Elsevier

Remote Sensing of Environment

Volume 182, 1 September 2016, Pages 169-191
Remote Sensing of Environment

Spatio-temporal variability of X-band radar backscatter and coherence over the Lena River Delta, Siberia

https://doi.org/10.1016/j.rse.2016.05.003Get rights and content

Highlights

  • We explored TerraSAR-X data with high temporal resolution in permafrost landscapes.

  • We analysed time series of backscatter and coherence and seasonal surface changes.

  • Backscatter responded on rain at the time of acquisition and on melt snow crust.

  • Coherence strongly decreased during snow cover onset and melt.

  • PCA revealed latent relationship between both time series and surface temperature.

Abstract

Satellite-based monitoring strategies for permafrost remain under development and are not yet operational. Remote sensing allows indirect observation of permafrost, a subsurface phenomenon, by mapping surface features or measuring physical parameters that can be used for permafrost modeling. We have explored high temporal resolution time series of TerraSAR-X backscatter intensity and interferometric coherence for the period between August 2012 and September 2013 to assess their potential for detecting major seasonal changes to the land surface in a variety of tundra environments within the Lena River Delta, Siberia. The TerraSAR-X signal is believed to be strongly affected by the vegetation layer, and its viability for the retrieval of soil moisture, for example, is therefore limited. In our study individual events, such as rain and snow showers, that occurred at the time of TerraSAR-X acquisition, or a refrozen crust on the snowpack during the spring melt were detected based on backscatter intensity signatures. The interferometric coherence showed marked variability; the snow cover onset and snow melt periods were identified by significant reduction in coherence. Principal component analysis provided a good spatial overview of the essential information contained in backscatter and coherence time series and revealed latent relationships between both time series and the surface temperature.

The results of these investigations suggest that although X-band SAR has limitations with respect to monitoring seasonal land surface changes in permafrost areas, high-resolution time series of TerraSAR-X backscatter and coherence can provide new insights into environmental conditions.

Introduction

Permafrost affects nearly 24% of the land surface in the northern hemisphere, making it one of the largest areal components of the cryosphere. According to Hugelius et al. (2014), permafrost soils in the northern hemisphere contain a total of about 1300 Pg of organic carbon. The release of carbon dioxide and methane from thawing arctic permafrost is currently a major focus of climate change science. Moreover, irreversible landscape transformations resulting from a warming climate (e.g. thaw subsidence, thermo-erosion, and slope failure) disturb the hydrology, fluxes of energy and matter, and moisture balance, affecting both ecosystems and human infrastructure (e.g. Nelson et al., 2001, Kääb, 2008, Osterkamp et al., 2009, Jorgenson et al., 2013). These far-reaching consequences make the monitoring of the thermal conditions and structural stability of permafrost and the prediction of its future state particularly important.

Sporadic and intermittent in situ observations are often inadequate for monitoring surface changes considering the vastness, remoteness, and poor accessibility of the Arctic region. Satellite remote sensing offers a possible alternative that is able to provide both regular observations and broad spatial coverage. Satellite monitoring systems are well established for other components of the cryosphere (e.g. glaciers, sea ice and snow cover), and provide essential information on the processes and impacts of a changing climate on the Arctic region. In contrast, no such monitoring systems currently exist for permafrost, mainly because permafrost is a sub-surface element and cannot be monitored directly through satellite observations (Westermann, Duguay, Grosse, & Kääb, 2015a).

However, various land surface changes that occur in association with permafrost degradation (such as the formation, expansion, and drainage of lakes, or the occurrence of thaw slumps and ground thaw subsidence) are observable by remote sensing and can therefore provide insight into the actual condition of the underlying permafrost (e.g. Duguay et al., 2005, Riordan et al., 2006, Mars and Houseknecht, 2007, Lantuit and Pollard, 2008, Jones et al., 2011, Günther et al., 2013). In addition, different remote sensing products such as land surface temperature and snow water equivalent can be used as forcing data for permafrost modeling (Langer et al., 2013, Westermann et al., 2015b). Despite a number of successes in the use of remote sensing for permafrost-related problems, operational satellite-based permafrost monitoring systems remain under development and in need of further improvement (Westermann et al., 2015a).

