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In this article, we present a method for combining image-based displacements to build time series. This method takes advantage of the redundancy of these displacements, which comes from multiple possible combinations between images. The proposed method combines common master series with a different master date to construct a single time series of relative displacements. We were able to test this method on displacements computed using images from a stereo time-lapse device recording images of the Argentière glacier during the summer and fall of 2019. Our method was compared to two other displacement aggregation strategies: a simple common master approach and the classical inversion method. In order to perform this comparison, displacement data from four permanent GPS are used. The results show that our method provides a more accurate time series of relative displacements than those obtained with the other methods. The higher accuracy of the proposed method makes the detection of seasonal variations in glacier velocity possible.

Antennas used in glacial environmental sensor networks and reported in the last two decades have been reviewed. A link budget framework for designing such antenna systems is presented and used to design an antenna system for deployment at the Thwaites glacier, Antarctica. Design details of two left hand circularly polarized cross dipole antennas, one for englacial sensor probes and the other for supraglacial surface receivers are presented. The probe antenna is a 3D bent cross dipole that fits within a borehole of 8 cm diameter while providing a 1 dBic gain at 433 MHz in ice. The surface receiver antenna is a planar printed antenna providing a gain of 6.1 dBic with a quarter wave reflector. Both antennas provide 3 dB beamwidths of at least 50° in the xz and yz vertical planes catering for transmitter-receiver antenna misalignments caused by extended deployments. The antennas displayed good circular polarization and polarization purity traits. The 3 dB axial ratio bandwidths of both the antennas remained 54.9 %. The total efficiencies of the bent cross dipole and the surface receiver antennas were noted as 69.7 % and 86.9 % respectively. Lastly, the 433 MHz band has been validated for achieving englacial communication ranges of up to 2300 metres.

The systematic monitoring of glaciers is essential to both evaluate water resource availability and better understand the effects of climate change. The increased speed of glacier changes observed in the past few years requires a more frequent update of the glacier inventories than in the past; however, the high human supervision required by the state-of-the-art techniques is discouraging their systematic application over large areas. This article proposes a novel approach to exploit the large volume of data provided by Copernicus Sentinel missions for detecting glacier outlines, including debris-covered glaciers. In detail, our method exploits the Sentinel-1 and Sentinel-2 multitemporal images to build a composite image representing the glacier conditions during the yearly maximum ablation period. The Sentinel-2 multispectral images are classified with a support vector machine and composed to a mosaic that represents the information of the maximum glacier ablation. At the same time, the Sentinel-1 time series are exploited to build a multitemporal coherence composite that represents all the snow-covered and glaciated areas together with all the moving surfaces. This information is used together with the Sentinal-2 composite to detect the debris-covered part of the glaciers. The proposed method was tested in the Central East Alps and presented an overall accuracy of 92% with respect to a reference inventory over South Tyrol and an agreement of 90% with respect to the latest glacier inventory of the Alps from Sentinel-2. The proposed approach enables to assist glacier experts in identifying glacier outlines over large areas and in short time

The contribution of polarimetric synthetic aperture radar (PolSAR) images compared with that of single-channel SAR images in terms of temporal scene characterization has been found and described to add valuable information in the literature. However, despite a number of recent studies focusing on single-polarized glacier monitoring, the potential of polarimetry to estimate the surface velocity of glaciers has not been explored due to the complex mechanism of polarization through glacier/snow. In this paper, a new approach to the problem of monitoring glacier surface velocity is proposed by means of temporal PolSAR images, using a basic concept from information theory, i.e., mutual information (MI). The proposed polarimetric tracking method applies the MI to measure the statistical dependence between temporal polarimetric images, which is assumed to be maximum if the images are geometrically aligned. Since the proposed polarimetric tracking method is very powerful and general, it can be implemented into any kind of multivariate remote sensing data such as multichannel optical and single-channel SAR images. The proposed polarimetric tracking is then used to retrieve the surface velocity of the Aletsch Glacier in Switzerland and the Inylchek Glacier in Kyrgyzstan with two different SAR sensors: the Experimental SAR airborne L-band (fully polarimetric) and Envisat C-band (single-polarized) systems, respectively. The effect of the number of channels (polarimetry) into tracking investigations demonstrated that the presence of snow, as expected, affects the location of the phase center in different polarization and frequency channels, as for the glacier tracking with temporal HH compared to temporal VV channels. In this paper, it is shown how it is possible to optimize these two different contributions, considering the multichannel SAR statistics. [ABSTRACT FROM AUTHOR]

