Global Navigation Satellite System-Reflectometry (GNSS-R) provides an effective remote sensing technique for accurate retrieval of sea surface height (SSH) measurements. However, accuracy is severely affected by environmental disturbances such as wind-induced sea clutter and wave interference, degrading Delay-Doppler Map (DDM)-derived measurements. In this study, we propose an advanced trajectory-based deep learning model, Crossformer, explicitly designed to capture temporal dependencies inherent in GNSS-R sequential data. The method leverages five distinct DDM features: Peak Power Point (PPP), Maximum Slope Point (MSP), Center Pixel Intensity (CPI), Average Power Point (APP), and Kurtosis (KUR). A dimension-segment-wise embedding technique combined with a two-stage attention mechanism effectively models both temporal and cross-dimensional correlations. Evaluation using CYGNSS data validated against Jason-3 Level 2 measurements demonstrates the superior performance of our approach, yielding a Root Mean Square Error (RMSE) of 0.93 m, Mean Absolute Error (MAE) of 0.65 m, and a coefficient of determination (R2) of 0.9901. Comparative analyses with baseline methods confirm significant improvements in robustness and predictive accuracy, particularly across varying sea states. This research underscores the potential of advanced temporal modeling techniques in GNSS-R altimetry applications.

A novel basic observable, termed the alternating-current texture (ACT), is defined from the original linear-polarization carrier-to-noise ratio (CNR) of Global Navigation Satellite System (GNSS) for monitoring wind vector. The amplitude distribution (AD), autocorrelation function (ACF), power spectral density (PSD), and fractal dimension (FD) of the ACT are explored. The results show that these statistical characteristics derived from the GNSS-IR CNR resemble those directly extracted from the reflected signal. Twelve statistics from the AD, ACF, PSD, and FD, as observables sensitive to wind speed, are analyzed. The findings suggest that these statistics exhibit geometric dependence, especially at the low elevation angle. Furthermore, the skewness of the AD, the correlation time of the ACF, PSD peak, PSD width, and fractal dimension respond more effectively to wind speed than other statistics, and are used to assess the capability of retrieving wind speed. Kernel principal component analysis (KPCA) is employed to fuse these statistics to produce a new sensitive observable to wind speed. When elevation and azimuth angles are confined to optimal regions with minimal interference, a root mean square error (RMSE) of 1.57 m/s is obtained with a minute-level temporal resolution. In contrast, a geodetic GNSS receiver provides an RMSE of 2.55 m/s. Additionally, based on the anisotropy of sea surface, the feasibility of these statistics in retrieving wind direction is investigated. Novel sensitive observables to wind direction, derived from the best-fit ellipse to the spatial distributions of the statistics, are defined. The fusion using KPCA achieves the best determination coefficient (DC) of 0.50 with wind direction, while the geodetic receiver yields a DC of only 0.16. These results conclude that low-cost GNSS sensors can be utilized for retrieving wind vector, and the linear-polarization GNSS-Interferometric Reflectometry (GNSS-IR) outperforms its right-handed circular polarization (RHCP) counterpart.

This paper revisits the Interferometric Complex Field (ICF) concept in coastal Global Navigation Satellite System-Reflectometry (GNSS-R), and proposes a novel baseband signal processing scheme for monitoring sea states. The scheme utilizes incoherent averaging to reduce hardware complexity while maintaining accuracy comparable to correlation-based methods. This eliminates requirements for the RHCP antenna, RF front-end, and baseband processor of the direct signal. Compared to the existing schemes, it has lower computational complexity, power, and cost. The primary observable, termed the alternating-current incoherent average power (aIAP), is derived by detrending a stable baseline component from the output of the proposed scheme. The coherence time and spectral width from the aIAP time series are defined as the observables of retrieving sea state, specifically wind speed and significant wave height (SWH). Three experimental data sets are used to demonstrate and assess the proposed scheme. Results indicate that the coherence time and spectral width of aIAP exhibit sea-state dependencies similar to those of alternating ICF within a wind speed range of 0 ∼ 15 m/s, and thus can be used to retrieve sea state. Spectral width retrieves sea state more effectively than coherence time. Coherence time and spectral width weakly depend on elevation angle so that an elevation correlation is unneeded. The proposed scheme, as a cost-efficient alternative, has the potential for operational sea state monitoring.

