ENVISAT Advanced Synthetic Aperture Radar Research Articles

Internal Wave Signature Extraction From SAR and Optical Satellite Imagery Based on Deep Learning

Internal waves (IWs) are a common characteristic of oceans and serve a crucial role in transmitting energies between large-scale tides and small-scale mixing. This study developed a deep-learning-based method for extracting IW signatures on multiple satellite imagery from synthetic aperture radar (SAR) and optical sensors in sun-synchronous and geostationary orbits with varying spatial resolution. We collected 1115 satellite images, including 116 ENVISAT ASAR (Advanced SAR), 839 MODIS (MODerate-resolution Imaging Spectroradiometer), and 160 Himawari-8 AHI (Advanced Himawari Imager) images with clear IW signatures in the South China Sea (SCS), Sulu Sea, and Celebes Sea for model training. Considering the distinct IW characteristics under different imaging mechanisms, the specially tailored IW Extraction network (IWE-Net) leverages three modifications to improve the accuracy and robustness: online data augmentation, squeeze and excitation blocks, and Matthews correlation coefficient loss. The overall mean Precision, Recall, and F1-score of the IWE-Net model are 85.75%, 85.67%, and 85.71%, demonstrating the model is accurate for IW signature extraction.We also proved the transferability of our method to sea areas worldwide, long-term periods, and Sentinel satellite sensors completely independent of the model training. Globally, the number of IW images and extracted pixels show an obvious tidal-related double-peak distribution. Furthermore, we processed 15461 MODIS images in the northeastern SCS to present a holistic IW distribution map over the past 22 years. An unreported IW silent zone caused by drastic topography changes has been discovered, indicating the great potential of deep learning in information retrieval from remote sensing imagery.

Estimation of Sea Ice Production in the North Water Polynya Based on Ice Arch Duration in Winter During 2006–2019

AbstractThe North Water Polynya (NOW) is the largest recurrent Arctic coastal polynya. The formation of the NOW is critically dependent on the development of an ice arch that defines its northern boundary. In this study, high‐resolution ENVISAT Advanced Synthetic Aperture Radar data, Sentinel‐1A data, and Moderate Resolution Imaging Spectroradiometer data were employed to identify the spatio‐temporal characteristics of the ice arch during 2006–2019. Polynya pixels were identified based on the thin ice thickness (TIT), using a threshold of TIT <0.2 m, from which the polynya extent, heat flux, and ice production (IP) were estimated. The results show the different locations of the ice arch in different years, with a mean duration of 132 ± 69 days. The average annual polynya extent over the 14 years is ∼38.8 ± 8 × 103 km2, and we found that it is more closely correlated with wind speed during the winter and air temperature during early spring. The average heat flux drops from about 248 W/m2 in the winter months to about 34 W/m2 in May. The average accumulated IP varies significantly every year, with an average of 144 ± 103 km3, and peak values in March in most years. No apparent interannual trends are shown for the polynya area, heat flux, and IP during 2006–2019. The results also show that IP calculated based on the ice arch data is approximately 25% lower than that obtained by assuming a fixed time, location, and duration for the polynya.

Wide-area observations of surface deformation in Mexican urban areas and geothermal fields using ENVISAT InSAR

There is an increasingly growing literature on the use of advanced multi-temporal Interferometric Synthetic Aperture Radar (InSAR) to monitor surface deformation with millimeter precision in urban and rural environments, including applications over wide areas spanning hundreds to thousands of square kilometers. In this work, we survey the eastern sector of the Trans-Mexican Volcanic Belt, extending from the state of Hidalgo, through Tlaxcala, Puebla and up to Veracruz, for a total of 19,000 km2. The surveyed area includes both the geothermal exploration site of the Acoculco caldera complex, and Los Humeros geothermal field, one of the largest geothermal exploitation sites of Mexico. Using the multi-temporal Small BAseline Subset (SBAS) InSAR method, we process the whole ENVISAT Advanced SAR (ASAR) IS2 image archive acquired in 2003-2010. Estimated subsidence rates are as high as 3.1 cm/year within the region, and a number of land deformation hotspots are identified within the urban areas of Tulancingo, Tlaxcala and Apizaco, the Tecamachalco valley, as well as within Los Humeros caldera. The surveying of such a scientifically interesting test region provides a proof of concept on how to investigate natural and anthropogenic processes in urban and rural areas using big SAR datasets and InSAR methods.