The TanDEM-X mission allows generation of digital elevation models (DEMs) with high potential for glacier monitoring, but the radar penetration into snow and ice remains a main source of uncertainty. In this study, we generate five new DEMs of the Mont-Blanc area from TanDEM-X interferometric pairs acquired in 2012/2013. We conducted a multitemporal analysis of the DEMs in comparison with two high-resolution DEMs obtained from Pléiades stereo satellite images in 2012 and 2013. A vertical precision of 1–3 m of the radar DEMs is estimated over ice and snow free areas and slopes less than 40°. DEM-derived elevation changes are compared with outputs of the snowpack model Crocus and snow accumulation measurements. The results show that at altitudes below $\sim$2500-m a.s.l., the radar penetration is negligible in our study area. The DEM-derived elevation changes agree, within uncertainty, with the modeled and field snow height. At higher altitudes, the comparison between the radar and optical DEMs acquired only a few weeks apart allows estimating the interferometric bias of the X-band DEM in the dry snowpack. At 4000-m a.s.l, it reaches 4 m on average in October and February. A geodetic glacier mass balance calculated using the October radar DEM would be biased. For the least favorable case, the highly elevated Bossons glacier, the bias would correspond to 1.66-m w.e. This error is too large to derive significant annual mass balances, but similar to elevation or seasonality uncertainties if integrated over a 10-years period. [ABSTRACT FROM PUBLISHER]

Mapping snow cover at the end of the ablation season allows us to extract the snow line altitude (SLA). The SLA is an important proxy for the equilibrium line altitude of a glacier and an indicator of glacier health. With the increase in both active and passive remote sensing satellites, the accuracy and effectiveness of glacier monitoring can be enhanced, as the two sensors offer complementary information. In this article, we focus on the combination of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data to perform glacial classification using an explainable neural network and thereafter determine SLA. In addition, confidence-aware inverse mapping dynamics is used to understand the result reliability and the individual sensor contributions. The proposed method is applied to the Great Aletsch Glacier in the European Alps, where an overall accuracy of 83% is observed compared to the ground truth data. We observe the glacier from 2015 to 2023, noting a retreat of the SLA to higher elevations by 36 to 133 m depending on the region. Apart from climate-related mass loss, the European Alps are also affected by dust deposited during Sahara dust events and contamination from algae. Thus, in this work, we assess the annual presence of contaminated snow on the glacier. The inverse mapping dynamics reveals the contributions of both SAR and optical sensor data in the classification. This multisensor approach is shown to mitigate the limitations of single-source data, providing a comprehensive understanding of glacier dynamics in the context of climate change.

The firn line, like other glacier facies, is mapped operationally as part of glacier monitoring activities for glaciological and climate studies. Synthetic aperture radar (SAR) images are commonly used to determine the firn line in dry snow imagery. The radiometric response of retreating firn has not previously been investigated. Rather, it has been assumed that firn line mapping is only useful where large scale advances or retreats of the lower limit of continuous firn have occurred. In this paper the radiometric signal of retreating firn on an icecap in north Norway is analyzed using multi-temporal SAR imagery. Using comparisons with firn well above the firn line and field investigations of the firn properties, backscattering mechanisms are inferred. It is found that retreating firn has a distinctive radiometric signal that can be used to identify the inception and progression of firn down-wasting prior to and during firn line retreat. [ABSTRACT FROM PUBLISHER]

A new generation of space-borne SAR sensors were launched in 2006–2007 with ALOS, TerraSAR-X, COSMO-Sky-Med and RadarSat-2 satellites. The data available in different bands (L, C and X bands), with High Resolution (HR) or multi-polarization modes offer new possibilities to monitor glacier displacement and surface evolution by SAR remote sensing. In this paper, the first results obtained with TerraSAR-X HR SAR image time series acquired over the temperate glaciers of the Chamonix Mont-Blanc test site are presented. This area involves well-known temperate glaciers which have been monitored and instrumented i.e. stakes for annual displacement/ablation, GPS for surface displacement and cavitometer for basal displacement, for more than 50 years. The potential of 11-day repeated X-band HR SAR data for Alpine glacier monitoring is investigated by a combined use of in situ measurements and multi-temporal images. Interpretations of HR images, analysis of interferometric pairs and performance assessments of target/texture tracking methods for glacier motion estimation are presented. The results obtained with four time series covering the Chamonix Mont-Blanc glaciers over one year show that the phase information is rarely preserved after 11 days on such glaciers, whereas the high resolution intensity information allows the main glacier features to be observed and displacement fields on the textured areas to be derived. [ABSTRACT FROM PUBLISHER]

