With the rapid development of the economy and technology, radio frequency identification (RFID) technology is growing rapidly. However, significant multipath effects in complex environments reduce the accuracy of traditional methods, limiting practical applications. To address these challenges, our work presents a novel synthetic aperture radar RFID (SAR RFID) localization method leveraging dual rotating antennas. By utilizing received signal strength indication (RSSI) and establishing its functional relationship with angular position, the horizontally and vertically rotating antennas improve coverage and reduce multipath errors. Additionally, a multivariate nonlinear regression algorithm integrated with particle filtering is introduced to enhance adaptability to nonlinear data, significantly improving localization performance. The experimental results show that compared with the traditional fixed antenna system, the positioning error of the proposed system in 3-D environments is less than 13 cm. Our system achieves a favorable tradeoff among performance, hardware cost, and deployment complexity, due to the flexible configuration enabled by rotating antennas and the SAR mechanism. The proposed method can provide accurate and stable state estimation in complex nonlinear scenes, and offers a robust and practical RFID positioning solution for practical applications.

Fractures represent vulnerable discontinuities formed under stress conditions, with their three-dimensional morphological parameters serving as pivotal indicators for assessing ice shelf dynamic stability. The current fracture monitoring system primarily focuses on two-dimensional feature analysis, and there is insufficient three-dimensional systematic monitoring of vertical extension processes. Based on REMA DEM data, this study integrates deep learning semantic segmentation with hydrological terrain analysis methods to construct a framework for extracting fracture depth information. For the first time, a comprehensive dataset of fracture depths across the Antarctic ice shelves is created, and based on this dataset, the three-dimensional extent of ice shelf damage is quantified and evaluated. The study shows that the average depth of fractures in ice shelves is 8.17 meters, with differences between ice shelves reaching up to ten times. Notable spatial variations in fracture depth are also observed within ice shelves. The depth distribution of fractures exhibits significant spatial coupling with the stretching stress field of the ice shelf. The three-dimensional morphological parameters of the ice shelf (average depth, area density, volume density, and penetration rate) exhibit significant spatial heterogeneity. This study fills the gap in the vertical dimension of fracture 3D modeling, providing essential data support for ice shelf stability research.

The 11-month data gap between Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) hinders monitoring long-term ice mass change and its further analysis. While many attempts have been made to bridge water storage gaps, few unified frameworks exist to bridge the ice mass change gaps for both Greenland Ice Sheet (GrIS) and Antarctic Ice Sheet (AIS). This study combined partial least squares regression (PLSR) and the Sparrow Search Algorithm optimized back propagation (SSA-BP) to fill this gap in GrIS and AIS. During this process, seasonal autoregressive integrated moving average with exogenous variables (SARIMAX), and multiple linear regression (MLR) were introduced as comparison. PSLR is utilized to select key variables for constructing predictive models. We found SSA-BP outperformed SARIMAX and MLR, with correlation coefficients and root mean square error at 0.99 and 39.22 Gt for GrIS, and 0.95 and 189.85 Gt for AIS within the testing period. SSA-BP demonstrated a reasonable mass change trend with less noise than other methods. SSA-BP reconstructed result shows superiority than other researches. And the reconstructed seasonal signals highlight the importance of filling the gap, showing decreased mass loss for GrIS and continuous mass loss acceleration for AIS post-2016.

The nominally coarse spatial resolution ($300\sim ~400$km) of gravity recovery and climate experiment (GRACE) and a 11-month data gap with GRACE follow-on (GRACE-FO) limits applications at the individual ice sheet drainage basin scale and complicates the evaluation of regional ice sheet mass changes. While numerous works have downscaled GRACE-estimated water storage, research on downscaling ice mass change in Antarctica is limited. This study employs joint partial least-squares regression (PSLR) and support vector machine (SVM) method to reconstruct GRACE-derived spatiotemporal data for the Antarctic ice sheet (AIS). The pixel-temporal downscaling (PTD) of random forest (RF) and pixel-spatial downscaling (PSD) of multiscale geographically weighted regression (MGWR) enhance spatial resolution of ice mass changes from 0.25° (~120 km) to 1.92 km. The downscaled results show consistent temporal variation and reduced noise compared to other reconstruction methods. Both RF and MGWR results exhibit high consistency with original GRACE data, with MGWR achieving a correlation coefficient (CC) of 0.99. The MGWR model effectively captures finer signals related to ice flow velocity. When compared to independent free air gravity anomalies, MGWR outperforms RF with improvements of 41.51% and 56.25% in mean correlation for group 1 and group 2 observation points, respectively. In addition, MGWR shows improvements of 16.90%/29.69% for flight Line A and 11.84%/19.72% for flight Line B compared to RF and original GRACE results. The enhanced spatial resolution offers valuable insights into ice dynamic changes within the Western AIS and Eastern AIS and smaller regions such as the Antarctic Peninsula.

