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Hubbard Glacier is an advancing tidewater glacier in Alaska, with large seasonal variations in flow speeds of 1500 to 5200 m a-1. The large size and fast speeds of Hubbard Glacier caused it to have possibly the largest rates of steady-state discharge of all glaciers outside of Greenland and Antarctica. Previous studies found the fastest flow on Hubbard Glacier to occur between May and June and the slowest flow between September to November. However, we observed a different seasonal pattern when looking at velocities at a higher temporal resolution than used before (image pairs of SAR and optical imagery with a separation in the range of ~4-30 days). In some years, the seasonal flow pattern of Hubbard Glacier changes to having higher velocities in the winter than what is seen in the spring/summer maximums. Here, we show that with the utilization of RADARSAT Constellation Mission (RCM) data, which has a higher temporal resolution than has previously been used to quantify glacier motion on Hubbard Glacier (4-day repeat pass), seasonality can be explored in much greater detail than formally possible. The results of velocities derived from RCM data will be compared to products derived from other SAR sensors (Sentinel-1, TerraSAR-X, and RADARSAT-2) to assess its feasibility in determining velocity patterns of Hubbard Glacier. This comparison provides an example of the improved ability to resolve glacier dynamics using RCM data and provides insight into how this SAR dataset can be used to characterize seasonality on other glaciers within the St. Elias region., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

The existence of supraglacial lakes influences debris-covered glaciers in two ways. The absorption of solar radiation in the water leads to a higher ice ablation, and water draining through the glacier to its bed leads to a higher velocity. Rising air temperatures and changes in precipitation patterns provoke an increase in the supraglacial lakes in number and total area. However, the seasonal evolution of supraglacial lakes and thus their potential for influencing mass balance and ice dynamics have not yet been sufficiently analyzed. We present a summertime series of supraglacial lake evolution on Baltoro Glacier in the Karakoram from 2016 to 2020. The dense time series is enabled by a multi-sensor and multi-temporal approach based on optical (Sentinel-2 and PlanetScope) and Synthetic Aperture Radar (SAR; Sentinel-1 and TerraSAR-X) remote sensing data. The mapping of the seasonal lake evolution uses a semi-automatic approach, which includes a random forest classifier applied separately to each sensor. A combination of linear regression and the Hausdorff distance is used to harmonize between SAR- and optical-derived lake areas, producing consistent and internally robust time series dynamics. Seasonal variations in the lake area are linked with the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) based on air temperature and precipitation data derived from the climate reanalysis dataset ERA5-Land. The largest aggregated lake area was found in 2018 with 5.783 km2, followed by 2019 with 4.703 km2, and 2020 with 4.606 km2. The years 2016 and 2017 showed the smallest areas with 3.606 and 3.653 km2, respectively. Our data suggest that warmer spring seasons (April–May) with higher precipitation rates lead to increased formation of supraglacial lakes. The time series decomposition shows a linear increase in the lake area of 11.12 ± 9.57% per year. Although the five-year observation period is too short to derive a significant trend, the tendency for a possible increase in the supraglacial lake area is in line with the pronounced positive anomalies of the SPI and STI during the observation period.

Arctic tundra landscapes are highly complex and are rapidly changing due to the warming climate. Datasets that document the spatial and temporal variability of the landscape are needed to monitor the rapid changes. Synthetic Aperture Radar (SAR) imagery is specifically suitable for monitoring the Arctic, as SAR, unlike optical remote sensing, can provide time series regardless of weather and illumination conditions. This study examines the potential of seasonal backscatter mechanisms in Arctic tundra environments for improving land cover classification purposes by using a time series of HH/HV TerraSAR-X (TSX) imagery. A Random Forest (RF) classification was applied on multi-temporal Sigma Nought intensity and multi-temporal Kennaugh matrix element data. The backscatter analysis revealed clear differences in the polarimetric response of water, soil, and vegetation, while backscatter signal variations within different vegetation classes were more nuanced. The RF models showed that land cover classes could be distinguished with 92.4% accuracy for the Kennaugh element data, compared to 57.7% accuracy for the Sigma Nought intensity data. Texture predictors, while improving the classification accuracy on the one hand, degraded the spatial resolution of the land cover product. The Kennaugh elements derived from TSX winter acquisitions were most important for the RF model, followed by the Kennaugh elements derived from summer and autumn acquisitions. The results of this study demonstrate that multi-temporal Kennaugh elements derived from dual-polarized X-band imagery are a powerful tool for Arctic tundra land cover mapping.