Long-Term Water Surface Area Monitoring and Derived Water Level Using Synthetic Aperture Radar (SAR) at Altevatn, a Medium-Sized Arctic Lake

Monitoring water storage in lakes and reservoirs is critical to water resource management, especially in a changing climate. Satellite microwave remote sensing offers a weather and light-independent solution for mapping water cover over large scales. We have used 13 years of synthetic aperture radar (SAR) data from three different sensors (Sentinel-1, RADARSAT-2, and Envisat advanced synthetic aperture radar (ASAR)) to develop a method for mapping surface water cover and thereby estimating the lake water extent (LWE). The method uses the unsupervised K-means clustering algorithm together with specific post-processing techniques to create binary maps of the water area. We have specifically tested and validated the method at Altevatn, a medium-sized arctic lake in Northern Norway, by using in-situ measurements of the water level. The multi-sensor SAR LWE time series were used in conjunction with the water level measurements to derive the lake hypsometry while at the same time quantifying the accuracy of our method. For Altevatn lake we estimated LWE with a root mean squared error (RMSE) of 0.89 km2 or 1.4% of the mean LWE, while the inferred lake water level (LWL) was associated with an RMSE of 0.40 m, or 2.5% of the maximum annual variation. We foresee that there is potential to further develop the algorithm by generalizing its use to other lakes worldwide and automating the process such that near real-time monitoring of LWE may be possible.

Change in regional land subsidence in Beijing after south-to-north water diversion project observed using satellite radar interferometry

ABSTRACTOver-exploitation of groundwater has caused severe land subsidence in Beijing during the past two decades. Since the middle route of South-to-North Water Diversion Project (SNWDP), the biggest water diversion project in China, started to deliver water to Beijing in December 2014, the groundwater shortage has been greatly alleviated. This study aims to analyze the impact of SNWDP on the spatiotemporal evolution of land subsidence in Beijing. Change in surface displacement in Beijing after SNWDP was retrieved and the spatiotemporal patterns of the change were analyzed based on long time-series Envisat Advanced Synthetic Aperture Radar (ASAR) (2004–2010), Radarsat-2 (2011–2014), and Sentinel-1 (2015–2017) satellite datasets using Permanent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) techniques. Land subsidence unevenness index (LSUI) was proposed to represent the spatial unevenness of surface displacement. PS-Time approach was then adapted to examine the time series evolution of LSUI. The results showed that the InSAR measurements agree well with leveling measurements with R2 over 0.96. Although the maximum annual displacement rate reached −159.7 mm/year by 2017, over 57% of the area within 25 mm/year contour line showed decreasing or unchanged displacement rate after the south-north water delivered to Beijing. The settlement rate in Chaoyang-Dongbalizhuang (CD) subsidence center has decreased for 26 mm/year from 2011–2014 to 2015–2017. Only around 15% of the area experienced continued accelerating settlement rate through the three time periods, which was mainly located in the area with the compressible layer thickness over 190 m, while the magnitude of velocity increment considerably decreased after SNWDP. Land subsidence unevenness, represented by LSUI, developed more slowly after SNWDP than that during 2011–2014. However, LSUI at the edge of settlement funnel has kept developing and reached 1.7‰ in 2017. Decreasing groundwater level decline after SNWDP and the positive relationship (R2 > 0.74) between land subsidence and groundwater level clearly showed impacts of SNWDP on the alleviating land subsidence. Other reasons include geological background, increasing precipitation, and strict water management policies implemented during these years.

A newly developed ocean significant wave height retrieval method from Envisat ASAR wave mode imagery

