Search

In dry southwestern South Tyrol, Italy, rock glaciers are dominant landforms of the high-mountain cryosphere. Their spatial distribution and degree of activity hold critical information on the current state of discontinuous permafrost and consequently on the response potential to climate warming. Traditional geomorphologic mapping, however, owing to the qualitative expert-based nature, typically displays a high degree of uncertainty and variability among operators with respect to the dynamic classification of intact (permafrost-bearing) and relict (permafrost-devoid) rock glaciers. This limits the reliability of geomorphologic rock glacier inventories for basic and applied purposes. To address this limitation, (i) we conduct a systematic evaluation of the improvements that synthetic aperture radar interferometry (InSAR) can afford to the detection and dynamic classification of rock glaciers and (ii) build an integrated inventory that combines the strengths of geomorphologic- and InSAR-based approaches. To exploit fully InSAR-based information towards a better understanding of the topo-climatic conditions that sustain creeping permafrost, we further explore how velocity and the spatial distribution of moving areas (MAs) within rock glaciers may vary as a function of simple topographic variables known to exert first-order controls on incoming solar radiation, such as elevation and aspect. Starting from a geomorphologic inventory (n=789), we characterize the kinematics of InSAR-based MAs and the relevant hosting rock glaciers on 36 Sentinel-1 interferograms in the 2018–2019 period. With respect to the original inventory, InSAR analysis allowed us to identify 14 previously undetected rock glaciers. Further, it confirmed that 246 (76 %) landforms, originally interpreted as intact, do exhibit detectable movement (i.e., ≥1 cm yr -1) and that 270 (60 %) of the relict labeled counterparts do not, whereas 144 (18 %) were kinematically undefined due to decorrelation. Most importantly, InSAR proved critical for reclassifying 121 (15 %) rock glaciers, clarifying that 41 (13 %) of those interpreted as being intact do not exhibit detectable movement and that 80 (17 %) of the original relict ones do move. Reclassification (i) allowed us to identify a cluster of intact rock glaciers below 2000 m a.s.l. associated with positive mean annual air temperature (MAAT), and (ii) by increasing the altitudinal overlap between intact and relict rock glaciers, it depicts a broad transition belt in the aspect–elevation space, which varies from 50 m on west-facing slopes to 500 m on easterly ones. This finding deteriorates the significance of elevation and aspect as topographic proxies for modeling permafrost occurrence and highlights the importance of using InSAR to inform such models. From a process-oriented standpoint, InSAR information proves fundamental for imaging how this altitudinal transition manifests through changing rates and styles of rock glacier surface deformation. Specifically, we find that, as rock glaciers move faster, an increasingly larger proportion of their surface becomes kinematically involved (i.e., percent MA cover) and that this proportion increases with elevation up to 2600–2800 m, beyond which an inflection occurs and consistent average values are attained. Considering that the inflection falls between the -1 and -2 °C MAAT – the lower boundary for discontinuous permafrost – and is independent of slope gradient, we conclude that this altitudinal pattern represents a geomorphic signature: the dynamic expression of increasing permafrost distribution, from sporadic to discontinuous. [ABSTRACT FROM AUTHOR]

A physical–statistical framework to estimate snow water equivalent (SWE) and snow depth from synthetic aperture radar (SAR) measurements is presented and applied to four SnowSAR flight-line data sets collected during the SnowEx'2017 field campaign in Grand Mesa, Colorado, USA. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, snow density and snow depth, and physical model parameters. Prior distributions are derived from multilayer snow hydrology predictions driven by downscaled numerical weather prediction (NWP) forecasts. To reduce the signal-to-noise ratio, SnowSAR measurements at 1 m resolution were upscaled by simple averaging to 30 and 90 m resolution. To reduce the number of physical parameters, the multilayer snowpack is transformed for Bayesian inference into an equivalent one- or two-layer snowpack with the same snow mass and volume backscatter. Successful retrievals meeting NASEM (2018) science requirements are defined by absolute convergence backscatter errors ≤1.2 dB and local SnowSAR incidence angles between 30 and 45 ∘ for X- and Ku-band VV-pol backscatter measurements and were achieved for 75 % to 87 % of all grassland pixels with SWE up to 0.7 m and snow depth up to 2 m. SWE retrievals compare well with snow pit observations, showing strong skill in deep snow with average absolute SWE residuals of 5 %–7 % (15 %–18 %) for the two-layer (one-layer) retrieval algorithm. Furthermore, the spatial distributions of snow depth retrievals vis-à-vis lidar estimates have Bhattacharya coefficients above 94 % (90 %) for homogeneous grassland pixels at 30 m (90 m resolution), and values up to 76 % in mixed forest and grassland areas, indicating that the retrievals closely capture snowpack spatial variability. Because NWP forecasts are available everywhere, the proposed approach could be applied to SWE and snow depth retrievals from a dedicated global snow mission. [ABSTRACT FROM AUTHOR]

Soil organic carbon storage is a well-identified climate change mitigation solution. Quantification of the soil carbon storage in cropland for agricultural policy and offset carbon markets using in situ sampling would be excessively costly, especially at the intrafield scale. For this reason, comprehensive monitoring, reporting, and verification (MRV) of soil carbon and its explanatory variables at a large scale need to rely on hybrid approaches that combine remote sensing and modelling tools to provide the carbon budget components with their associated uncertainties at intrafield scale. Here, we present AgriCarbon-EO v1.0.1: an end-to-end processing chain that enables the estimation of carbon budget components for major and cover crops at intrafield resolution (10 m) and regional extents (e.g. 10 000 km 2) by assimilating remote sensing data (e.g. Sentinel-2 and Landsat8) in a physically based radiative transfer (PROSAIL) and agronomic models (SAFYE-CO2). The data assimilation in AgriCarbon-EO is based on a novel Bayesian approach that combines normalized importance sampling and look-up table generation. This approach propagates the uncertainties across the processing chain from the reflectances to the output variables. After a presentation of the chain, we demonstrate the accuracy of the estimates of AgriCarbon-EO through an application over winter wheat in the southwest of France during the cropping seasons from 2017 to 2019. We validate the outputs with flux tower data for net ecosystem exchange, biomass destructive samples, and combined harvester yield maps. Our results show that the scalability and uncertainty estimates proposed by the approach do not hinder the accuracy of the estimates (net ecosystem exchange, NEE: RMSE =1.68 –2.38 gC m -2 , R2=0.87 –0.77; biomass: RMSE =11.34 g m -2 , R2=0.94). We also show the added value of intrafield simulations for the carbon components through scenario testing of pixel and field simulations (biomass: bias =-47 g m -2 , - 39 % variability). Our overall analysis shows satisfying accuracy, but it also points out the need to represent more soil processes and include synthetic aperture radar data that would enable a larger coverage of AgriCarbon-EO. The paper's findings confirm the suitability of the choices made in building AgriCarbon-EO as a hybrid solution for an MRV scheme to diagnose agro-ecosystem carbon fluxes. [ABSTRACT FROM AUTHOR]

Some of the highest specific mass change rates in Antarctica are reported for the Antarctic Peninsula. However, the existing estimates for the northern Antarctic Peninsula (<70 ∘ S) are either spatially limited or are affected by considerable uncertainties. The complex topography, frequent cloud cover, limitations in ice thickness information, boundary effects, and uncertain glacial–isostatic adjustment estimates affect the ice sheet mass change estimates using altimetry, gravimetry, or the input-output method. Within this study, the first assessment of the geodetic mass balance throughout the ice sheet of the northern Antarctic Peninsula is carried out employing bi-static synthetic aperture radar (SAR) data from the TanDEM-X satellite mission. Repeat coverages from the austral winters of 2013 and 2017 are employed. Overall, coverage of 96.4 % of the study area by surface elevation change measurements and a total mass budget of -24.1±2.8 Gt a -1 are revealed. The spatial distribution of the surface elevation and mass changes points out that the former ice shelf tributary glaciers of the Prince Gustav Channel, Larsen A and B, and Wordie ice shelves are the hotspots of ice loss in the study area and highlights the long-lasting dynamic glacier adjustments after the ice shelf break-up events. The highest mass change rate is revealed for the Airy–Seller–Fleming glacier system at -4.9±0.6 Gt a -1 , and the highest average surface elevation change rate of -2.30±0.03 m a -1 is observed at Drygalski Glacier. The comparison of the ice mass budget with anomalies in the climatic mass balance indicates, that for wide parts of the southern section of the study area, the mass changes can be partly attributed to changes in the climatic mass balance. However, imbalanced high ice discharge drives the overall ice loss. The previously reported connection between mid-ocean warming along the southern section of the west coast and increased frontal glacier recession does not repeat in the pattern of the observed glacier mass losses, excluding in Wordie Bay. The obtained results provide information on ice surface elevation and mass changes for the entire northern Antarctic Peninsula on unprecedented spatially detailed scales and with high precision and will be beneficial for subsequent analysis and modeling. [ABSTRACT FROM AUTHOR]