Inuit have reported greater inter-annual variability in seasonal sea ice conditions. For Deception Bay (Nunavik), an area prized for seal and caribou hunting, an increase in solid precipitation and a shorter snow cover period is expected in the near future. In this context, and considering ice-breaking transport in the fjord by mining companies, we monitored sea ice in the area for three seasons of ice between 2015 and 2018. This article presents a case study for the combined use of TerraSAR- X and time-lapse photography time-series in order to monitor snow-covered sea ice seasonal processes. The X-band median backscattering is shown to reproduce the seasonal evolution expected from C-band data. Two different freeze-up and breakup processes are characterized. New X-band backscattering values from newly formed ice types are reported. The monitoring approach presented in this article has the potential to be applied in other remote locations, and processes outlined here may inform our understanding of other fjords or bays where ice-breakers transit.

Accelerating melt on the Greenland ice sheet leads to dramatic changes at a global scale. Especially in the last decades, not only the monitoring, but also the quantification of these changes has gained considerably in importance. In this context, Interferometric Synthetic Aperture Radar (InSAR) systems complement existing data sources by their capability to acquire 3D information at high spatial resolution over large areas independent of weather conditions and illumination. However, penetration of the SAR signals into the snow and ice surface leads to a bias in measured height, which has to be corrected to obtain accurate elevation data. Therefore, this study purposes an easy transferable pixel-based approach for X-band penetration-related elevation bias estimation based on single-pass interferometric coherence and backscatter intensity which was performed at two test sites on the Northern Greenland ice sheet. In particular, the penetration bias was estimated using a multiple linear regression model based on TanDEM-X InSAR data and IceBridge laser-altimeter measurements to correct TanDEM-X Digital Elevation Model (DEM) scenes. Validation efforts yielded good agreement between observations and estimations with a coefficient of determination of R\(^2\) = 68% and an RMSE of 0.68 m. Furthermore, the study demonstrates the benefits of X-band penetration bias estimation within the application context of ice sheet elevation change detection.

Since its launch in 2007, TerraSAR-X observations have been widely used in a broad range of scientific applications. Particularly in wetland research, TerraSAR-X’s shortwave X-band synthetic aperture radar (SAR) possesses unique capabilities, such as high spatial and temporal resolution, for delineating and characterizing the inherent spatially and temporally complex and heterogeneous structure of wetland ecosystems and their dynamics. As transitional areas, wetlands comprise characteristics of both terrestrial and aquatic features, forming a large diversity of wetland types. This study reviews all published articles incorporating TerraSAR-X information into wetland research to provide a comprehensive study of how this sensor has been used with regard to polarization, and the function of the data, time-series analyses, or the assessment of specific wetland ecosystem types. What is evident throughout this literature review is the synergistic fusion of multi-frequency and multi-polarization SAR sensors, sometimes optical sensors, in almost all investigated studies to attain improved wetland classification results. Due to the short revisiting time of the TerraSAR-X sensor, it is possible to compute dense SAR time-series, allowing for a more precise observation of the seasonality in dynamic wetland areas as demonstrated in many of the reviewed studies.

Lac Bam is the largest natural lake in Burkina Faso, and is of high importance for the local inhabitants for irrigated farming, animal watering, and extraction of water for drinking and sanitation. Wetlands in semi-arid African areas are prone to strong seasonal fluctuations and their waters are often covered by floating or standing vegetation, and can be very turbid and sediment-rich. During the rainy season cloud-cover is very strong, and during this time, only active microwave-based remote sensing systems like SAR sensors are suitable. While SAR intensity images have been successfully used to detect open water, polarimetric SAR applications have shown promising results to also distinguish between open water, floating, flooded, and standing vegetation, and land of the wetland’s environment. A time series of StripMap data from TerraSAR-X has been acquired from August 2013 (rainy season) to May 2014 (end of dry season), with an interval of 11 days. Images are geocoded and calibrated, and further processed to Multi-scale Multi-looked normalized Kennaugh elements. Results clearly show that using the Kennaugh elements from co-polarized HH-VV bands open water can be well identified from one Kennaugh element (K0), and two more Kennaugh elements (K3, K4) clearly enable the detection of floating and standing vegetation in water. Applied on the full time series, seasonal fluctuations become apparent, and proof that monitoring the temporal development of wetlands is possible despite water being covered by vegetation. The method will also be tested for a dual-pol HH-HV Radarsat-2 time series in preparation for Sentinel-1 data. Results of the analysis are validated based on optical very high resolution data from WorldView-2 and RapidEye, available for selected dates. This study contributes to a better understanding of dynamics of African wetlands, as well as concludes to the capability for the use of dual-polarimetric SAR imagery for regular wetland monitoring which is essential for water management and wetland ecology.