The potential of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) is explored for detecting and estimating global mesoscale oceanic eddies. A fractional Fourier transform-residual network (F-ResNet) model is used to achieve centimeter-level absolute dynamic topography (ADT), demonstrating a root mean square error (RMSE) of 5.05 cm, which is subsequently used for detecting oceanic eddies based on their height characteristics. Using CYGNSS data from 2020, comprising over 750 million samples, the research demonstrates that GNSS-R technology effectively supports daily oceanic eddy detection, achieving precision rates of 50.63% and 47.80%, recall rates of 89.95% and 90.48%,$F1$scores of 64.79% and 62.56%, and accuracy rates of 47.92% and 45.52% for cyclonic and anticyclonic eddies, respectively. Although the method proves both effective and feasible, its performance remains suboptimal. To improve detection accuracy, the study explores high-bandwidth signals, whose sharper autocorrelation function enhances ADT retrieval and improves oceanic eddy detection accuracy. Simulation results from the ACE-BOC (15, 10) signal suggest precision rates of 85.51% and 84.14%, recall rates of 91.93% and 91.11%,$F1$scores of 88.76% and 87.48%, and accuracy rates 79.80% and 77.75% for cyclonic and anticyclonic eddies, respectively. Moreover, supplementary metrics for eddy features indicate significant improvements with high-bandwidth signals. The application of these two signals for sustained, long-term monitoring of oceanic eddies in a specific region is also demonstrated, providing a foundation for eddy tracking. The tracking results show that the ACE-BOC (15, 10) signal can effectively track eddy trajectories, achieving an RMSE of 1.11 km, whereas CYGNSS yields an RMSE of 8.4 km. Finally, this article discusses the characteristics of ACE-BOC and carrier-altimeter signals, highlighting the potential of advanced spaceborne GNSS-R altimeters and discussing future directions in this field.

The integrated sensing and communication-enhanced (ISAC-enhanced) intelligent transportation system (ITS) has great development potential and market value in future urban vehicle-to-infrastructure (V2I) scenarios. With limited total power consumption, joint design of power distribution and beam steering of sensing and communication subsystems is required to achieve optimal system performance. This paper introduces an intelligent power allocation and beam-steering control system, where both the system actions are determined based on the radar sensing data cube. The adaptive power allocation module employs the Actor-Critic reinforcement learning agents to optimize power distribution based on vehicle GPS data, communication channel capacity, and historical power allocation, balancing the overall system performance including the sensing and communication functions. The automatic beam control module, utilizing the convolutional neural network (CNN) for dynamic beam control, intelligently adapts communication beams for vehicles navigating complex roadways, enhancing the beam tracking accuracy and quality of service. Validation on real-world datasets showcases the effectiveness of these modules, highlighting the potential to enhance vehicular communication performance.

This article presents the statistical analysis of the reflected global navigation satellite system-reflectometry (GNSS-R) signal from a coastal experiment, including the non-Gaussianity, probability distribution functions, autocorrelations, and fractal dimensions of the speckle and texture components. The results clearly show that the amplitude distribution is modeled well by a Weibull model. The texture component of the reflected GNSS signal has a log-normal distribution. Due to the presence of the coherent and non-coherent components, the phase of the reflected signal is not uniformly distributed with$\left [{{-\pi, \pi }}\right]$. The autocorrelation functions (ACFs) of the speckle and texture components both are Gaussian-shaped, with the correlation times on the order of hundreds of milliseconds and a few seconds, respectively. Some statistical properties of the reflected GNSS signal depend on GNSS-R geometry and sea state; therefore, once the influence of GNSS-R geometry is corrected, they can be used to determine sea state. The speckle and texture correlation times of the reflected GNSS signal, as an example, are used to retrieve wind speed. The speckle and texture correlation times provide retrieved wind speeds with root mean square errors (RMSEs) of 1.66 and 1.75 m/s. When a minimum variance estimator is used to fuse two retrieved wind speeds, the RMSE is reduced to 1.46 m/s. The work is helpful for developing a GNSS signal scattering model over the sea surface and further studies on coastal GNSS-R to monitor sea state and maritime target.