Microwave imagery has a distinct advantage over optical imagery in polar areas as it allows data to be acquired independently of cloud cover and solar illumination. Synthetic Aperture Radar (SAR) is an active microwave system that transmits pulses of energy to the target and receives an echo of these pulses through an antenna. The backscatter intensity (subsequently referred to simply as backscatter) of an SAR signal is the portion of transmitted energy that is received by the system. Two properties of the backscattering surface that mainly define the backscatter are the surface roughness and the dielectric constant of the backscattering media. Since the presented study has one focus on radar backscatter, we outline those factors that have the potential to influence backscatter in the context of a typical tundra environment underlain by permafrost. There are static conditions such as the variations in surface roughness associated with different land cover types, and dynamic conditions associated with, for example, soil moistening and drying, soil freezing and thawing, or snow cover onset and melt. Higher surface roughness (relative to the SAR wavelength) typically causes diffuse scattering resulting in higher backscatter, whereas smooth surfaces cause more specular reflection of the signal resulting in lower backscatter. Where surfaces are vegetated it is important to take into account volume scattering within the vegetation which can significantly alter the total backscatter either due to increased backscatter by leaves and branches or due to attenuation of the backscattered signal from the terrain beneath the vegetation layer.

The moisture of soil or vegetation affects the backscatter due to variations in their dielectric properties with water content. Moister soil normally results in higher backscatter than the drier soil. The relationships between radar backscatter and soil moisture have been investigated by, for example, Ulaby, Batlivala, and Dobson (1978); Ulaby, Bradley, and Dobson (1979); Kane, Hinzman, Haofang, and Goering (1996); Lu and Meyer (2002). The backscatter from frozen ground appears similar to that from dry ground because the dielectric constant is much lower for water in a frozen state than for water in a liquid state. Freezing of the ground therefore typically reduces the backscatter and thawing results in a higher backscatter. The influence of ground freezing and thawing on backscatter has been investigated by, for example, Wegmüller (1990) and Rignot and Way (1994). The presence of snow cover affects backscatter in a more complicated manner than freezing and thawing or variations in soil moisture. This is due to complex backscattering within the snowpack and from its surface, as well as from the surface of the ground beneath (if reached by the radar waves). SAR system properties, acquisition parameters, and snowpack conditions all have a strong influence on the observed backscatter. Dry snow typically appears “transparent” to SAR frequencies such as those in X-band and lower, and backscattering occurs mainly from the ground surface beneath the snowpack (e.g. Mätzler & Schanda, 1984). Wetting of the snow can have a major effect on the backscatter as the dielectric contrast at the air-snow interface becomes significant and energy transmission into the snowpack is reduced. The snow surface properties then begin to have a major effect on the backscattered signal, with smooth surfaces (relative to the wavelength of the signal) resulting in low backscatter due to specular reflection of the signal and rough surfaces resulting in higher backscatter.

Volume scattering within the snowpack is influenced by the SAR frequency and acquisition incidence angle, as well as by the properties of the snow such as its density, liquid water content, and grain size. Snow metamorphism, such as changes in crystal size and structure or the formation of ice lenses or layers, can also influence backscatter (e.g. Du, Shi, & Rott, 2010). Backscatter signatures from different snow conditions have been investigated by, for example, Strozzi, Wiesmann, and Mätzler (1997) and Nagler and Rott (2000).

Radar parameters such as frequency, incidence angle, and polarization configuration had a strong influence on the results of the above-mentioned investigations, for which C-band scatterometers and SAR were commonly used. Far fewer investigations into the conditions and processes related to permafrost have been carried out using higher frequency X-band sensors due to the limited availability of such data to date. Among these are investigations by Regmi, Grosse, Jones, Jones, and Anthony (2012) who used TerraSAR-X backscatter for dating drained thermokarst lake basins, by Ullmann et al. (2014) who carried out polarimetric analysis of TerraSAR-X data for different tundra surfaces in the Canadian Arctic, and by Duguay, Bernier, Lévesque, and Tremblay (2015) who tested the use of TerraSAR-X backscatter for monitoring tundra shrub growth.