The main objective of this paper is to propose a newly developed ocean Significant Wave Height (SWH) retrieval method from Envisat Advanced Synthetic Aperture Radar (ASAR) imagery. A series of wave mode imagery from January, April and May of 2011 are collocated with ERA-Interim reanalysis SWH data. Based on the matched datasets, a simplified empirical relationship between 22 types of SAR imagery parameters and SWH products is developed with the Genetic Algorithms Partial Least-Squares (GA-PLS) model. Two major features of the backscattering coefficient S10 and the frequency parameter S10 are chosen as the optimal training feature subset of SWH retrieval by using cross validation. In addition, we also present a comparison of the retrieval results of the simplified empirical relationship with the collocated ERA-Interim data. The results show that the assessment index of the correlation coefficient, the bias, the root-mean-square error of cross validation (RMSECV) and the scattering index (SI) are 0.78, 0.07 m, 0.76 m and 0.5, respectively. In addition, the comparison of the retrieved SWH data between our simplifying model and the Jason-2 radar altimeter data is proposed in our study. Moreover, we also make a comparison of the retrieval of SWH data between our developed model and the wellknown CWAVE_ENV model. The results show that satisfying retrieval results are acquired in the low-moderate sea state, but major bias appears in the high sea state, especially for SWH>5 m.

An adaptive machine learning approach to improve automatic iceberg detection from SAR images

Iceberg distribution, dispersion and melting patterns are fundamental aspects in the balance of heat and freshwater in the Southern Ocean; yet these features are not fully understood. This lack of understanding is, in part, due to the difficulties in accurately identifying icebergs in different environmental conditions. To improve the understanding, reliable iceberg detection tools are necessary to achieve a detailed picture of iceberg drift and disintegration patterns, an thus to gain further information on the freshwater input into the Southern Ocean. Here, we present an accurate automatic large-scale iceberg detection method using an alternative machine learning architecture applied to high resolution Synthetic Aperture Radar (SAR) images. Our method is based on the concept of adaptability and focuses on improving the performance of identifying icebergs in ambiguous environmental contexts with wide radiometric, textural, size and shape variability. The fundamentals of the method are centred on superpixel segmentation, ensemble learning and incremental learning. The method is applied to a dataset containing 586 ENVISAT Advanced SAR images acquired during 2003–2005 (Weddell Sea region) and to the Radarsat-1 Antarctic Mapping Project (RAMP) mosaic, covering the Antarctic wide near-coastal zone. These images cover scenes under heterogenous backscattering signatures for all seasons with variable meteorological, oceanographic and acquisition parameters (e.g. band, polarization). Our method is highly adaptable to distinguish icebergs from ambiguous objects hidden in the images. The average false positive rate and miss rate are 2.3 ± 0.4% and 3.3 ± 0.4%, respectively. Overall, 9512 icebergs with sizes varying from 0.1 to 4567.82 km2 are detected with average classification accuracy of 97.5 ± 0.6%. The results confirm that the method presented here is robust for widespread iceberg detection in the Antarctic seas.

Determining hydrogeological parameters of an aquifer in Sirjan Basin using Envisat ASAR interferometry and groundwater modelling

ABSTRACT Excessive groundwater exploitation in Sirjan plain, located in south of Iran, has caused severe land subsidence. The main purpose of this paper is to determine the hydrogeological parameters of the aquifer system in Sirjan plain. To this end, the groundwater flow was simulated in both steady and transient states using the United States Geological Survey Modular Three-Dimensional Finite-Difference Groundwater Flow Model, MODFLOW. The hydraulic conductivity parameter of the aquifer system was first estimated and calibrated in the steady state by simulating the hydraulic head at 63 piezometric wells. The specific yield and recharge parameters were then calibrated in the transient state by simulating the drawdown within a period of interest, i.e. 23 September 2004 to 22 September 2005. The main innovation of this paper, however, is to use the potential of Synthetic Aperture Radar (SAR) interferometry time-series analysis in the last step of the proposed algorithm. Small Baseline Subset (SBAS) time-series analysis was applied using Envisat Advanced SAR (ASAR) images acquired from 1 June 2004 to 28 September 2010. The merit of SBAS time series is that it enables us to calculate the amount of subsidence in each period of interest while minimizing the temporal decorrelation effect. In the last step, the SBAS time-series analysis results were obtained to extract the accumulated subsidence from 19 October 2004 to 30 August 2005. The aquifer compaction due to this period was calculated in the Interbed Storage Package (IBS1) for MODFLOW in order to calibrate storage coefficient in a trial-and-error manner so as the calculated subsidence and the observed one by SAR interferometry be in a good agreement. The Root Mean Square Error (RMSE) between the simulated subsidence and the observed one is calculated as 0.019 m which is an indication of the high performance of modelling.