The fracturing of glaciers and ice shelves in Antarctica influences their dynamics and stability. Hence, data on the evolving distribution of crevasses are required to better understand the evolution of the ice sheet, though such data have traditionally been difficult and time-consuming to generate. Here, we present an automated method of mapping crevasses on grounded and floating ice with the application of convolutional neural networks to Sentinel-1 synthetic aperture radar backscatter data. We apply this method across Antarctica to images acquired between 2015 and 2022, producing a 7.5-year record of composite fracture maps at monthly intervals and 50 m spatial resolution and showing the distribution of crevasses around the majority of the ice sheet margin. We develop a method of quantifying changes to the density of ice shelf fractures using a time series of crevasse maps and show increases in crevassing on Thwaites and Pine Island ice shelves over the observational period, with observed changes elsewhere in the Amundsen Sea dominated by the advection of existing crevasses. Using stress fields computed using the BISICLES ice sheet model, we show that much of this structural change has occurred in buttressing regions of these ice shelves, indicating a recent and ongoing link between fracturing and the developing dynamics of the Amundsen Sea sector. [ABSTRACT FROM AUTHOR]

Short-term sea ice predictability is challenging despite recent advancements in sea ice modelling and new observations of sea ice deformation that capture small-scale features (open leads and ridges) at the kilometre scale. A new method for assimilation of satellite-derived sea ice deformation into numerical sea ice models is presented. Ice deformation provided by the Copernicus Marine Service is computed from sea ice drift derived from synthetic aperture radar at a high spatio-temporal resolution. We show that high values of ice deformation can be interpreted as reduced ice concentration or increased ice damage – i.e. scalar variables responsible for ice strength in brittle or visco-plastic sea ice dynamical models. This method is tested as a proof of concept with the neXt-generation Sea Ice Model (neXtSIM), where the assimilation scheme uses a data insertion approach and forecasting with one member. We obtain statistics of assimilation impact over a long test period with many realisations starting from different initial times. Assimilation and forecasting experiments are run on synthetic and real observations in January 2021 and show increased accuracy of deformation prediction for the first 3–4 d. Similar conclusions are obtained using both brittle and visco-plastic rheologies implemented in neXtSIM. Thus, the forecasts improve due to the update of sea ice mechanical properties rather than the exact rheological formulation. It is demonstrated that the assimilated information can be extrapolated in space – gaps in spatially discontinuous satellite observations of deformation are filled with a realistic pattern of ice cracks, confirmed by later satellite observations. The limitations and usefulness of the proposed assimilation approach are discussed in a context of ensemble forecasts. Pathways to estimate intrinsic predictability of sea ice deformation are proposed. [ABSTRACT FROM AUTHOR]

Wilkes Land, East Antarctica, has been losing mass at an accelerating rate over recent decades in response to enhanced oceanic forcing. Overlying the Aurora Subglacial Basin, it has been referred to as the "weak underbelly" of the East Antarctic Ice Sheet and is drained by several major outlet glaciers. Despite their potential importance, few of these glaciers have been studied in detail. This includes the six outlet glaciers which drain into Vincennes Bay, a region recently discovered to have the warmest intrusions of modified Circumpolar Deep Water (mCDW) ever recorded in East Antarctica. Here, we use satellite imagery; differential synthetic aperture radar interferometry (DInSAR); and remotely sensed datasets of ice-surface velocity, ice-surface elevation and grounding line position to investigate ice dynamics between 1963 and 2022. Our results support previous observations of extensive grounding line retreat at Vanderford Glacier, measured at 18.6 km between 1996 and 2020. The persistent grounding line retreat, averaging 0.8 km yr -1 , places Vanderford Glacier as the fastest retreating glacier in East Antarctica, and the third fastest in Antarctica, across decadal timescales. Such rapid retreat is consistent with the hypothesis that warm mCDW is able to access deep cavities formed below the Vanderford Ice Shelf, driving high rates of basal melting close to the grounding line. With a retrograde slope observed inland along the Vanderford Trench, such oceanic forcing may have significant implications for the future stability of Vanderford Glacier. [ABSTRACT FROM AUTHOR]

Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. However, there is no high-resolution map of rice covering all of China. This study developed a new rice-mapping method by combining optical and synthetic aperture radar (SAR) images in cloudy areas based on the time-weighted dynamic time warping (TWDTW) method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at a 10 or 20 m resolution. The accuracy was examined using 108 195 survey samples and county-level statistical data. On average, the user's, producer's, and overall accuracy values over all investigated provincial administrative regions were 73.08 %, 82.81 %, and 85.23 %, respectively. Compared with the statistical data from 2017 to 2019, the distribution maps explained 83 % of the spatial variation of county-level planting areas on average. The distribution maps can be obtained at 10.57760/sciencedb.06963 (Shen et al., 2023). [ABSTRACT FROM AUTHOR]

Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. However, there is no high-resolution map of rice covering all of China. This study developed a new rice mapping method by combining optical and synthetic aperture radar (SAR) images in cloudy areas based on the time-weighted dynamic time warping (TWDTW) method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at 10 or 20-m resolution. The accuracy was examined by using 108195 survey samples and countylevel statistical data. On average, the user's, producer's, and overall accuracy over all investigated provincial administrative regions were 73.08 %, 82.81 %, and 85.23 %, respectively. Compared with the statistical data from 2017 to 2019, the distribution map explained 83 % of the spatial variation of county-level planting areas on average. The distribution maps can be obtained at https://doi.org/10.57760/sciencedb.06963 (Shen et al., 2023). [ABSTRACT FROM AUTHOR]

Iceberg calving accounts for up to half of mass loss from the Greenland Ice Sheet (GrIS), with their size distributions providing insights into glacier calving dynamics and impacting fjord environments through their melting and subsequent freshwater release. Iceberg area and volume data for the GrIS are currently limited to a handful of fjord locations, while existing approaches to iceberg detection are often time-consuming and are not always suited for long time series analysis over large spatial scales. This study presents a highly automated workflow that detects icebergs and appends their associated metadata within Google Earth Engine using high spatial resolution timestamped ArcticDEM (Arctic Digital Elevation Model) strip data. This is applied to three glaciers that exhibit a range of different iceberg concentrations and size distributions: Sermeq Kujalleq (Jakobshavn Isbræ), Umiammakku Isbræ and Kangiata Nunaata Sermia. A total of 39 ArcticDEM scenes are analysed, detecting a total of 163 738 icebergs with execution times of 6 min to 2 h for each glacier depending on the number of DEMs available and total area analysed, comparing well with the mapping of manually digitised outlines. Results reveal two distinct iceberg distributions at Sermeq Kujalleq and Kangiata Nunaata Sermia where iceberg density is high, and one distribution at Umiammakku Isbræ where iceberg density is low. Small icebergs (< 1000 m 2) are found to account for over 80 % of each glacier's icebergs; however, they only contribute to 10 %–37 % of total iceberg volume suggesting that large icebergs are proportionally more important for glacier mass loss and as fjord freshwater reservoirs. The overall dataset is used to construct new area-to-volume conversions (with associated uncertainties) that can be applied elsewhere to two-dimensional iceberg outlines derived from optical or synthetic aperture radar imagery. When data are expressed in terms of total iceberg count and volume, insight is provided into iceberg distributions that have potential applicability to observations and modelling of iceberg calving behaviour and fjord freshwater fluxes. Due to the speed and automated nature of our approach, this workflow offers the potential to interrogate iceberg data on a pan-Arctic scale where ArcticDEM strip data coverage allows. [ABSTRACT FROM AUTHOR]

Satellite data transmission is usually limited between hundreds of kilobits-per-second (kb/s) and several megabitsper-second (Mb/s) while the space-to-ground data volume is becoming larger as the resolution of the instruments increases while the bandwidth remains limited, typically. The Surface Water and Ocean Topography (SWOT) altimetry mission is a partnership between the National Aeronautics and Space Administration (NASA) and the Centre National des Études Spatiales (CNES) which uses the innovative KaRin instrument, a Ka band (35.75 GHz) synthetic aperture radar combined with an interforemeter. Its launch is expected for 2022 for oceanographic and hydrological levels measurement and it will generate 7 TeraBytes-per-day, for a lifetime total of 20 PetaBytes. That is why data compression needs to be implemented at both ends of satellite communications. This study compares the compression results obtained with 672 algorithms, mostly based on the Huffman coding approach which constitute the state-of-the-art for scientific data manipulation, including Computational Fluid Dynamics (CFD). We also have incorporated data preprocessing such as shuffle and bitshuffle, and a novel algorithm known as SL6. [ABSTRACT FROM AUTHOR]

