, and Ice Duration: Winters 1973–2002

    (2003)
  • R. Assel et al.

    Great Lakes Ice Cover Data - Ice Year 2017

    (2017)
  • R.A. Assel et al.

    Analysis of Great Lakes Ice Cover Climatology: Winters 2006–2011

    (2013)
  • D.K. Atwood et al.

    Microwave backscatter from Arctic lake ice and polarimetric implications

    IEEE Trans. Geosci. Remote Sens.

    (2015)
  • F. Baup et al.

    Combining high-resolution satellite images and altimetry to estimate the volume of small lakes

    Hydrol. Earth Syst. Sci.

    (2014)
  • J.F. Beckers et al.

    Retrievals of lake ice thickness from Great Slave Lake and Great Bear Lake using CryoSat-2

    IEEE Trans. Geosci. Remote Sens.

    (2017)
  • C.M. Birkett

    The contribution of TOPEX/POSEIDON to the global monitoring of climatically sensitive lakes

    J. Geophys. Res.

    (1995)
  • C.M. Birkett et al.

    Investigating the performance of the Jason-2/OSTM radar altimeter over lakes and reservoirs

    Mar. Geod.

    (2010)
  • C. Birkett et al.

    From research to operations: the USDA global reservoir and lake monitor

  • S. Bogning et al.

    Monitoring water levels and discharges using radar altimetry in an ungauged river basin: the case of the Ogooué

    Remote Sens.

    (2018)
  • D.B. Chelton et al.

    Pulse compression and sea level tracking in satellite altimetry

    J. Atmos. Ocean. Technol.

    (1989)
  • J.F. Crétaux et al.

    An absolute calibration site for radar altimeters in the continental domain: Lake Issykkul in Central Asia

    J. Geod.

    (2009)
  • J.-F. Cretaux et al.

    Hydrological applications of satellite altimetry rivers, lakes, man-made reservoirs, inundated areas

  • S. Dinardo et al.

    Sentinel-3 STM SAR ocean retracking algorithm and SAMOSA model

  • B.M. Fekete et al.

    The current status of global river discharge monitoring and potential new technologies complementing traditional discharge measurements

  • M. Fernandes et al.

    Atmospheric corrections for altimetry studies over inland water

    Remote Sens.

    (2014)
  • W.D. Forrester

    Establishment of temporary water level gauge

  • J.L. Foster et al.

    An overview of passive microwave snow research and results

    Rev. Geophys.

    (1984)
  • F. Frappart et al.

    Water volume change in the lower Mekong from satellite altimetry and imagery data

    Geophys. J. Int.

    (2006)
  • F. Frappart et al.

    Preliminary assessment of SARAL/AltiKa observations over the Ganges-Brahmaputra and Irrawaddy rivers

    Mar. Geod.

    (2015)
  • F. Frappart et al.

    Satellite altimetry: principles and applications in earth sciences

  • J.J. Gibson et al.

    Hydroclimatic controls on water balance and water level variability in Great Slave Lake

    Hydrol. Process.

    (2006)
  • Global Runoff Data Center

    Long-term Mean Monthly Discharges and Annual Characteristics of GRDC Stations

    (2009)
  • G.E. Gunn et al.

    Observation and modeling of X- and Ku-band backscatter of snow-covered freshwater lake ice

    IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.

    (2015)
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