Stress–strain analysis by genetic algorithm-based integration of long-term subsidence time series from different synthetic aperture radar platforms in Darab, Iran

The potential of synthetic aperture radar (SAR) interferometry was shown to study the compaction of the aquifer system in Darab plain, Iran. In so doing, two different datasets, including Envisat advanced SAR (ASAR) spanning 2010 and Sentinel-1A spanning 2016 to 2017, were applied in small baseline subset time series analysis. To estimate the subsidence in the time period for which there is no SAR data available, i.e., 2010 to 2016, the time series analysis results separately obtained from the two datasets were to be integrated using an appropriate model, which should have been fitted to both sets of results. However, as both deformation time series results were calculated taking into account a distinct temporal reference, fitting the model was not a straightforward task. Accordingly, the main attempt was to find the subsidence value corresponding to the temporal reference of Sentinel-1A time series with respect to that of Envisat ASAR. This shift value was optimally determined using a genetic algorithm so as to minimize the misfit between the model and the deformation time series corresponding to the entire period. The average value of the root mean square error estimated as the misfit between the model and the calculated time series at all pixels is 0.011 m, which is an indication of the high performance of the proposed method for modeling the deformation time series. The integration results were further used to derive the stress–strain relationships to study the storage properties of the aquifer system. The fact that the strain linearly increases along with the decrease in water level in most piezometric wells indicates that the subsidence is highly correlated with groundwater exploitation.

Seven years of surface deformation above the buried Nasr-Abad salt diapir using InSAR time-series analysis, Central Iran

This study employs Interferometric Synthetic Aperture Radar (InSAR) data sets to monitor the surface deformation of the Nasr-Abad buried salt diapir in the Central Basin of Iran. The Nasr-Abad salt diapir is one of the largest buried salt diapir in Iran and could be ideal site for oil/gas storage and industrial waste disposal. Forty ENVISAT Advanced Synthetic Aperture Radar (ASAR) images acquired during 2003–2010 were used to monitor surface activities of this diapir and its surrounding regions in that time window. One hundred and one interferograms were generated from both descending and ascending orbits by applying the Small-BAseline Subset (SBAS) method. Tropospheric artefacts in the displacement interferograms were mitigated using the power law correction method in TRAIN software. A time series of line-of-sight (LOS) displacements on the residual cap of Lower Red Formation above the buried diapir was also generated as well as the area. Finally, the data for temperature, precipitation, and tidal forces were correlated with the time-series displacement results of four points A, B, C, and D that located on the residual cap. Our analyses of our data indicate that surface above the diapir and an area of about 28 square kilometres subsided with maximum LOS velocity of about 7 mm/year for ascending images and 5 mm/year for descending images. Comparing the temperature and precipitation data with the time-series of displacement confirmed that they correlated with the same seasonal effects. Monitoring the activities of the Nasr-Abad region over 7 years shows that the region of surface subsidence is confined to the area that relates directly to the geological structures of the region.

The Dependence of Sea SAR Image Distribution Parameters on Surface Wave Characteristics

Modeling the statistical distribution of synthetic aperture radar (SAR) images is essential for sea target detection, which is an important aspect of marine SAR applications. The main goal of this study is to determine the effects of sea states and surface wave texture characteristics on the statistical distributions of sea SAR images. A statistical analysis of the Envisat Advanced Synthetic Aperture Radar (ASAR) wave mode images (imagettes), covering a variety of sea states and wave conditions, was carried out to investigate the suitability of the statistical distributions often used in the literature for sea states parameters. The results revealed the variation in the distribution parameters in terms of their azimuthal cutoff wavelength (ACW) and the peak-to-background ratio (PBR) of the SAR image intensity spectra. The shape parameters of Gamma and Weibull distribution are sensitive and monotonously decreasing with respect to PBR, while the scale parameter is sensitive to ACW. The K distribution was shown to perform well, with both high and stable accuracy. The results of this paper provide a parameterized scheme for sea state classifications and can potentially be used for choosing the most suitable distribution model according to sea state when performing sea target detection.