The performance of a tracking algorithm considering remotely sensed data strongly depends on a correct statistical description of the data, i.e., its noise model. The objective of this paper is to introduce a new intensity tracking algorithm for synthetic aperture radar (SAR) data, considering its multiplicative speckle! noise model. The proposed tracking algorithm is discussed regard- ing the measurement of glacier velocities. Glacier monitoring exhibits complex spatial and temporal dynamics including snowfall, melting, and ice flows at a variety of spatial and temporal scales. Due to these complex characteristics, most traditional methods based on SAR suffer from speckle decorrelation that results in a low signal-to-noise ratio. The proposed tracking technique improves the accuracy of the classical intensity tracking technique by making use of the temporal speckle structure. Even though a new intensity-based matching algorithm is proposed, particularly for incoherent data sets, the analysis of the proposed technique was also performed for correlated data sets. As it is demonstrated, the velocity monitoring can be continuously performed by using the maximum likelihood (ML) texture tracking without any assumption concerning the correlation of the data set. The ML texture tracking approach was tested on ENVISAT-ASAR data acquired during summer 2004 over the Inyltshik glacier in Kyrgyzstan, representing one of the largest alpine glacier systems of the world. It will be demonstrated that the proposed technique is capable of robustly and precisely detecting the surface velocity field and velocity changes in time. [ABSTRACT FROM AUTHOR]

Parks and national institutions all over the world have realized the benefits of using geographical information systems (GTSs) to complement exploration route profiles, which enable them to monitor, maintain, and define intervention actions along those routes. To do this, it is necessary to combine global positioning systems (GPSs) with a GIS to ensure position accuracy. In 2003, the authors helped survey an extended trekking route in a remote area of the Sagarmatha National Park, the national park of Mount Everest in the Himalayan range. The group joined the Changri Nup Glacier Monitoring Expedition under the auspices of the Italian Research Council (EV-K2-CNR project) and the Nepali National Research Institutes (Ronast). The research activity goal was to retrieve the geometrical profile of both the park routes and the tracks leading to the base camps of principal mountains and to develop a GIS for the park. This article presents the results from this trekking experience: the planning phase, the palmtop database design and methods of operation during the experience on the trail, and the incorporation of the data on a GIS Web site, as well as a recent history of route surveys and resources [ABSTRACT FROM PUBLISHER]

Accurate multitemporal glacier change investigations and analyses are lacking on the southeastern Tibetan Plateau (SETP). A combination of photogrammetry, optical remote sensing, and synthetic aperture radar datasets can accurately identify large-scale glaciers; In this article, glaciers in three periods on the SETP (1970s, 2000, 2020) were identified from multisource remote sensing data based on a deep learning method and manual visual interpretation, and multitemporal glacial inventory data from relatively high-frequency source imagery. Totals of 11648, 12993, and 11875 glaciers were identified in the 1970s, 2000, and 2020, with total areas of 13372.08 km2, 11692.31 km2, and 10612.94 km2, respectively. The general distribution of SETP glaciers was identified to be typical of alpine glaciers dominated by small-sized glaciers. The average elevation of glaciers was approximately 5000 m; the slopes were mostly lower than 40°, and the main aspect was southeast, followed by south and southwest. The glaciers retreated from the 1970s to 2020, and a total glacier area of approximately 2759.14 km2 was degraded during this time, with an average annual melting rate of 0.45% yr−1. Rising summer temperatures may be the driving force behind the continuous decline in the glacier area. Overall, the results obtained in this article showed relatively low uncertainty involved in the identification of glaciers compared to some previous studies. The results can provide accurate glacier information for glacier monitoring and modeling studies on the SETP.

The use of Kalman filtering techniques for landslide monitoring has proved effective as a tool for estimating and predicting land displacements. Ground-Based Synthetic Aperture Radars (GBSAR) are popular remote sensing instruments able to provide displacement maps of the investigated area, with submillimeter precision. These instruments outperform other sensors in several respects, such as all-weather and all-day monitoring. However, in some cases, for instance in vegetated scenarios, the displacement is affected by a significant uncertainty due to the decorrelation of the radar signal. In such a case, to retrieve any reliable information, noise must be filtered out using appropriate methods. Given the success of kinematic modeling of landslide movements through Kalman filtering, this technique seems to be the optimal candidate for processing the displacement measured by interferometric GBSAR data. This paper investigates this idea, by applying Kalman Filter to GBSAR measurements acquired in two different campaigns: a landslide monitoring in north Spain, and a sliding glacier monitoring in the Alpes, Italy. A proper initialization of the algorithm parameters is fundamental for a correct application of the Kalman filter. In this work, we present a strategy that exploits information from coherent pixels for tuning the filtering parameters and optimizing the filter performance on areas with low coherence.