Monitoring of wetlands is a very important task for water management as well as for wetland ecology. Water bodies and wetlands in semi-arid African areas show strong seasonal dynamics throughout the year that require time series imagery with narrow temporal intervals for satellite-based monitoring. The study site for this work is Lac Bam, which is the largest natural lake in Burkina Faso, and as such of high importance for the local inhabitants for irrigated farming, animal watering, and extraction of water for drinking and sanitation. Semi-arid African wetlands often contain areas of floating vegetation or flooded vegetation, which causes difficulties for water body detection and monitoring over time when using synthetic aperture radar (SAR) intensity data or medium to high resolution optical data. The water of Lac Bam is very turbid and sediment-rich, particularly during the rainy season when also strong cloud-cover is a limiting factor for the use of optical data. SAR intensity data has been frequently used to detect open water surfaces. Multi-polarized SAR data, in addition, have proven to be able to detect flooded vegetation, thanks to their ability to distinguish between different scattering mechanisms in different polarizations. Two different repeat-pass time series have been acquired in X-band and C-band dual-polarimetric mode: TerraSAR-X StripMap data with HH (transmitted and received horizontally)–VV (transmitted and received vertically) polarization with an interval of 11 days have been acquired from August 2013 until present (January 2015). Radarsat-2 fine beam data with HH (transmitted and received horizontally)–HV (transmitted horizontally, received vertically) polarization with an interval of 22 days have been acquired since October 2014 until present. The Kennaugh decomposition produces four normalized Kennaugh elements from the complex HH and VV, or HH and HV input channels, respectively. A novel image enhancement algorithm called Multi-scale Multi-looking stabilizes the radiometric measurements without loss of geometric resolution. Results demonstrate that open water could be well detected in the Kennaugh element representing the total intensity from both co-polarized HH-VV (X-band) as well as cross-polarized HH-HV (C-band) data. The three remaining polarimetric Kennaugh elements deduced from X-band clearly enable the detection of vegetation in or on water, and can discriminate it from vegetation on land. Applied onto the full time series surface water fluctuations and changes in flooded vegetation can be monitored throughout the year. Seasonal curves extracted from time series of the Kennaugh elements allow concluding to different types of flooded vegetation and the duration of flooding. Results of the analysis are validated based on optical very high resolution data from WorldView-2 with a resolution of 0.5 m, and RapidEye with a resolution of about 6 m for selected dates throughout the time series. Moreover, the performance of TerraSAR-X and Radarsat-2 is compared using data acquired on the same date in October 2014. Despite the great importance of water management and ecology in African wetlands, regular monitoring is often not carried out and applications of polarimetric SAR imagery in Africa are very scarce. This study contributes to understanding of the dynamics of surface water including flooded vegetation in or floating vegetation on water. It demonstrates the capability of dual-polarimetric SAR imagery for wetland delineation, and concludes to the applicability of Sentinel-1 dual-polarimetric data for almost continuous wetland monitoring.

The main goal of the TanDEM-X mission is the production of a global Digital Elevation Model (DEM). A byproduct is the so-called Water Indication Mask (WAM). The purpose of this supplementary information layer is to support the DEM editing process where the DEM is noisy. The WAM is derived from the SAR amplitude and the single-pass coherence. In this paper, the methodology of the water body detection is briefly explained and the results of four test sites covering different climatic regions are evaluated. The different characteristics of the WAM using amplitude and coherence image are described and their respective pros and cons are discussed.

The aimed accuracies for the final TanDEM-X DEM of 10m absolute and 2m relative height error will be ensured by calibration data. One crucial data set for the relative accuracy is tie-points that connect adjacent DEM acquisitions in the approximately 4km-overlap-area with each other. In this paper an improved concept for tie-point candidates is presented that is based on averaging a larger region instead of comparing single points. This concept should be more robust against noise. It is validated by applying the DEM calibration on a simulated test area and if available on real TanDEM-X data. Also, the DEM calibration will be validated for the first time on a larger “real” test site by applying the TanDEM-X processing scenario.