Combining InSAR and Hydraulic Head Measurements to Estimate Aquifer Parameters and Storage Variations of Confined Aquifer System in Cangzhou, North China Plain

AbstractGroundwater storage (GWS) depletion and land subsidence caused by groundwater overexploitation threaten water supply and infrastructure security in the North China Plain. However, the aquifer system responses to groundwater changes are poorly understood in this region, although it is extremely important for groundwater management and land subsidence control. In this study, aquifer parameters and GWS variations of the confined aquifer system in central Cangzhou are quantified by using Interferometric Synthetic Aperture Radar (InSAR) and hydraulic head measurements. We apply the multitemporal InSAR method to detect land deformation time series with Envisat Advanced Synthetic Aperture Radar (ASAR) data from 2003 to 2010. Seasonal signals in land deformation and head data are isolated by multichannel singular spectrum analysis to derive the elastic skeletal storativity (Ske). The results reveal a spatially heterogeneous Ske pattern with values ranging from 1.7 × 10−4 to 4.4 × 10−3 that agree well with those derived from pumping tests. An abnormal Skezone is found in the land uplift zone, and a gradual increase of Ske is revealed in the eastern coastal plain, which are likely attributed to the hydrological properties in the aquifer systems. Additionally, we characterize spatiotemporal variations in the total GWS, recoverable GWS, and irreversible GWS. The annual depletion rates of recoverable GWS and irreversible GWS are −0.52 ± 1.27 × 107 and −7.68 ± 2.68 × 107 m3/year, accounting for 6.4% and 93.6% of annual total GWS depletion, respectively, suggesting that previous groundwater pumping practices are unsustainable in this region. Finally, we find a 1‐year lag of the IGWS depletion variations behind the head changes due to low vertical hydraulic conductivities in aquitards.

Signature of the Agulhas Current in high resolution satellite derived wind fields

5 years of Systematic Envisat Advanced Synthetic Aperture Radar (ASAR) acquisitions and 8 years of observations from the Jason 2 altimeter are used to investigate the signature of the Agulhas Current on high resolution (between 1 and 5 km) satellite-derived winds. The satellite wind observations are analysed together with co-located ocean current, Sea Surface Temperature (SST) and significant wave height information. Satellite-derived winds cannot be considered current relative over the Agulhas Current. SAR and altimeter winds increase in magnitude over the Agulhas Current in all up-, down- and cross-current wind conditions, with contributions from both the ocean surface current and SST. When winds blow against the current, strong accelerations in satellite winds are observed at the Agulhas Current's inshore front, with the strongest winds observed 10 km offshore from the location of maximum SST gradient. The strong SST gradient appears to drive a thermal wind with a magnitude of about 1 m/s and which intensifies/abates the predominant alongfront winds at the Agulhas Current's northern wall. The SAR dataset shows abnormally high increases in wind speeds during up-current conditions in comparisons to the Jason-2 derived winds. We argue that these differences are caused by the inability of the CMOD_type algorithms to taken into account the wave field and provide accurate wind speed estimates in conditions where wave height exceed the expected value for a given wind speed. Our analysis suggests that wave-current interactions in regions of strong current shear produce enhanced sea surface roughness signatures and lead to artificially high estimates of SAR-derived wind speeds. There is future potential in using high resolution SAR imagery to map strong ocean current fronts and thus improve our ability to monitor ocean surface currents from space.

The Locking Depth of the Cholame Section of the San Andreas Fault from ERS2-Envisat InSAR

The Cholame section of the San Andreas Fault (SAF), which has been considered locked since 1857, has been little studied using geodetic methods. In this study, we propose to use Interferometric Synthetic Aperture Radar (InSAR) to contribute to the improvement of the knowledge of this section of the SAF. In particular, the objective of this work is to provide a description of the transition between the Parkfield and Cholame-Carrizo segments further southeast by producing an estimate of the locking depth of the Cholame segment by combining ERS2 (European Remote Sensing) and Envisat Advanced SAR (ASAR) satellites data. Our results indicate that the locking depth between the Parkfield and the Cholame-Carrizo segments deepens to the southeast. We then use these results as a hint to refine the tectonic loading on this section of the SAF.