Exact information on the calving front positions of marine- or lake-terminating glaciers is a fundamental glacier variable for analyzing ongoing glacier change processes and assessing other variables like frontal ablation rates. In recent years, researchers started implementing algorithms that can automatically detect the calving fronts on satellite imagery. Most studies use optical images, as calving fronts are often easy to distinguish in these images due to the sufficient spatial resolution and the presence of different spectral bands, allowing the separation of ice features. However, detecting calving fronts on synthetic aperture radar (SAR) images is highly desirable, as SAR images can also be acquired during the polar night and are independent of weather conditions (e.g., cloud cover), facilitating year-round monitoring worldwide. In this paper, we present a benchmark dataset of SAR images from multiple regions of the globe with corresponding manually defined labels providing information on the position of the calving front (https://doi.org/10.1594/PANGAEA.940950). With this dataset, different approaches for the detection of glacier calving fronts can be implemented, tested, and their performance fairly compared so that the most effective approach can be determined. The dataset consists of 681 samples, making it large enough to train deep learning segmentation models. It is the first dataset to provide long-term glacier calving front information from multi-mission data. As the dataset includes glaciers from Antarctica, Greenland, and Alaska, the wide applicability of models trained and tested on this dataset is ensured. The test set is independent of the training set so that the generalization capabilities of the models can be evaluated. We provide two sets of labels: one binary segmentation label to discern the calving front from the background, and one label for multi-class segmentation of different landscape classes. Unlike other calving front datasets, the presented dataset contains not only the labels but also the corresponding preprocessed and geo-referenced SAR images as PNG files. The ease of access to the dataset will allow scientists from other fields, such as data science, to contribute their expertise. With this benchmark dataset, we enable comparability between different front detection algorithms and improve the reproducibility of front detection studies. Moreover, we present one baseline model for each kind of label type. Both models are based on the U-Net, one of the most popular deep learning segmentation architectures. In the following two post-processing procedures, the segmentation results are converted into 1-pixel-wide front delineations. By providing both types of labels, both approaches can be used to address the problem. To assess the performance of different models, we suggest first reviewing the segmentation results using the recall, precision, F1 score, and the Jaccard index. Second, the front delineation can be evaluated by calculating the mean distance error to the labeled front. The presented vanilla models provide a baseline of 150 m ± 24 m mean distance error for the Mapple Glacier in Antarctica and 840 m ± 84 m for the Columbia Glacier in Alaska, which has a more complex calving front, consisting of multiple sections, compared with a laterally well constrained, single calving front of Mapple Glacier. [ABSTRACT FROM AUTHOR]

Marine-terminating outlet glacier terminus traces, mapped from satellite and aerial imagery, have been used extensively in understanding how outlet glaciers adjust to climate change variability over a range of timescales. Numerous studies have digitized termini manually, but this process is labor intensive, and no consistent approach exists. A lack of coordination leads to duplication of efforts, particularly for Greenland, which is a major scientific research focus. At the same time, machine learning techniques are rapidly making progress in their ability to automate accurate extraction of glacier termini, with promising developments across a number of optical and synthetic aperture radar (SAR) satellite sensors. These techniques rely on high-quality, manually digitized terminus traces to be used as training data for robust automatic traces. Here we present a database of manually digitized terminus traces for machine learning and scientific applications. These data have been collected, cleaned, assigned with appropriate metadata including image scenes, and compiled so they can be easily accessed by scientists. The TermPicks data set includes 39 060 individual terminus traces for 278 glaciers with a mean of 136 ± 190 and median of 93 of traces per glacier. Across all glaciers, 32 567 dates have been digitized, of which 4467 have traces from more than one author, and there is a duplication rate of 17 %. We find a median error of ∼ 100 m among manually traced termini. Most traces are obtained after 1999, when Landsat 7 was launched. We also provide an overview of an updated version of the Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for future manual picking of the Greenland Ice Sheet. [ABSTRACT FROM AUTHOR]

We present a method to combine CryoSat-2 (CS2) radar altimeter and Sentinel-1 synthetic aperture radar (SAR) data to obtain sea ice thickness (SIT) estimates for the Barents and Kara seas. From the viewpoint of tactical navigation, along-track altimeter SIT estimates are sparse, and the goal of our study is to develop a method to interpolate altimeter SIT measurements between CS2 ground tracks. The SIT estimation method developed here is based on the interpolation of CS2 SIT utilizing SAR segmentation and segmentwise SAR texture features. The SIT results are compared to SIT data derived from the AARI ice charts; to ORAS5, PIOMAS and TOPAZ4 ocean–sea ice data assimilation system reanalyses; to combined CS2 and Soil Moisture and Ocean Salinity (SMOS) radiometer weekly SIT (CS2SMOS SIT) charts; and to the daily MODIS (Moderate Resolution Imaging Spectroradiometer) SIT chart. We studied two approaches: CS2 directly interpolated to SAR segments and CS2 SIT interpolated to SAR segments with mapping of the CS2 SIT distributions to correspond to SIT distribution of the PIOMAS ice model. Our approaches yield larger spatial coverage and better accuracy compared to SIT estimates based on either CS2 or SAR data alone. The agreement with modelled SIT is better than with the CS2SMOS SIT. The average differences when compared to ice models and the AARI ice chart SIT were typically tens of centimetres, and there was a significant positive bias when compared to the AARI SIT (on average 27 cm) and a similar bias (24 cm) when compared to the CS2SMOS SIT. Our results are directly applicable to the future CRISTAL mission and Copernicus programme SAR missions. [ABSTRACT FROM AUTHOR]

Area-based measurements of snow water equivalent (SWE) are important for understanding earth system processes such as glacier mass balance, winter hydrological storage in drainage basins, and ground thermal regimes. Remote sensing techniques are ideally suited for wide-scale area-based mapping with the most commonly used technique to measure SWE being passive microwave, which is limited to coarse spatial resolutions of 25 km or greater and to areas without significant topographic variation. Passive microwave also has a negative bias for large SWE. Another method is repeat-pass synthetic aperture radar interferometry (InSAR) that allows measurement of SWE change at much higher spatial resolution. However, it has not been widely adopted because (1) the phase unwrapping problem has not been robustly addressed, especially for interferograms with poor coherence, and (2) SWE change maps scaled directly from repeat-pass interferograms are not an absolute measurement but contain unknown offsets for each contiguous coherent area. We develop and test a novel method for repeat-pass InSAR-based dry-snow SWE estimation that exploits the sensitivity of the dry-snow refraction-induced InSAR phase to topographic variations. The method robustly estimates absolute SWE change at spatial resolutions of < 1 km without the need for phase unwrapping. We derive a quantitative signal model for this new SWE change estimator and identify the relevant sources of bias. The method is demonstrated using both simulated SWE distributions and a 9-year RADARSAT-2 (C-band, 5.405 GHz) spotlight-mode dataset near Inuvik, Northwest Territories (NWT), Canada. SWE results are compared to in situ snow survey measurements and estimates from ERA5 reanalysis. Our method performs well in high-relief areas, thus providing complementary coverage to passive-microwave-based SWE estimation. Further, our method has the advantage of requiring only a single wavelength band and thus can utilize existing spaceborne synthetic aperture radar systems. [ABSTRACT FROM AUTHOR]

Glaciers in the Russian High Arctic have been subject to extensive atmospheric warming due to global climate change, yet their contribution to sea level rise has been relatively small over the past decades. Here we show surface elevation change measurements and geodetic mass balances of 93 % of all glacierized areas of Novaya Zemlya, Severnaya Zemlya, and Franz Josef Land using interferometric synthetic aperture radar measurements taken between 2010 and 2017. We calculate an overall mass loss rate of -22±6 Gt a -1 , corresponding to a sea level rise contribution of 0.06±0.02 mm a -1. Compared to measurements prior to 2010, mass loss of glaciers on the Russian archipelagos has doubled in recent years. [ABSTRACT FROM AUTHOR]

3.3 Comparison of the composite paddy rice map and IRRI dataset The composite paddy rice map is a mosaic of rice planting areas in 3 years (2017-2019) where rice has been detected in 1 or more years. MAP: Figure 10 Estimated distribution of paddy rice in 2017 in mountainous regions in South Korea: (a) flooding signal based on MODIS indices, a (b) paddy rice map generated by our method, and (c) mountainous landscapes acquired from © Google Earth.. The example data for b can be found at the following link: (Han et al., 2021b; see example 04). To summarize, we provided more accurate paddy rice maps at continental scales using the improved method for paddy rice mapping. [Extracted from the article]