The rapid evolution of mobile networks presents challenges for devices with limited computing power. Mobile or multi-access edge computing (MEC) addresses this by providing computing resources in proximity to end devices. However, MEC servers face constraints in resource sharing, necessitating efficient allocation. We propose the Balanced Offload for Multi-type Tasks (BOMT) algorithm. Tasks are prioritized based on type, size, and maximum tolerable delay. Different offloading algorithms are applied for varying priority tasks, considering current server load. Simulation results demonstrate BOMT’s effectiveness in reducing latency, enhancing user coverage, and improving task completion rates.

Simulations of future urban form (road networks, land use type, building density and building type) are needed to provide greater clarity of future urban flooding dynamics. This paper proposes an interdisciplinary and novel geospatial approach involving advanced geosimulations, artificial intelligence algorithms and hydrodynamic modelling to assess how flood risk is exacerbated under different urban form scenarios. It is envisioned that the final output will have a pivotal impact on urban growth modelling research and enhance community-level knowledge and resilience to urban flooding.

Layover is a kind of geometric distortion in radar systems with side-look imaging, especially in mountainous and dense urban areas. It causes phase distortion and alters target characteristics in the acquired images, which directly hinders the application of radar images. In this letter, the multilayer feature fusion attention mechanism (MF2AM) is proposed to extract layover from interferometric synthetic aperture radar (InSAR) imagery automatically. First, the SAR image, the corresponding coherence map, and interferometric phases are channel-fused to enhance semantic information of layover areas. Then, the fused image is fed into MF2AM to extract the essential features of layover. Finally, the detection results are produced via MF2AM. MF2AM consists of the encoder and the decoder. The encoder contains three parts: the resnet101, attention-based atrous spatial pyramid (AASP), and the semantic embedding branch (SEB). In the decoder, step decoding is used to better fuse high- and low-level features and improve the effect of edge segmentation. To verify the proposed method, the images of millimeter wave InSAR system are used for the experiment, and the performance is compared with DeepLabV3+ and Geospatial Contextual Attention Mechanism (GCAM). The results show that the MF2AM has achieved obvious performance advantages. The average pixel accuracy and average intersection over union (IOU) are 0.9601 and 0.9310, respectively, and the average test time is only 7.97 s.

The signals at higher elevation angles could be received by a left-handed circularly polarized (LHCP) antenna because LHCP components dominate the reflected signals at higher elevation angles. This letter explores a four-channel interference method of dual-antenna global navigation satellite system (GNSS) reflectometry capable and its application in retrieving the water level. Compared to single-antenna observation using a oscillating signal-to-noise ratio (SNR), due to the capability of receiving the reflected signals at high elevation angles, the proposed method has higher temporal resolution. The criterion based on the effective value of the time series is proposed to evaluate the quality of oscillating carrier phase difference. The Lomb–Scargle method and cosine fitting are used to estimate the oscillated frequency of carrier phase difference. Through normalizing cosine oscillation and time scale by the GNSS signal amplitude and wavelength, the time-series from multisatellites could be combined to retrieve the height. At last, the experiment is conducted to demonstrate the proposed method, and the results show that when the criterion threshold is under 0.2, the centimeter-level precision of the retrieved height could be achieved. Two-satellite observation combining satellite pseudorandom noise (PRN) 15 and 24 could decrease the time span of the observation to 320 s from 480 s for only using single satellite, and the root mean square error (RMSE) reduces to 6.2 cm from 9.5 and 18.9 cm of the satellite PRN 15 and 24.

Although deep learning techniques have achieved noticeable success in aircraft detection, the scale heterogeneity, position difference, complex background interference, and speckle noise keep aircraft detection in large-scale synthetic aperture radar (SAR) images challenging. To solve these problems, we propose the geospatial transformer framework and implement it as a three-step target detection neural network, namely, the image decomposition, the multiscale geospatial contextual attention network (MGCAN), and result recomposition. First, the given large-scale SAR image is decomposed into slices via sliding windows according to the image characteristics of the aircraft. Second, slices are input into the MGCAN network for feature extraction, and the cluster distance nonmaximum suppression (CD-NMS) is utilized to determine the bounding boxes of aircraft. Finally, the detection results are produced via recomposition. Two innovative geospatial attention modules are proposed within MGCAN, namely, the efficient pyramid convolution attention fusion (EPCAF) module and the parallel residual spatial attention (PRSA) module, to extract multiscale features of the aircraft and suppress background noise. In the experiment, four large-scale SAR images with 1-m resolution from the Gaofen-3 system are tested, which are not included in the dataset. The results indicate that the detection performance of our geospatial transformer is better than Faster R-CNN, SSD, Efficientdet-D0, and YOLOV5s. The geospatial transformer integrates deep learning with SAR target characteristics to fully capture the multiscale contextual information and geospatial information of aircraft, effectively reduces complex background interference, and tackles the position difference of targets. It greatly improves the detection performance of aircraft and offers an effective approach to merge SAR domain knowledge with deep learning techniques.