With the rapid development of the Internet of Things (IoT), an increasing number of industrial demands have become urgent. Radio-frequency identification (RFID) system plays a crucial role in addressing these challenges. It has now become a significant direction for the development of industrial automatic identification and data collection technology. However, the existing 3-D high-precision absolute localization methods have issues that need to be resolved. For example, the antenna sampling position needs to be used as known prior information, and the timestamp information of the samples is not fully utilized. Additionally, relying on multiple antennas or reference tags as auxiliary measures reduces the system flexibility. To overcome these challenges, we propose a single-antenna, multitarget, and 3-D localization method based on an attention mechanism and neural network regression model inspired by the synthetic aperture (SA) method. This method suggests using a single antenna moving uniformly on a slider for continuous motion and sampling. By utilizing sample phase and time-domain information, it achieves 3-D localization of cargo boxes in intelligent warehousing environments. In comparison with the current state-of-the-art localization solutions, our method does not give the use of reference tags and utilizes a reduced number of antennas. The experimental results prove the feasibility of using only mobile antennas without reference tags to perform 3-D multitarget positioning tasks. The combined dimensional average positioning error of 3-D indoor positioning achieves a high-precision positioning, reaching 2.04 cm and exhibits good robustness.

Satellite altimetry is a primary method for monitoring dynamic changes in polar ice and snow. However, recent studies on extreme melting events have revealed the notable impact of radar altimeter signals penetrating through snow, challenging previous assumptions that monitoring imparted negligible effects. Current approaches for mitigating penetration errors offer only partial alleviation instead of complete elimination. This study estimates the penetration values of Greenland satellite monitoring from February 2004 to November 2020 by comparing the temporal sequence of elevation changes obtained from laser altimetry and radar altimetry. We investigated the temporal and spatial evolution characteristics of penetration, particularly focusing on the extreme melting event in 2019. Interestingly, laser altimeter signals are also determined to penetrate water bodies, introducing a potential source of inaccuracy in laser altimeter measurements during the melting season. Furthermore, the analysis estimated the impact of time-variable penetration depths on Greenland’s mass balance, revealing that regions with severe mass loss have the potential to exceed loss rates of 10 Gt/yr. These findings enhance our understanding of the impact of penetration errors in satellite altimetry and emphasize the crucial need for their correction to ensure accurate mass balance calculations in Greenland.

The segmentation of crevasses in remote sensing images plays a pivotal role in diverse domains, including crevasse change monitoring, analysis of ice shelf surface water systems, and investigations into ice shelf stability. In response to the limitations in existing crevasses segmentation methods, which struggle to concurrently capture global structures while preserving local details, this letter introduces CREVNet. CREVNet is designed to achieve precise crevasse segmentation, comprising two integral components: the Transformer Path for enhanced local and global feature extraction, and the Convolutional Path for detailed depiction of crevasses. Evaluation on crevasses dataset, created through the integration of optical remote sensing imagery and laser altimetry data, reveals impressive results. CREVNet achieves F1-score, MIoU, and OA values of 80.40%, 80.98%, and 95.24%, respectively. Notably, CREVNet surpasses the performance of prominent deep learning methods, including Unet, DeepLabV3Plus, DFANet, FPN, MobileViT, and TransUnet. These outcomes underscore CREVNet’s practical potential for effective crevasses segmentation.