This paper presents a processing chain for the change detection of Arctic glaciers from multitemporal multipolarization synthetic aperture radar (SAR) images. We produce terrain-corrected multilook complex covariance data by including the effects of topography on both geolocation and SAR radiometry as well as azimuth slope variations on polarization signature. An unsupervised contextual non-Gaussian clustering algorithm is employed for the segmentation of each terrain-corrected polarimetric SAR image and subsequently labeled with the aid of ground-truth data into glacier facies. We demonstrate the consistency of the segmentation algorithm by characterizing the expected random error level for different SAR acquisition conditions. This allows us to determine whether an observed variation is statistically significant and therefore can be used for the postclassification change detection of Arctic glaciers. Subsequently, the average classified images of succeeding years are compared, and changes are identified as the detected differences in the location of boundaries between glacier facies. In the current analysis, a series of dual-polarization C-band ENVISAT ASAR images over the Kongsvegen glacier, Svalbard, is used for demonstration. [ABSTRACT FROM PUBLISHER]

Synthetic aperture radar (SAR) interferometric data offer the opportunity to measure temperate glacier surface topography and displacement. The increase of the resolution provided by the most recent SAR systems has some critical implications. For instance, a reliable estimate of the phase gradient can only be achieved by using interferogram local frequencies. In this paper, an original two-step method for estimating local frequencies is proposed. The 2-D phase signal is considered to have two deterministic components corresponding to low-resolution (LR) fringes and high-resolution (HR) patterns due to the local microrelief, respectively. The first step of the proposed algorithm consists in the LR phase flattening. In the second step, the local HR frequencies are estimated from the phase 2-D autocorrelation function computed on adaptive neighborhoods. This neighborhood is the set of connected pixels belonging to the same HR spatial feature and respecting the "local stationarity" hypothesis. Results with both simulated TerraSAR-X interferograms and real airborne E-SAR images are presented to illustrate the potential of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, we develop a novel method to automatically detect areas of snow avalanche debris using a color space segmentation technique applied to synthetic aperture radar (SAR) image time series through January 2018 in the Swiss Alps. Debris avalanche zones are detected assuming that these areas are characterized by a significant and localized increase in SAR signal relative to the surrounding environment. We undertake a sensitivity study by calculating debris products by varying the D-M reference images (a stable reference image taken several weeks before the event). We examine the results according to the direction of the orbit, the characteristics of the terrain (slope, altitude, orientation), and also by evaluating the relevance of the detection with the help of an independent SPOT database by Hafner and Buhler including 18 737 avalanche events. Small avalanches are not detected by SAR images, and depending on the orientation of the terrain some avalanches are not detected by either the ascending or the descending orbit. The detection results vary with the reference image; best detection results are obtained with some selected individual dates with almost 70% of verified avalanche events using the ascending orbit. [ABSTRACT FROM AUTHOR]

Change detection is one of the most addressed topics in the remote sensing community. When performed on synthetic aperture radar images, the most critical issues are as follows: 1) the labeling of the identified changing patterns and 2) the scarce robustness of classic pixel-based approaches based on threshold segmentation of an appropriate change index, which tend to fail when multiple changes are present in the study area. In this work, a new methodology for unsupervised change detection in vegetation canopy is presented. It overcomes these limitations by exploiting multitemporal geographical object-based image analysis with the aim to make the intrinsic semantic of data emerge and direct the processing toward the identification of precise classes of changes through dictionary-based preclassification and fuzzy combination of class-specific information layers. The proposed methodology has been tested in ten different experiments covering agriculture and clear-cut deforestation applications. The results, validated against literature methods, highlighted the superiority of the proposed approach, which was quantitatively assessed in terms of standard classification quality parameters. On agriculture experiments, it allowed for an average increase in the detection accuracy of about 11% with respect to the best performing literature method, with an increment of the false alarm rate in the order of 0.5%. In case of deforestation, the registered detection accuracy was comparable to that achieved by the literature, while the most significant benefit was the reduction, of more than one-third, of the number of detected false deforestation patterns. Overall, the main characteristics of the proposed architecture are the robustness and the lack of any supervision, which makes it very well-suited for operational scenarios. [ABSTRACT FROM AUTHOR]