Surface meltwater drains on several Antarctic ice shelves, resulting in surface and sub-surface lakes that are potentially critical for the ice shelf collapse. Despite these phenomena, our understanding and assessment of the drainage and refreezing of these lakes is limited, mainly due to lack of field observations and to the limitations of optical satellite imagery during polar night and in cloudy conditions. This paper explores the potential of backscatter intensity and of interferometric coherence and phase from synthetic aperture radar (SAR) imagery as an alternative to assess the dynamics of meltwater lakes. In four case study regions over Amery and Roi Baudouin ice shelves, East Antarctica, we examine spatial and temporal variations in SAR backscatter intensity and interferometric (InSAR) coherence and phase over several lakes derived from Sentinel-1A/B C-band SAR imagery. Throughout the year, the lakes are observed in a completely frozen state, in a partially frozen state with a floating ice lid and as open-water lakes. Our analysis reveals that the meltwater lake delineation is challenging during the melting period when the contrast between melting snow and lakes is indistinguishable. Despite this finding, we show using a combination of backscatter and InSAR observations that lake dynamics can be effectively captured during other non-summertime months. Moreover, our findings highlight the utility of InSAR-based observations for discriminating between refrozen ice and sub-surface meltwater and indicate the potential for phase-based detection and monitoring of rapid meltwater drainage events. The potential of this technique to monitor these meltwater change events is, however, strongly determined by the satellite revisit interval and potential changes in scattering properties due to snowfall or melt events. [ABSTRACT FROM AUTHOR]

Iceberg calving is the main process that facilitates the dynamic mass loss of ice sheets into the ocean, which accounts for approximately half of the mass loss of the Antarctic ice sheet. Fine-scale calving variability observations can help reveal the calving mechanisms and identify the principal processes that influence how the changing climate affects global sea level through the ice shelf buttressing effect on the Antarctic ice sheet. Iceberg calving from entire ice shelves for short time intervals or from specific ice shelves for long time intervals has been monitored before, but there is still a lack of consistent, long-term, and high-precision records on independent calving events for all of the Antarctic ice shelves. In this study, a 15-year annual iceberg calving product measuring every independent calving event larger than 1 km2 over all of the Antarctic ice shelves that occurred from August 2005 to August 2020 was developed based on 16 years of continuous satellite observations. First, the expansion of the ice shelf frontal coastline was simulated according to ice velocity; following this, the calved areas, which are considered to be the differences between the simulated coastline, were manually delineated, and the actual coastline was derived from the corresponding satellite imagery, based on multisource optical and synthetic aperture radar (SAR) images. The product provides detailed information on each calving event, including the associated year of occurrence, area, size, average thickness, mass, recurrence interval, and measurement uncertainties. A total of 1975 annual calving events larger than 1 km2 were detected on the Antarctic ice shelves from August 2005 to August 2020. The average annual calved area was measured as 3549.1 km2 with an uncertainty value of 14.3 km2 , and the average calving rate was measured as 770.3 Gtyr-1 with an uncertainty value of 29.5 Gtyr-1. The number of calving events, calved area, and calved mass fluctuated moderately during the first decade, followed by a dramatic increase from 2015/2016 to 2019/2020. During the dataset period, large ice shelves, such as the Ronne–Filchner and Ross ice shelves, advanced with low calving frequency, whereas small- and medium-sized ice shelves retreated and calved more frequently. Iceberg calving of ice shelves is most prevalent in West Antarctica, followed by the Antarctic Peninsula and Wilkes Land in East Antarctica. The annual iceberg calving event dataset of Antarctic ice shelves provides consistent and precise calving observations with the longest time coverage. The dataset provides multidimensional variables for each independent calving event that can be used to study detailed spatial–temporal variations in Antarctic iceberg calving. The dataset can also be used to study ice sheet mass balance, calving mechanisms, and responses of iceberg calving to climate change. The dataset, entitled "Annual iceberg calving dataset of the Antarctic ice shelves (2005–2020)", is shared via the National Tibetan Plateau Data Center: 10.11888/Glacio.tpdc.271250 (Qi et al., 2021). In addition, the average annual calving rate of 18.4±6.7 Gtyr-1 for calving events smaller than 1 km2 of the Antarctic ice shelves and the calving rate of 166.7±15.2 Gtyr-1 for the marine-terminating glaciers were estimated. [ABSTRACT FROM AUTHOR]

Climate change has reduced global ice mass over the last 2 decades as enhanced warming has accelerated surface melt and runoff rates. Glaciers have undergone dynamic processes in response to a warming climate that impacts the surface geometry and mass distribution of glacial ice. Until recently no single technique could consistently measure the evolution of surface flow for an entire glaciated region in three dimensions with high temporal and spatial resolution. We have improved upon earlier methods by developing a technique for mapping, in unprecedented detail, the temporal evolution of glaciers. Our software computes north, east, and vertical flow velocity and/or displacement time series from the synthetic aperture radar (SAR) ascending and descending range and azimuth speckle offsets. The software can handle large volumes of satellite data and is designed to work on high-performance computers (HPCs) as well as workstations by utilizing multiple parallelization methods. We then compute flow velocity–displacement time series for glaciers in southeastern Alaska during 2016–2021 and observe seasonal and interannual variations in flow velocities at Seward and Malaspina glaciers as well as culminating phases of surging at Klutlan, Walsh, and Kluane glaciers. On a broader scale, this technique can be used for reconstructing the response of worldwide glaciers to the warming climate using archived SAR data and for near-real-time monitoring of these glaciers using rapid revisit SAR data from satellites, such as Sentinel-1 (6 or 12 d revisit period) and the forthcoming NISAR mission (12 d revisit period). [ABSTRACT FROM AUTHOR]

Widely used sea ice concentration and sea ice cover in polar regions are derived mainly from spaceborne microwave radiometer and scatterometer data, and the typical spatial resolution of these products ranges from several to dozens of kilometers. Due to dramatic changes in polar sea ice, high-resolution sea ice cover data are drawing increasing attention for polar navigation, environmental research, and offshore operations. In this paper, we focused on developing an approach for deriving a high-resolution sea ice cover product for the Arctic using Sentinel-1 (S1) dual-polarization (horizontal-horizontal, HH, and horizontal-vertical, HV) data in extra wide swath (EW) mode. The approach for discriminating sea ice from open water by synthetic aperture radar (SAR) data is based on a modified U-Net architecture, a deep learning network. By employing an integrated stacking model to combine multiple U-Net classifiers with diverse specializations, sea ice segmentation is achieved with superior accuracy over any individual classifier. We applied the proposed approach to over 28 000 S1 EW images acquired in 2019 to obtain sea ice cover products in a high spatial resolution of 400 m. The validation by 96 cases of visual interpretation results shows an overall accuracy of 96.10 %. The S1-derived sea ice cover was converted to concentration and then compared with Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration data, showing an average absolute difference of 5.55 % with seasonal fluctuations. A direct comparison with Interactive Multisensor Snow and Ice Mapping System (IMS) daily sea ice cover data achieves an average accuracy of 93.98 %. These results show that the developed S1-derived sea ice cover results are comparable to the AMSR and IMS data in terms of overall accuracy but superior to these data in presenting detailed sea ice cover information, particularly in the marginal ice zone (MIZ). Data are available at 10.11922/sciencedb.00273 (Wang and Li, 2020). [ABSTRACT FROM AUTHOR]

Ground-based inverse synthetic aperture radar is a tool that can provide insights into the early history and formative processes of planetary bodies in the inner solar system. This information is gathered by measuring the scattering matrix of the target body, providing composite information about the physical structure and chemical makeup of its surface and subsurface down to the penetration depth of the radio wave. This work describes the technical capabilities of the upcoming 233 MHz European Incoherent Scatter Scientific Association (EISCAT) 3D radar facility for measuring planetary surfaces. Estimates of the achievable signal-to-noise ratios for terrestrial target bodies are provided. While Venus and Mars can possibly be detected, only the Moon is found to have sufficient signal-to-noise ratio to allow high-resolution mapping to be performed. The performance of the EISCAT 3D antenna layout is evaluated for interferometric range–Doppler disambiguation, and it is found to be well suited for this task, providing up to 20 dB of separation between Doppler northern and southern hemispheres in our case study. The low frequency used by EISCAT 3D is more affected by the ionosphere than higher-frequency radars. The magnitude of the Doppler broadening due to ionospheric propagation effects associated with traveling ionospheric disturbances has been estimated. The effect is found to be significant but not severe enough to prevent high-resolution imaging. A survey of lunar observing opportunities between 2022 and 2040 is evaluated by investigating the path of the sub-radar point when the Moon is above the local radar horizon. During this time, a good variety of look directions and Doppler equator directions are found, with observations opportunities available for approximately 10 d every lunar month. EISCAT 3D will be able to provide new, high-quality polarimetric scattering maps of the nearside of the Moon with the previously unused wavelength of 1.3 m, which provides a good compromise between radio wave penetration depth and Doppler resolution. [ABSTRACT FROM AUTHOR]