Basal channels are the expression of basal melting in detail. In extreme cases, they will cause the ice shelf calving and seriously threaten the ice shelf stability, which has attracted wide attention. In this study, we used reference elevation model of Antarctica (REMA) digital elevation models (DEMs) data, IceBridge data, and Amery Ice Shelf thickness data to accurately identify the distribution location of Antarctic Ice Shelf Basal Channel (AISBC). In addition, we counted the basal channels length (BCL) and basal channels concentration (BCC) and analyzed the main formation mechanism of basal channels in different sea. The accuracy of identifying basal channels was at least more than 90%. Our identification results were more accurate compared with Alley et al. (2016), and there were especially basal channels on Amery Ice Shelf and Larsen-C Ice Shelf. AISBC network is developed, and AISBC’s total length is about 16965 km. In particular, the transverse basal channel has a potential impact on the ice shelf calving. BBC along the coast of Dumont D’Urville Sea was the highest, which is closely related to the active degree of the formation mechanism of the basal channels. The types of AISBC show obvious regional characteristics, which were mainly affected by the formation mechanism of basal channels. Our research results can provide scientific reference data for studying ice shelf stability in different sea regions.

Sea ice concentration (SIC) is an indispensable parameter for the study of polar sea ice. The existing methods can obtain accurate SICs for most situations, but they usually perform poorly in low SIC regions because of the spatial differences in the neighboring pixels induced by the discontinuity of the sea ice cover. In this letter, to cope with the difficulty of this problem, an improved SIC estimation method is proposed to retrieve SIC, focusing on low SIC regions. The proposed method introduces the spatial relationships into SIC estimation by employing a total variation (TV) regularizer. Moreover, nonnegative constrained least squares (NCLS) is used to derive the optimal solutions from the SIC estimation equation. Verification was conducted in low SIC regions (0%–50%) of the Antarctic utilizing ship-based in situ data and the Moderate Resolution Imaging Spectroradiometer (MODIS), and the results were compared with those of some of the mature methods. The results indicated that the proposed method can obtain a superior accuracy with a smaller root-mean-square error (RMSE) (6.0%–14.61%) than the other algorithms in low SIC regions. Furthermore, the proposed method can accurately estimate the SIC of both first-year ice and multiyear ice. The findings of this study confirm the need to consider the spatial relationships in the processing of SIC estimation.

Global Navigation Satellite System (GNSS) ionospheric tomography is a typical ill-posed problem. Joint inversion with external observation data is one of the effective ways to mitigate the problem. In this article, by fusing 3-D multisource ionospheric data, and improving the stochastic model, an improved GNSS tomographic algorithm MFCIT [computerized ionospheric tomography (CIT) using mapping function] is presented. The accuracy of the algorithm is validated by selected data under different geomagnetic and solar conditions acquired in Europe. The results show that the estimated, statistically significant uncertainty for each of the layers is about 0.50-3.0TECU, with the largest absolute error within 6.0TECU. The advantage of the MFCIT is that it is based on the Kalman filter, which enables efficient near real-time 3-D monitoring of ionosphere. The temporal resolution can reach ~1 min level. Here, we apply the ionospheric tomography inversion to the magnetic storm on January 7, 2015, in the European region, and quantified the evolution of the storm. The results show that the difference of the core region between the MFCIT and CODE GIM is less than 1TECU. More importantly, during the initial phase of the storm, when the ionospheric disturbance is not evident in the single layer CODE GIM model, the MFCIT shows obvious positive disturbances in the upper ionosphere, although there is no disturbance in the F2 layer. The MFCIT further tracks the evolution of the magnetic storm that the ionospheric disturbance expands from the upper to the lower ionosphere layers, and at UT12:00, the disturbance continues to spread to the F2 layer.