To study the applicability of full polarimetric synthetic aperture radar (SAR) data to identify alpine glaciers in the central Himalayas, six polarimetric decomposition methods were used to obtain 20 polarimetric characteristic parameters based on the Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band SAR (PALSAR) data. Object-oriented multiscale segmentation was performed on a Landsat 8 Operational Land Imager (OLI) image prior to classification, and the vector boundaries of different types of training samples were selected from the segmented results. We performed a support vector machine (SVM)-based classification on the characteristic parameters from each polarimetric decomposition. All 20 parameters were then screened and combined according to different requirements: the degree of separability of different types of training samples and the type of scattering mechanisms. The results show that the classification accuracy of the incoherent decomposition characteristics based on the covariance matrix is the best, reaching 87%, and it can exceed 91% after adding the local incidence angle to the suite of classifiers. Eventually, more than 93% accuracy was achieved using a combination of multiple polarimetric parameters, which reduced the misclassification between bare ice and rock. We also analyzed the use of controlling factors on the accuracy of alpine glacier identification and found that the polarimetric information and aspect of the glacier surface are the most important factors. The former is the main basis for identification but the latter will confuse the feature distributions of different categories and cause misclassification. [ABSTRACT FROM AUTHOR]

The article presents the design of a Ka-band synthetic aperture radar (SAR) instrument based on innovative phased-array antennas, with steering capability in azimuth and elevation. To limit the complexity and costs associated with conventional large active arrays with regular layouts, a sparse array concept is presented. The proposed approach results in a 40% reduction in the number of active controls, with a marginal reduction in performance compared to conventional phasedarray solutions previously developed for SAR instruments. Additionally, a preliminary thermomechanical design is presented to assess the challenges associated with the industrial production and to identify options for accommodation on the spacecraft. [ABSTRACT FROM AUTHOR]

Illumination variation is one of the major problems in multitemporal earth observation (EO) image matching. Despite much research has been focused on illumination invariant image matching, subpixel image matching under large illumination variation without prior knowledge is still a challenge. This paper proposes a phase correlation decomposition (PCD) theory model in order to analyze the joint effects of zenith and azimuth angles of the lighting source. A novel stepwise least-squares fitting-based PC (SLSF-PC) is proposed to accurately calculate the subpixel image shift by a stepwise function in the frequency domain. Our mathematical investigation is validated by image alignment and stereo dense matching experiments using simulated terrain shading images representing four different landscapes and a multi-illumination remotely sensed image data set containing eight different scenes under seasonal and daily illumination variation. Image matching experiments demonstrate the superior performance of the proposed SLSF-PC compared to the state-of-the-art image matching algorithms, such as speeded up robust features (SURF), mutual information (MI), and normalized cross correlation (NCC). Even under great illumination angle change, the proposed SLSF-PC is able to achieve 0.1 subpixel matching accuracy on average while other methods fail to find the correspondence. [ABSTRACT FROM AUTHOR]

For more than 40 years, Earth observation satellites have been regularly providing images of glaciers that can be used to derive surface displacement fields and study their dynamics. In the context of global warming, the analysis ofdisplacement field time series (DFTS)can provide useful information. Efficient data mining techniques are, thus, required to extract meaningful displacement evolutions from such large and complex datasets. In this paper, a pattern-based data mining approach, which handles confidence measures, is proposed to analyze DFTS. In order to focus on the most reliable measurements, a displacement evolution reliability measure is defined. It is aimed at assessing the quality of each evolution and pruning the search space. Experiments on two different DFTS (annual displacement fields derived from optical data over Greenland ice sheet and 11-day displacement fields derived from synthetic aperture radar data over Alpine glaciers) show the potential of the proposed approach. [ABSTRACT FROM AUTHOR]

Efficient and low power utilizing clock synchronization is a challenging task for a wireless-sensor network (WSN). Therefore, it is crucial to design a light weight clock synchronization protocols for these networks. An adaptive clock offset prediction model for WSN is proposed in this paper that exchanges fewer synchronization messages to improve the accuracy and efficiency. Timing information required is collected by setting a small WSN set up to investigate the soil condition to control the irrigation in agriculture. The networks investigate soils moisture, temperature, humidity, and pressure content along with the sensors clock offset. First, the prediction model perceives the existing sensor clock offset to observe the clock characteristics and delay. Then, a Gaussian function is applied for adjusting the parameters weight of the observed value in the prediction model. The system results demonstrate that the proposed adaptive non-linear Gaussian regression synchronization model utilizes 20% less energy as consumed by time sync protocol for sensor-network and reference broadcast synchronization Protocol. It also reduces the synchronization error with respect to root-mean-square error (RMSE) by 24.85% as compared to linear prediction synchronization with RMSE 28.72% in terms of accuracy. [ABSTRACT FROM AUTHOR]