Regions of anomalously low backscatter in C-band synthetic aperture radar (SAR) imagery of lake ice of Lake Neyto in northwestern Siberia have been suggested to be caused by emissions of gas (methane from hydrocarbon reservoirs) through the lake's sediments. However, to assess this connection, only analyses of data from boreholes in the vicinity of Lake Neyto and visual comparisons to medium-resolution optical imagery have been provided due to a lack of in situ observations of the lake ice itself. These observations are impeded due to accessibility and safety issues. Geospatial analyses and innovative combinations of satellite data sources are therefore proposed to advance our understanding of this phenomenon. In this study, we assess the nature of the backscatter anomalies in Sentinel-1 C-band SAR images in combination with very high resolution (VHR) WorldView-2 optical imagery. We present methods to automatically map backscatter anomaly regions from the C-band SAR data (40 m pixel spacing) and holes in lake ice from the VHR data (0.5 m pixel spacing) and examine their spatial relationships. The reliability of the SAR method is evaluated through comparison between different acquisition modes. The results show that the majority of mapped holes (71 %) in the VHR data are clearly related to anomalies in SAR imagery acquired a few days earlier, and similarities to SAR imagery acquired more than a month before are evident, supporting the hypothesis that anomalies may be related to gas emissions. Further, a significant expansion of backscatter anomaly regions in spring is documented and quantified in all analysed years 2015 to 2019. Our study suggests that the backscatter anomalies might be caused by lake ice subsidence and consequent flooding through the holes over the ice top leading to wetting and/or slushing of the snow around the holes, which might also explain outcomes of polarimetric analyses of auxiliary L-band Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) data. C-band SAR data are considered to be valuable for the identification of lakes showing similar phenomena across larger areas in the Arctic in future studies. [ABSTRACT FROM AUTHOR]

Iceberg calving is the main process that facilitates the dynamic mass loss of ice sheets into the ocean, which accounts for approximately half of the net mass loss of all Antarctic ice shelves. Fine-scale calving variability observations can help reveal the involved calving mechanisms and identify the principal processes that influence how the changing climate affects the mass loss of ice sheets. Iceberg calving from specific ice shelves or regions has been monitored before, but there is still a lack of consistent, long-term and high-precision records on independent calving events for all Antarctic ice shelves. In this study, we developed a circum-Antarctic annual iceberg calving product measuring every independent calving event larger than 1 km2 that occurred from August 2005 to August 2019. We first simulated the expansion of the coastline according to ice velocity, and then manually delineated the calved areas, which are considered to be the differences between the simulated coastline and the actual coastline derived from the corresponding satellite imagery, based on 15 years of continuous multisource optical and synthetic aperture radar images. This product provides detailed information on each calving event, including the associated year of occurrence, area, size, average thickness, mass, recurrence interval, type, and measurement uncertainties. In total, 1786 annual calving events occurred on the Antarctic ice shelves from August 2005 to August 2019. The average annual calving area was measured as 3411.4 km2 with an uncertainty value of 17.1 km2, and the average calving rate was measured as 771.1 Gt/yr with an uncertainty value of 10.2 Gt/yr. The calving frequency, area, and mass fluctuated moderately during the first decade, followed by a dramatic increase from 2015/16 to 2018/19. During the dataset period, large ice shelves, such as the Ronne-Filchner, Ross and Amery Ice Shelves, advanced with low calving frequency, while small and medium-sized ice shelves retreated and calved more frequently. Iceberg calving is most prevalent in West Antarctica, followed by the Antarctic Peninsula and Wilkes Land in East Antarctica. The annual circum-Antarctic iceberg calving dataset provides consistent and precise calving observations with the longest time coverage. The dataset provides multidimensional variables for each independent calving event that can be used to study detailed spatiotemporal variations in Antarctic iceberg calving. The dataset can also be used to study ice sheet mass balance, calving mechanisms and the responses of iceberg calving to climate change. The dataset is shared via Global Change Data Repository (href="http://www.geodoi.ac.cn/WebEn/doi.aspx?Id=1516), and entitled Annual iceberg calving dataset of the Antarctic ice shelves (2005-2019) with DOI: https://doi.org/10.3974/geodb.2020.04.09.V1. [ABSTRACT FROM AUTHOR]

In this paper two simple synthetic aperture radar (SAR) methods are applied on data from a 24 GHz FMCW radar implemented on a linear drive for educational purposes. The data of near and far range measurements are evaluated using two different SAR signal processing algorithms featuring 2D-FFT and frequency back projection (FBP) method. A comparison of these two algorithms is performed concerning runtime, image pixel size, azimuth and range resolution. The far range measurements are executed in a range of 60 to 135 m by monitoring cars in a parking lot. The near range measurement from 0 to 5 m are realised in a measuring chamber equipped with absorber foam and nearly ideal targets like corner reflectors. The comparison of 2D-FFT and FBP algorithm shows that both deliver good and similar results for the far range measurements but the runtime of the FBP algorithm is up to 150 times longer as the 2D-FFT runtime. In the near range measurements the FBP algorithm displays a very good azimuth resolution and targets which are very close to each other can be separated easily. In contrast to that the 2D-FFT algorithm has a lower azimuth resolution in the near range, thus targets which are very close to each other, merge together and cannot be separated. [ABSTRACT FROM AUTHOR]

We present a new, fully automated method of mapping the Antarctic Ice Sheet's grounding zone using a repeat-track analysis and crossover analysis of newly acquired ICESat-2 laser altimeter data. We map the position of the landward limit of tidal flexure and the inshore limit of hydrostatic equilibrium, as demonstrated over the mountainous and hitherto difficult to survey grounding zone of Larsen C Ice Shelf. Since the start of data acquisition in 2018, our method has already achieved a near 9-fold increase in the number of grounding zone observations compared with ICESat, which operated between 2003 and 2009. We have improved coverage in particular over the previously poorly mapped the Bawden and Gipps ice rises and Hearst Island. Acting as a reliable proxy for the grounding line, which cannot be directly imaged by satellites, our ICESat-2-derived landward limit of tidal flexure locations agrees well with independently obtained measurements, with a mean absolute difference and standard deviation of 0.39 and 0.32 km, respectively, compared to interferometric synthetic-aperture-radar-based observations. Our results demonstrate the efficiency, density, and high spatial accuracy with which ICESat-2 can image complex grounding zones and its clear potential for future mapping of the pan-ice sheet grounding zone. [ABSTRACT FROM AUTHOR]

New possibilities for horizontal current retrieval in marginal ice zones (MIZs) from sequential Sentinel-1 synthetic aperture radar (SAR) images are demonstrated. Daily overlapping SAR acquisitions within 70–85 ∘ S/N at time intervals < 1 h enable estimation of high-resolution velocity fields, revealing MIZ dynamics down to submesoscales. An example taken from the Fram Strait MIZ reveals energetic eddies and filaments with Rossby numbers reaching O(1) magnitudes. The SAR-derived velocity estimations at such high spatial resolution can be critical for monitoring the evolving MIZ dynamics and model validation of submesoscale processes in polar oceans. [ABSTRACT FROM AUTHOR]