A second focus of this study is on radar coherence over permafrost landscapes. In contrast to SAR backscatter analysis, SAR interferometry (InSAR) exploits the phase component of the microwave signal. It uses the phase difference between two SAR images covering the same area but acquired at different times to detect surface displacements. InSAR has been shown to be a powerful technique for detecting ground displacement associated with earthquakes and volcanic eruptions (e.g. Massonnet et al., 1993). The method has also recently been tested for monitoring permafrost thaw subsidence and frost heave (e.g. Rykhus and Lu, 2008, Liu et al., 2010, Short et al., 2011, Strozzi et al., 2012, Liu et al., 2014, Liu et al., 2015, Beck et al., 2015). Other applications of InSAR are for monitoring water level and inundations in the wetlands (e.g. Alsdorf et al., 2000, Hong et al., 2010, Xie et al., 2013) and for the retrieval of snow water equivalent (Guneriussen et al., 2001, Rott et al., 2003, Deeb et al., 2011).

One of the main limitations of InSAR is the signal loss due to insufficient phase coherence between SAR datasets. This phase coherence (or interferometric correlation) over time indicates the quality of the interferometric phase. There are a number of possible reasons for a loss of phase coherence including thermal noise from the antenna, a large interferometric baseline, topographic effects, misregistration between the SAR images, and atmospheric effects, but it can also be due to land surface changes that occur between SAR acquisitions (Zebker & Villasenor, 1992). The latter are of special interest here because if all other factors causing decorrelation are minimized, the temporal decorrelation due to changes in the land surface can be used as a direct geophysical signal for the detection and interpretation of such changes (e.g. Rignot and Van Zyl, 1993, Wegmüller and Werner, 1997). Interferometric coherence is defined by both amplitude and phase components of the SAR signal and is therefore potentially more sensitive to changes in the land surface than amplitude variations alone. The use of coherence has, however, been less investigated compared to the use of changes in backscatter. Examples of coherence use include the detection of changes in glacier surfaces (Weydahl, 2001a), delineation of the extent of glaciers (Atwood et al., 2010, Frey et al., 2012), Arctic ecozones classification (Hall-Atkinson & Smith, 2001), and mapping wet snow covers (Strozzi, Wegmüller, & Mätzler, 1999).

Similar factors to those influencing backscatter (as discussed in the previous sub-section), may also affect coherence. Some studies have used SAR coherence to supplement backscatter for the detection of temporal changes to the ground conditions (e.g. Rignot and Van Zyl, 1993, Strozzi et al., 1999, Moeremans and Dautrebande, 2000, Barrett et al., 2012). The results of these investigations have suggested a limited sensitivity of coherence to moisture variations or soil freeze and thaw (i.e. to changes in dielectric properties) compared to variations in backscatter. Far more marked decorrelation can occur due to physical displacement of backscattering elements (for example, due to soil ploughing), a complete change in the nature of backscattering elements (such as from snow-free to snow-covered surfaces, or from bare ground to vegetated ground), or to volumetric decorrelation caused, for instance, by volume scattering in the vegetation layer. The level of decorrelation also depends on the time span between the SAR images used to compose coherence images relative to the rate of change of the backscattering elements, and on the wavelength of the SAR signal. Wickramanayake et al. (2016) investigated all possible interferometric combinations of 34 RADARSAT-2 (C-band) images in order to evaluate coherence variability with respect to temporal baseline and master image.

PCA is a well-established technique in remote sensing for the visualization of multidimensional data (e.g. Gonzalez & Woods, 2002). It reduces redundancy in multiband or multitemporal imagery, increases the signal-to-noise ratio and provides an opportunity to use multitemporal datasets for change detection. PCA transforms the axes of multidimensional data in such way that the new axes (the principal components) account for variances within the data, with the first PC accounting for the largest variance and the last PC accounting for the smallest variance. PCA has mainly been used in optical remote sensing applications, such as for multitemporal analysis of Normalized Difference Vegetation Index images (Townshend, Goff, & Tucker, 1985) or landscape change detection based on multisensor data (Millward, Piwowar, & Howarth, 2006). Few investigations have attempted to use PCA on SAR imagery for soil wetness assessments (Verhoest et al., 1998, Bourgeau-Chavez et al., 2005, Kong and Dorling, 2008).