Near‐Real‐Time Assimilation of SAR‐Derived Flood Maps for Improving Flood Forecasts

AbstractShort‐ to medium‐range flood forecasts are central to predicting and mitigating the impact of flooding across the world. However, producing reliable forecasts and reducing forecast uncertainties remains challenging, especially in poorly gauged river basins. The growing availability of synthetic aperture radar (SAR)‐derived flood image databases (e.g., generated from SAR sensors such as Envisat advanced synthetic aperture radar) provides opportunities to improve flood forecast quality. This study contributes to the development of more accurate global and near real‐time remote sensing‐based flood forecasting services to support flood management. We take advantage of recent algorithms for efficient and automatic delineation of flood extent using SAR images and demonstrate that near real‐time sequential assimilation of SAR‐derived flood extents can substantially improve flood forecasts. A case study based on four flood events of the River Severn (United Kingdom) is presented. The forecasting system comprises the SUPERFLEX hydrological model and the Lisflood‐FP hydraulic model. SAR images are assimilated using a particle filter. To quantify observation uncertainty as part of data assimilation, we use an image processing approach that assigns each pixel a probability of being flooded based on its backscatter values. Empirical results show that the sequential assimilation of SAR‐derived flood extent maps leads to a substantial improvement in water level forecasts. Forecast errors are reduced by as much as 50% at the assimilation time step, and improvements persist over subsequent time steps for 24 to 48 hr. The proposed approach holds promise for improving flood forecasts at locations where observed data availability is limited but satellite coverage exists.

SAR-Based Urban Extents Extraction: From ENVISAT to Sentinel-1

There are only a few examples in technical literature about the use of Synthetic Aperture Radar (SAR) dataset to extract human settlement extents. This paper shows how it is possible to extract a global urban layer from multiyear ENVISAT Advanced SAR (ASAR) backscatter measurements, overcoming some of the drawbacks of a preliminary release. Additionally, this research shows that the same technique can be applied with minor changes to SAR data from the new Sentinel-1A platform. The current ASAR urban layer shows improvements with respect to the preliminary version in the test sites of the 2011 Round Robin (RR) on urban mapping promoted by the European Space Agency (ESA). For Sentinel-1, results are similarly validated on the test sites of the 2015 edition of the RR on urban mapping. Results prove that UEXT can be used for SAR-based urban extent extraction using multiple sensors.

Multi-Scale Analysis of the Relationship between Land Subsidence and Buildings: A Case Study in an Eastern Beijing Urban Area Using the PS-InSAR Technique

Beijing is severely affected by land subsidence, and rapid urbanisation and building construction might accelerate the land subsidence process. Based on 39 Envisat Advanced Synthetic Aperture Radar (ASAR) images acquired between 2003–2010, 55 TerraSAR-X images acquired between 2010–2016, and urban building information, we analysed the relationship between land subsidence and buildings at the regional, block, and building scales. The results show that the surface displacement rate in the Beijing urban area ranged from −109 mm/year to +13 mm/year between 2003–2010, and from −151 mm/year to +19 mm/year between 2010–2016; two subsidence bowls were mainly distributed in the eastern part of the Chaoyang District. The displacement rate agreed well with the levelling measurements, with an average bias of less than six mm/year. At the regional scale, the spatial pattern of land subsidence was mainly controlled by groundwater extraction, compressible layer thickness, and geological faults. Subsidence centres were located in the area around ground water funnels with a compressible layer depth of 50–70 m. The block-scale analysis demonstrated a clear correlation between the block construction age and the spatial unevenness of subsidence. The blocks constructed between 1998–2005 and after 2005 showed considerably more subsidence unevenness and temporal instability than the blocks constructed before 1998 during both time periods. The examination of the new blocks showed that the spatial unevenness increased with building volume variability. For the 16 blocks with a high building volume, variability, and subsidence unevenness, the building-scale analysis showed a positive relationship between building volume and settlement in most blocks, although the R2 was lower than 0.5. The results indicate that intense building construction in urban areas could cause differential settlement at the block scale in Beijing, while the settlement of single buildings could be influenced by the integrated effects of building volume, foundation structures, and the hydrogeological background.