A new Sentinel-1 image-based sea ice classification algorithm using a machine-learning-based model trained in a semi-automated manner is proposed to support daily ice charting. Previous studies mostly rely on manual work in selecting training and validation data. We show that the readily available ice charts from the operational ice services can reduce the amount of manual work in preparation of large amounts of training/testing data. Furthermore, they can feed highly reliable data to the trainer by indirectly exploiting the best ability of the sea ice experts working at the operational ice services. The proposed scheme has two phases: training and operational. Both phases start from the removal of thermal, scalloping, and textural noise from Sentinel-1 data and calculation of grey level co-occurrence matrix and Haralick texture features in a sliding window. In the training phase, the weekly ice charts are reprojected into the SAR image geometry. A random forest classifier is trained with the texture features on input and labels from the rasterized ice charts on output. Then, the trained classifier is directly applied to the texture features from Sentinel-1 images operationally. Test results from the two datasets spanning winter (January–March) and summer (June–August) seasons acquired over the Fram Strait and the Barents Sea showed that the classifier is capable of retrieving three generalized cover types (open water, mixed first-year ice, old ice) with overall accuracies of 87 % and 67 % in winter and summer seasons, respectively. For the summer season, the classifier failed in distinguishing mixed first-year ice from old ice with accuracy of only 12 %; however, it performed rather like an ice–water discriminator with high accuracy of 98 % as the misclassification between the mixed first-year ice and old ice was between them. The accuracy for five cover types (open water, new ice, young ice, first-year ice, old ice) in the winter season was 60 %. The errors are attributed both to incorrect manual classification on the ice charts and to the semi-automated algorithm. Finally, we demonstrate the potential for near-real-time service of the ice map using daily mosaicked Sentinel-1 images. [ABSTRACT FROM AUTHOR]

Differential interferometric synthetic aperture radar (DInSAR) is an essential tool for detecting ice-sheet motion near Antarctica's oceanic margin. These space-borne measurements have been used extensively in the past to map the location and retreat of ice-shelf grounding lines as an indicator for the onset of marine ice-sheet instability and to calculate the mass balance of ice sheets and individual catchments. The main difficulty in interpreting DInSAR is that images originate from a combination of several SAR images and do not indicate instantaneous ice deflection at the times of satellite data acquisitions. Here, we combine the sub-centimetre accuracy and spatial benefits of DInSAR with the temporal benefits of tide models to infer the spatio-temporal dynamics of ice–ocean interaction during the times of satellite overpasses. We demonstrate the potential of this synergy with TerraSAR-X data from the almost-stagnant southern McMurdo Ice Shelf (SMIS). We then validate our algorithm with GPS data from the fast-flowing Darwin Glacier, draining the Antarctic Plateau through the Transantarctic Mountains into the Ross Sea. We are able to reconstruct DInSAR-derived vertical displacements to 7 mm mean absolute residual error and generally improve traditional tide-model output by up to 39 % from 10.8 to 6.7 cm RMSE against GPS data from areas where ice is in local hydrostatic equilibrium with the ocean and by up to 74 % from 21.4 to 5.6 cm RMSE against GPS data in feature-rich coastal areas where tide models have not been applicable before. Numerical modelling then reveals Young's modulus of E=1.0±0.56 GPa and an ice viscosity of ν=10±3.65 TPa s when finite-element simulations of tidal flexure are matched to 16 d of tiltmeter data, supporting the hypothesis that strain-dependent anisotropy may significantly decrease effective viscosity compared to isotropic polycrystalline ice on large spatial scales. Applications of our method include the following: refining coarsely gridded tide models to resolve small-scale features at the spatial resolution and vertical accuracy of SAR imagery, separating elastic and viscoelastic contributions in the satellite-derived flexure measurement, and gaining information about large-scale ice heterogeneity in Antarctic ice-shelf grounding zones, the missing key to improving current ice-sheet flow models. The reconstruction of the individual components forming DInSAR images has the potential to become a standard remote-sensing method in polar tide modelling. Unlocking the algorithm's full potential to answer multi-disciplinary research questions is desired and demands collaboration within the scientific community. [ABSTRACT FROM AUTHOR]

Increasing global demand of vegetable oils and biofuels results in significant oil palm expansion in Southeast Asia, predominately in Malaysia and Indonesia. The land conversion to oil palm plantations leads to deforestation, loss of biodiversity, and greenhouse gas emission over the past decades. Quantifying the consequences of oil palm expansion requires fine scale and frequently updated datasets of land cover dynamics. Previous studies focused on total changes for a multi-year interval without identifying the exact time of conversion, causing uncertainty in the timing of carbon emission estimates from land cover change. Using Advanced Land Observing Satellite (ALOS) Phased Array Type L-band Synthetic Aperture Radar (PALSAR), ALOS-2 PALSAR-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, we produced an Annual Oil Palm Area Dataset (AOPD) at 100-meter resolution in Malaysia and Indonesia from 2001 to 2016. We first mapped the oil palm extent using PALSAR/PALSAR-2 data for 2007–2010 and 2015–2016 and then applied a disturbance and recovery algorithm (BFAST) to detect land cover change time-points using MODIS data during the years without PALSAR data (2011–2014 and 2001–2006). The new oil palm land cover maps are assessed to have an accuracy of 86.61 % in the mapping step (2007–2010 and 2015–2016). During the intervening years when MODIS data are used, 75.74 % of the change detected time matched the timing of actual conversion using Google Earth and Landsat images. The AOPD dataset revealed spatiotemporal oil palm dynamics every year and shows that plantations expanded from 2.59 to 6.39 M ha and from 3.00 to 12.66 M ha in Malaysia and Indonesia, respectively (i.e., a net increase of 146.60 % and 322.46 %) between 2001 and 2016. The increasing trends from our dataset are consistent with those from the national inventories, but slightly greater because of inclusion of smallholder oil palm plantations in our dataset. We highlight the capability of combining multiple resolution radar and optical satellite datasets in annual plantation mapping at large extent using image classification and statistical boundary-based change detection to achieve long time-series. The consistent characterization of oil palm dynamics can be further used in downstream applications. [ABSTRACT FROM AUTHOR]

Changes in snowpack associated with climatic warming has drastic impacts on surface energy balance in the cryosphere. Yet, traditional monitoring techniques, such as punctual measurements in the field, do not cover the full snowpack spatial and temporal variability, which hampers efforts to upscale measurements to the global scale. This variability is one of the primary constraints in model development. In terms of spatial resolution, active microwaves (synthetic aperture radar – SAR) can address the issue and outperform methods based on passive microwaves. Thus, high-spatial-resolution monitoring of snow depth (SD) would allow for better parameterization of local processes that drive the spatial variability of snow. The overall objective of this study is to evaluate the potential of the TerraSAR-X (TSX) SAR sensor and the wave co-polar phase difference (CPD) method for characterizing snow cover at high spatial resolution. Consequently, we first (1) investigate SD and depth hoar fraction (DHF) variability between different vegetation classes in the Ice Creek catchment (Qikiqtaruk/Herschel Island, Yukon, Canada) using in situ measurements collected over the course of a field campaign in 2019; (2) evaluate linkages between snow characteristics and CPD distribution over the 2019 dataset; and (3) determine CPD seasonality considering meteorological data over the 2015–2019 period. SD could be extracted using the CPD when certain conditions are met. A high incidence angle (>30∘) with a high topographic wetness index (TWI) (>7.0) showed correlation between SD and CPD (R2 up to 0.72). Further, future work should address a threshold of sensitivity to TWI and incidence angle to map snow depth in such environments and assess the potential of using interpolation tools to fill in gaps in SD information on drier vegetation types. [ABSTRACT FROM AUTHOR]

We have derived digital elevation models (DEMs) over the western part of the Devon Ice Cap in Nunavut, Canada, using "swath processing"of interferometric data collected by Cryosat between February 2011 and January 2012. With the standard ESA (European Space Agency) SARIn (synthetic aperture radar interferometry) level 2 (L2) data product, the interferometric mode is used to map the cross-track position and elevation of the "point-of-closestapproach" (POCA) in sloping glacial terrain. However, in this work we explore the extent to which the phase of the returns in the intermediate L1b product can also be used to map the heights of time-delayed footprints beyond the POCA. We show that there is a range of average cross-track slopes (~0.5 to ~2°) for which the returns will be dominated by those beneath the satellite in the main beam of the antenna so that the resulting interferometric phase allows mapping of heights in the delayed range window beyond the POCA. In this way a swath of elevation data is mapped, allowing the creation of DEMs from a sequence of L1b SARIn Cryosat data takes. Comparison of the Devon results with airborne scanning laser data showed a mean difference of order 1m with a standard deviation of about 1 m. The limitations of swath processing, which generates almost 2 orders of magnitude more data than traditional radar altimetry, are explored through simulation, and the strengths and weaknesses of the technique are discussed. [ABSTRACT FROM AUTHOR]

The article presents a study which analyses the behavior of superglacial lakes on the Greenland ice sheet using a new simulation model. The study uses a model that combines digital elevation model (DEM) derived from interferometric synthetic aperture radar (InSAR) data with prehistoric hydraulic flow equations to route and pond surface water runoff. Results show accurate simulation of maximum cumulative lake area with only 1.5% overestimate.