Ice surface temperature (IST) is a key parameter for the study of polar ice sheets and ice shelves. In this study, an improved single-channel (ISC) algorithm based on the radiative transfer equation is proposed for IST retrieval from Landsat-8 band 10 data. The main steps in the proposed ISC algorithm include: 1) simulation of atmospheric radiative parameters by regression against the atmospheric water vapor content and the effective mean atmospheric temperature; 2) calculation of IST using Planck's equation, instead of using Taylor's approximation; and 3) implementation of an iterative scheme for IST calculation. The errors from using Taylor's approximation and the atmospheric radiative parameter simulation were quantitatively estimated. A sensitivity analysis of ISC to possible errors in atmospheric water vapor content, brightness temperature, and satellite observations was also conducted. The results of the sensitivity analysis showed that the proposed algorithm is robust to the atmospheric water vapor content, but is sensitive to the calibration precision of the thermal infrared sensor. Verification using a simulated approach showed better IST variability from ISC than the original SC algorithm [the root-mean-square errors (RMSEs) were 0.3252 and 0.7176 K, respectively]. When compared with near-surface air temperatures from 68 automatic weather stations data in Greenland and 25 data in the Antarctic, the bias and RMSE from the ISC algorithm were again better than those from the SC algorithm. The IST from Moderate Resolution Imaging Spectroradiometer (MODIS) was found to be underestimated with respect to the results of both the SC and ISC algorithms. Maps of the spatial distributions of IST derived from samples of Landsat-8 images are presented. The rationale of each step in the proposed ISC algorithm is also presented so that this can provide further support to the authenticity of the results.

The influence of the ionosphere on spaceborne SAR signals can be significant, predominantly at the L-band and lower frequencies. In particular, low band polarimetric SAR's applications are mainly limited by Faraday rotation (FR) effects. In this paper a new FR estimator is proposed from linearly polarized coherency matrix data and validated by ALOS PALSAR full-pol data processing. FR angles computed by the new estimator are in good consistent with FR angles computed by physical model. The estimator is also used to assess the impact on sea ice. The results show that a FR value exceeding 2° could reduce the accuracy of geophysical parameter recovery of sea ice in the cross-polarized channels.

Ice surface temperature (IST), which can indicates surface melt in polar areas, is one of the key parameters in ice surface monitoring. However, the existing studies about this topic are relatively limited. In this context, this research proposes an effective IST retrieval approach using MODIS data. In this approach, an improved split-window algorithm (SWA) is introduced by combining it with a polynomial fitting for atmospheric transmittance simulation. A comparative study between the satellite-derived ISTs (retrieved IST and MODIS IST product) and automatic weather station data from Zhongshan Station and the Ross Ice Shelf (from 2004 to 2013) was undertaken to assess the effectiveness of the proposed method. The experimental results show that the proposed method performs better than the existing MOD29 product. The retrieved ISTs have a bias of -0.62 K and a root-mean-square error (RMSE) of 1.32 K for the Zhongshan Station data, and a bias of -1.62 K and an RMSE of 2.34 K for the Ross Ice Shelf data, respectively.

To improve the accuracy of the traditional NASA Team (NT) sea ice concentration (SIC) algorithm, a new SIC estimation method is proposed by combining the NT algorithm and a numerical optimization technique with Special Sensor Microwave/Imager (SSM/I) data. In this method, the noise is taken into consideration to improve the SIC estimation equation, and then, the least squares method is used to further optimize the estimation results from the improved equation. Validation was performed using a comparison between the results from the SSM/I-based SICs (the proposed method, the NT algorithm, and the bootstrap algorithm) and in situ data. The quantitative results show that the proposed method generates a more accurate SIC with smaller bias (-3.2-2.8) and root-mean-square error (7.7-18.4) than the other two algorithms.

Differential synthetic aperture radar interferometry (D-InSAR) is a powerful method for measuring surface deformation, such as in studies of the earthquake cycle, volcano deformation monitoring, land subsidence monitoring, and glaciological studies. However, its application to glaciological studies is limited by the lack of accurate digital elevation models (DEMs), particularly over the Antarctic ice sheet. Previous studies on ice motion using D-InSAR are mostly based on short-baseline interferograms because these data sets are insensitive to DEM errors. Unfortunately, short-baseline interferograms are often unavailable. In this paper, we refine the InSAR technique by using a combination of two interferograms to make accurate ice-flow velocity measurements. The refined technique is tested in the Grove Mountains area, East Antarctica. Ice-flow velocities from the baseline-combination method are in good agreement with those measured by short-baseline interferograms. This method is also capable of reducing phase errors by combining the appropriate data sets. The reliability of the data sets is assessed by defining a baseline-combination parameter and ensuring that it is less than or equal to 1.0. With this method, we are able to extend the usefulness of D-InSAR for glaciological studies.