GaoFen (GF) series are the space-based system as part of the China High-Resolution Earth Observation System (CHEOS), which is established as one of the Chinese National Science and Technology Major Project. Three satellites of the series are already launched till now, and providing near-real time Earth observation with high spatial resolution of 1m and high spectral resolution reaching nanometer level. Data acquired from all these satellites has served a wide range of applications covering many topics. In particular, data of GF-1 is applied in monitoring land cover, soil salinization, sea ice, marine oil spill and so on. And GF-2, with high spatial resolution of 1m, has been used to monitor urban changes and survey the coastline. In addition, all users can access all available data through the data service platform of CHEOS, where also all practical applications cases are detailed.

Big data have shifted spatial optimization from a purely computational-intensive problem to a data-intensive challenge. This is especially the case for spatiotemporal (ST) land use/land cover change (LUCC) research. In addition to greater variety, for example, from sensing platforms, big data offer datasets at higher spatial and temporal resolutions; these new offerings require new methods to optimize data handling and analysis. We propose a LUCC-based geospatial cyberinfrastructure (GCI) that optimizes big data handling and analysis, in this case with raster data. The GCI provides three levels of optimization. First, we employ spatial optimization with graph-based image segmentation. Second, we propose ST Atom Model to temporally optimize the image segments for LUCC. At last, the first two domain ST optimizations are supported by the computational optimization for big data analysis. The evaluation is conducted using DMTI (DMTI Spatial Inc.) Satellite StreetView imagery datasets acquired for the Greater Montreal area, Canada in 2006, 2009, and 2012 (534 GB, 60 cm spatial resolution, RGB image). Our LUCC-based GCI builds an optimization bridge among LUCC, ST modelling, and big data.

With increasing amounts of spatial, spectral and temporal remote sensing data and heterogeneity of platforms, we have entered an era of big data in remote sensing research. Imagery now routinely exceeds the memory size of personal computers so splitting/distributing big remote sensing data becomes a necessary pre-processing step. Standard rectangle based splitting methods can distort existing geometric and topological information and lose features as images are split into tiles. To address these challenges, we propose a sampling based image splitting method, which models the dataset as a streaming service and splits the dataset with a Voronoi diagram. The streaming data is systematically sampled to initially select the seeds of a Voronoi diagram. Voronoi regions are then generated according to spatial and spectral distances using Fortune's sweepline algorithm [1]. We test the splitting method with AVIRIS imagery of North America in 2013 (courtesy of NASA/JPL-Caltech) to evaluate the ability to detect objects of our splitting method. For evaluation we employ the object-based classification method of Hay and Castilla [2]. In contrast to rectangle based splitting approaches, most polygon borders generated by our method are found to converge with object borders (e.g., trees, building, and roads). When deployed with MapReduce, our sampling based splitting method also helps balance the computation intensity between each computing node.

On 29 and 31 May 2006, a comprehensive vicarious calibration experiment for the Moderate Resolute Imaging Spectroradiometer(MODIS) visible and near-infrared bands was performed at Gongger test site located in Inner Mongolia, which is a flat and uniform area. The reflectance-based method was used for calibration of MODIS visible and near-infrared bands. In situ measurements of surface and atmospheric conditions were carried out. By computing the surface reflectance of the site, it was concluded that the site was appropriate for calibration because of its stable and uniform characteristics. These data were then inputted to a radiative transfer code, 6S, to compute top-of- atmosphere (TOA) radiances and TOA reflectances, which were compared with the MODIS on-board calibration results. The in situ estimated results were in good agreement with the MODIS on-board calibration results on May 31 with the variations about 2%, while vicarious calibration results on May 29 were slightly inconsistent with those of on-board calibration, whose differences were about 7%.