Inferring Water Table Depth Dynamics from ENVISAT-ASAR C-Band Backscatter over a Range of Peatlands from Deeply-Drained to Natural Conditions

Water table depth (WTD) is one of the key variables controlling many processes in peatlands. Reliable WTD estimates based on remote sensing data would advance peatland research from global-scale climate monitoring to field-scale ecosystem management. Here, we evaluate the relationship between ENVISAT Advanced Synthetic Aperture Radar (ASAR) C-band backscatter (σ°) and in situ observed WTD dynamics over 17 peatlands in Germany covering deeply-drained to natural conditions, excluding peatlands dominated by forest or inundation periods. The results show increasing σ° with shallower WTD (=wetter conditions), with average temporal Pearson correlation coefficients of 0.38 and 0.54 (-) for natural (also including disturbed and rewetted/restored states) and agriculturally-used drained peatlands, respectively. The anomaly correlation further highlights the potential of ASAR backscatter to capture interannual variations with values of 0.33 and 0.43 (-), for natural and drained peatlands. The skill metrics, which are similar to those for evaluations of top soil moisture from C-band over mineral soils, indicate a strong capillary connection between WTD and the ‘C-band-sensitive’ top 1–2 cm of peat soils, even during dry periods with WTD at around −1 m. Various backscatter processing algorithms were tested without significant differences. The cross-over angle concept for correcting dynamical vegetation effects was tested, but not superior, to constant incidence angle correction.

Ground Deformations around the Toktogul Reservoir, Kyrgyzstan, from Envisat ASAR and Sentinel-1 Data—A Case Study about the Impact of Atmospheric Corrections on InSAR Time Series

We present ground deformations in response to water level variations at the Toktogul Reservoir, located in Kyrgyzstan, Central Asia. Ground deformations were measured by Envisat Advanced Synthetic Aperture Radar (ASAR) and Sentinel-1 Differential Interferometric Synthetic Aperture Radar (DInSAR) imagery covering the time periods 2004–2009 and 2014–2016, respectively. The net reservoir water level, as measured by satellite radar altimetry, decreased approximately 60 m (∼13.5 km3) from 2004–2009, whereas, for 2014–2016, the net water level increased by approximately 51 m (∼11.2 km3). The individual Small BAseline Subset (SBAS) interferograms were heavily influenced by atmospheric effects that needed to be minimized prior to the time-series analysis. We tested several approaches including corrections based on global numerical weather model data, such as the European Centre for Medium-RangeWeather Forecasts (ECMWF) operational forecast data, the ERA-5 reanalysis, and the ERA-Interim reanalysis, as well as phase-based methods, such as calculating a simple linear dependency on the elevation or the more sophisticated power-law approach. Our findings suggest that, for the high-mountain Toktogul area, the power-law correction performs the best. Envisat descending time series for the period of water recession reveal mean line-of-sight (LOS) uplift rates of 7.8 mm/yr on the northern shore of the Toktogul Reservoir close to the Toktogul city area. For the same area, Sentinel-1 ascending and descending time series consistently show a subsidence behaviour due to the replenishing of the water reservoir, which includes intra-annual LOS variations on the order of 30mm. A decomposition of the LOS deformation rates of both Sentinel-1 orbits revealed mean vertical subsidence rates of 25 mm/yr for the common time period of March 2015–November 2016, which is in very good agreement with the results derived from elastic modelling based on the TEA12 Earth model.

SAR Observation and Numerical Simulation of Internal Solitary Wave Refraction and Reconnection Behind the Dongsha Atoll

AbstractThe refraction and reconnection of internal solitary waves (ISWs) around the Dongsha Atoll (DSA) in the northern South China Sea (SCS) are investigated based on spaceborne synthetic aperture radar (SAR) observations and numerical simulations. In general, a long ISW front propagating from the deep basin of the northern SCS splits into northern and southern branches when it passes the DSA. In this study, the statistics of Envisat Advanced SAR (ASAR) images show that the northern and southern wave branches can reconnect behind the DSA, but the reconnection location varies. A previously developed nonlinear refraction model is set up to simulate the refraction and reconnection of the ISWs behind the DSA, and the model is used to evaluate the effects of ocean stratification, background currents, and incoming ISW characteristics at the DSA on the variation in reconnection locations. The results of the first realistic simulation agree with consecutive TerraSAR‐X (TSX) images captured within 12 h of each other. Further sensitivity simulations show that ocean stratification, background currents, and initial wave amplitudes all affect the phase speeds of wave branches and therefore shift their reconnection locations while shapes and locations of incoming wave branches upstream of the DSA profoundly influence the subsequent propagation paths. This study clarifies the variation in reconnection locations of ISWs downstream of the DSA and reveals the important mechanisms governing the reconnection process, which can improve our understanding of the propagation of ISWs near the DSA.