To achieve high range resolution in synthetic aperture radar imaging a frequency synthesizer with high bandwidth is a possible solution. To operate in the required frequency band an LF-signal usually has to be upconverted. In this paper we describe the design and realization of a broadband Single-Side-Band Mixer with a very short settling time between frequency steps of arbitrary length inside a high bandwidth. Compared to already existing SSB-Mixers, our novel concept has three major advantages: At first, the mixer could be used in combination with an arbitrary signal source. Due to a modular circuit concept it is possible to use the system for different input frequency ranges. Moreover, just by changing single modules, the output frequency-range can be adapted to individual requirements. Thirdly, as a main advantage, the system is able to generate a high frequency output span with a very fast settling time between frequency steps. Even with applied steps up to 400 MHz, the settling time remains below 3 µs, which is more than 5 times faster than the settling time of similar synthesizers. [ABSTRACT FROM AUTHOR]

Volcano slope instability manifests in many forms, ranging from steady state to punctuated movement, or shallow erosion to deep-seated spreading. The interplay of gravity, magmatic or hydrothermal fluids, and often active tectonics, results in complex spatial and temporal variations in deformation kinematics; deformation from magma rise or variations in hydrothermal systems may be inseparable from deep-seated and persistent flank motion. Similarly, shallow or localized ground motion may overlap with thermal contraction or subsidence of newly emplaced lava or tephra deposits. While this makes the recognition, assessment, and monitoring of volcano slope instability challenging, advancements in Synthetic Aperture Radar (SAR) technology have accelerated our knowledge of instability phenomena and our ability to assess their hazards. SAR amplitude images are powerful tools for mapping areas of geomorphological changes, which can be combined using Interferometric SAR (InSAR) to create multi-temporal deformation maps that provide unique information on the evolution of slope failures. Space-borne InSAR has become an economic way to detect changes at volcanoes at very high resolution. Ground-Based InSAR (GBInSAR) can produce frequent SAR images, propelling InSAR from monitoring to surveillance and early-warning applications. Various InSAR techniques have been used to measure persistent, transient, or surficial slope movement of at least ten volcanoes, some of which have had several periods or instances of flank motion. To highlight the utility of SAR for measuring and monitoring mass movements, studies at Stromboli (Italy) and Pacaya (Guatemala) volcanoes are reviewed in detail, where recent instability events, persistent volcanic activity, and ground truth constraints have resulted in excellent case-histories in applying SAR imagery to understand these potentially hazardous slope instabilities. At Stromboli, years of monitoring with GBInSAR has led to an operational approach to mitigate landslide risks by defining a relationship between measured displacement rates and associated instability and eruptive hazards. This aided in anticipating a flank eruption 11 hours in advance in 2014. At Pacaya, the combined use of aerial and satellite InSAR time series successfully measured a large transient co-eruptive event in 2010. This was determined to have initiated a new stage of rifting, implicating new potential hazards for future eruptions. Monitoring slope instability over the world's volcanoes will continue to increase with new radar satellites schedule to launch around 2020-2022, including ALOS-4 PALSAR by JAXA, NISAR by NASA-India, and SAOCOM by Argentine Space Agency. These satellites will provide more frequent images and improved coherence and coverage than previous sensors, with the potential to move from observing and explaining volcanic slope instability to tracking and forecasting failures. However, this enhanced mitigation potential relies on the manageability, rapidity, and fusing of these large datasets, and the integration of measured persistent or flank unrest into emergency planning. [ABSTRACT FROM AUTHOR]

Aso volcano (central part of the Kyushu island, Japan) stands out for its large caldera (18 km x 25 km) and intracaldera volcanic central cones younger than 0.1 Ma. Among 17 cones, the only crater which has been active for 80 years is Nakadake, composed by seven craterlets. In the considered time span (2007-2018), only few eruptions occurred. Despite the low intensity activity, ground displacements, detectable through different satellite sensors, can reflect the inflation-deflation cycles of the plumbing system below one of the main inactive crater (Kusasenri). Using Small Baseline Subset (SBAS), an Advanced Differential Interferometric Synthetic Radar Aperture (A-DInSAR) technique, ALOS Palsar-1 from 2007 to 2011, Sentinel-1 from 2014 to 2018 and ALOS Palsar-2 from 2016 to 2018, Synthetic Aperture Radar (SAR) datasets have been integrated with Global Navigation Satellite System (GNSS) measurements. With the employment of SARscape software, for each time span, velocity maps and displacement time series have been generated to analyse and identify the motion related to the volcanic activity. An important seismic event occurred during the investigated time period: the Mw 7.0 April 16, 2016 Kumamoto earthquake, 60 km far from the caldera rim. Both in SAR and GPS time series, we excluded the coseismic effects to estimate the trend movement due to the Aso activity. The different SAR datasets were compared to each other considering the temporal evolution of deformations: the fluctuating trend detected by C-band Sentinel-1 can be associated with other factors as earthquakes, beyond the volcanic activity. In addition, during Aso volcano unrest periods, a prevalent subsidence of the caldera central part has been observed simultaneously with the degassing activity, as confirmed by L-band Palsar-1 and 2. In conclusion, the results show that, in case of short satellite revisiting time and wavelength, it is possible to detect smaller displacements related to low intensity activities. Nonetheless, sometimes longer revisiting time and wavelength increase the spatial information in vegetated area, as in the case of Aso caldera. [ABSTRACT FROM AUTHOR]

The Synthetic Aperture Laboratory at Simon Fraser University has recently acquired an experimental airborne miniature SAR sensor. The small (ca. 10x20x40 cm^3) sensor was developed to our specifications by Artemis Inc based on their SlimSAR line heritage. The SARlab's SlimSAR is a dual frequency (X- and L-band) pulsed SAR system with chirp bandwidths providing slightly better than 1 meter range resolution at both bands. There are two monostatic fully polarimetric L-band patch antennas, mounted in an along-track InSAR (ATI) configuration (2 x 4 channels), and three bi-static X-band horn antennas mounted across-track for single-pass acquisition (2 channels). The system can operate any combination of maximum 6 channels simultaneously, so e.g. we can acquire single pass X-band and fully polarimetric L-band on the same flight trajectory. Transmit power (60 W at L-band and 25 W at X-band) allows flying heights up to 2000 m above ground level with reasonable SNR for most surfaces. The system includes an IMU/GNSS module allowing accurate mocomp and after differential processing also repeat pass InSAR at L-band. A first comprehensive demonstration test of the SARlab SlimSAR using a helio courier aircraft as platform was conducted from 30 August to 3 September 2018 over areas in and around Kluane National Park, Yukon. We will present preliminary analysis results of the acquired datasets covering a variety of phenomena, glaciers, landslides, tundra and forest terrains, airfields and roads, and water surface over Kluane Lake. Data configuration examples span (alone or in combination) single pass DEMs, polarimetry, ATI, and repeat pass InSAR. We will show encountered challenges during raw data focusing with back propagation time domain processing algorithms and how they can be overcome by using differentially processed IMU/GNSS and autofocus algorithms and mocomp on multiple SAR channels simultaneously. In addition, we will present initial results of polarimetric decomposition-based classification as well as single- and repeat-pass InSAR examples. We will outline our plans for accurate calibration next field season using artificial targets and simultaneous spaceborne data acquisitions (TerraSAR-X, ALOS-2, Sentinel-1). [ABSTRACT FROM AUTHOR]

THE frequency band from 50 to 56 GHz has been used to retrieve atmospheric temperatureprofiles through radiometric measurements at and near absorption maxima. Sensors workingaround 50–56 GHz are now only available on low earth orbit (LEO), and are still lacked inthe geostationary earth orbit (GEO) application. Compared with LEO sounding,sensors working in GEO orbit can continuously monitor the full earth disk andperform. The Geostationary Interferometric Microwave Sounder (GIMS) is a synthetic aperturemicrowave sounder working in time-sharing sampling mode with a rotating circular antennaarray. Real-time forecasting for short-term meteorological phenomena such as tropical cyclones,which is one of the most important natural disasters that cause severe damage in coastal areasaround the world. Furthermore, since information available in microwave band is differentfrom that available in visible/ infrared frequency, microwave sensor in GEO orbit can complement the existing sensorsin GEO orbit that work in visible/infrared frequency to determine vertical temperaturedistribution and thus help investigate inner structure of tropical cyclone. Till 2018, a lot of improvement of WRFDA has been realized, such as radar data and LEOmicrowave data. It has the ability of providing initial conditions for the WRF model andassessing observing system. However, one major constraint of WRFDA is the ability ofassimilating GEO microwave observations into the assimilation model and verify how theGIMS sensor effect the output data of model, especially for synthetic aperture microwavesounder. In this paper, we focus on surface pressure and precipitation in hurricane and typhoonareas based on WRF and WRFDA model with GEO microwave observations assmilated andsome significant results have been achieved. Keyword: geostationary earth orbit (GEO); full earth disk; The GeostationaryInterferometric Microwave Sounder; Real-time forecasting [ABSTRACT FROM AUTHOR]