TerraSAR-X is a new generation X-band radar satellite that has been providing high spatial resolution (up to 1 m) and high temporal resolution (11 day repeat orbit) SAR imagery since 2008. On the one hand its short wavelength (2.5–3.75 cm) is expected to generate greater loss of coherence than longer wavelengths such as C-band (3.75–7.5 cm) and L-band (15–30 cm) over a given time span due higher probability of temporal decorrelation, because scatterers change or motion of a significant fraction of an X-band wavelength is more likely to occur on the surface. On the other hand exactly this effect could be beneficial for a more sensitive analysis of surface changes. However, the shorter revisit time of TerraSAR-X satellite compared to ERS, ENVISAT, RADARSAT or ALOS satellites (but not Sentinel-1 with 12 days, or 6 days once both satellites are in operation) may lead to improved coherence, thus to some extent potentially compensating for the greater loss of coherence due to the shorter wavelength.

In order to investigate the potential of TerraSAR-X for monitoring surface changes within typical Arctic lowland permafrost areas, this study made use of a time series of TerraSAR-X data from August 2012 to September 2013, covering the full range of seasonal surface changes. These changes are important for the assessment of regional climate change impacts and as input and validation for numerical permafrost models. We have explored time series for both radar backscatter intensity and interferometric coherence over a variety of landscapes in the Lena River Delta region, also making use of detailed supporting information that is available on meteorological conditions and snow coverage.

This is, to the best of our knowledge, one of the first studies of Arctic Siberian lowland permafrost to analyze such a high-resolution time series of SAR imagery in terms of backscatter and interferometric coherence. The explorative nature of this work meant that our objective was not to come up with concrete models or algorithms for SAR analysis, but rather to highlight the potential and limitations of X-band time series for monitoring surface related processes in tundra permafrost landscapes.

Section snippets

Study area

Our investigation focused on the southern part of the Lena River Delta in Siberia (Fig. 1). The delta occupies an area of about 30,000 km2 and includes > 1500 islands of various sizes. It lies within the zone of continuous permafrost, with permafrost thicknesses of up to 600 m (Grigoriev, 1960). The delta can be divided into three geomorphological units or terraces on the basis of their height above sea level, geological age, and the composition of the deposits (Fig. 1b). The first terrace (1–12 m 

SAR data

The German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR) has designated the central Lena Delta as a long-term monitoring site that meets the Requirements for Monitoring of Permafrost in Polar Regions for the WMO Polar Space Task Group (Bartsch, 2014). The DLR has provided access to a unique dataset acquired by the TerraSAR-X satellite. TerraSAR-X (TSX) is a radar satellite launched in 2007, operating in X-band (wavelength 3.1 cm, frequency 9.6 GHz). The SAR dataset used in

Time series of backscatter for regions of interest

We first compared time series of backscatter for the different ROIs; the mean backscatter over the 30 × 30 pixel ROIs are plotted against the dates of acquisition in Fig. 3. There is a notable difference in backscatter values over the different types of land cover and the ROIs yield different ranges of values during the different seasons. During the first snow-free season (03.08-27.09.2012) the rocky outcrops with very poorly developed and sparse vegetation yielded the highest backscatter (− 10 

Backscatter intensity time series

Comparing backscatter time series for different ROIs revealed that most of the ROIs can be distinguished from each other by general differences in backscatter. The highest backscatter was generally from the rocky outcrops ROI and the lowest from the sandbank ROI (except of the summer of 2013), while the backscatter time series from the other (vegetated) ROIs were less clearly distinguished from each other, typically lying between the time series for the two unvegetated ROIs. Rayleigh's

Conclusions

Time series of X-band SAR backscatter intensity and 11-days interferometric coherence with high temporal resolutions have been used to interpret major seasonal land surface changes in a variety of tundra environments, namely an area of wet polygonal tundra, a drier Ice Complex upland area, a recently drained well-vegetated lake basin, a partly well-vegetated floodplain, a bare sandbank, and a very dry area of rocky outcrops. Seasonal variations in intensity and coherence were evaluated in the

Acknowledgements

We thank the editor, Prof. Dr. Eric Kasischke, and the referees for their thoughtful and valuable comments. Special thanks go to the German Aerospace Center DLR and Achim Roth for invaluable help with the data access. We also thank Prof. Dr. Claude Duguay and Dr. Sebastian Westermann for valuable discussions. Parts of the study were conducted in preparation for the ESA GlobPermafrost project. A. Kääb acknowledges support by the European Research Council under the European Union's Seventh

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