Mountain landscapes are shaped by hillslope and fluvial processes that remove and transport material and sediment. These processes act by fundamentally different mechanisms and have correspondingly different impacts on the landscape. The transition from hillslope to fluvial erosional processes depends on several factors, including local geology, climate, and environment. However, precisely mapping where these transitions occur can prove challenging and is limited by the quality and availability of data and observations for a given region. The growing availability of satellite synthetic aperture radar (SAR) observations of Earth's surface provides new opportunities to map and quantify erosional processes over large regions. SAR data, which can track changes on the Earth's surface in the radio spectrum through time, are a valuable addition to established analyses of digital elevation and digital terrain models. Here, we propose a new method that exploits Sentinel-1 SAR interferometric coherence and digital elevation models to identify transitions between hillslope, fluvial, and alluvial erosional processes.The coherence, or the spatial correlation, between two SAR images is sensitive to changes in both the phase and amplitude of the received radar signal. Therefore, surficial processes such as landsliding, hillslope slump, fluvial cobble movement, or alluvial sediment transport result in loss of SAR coherence. We construct a timeseries of Sentinel-1 coherence images for arid and semi-arid regions of the Argentinian Central Andes, covering multiple wet and dry seasons. We compare the temporally averaged coherence maps to the high-resolution TanDEM-X 12-m DEM and observe two distinct transitions in coherence as a function of increasing drainage area: (1) A hillslope-to-fluvial transition represented by a significant decrease in the range of coherence values and moderate decrease in median coherence. (2) A fluvial-to-alluvial transition represented by a marked decrease in median coherence and a corresponding increase in coherence range. We therefore propose that coherence loss can be used as a proxy for surface sediment movement in hillslope, fluvial, and alluvial settings and provide valuable insight into where these processes transition across a landscape. Mapping these transitions remotely over broad regions allows us to compare morphometrics, such as hillslope length and flow lengths, across differing climatic and geologic environments. [ABSTRACT FROM AUTHOR]

Englacial layers in Antarctica and Greenland are indicators of the dynamic, rheological, and subglacial configuration of the ice sheets. Airborne radar sounder data is currently the primary remote sensing solution for directly observing englacial layers and imaging englacial structures at the glacier-catchment to ice-sheet scale. Traditional along-track synthetic aperture (SAR) processing can limit the detectability and interpretability of englacial layer geometry due to destructive interference in the processed signal. This reflection loss can destroy layer signals and obfuscate automatic or manual tracing and interpretation.To address this challenge, we provide a reconstruction algorithm to address the problem of destructive phase interference during the radargram formation. We develop and apply a novel SAR processor optimized for layer detection that enhances the Signal-to-Noise ratio of specular reflectors of any steepness. The algorithm also enables the automatic estimation of layer slope. Notably, this approach does not require layers to be long or continuous to produce usable slope fields, overcoming limitations of previous approaches developed for shallower and slower-flowing areas with intact layers. We illustrate the application of this new processing approach to create 2D slope fields for inverse modelling to constrain bed slipperiness flow history in ice sheet models using data acquired in by the British Antarctic Survey (BAS) Polarimetric radar Airborne Science Instrument (PASIN) in the Institute Ice Stream catchment of West Antarctica. [ABSTRACT FROM AUTHOR]

Earth Observation (EO) satellites have been operated for a century and a large amount of EO data with global coverage is available nowadays. Previous studies have focussed on the development and evaluation of algorithms using optical and Synthetic Aperture Radar (SAR) data in the field of flood mapping. When compared with optical data, SAR data has as main advantages that it can be acquired with equal quality regardless of the sun-illumination and weather conditions. Moreover, the low backscatter generated by calm and open water bodies allows them to be separated from other land cover classes.Most of the flood mapping methods have been developed for specific regions and specific flood events and may thus be not suitable for large scale applications. Moreover, systematic and fully automated applications of flood mapping algorithms on large datasets are still rare. In the previous studies, change detection was widely used since the number of false alarms caused by water lookalike areas (e.g. vegetated areas, tarmac, shadow regions) can be reduced rather easily. However, the selection of an adequate reference image remains a critical issue since the accuracy of the final flood extent map clearly depends on the selected reference image. Several indices for selecting reference images have been proposed in the literature and can be applied as part of an automatic change detection method with the previously determined flooded image. Moreover, when it comes to the generation of a flood record at the global scale using large collections of SAR images, the so-called 'flood images' are generally unknown a priori. Overall, two unknowns thus exist in any change detection-based flood mapping application based on large SAR datasets: the image acquired during the flood event and the corresponding optimal reference image. To address the problems mentioned above, we propose a fully automatic change detection method, consisting of three main processing steps: (i) flood image identification; (ii) reference image selection; (iii) flood mapping using change detection. Images from different ENVISAT orbital tracks over the UK have been processed with the proposed method. Due to a lack of ground truth data at equally large scale, we adopted a two-step approach for evaluating the resulting flood maps covering the 405 km swath width. First, subsets of the flood maps over the flooded regions are evaluated using a flood extent map derived from high-resolution aerial imagery. Next, for the other regions and images without any ground truth data, flood maps derived from a hydraulic model are considered for the evaluation. According to our results, the overall accuracies of two flood maps' subsets are higher than 85% while the users' accuracies of the flood class are higher than 88%. Moreover, a good agreement has been found between the entire frame of SAR-derived flood maps and model-derived results published by the Environment Agency. The experimental results over the UK indicate that the proposed method has a great potential to generate a global flood record from the large collection of SAR datasets. [ABSTRACT FROM AUTHOR]

We propose a novel approach of estimating stream flow discharge in small to large size channels using the European Space Agency Sentinel-1 satellite data. Fifteen streams with difference sizes in the Han River basin, Korea were chosen as study sites, and a series of Sentinel-1 Synthetic Aperture Radar (SAR) images and the observed stream flow data were used to develop the methodology. The methodology relates the stream flow discharge to the water body area that is optimally filtered from the satellite images. Here, the satellite image analysis involves the following sequential procedures: (1) The SAR images for each test site were processed for radiometric calibration, speckle errors and terrain correction; (2) The histogram matching technique was applied to the stack of images to normalize the brightness intensity of all images; (3) The area in the satellite image to extract the water bodies from is manually delineated; (4) A single threshold value is applied to all the brightness-adjusted images to extract the waterbody; (5) the power-law relationship between the extracted waterbody area and stream flow discharge is established and the coefficient of determination (R2) of the relationship is calculated; (6) The process of (4) and (5) is repeated to obtain the threshold value of water body extraction that maximizes the R2 of the relationship. A clear relationship could be developed for the 13 stream locations except for the 2 of which flow is highly influenced by hydraulic structures such as dam. At these 13 stream locations, the R2 value of the power-law relationship between the satellite-based water body area and flow discharge varied between 0.47 and 0.98 with a mean value of 0.82. The relationship was influenced by the geometrical properties of the stream such as the size, side slope and the sensitivity of the waterbody area to the depth of flow.AcknowledgementThis research was supported by a grant(MOIS-DP-2015-05) of Disaster Prediction and Mitigation Technology Development Program funded by Ministry of Interior and Safety(MOIS, Korea). [ABSTRACT FROM AUTHOR]

Effects of sea surface wind on the wakes induced by the Kuroshio at lee of Green Island, Taiwan are investigated using satellite imagery. The spatial scale and characteristics of the wakes are analyzed using optical and synthetic aperture radar images acquired from the Satellite Pour l'Observation de la Terre, Formosat-2, Europeon Remote Sensing Satellite, Advanced Land Observing Satellite, and Sentinel-1. Kuroshio velocities around Green Island are derived from satellite altimeter data and acoustic Doppler current profilers. From high-spatial resolution satellite images, the average aspect ratio of the wakes is 2.09 under southerly winds and 1.91 under northerly winds. The dimensionless width of the wakes is 2.02 and 2.76 under southerly winds and northerly winds, respectively. Under the same conditions of the Kuroshio velocity, southerly winds lead to the expansion of the wakes and eddy shedding is transmitted farther downstream. However, northerly winds constrict the development of eddy shedding and cause the wake to be close to the island. Observations from Moderate Resolution Imaging Spectroradiometer images show that the area of the sea surface temperature drop is relatively large under southerly winds and small under northerly winds. [ABSTRACT FROM AUTHOR]