Songhua Yan

Airborne Global Navigation Satellite System-Reflectometry (GNSS-R) offers high-resolution observations but is constrained by time-varying satellite geometries, and currently lacks dedicated trajectory planning methods. To address this, this letter proposes a multi-objective trajectory planning method to balance the trade-off between coverage rate (CR) and flight cost. A trajectory optimization model is constructed by classifying flight parameters, where mission start time, track spacing, and flight speed are optimized using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) under cost constraints. To address computational challenges, efficient strategies such as satellite filtering and look-up table approximations are introduced, reducing processing time by over 90%. Validated against field data from the Rongowai mission over Lake Taupo, New Zealand, the optimized trajectory achieves a 96.6% CR within a single 130-minute mission. This significantly outperforms the 34% CR obtained from a 14-day historical campaign, demonstrating the proposed method's efficiency and practical value.

Soil moisture (SM) is one of the important driving factors for promoting photosynthesis of vegetation to produce organic carbon. Global satellite navigation system reflectometry (GNSS-R) has considerable potential in monitoring regional SM due to its penetrability and high temporal resolution. However, the existing researches have two problems: 1) the GNSS-R SM retrieval methods lack the uncertainty quantification for results; 2) the application potential of the retrieved GNSS-R SM has not been evaluated from the perspective of agricultural production. The core scientific basis for this evaluation is the tight biogeochemical coupling mechanism existing among SM, vegetation (crops grown in plain) physiology and atmospheric exchange (i.e., soil-plant-atmosphere continuum), which forms a quantitatively describable response relationship. This paper first proposes a Natural Gradient Boosting (NGBoost)-based framework to quantify the uncertainty and feed it back to improv the accuracy of retrieved results. Secondly, gross primary productivity (GPP) and evapotranspiration (ET) are used to conduct the potential evaluation of the retrieved GNSS-R SM. Compared with the frameworks based on the existing advanced uncertainty quantification techniques, such as Monte Carlo (MC) dropout-based and Deep Ensembles-based frameworks, the correlation (R) is improved by 4% 5%, the root mean square error (RMSE) is reduce by 17% 25%, and the uncertainty is decreased by 21% 25%. Compared with the official SM released by Cyclone GNSS, the R is increased by 0.2 and the RMSE is decreased by 0.01 cm3/cm3. From the responses of SM to GPP and ET, the retrieved SM has better response (9% 17% enhancement) to vegetation productivity compared with that of other frameworks. Meanwhile, compared with the responses of other remote sensing SM data to vegetation productivity, such as Soil Moisture Active and Passive, Climate Change Initiative, and the official SM released by Cyclone GNSS, R has increased by 20% to 60%. The results shows that the proposed framework can improve the accuracy of GNSS-R SM retrieval by quantifying uncertainty and the retrieved GNSS-R SM can better reflect the agricultural production. In addition, by analyzing the importance of physical parameters in the GNSS-R SM retrieval, we conclude that the signal reflectivity under different roughness conditions should be considered at the regional scale.

Changes in wetlands, rivers, and other water resources significantly impact the environment and economy, highlighting the need for their real-time, high-accuracy detection. Current GNSS reflectometry methods use reflected signal amplitude for water body detection, ensuring near real-time capability due to the high revisit period of source satellites. However, these methods face challenges in distance resolution due to limited signal features. This paper proposes a method utilizing CYGNSS raw intermediate frequency (IF) data for multidimensional analyses, including amplitude, time delay, phase, and frequency, to improve water body discrimination. The high sampling rate of IF raw data enables a minimum interval of 3 meters between sampling points, enhancing distance resolution. The preprocessing of raw IF data involves creating complex correlation matrices and applying three compensation methods: direct signal delay, reflected signal frequency, and doppler drift correction. Features such as amplitude, delay, phase, and frequency are extracted from the matrices using techniques like complex amplitude calculation, peak localization, phase extraction, and wavelet transforms. Various discrimination methods, including threshold, normalized variance, Z score, variance, and circular correlation discriminations, are used based on these features. The fusion of these indicators provides the final water body discrimination results. Case studies in the Amazon River area show that this multi-feature method achieves high discrimination accuracy (~90%) and provides high temporal resolution (20 Hz). This approach offers high precision in water body retrieval, particularly for smaller water bodies, reducing the distance resolution from the kilometer level to the meter level.

Researches have demonstrated that Passive interferometric microwave sensors (PIMSs) can be used in the imagery acquired and target detections, especially in low visibility and/or high sea clutter environments. The target detection by PIMSs is based on the high contrast against the background in the brightness temperature (TB) maps, and it is found that the targets still exhibit a high contrast within the alias field of view (FOV) of the background cancellation TB maps obtained by a background cancelling method in the target detection algorithm. As a result, the targets can also be detected within the alias FOV of PIMSs, and PIMSs exhibit a wider FOV for target detection than that for Earth’s remote sensing. However, the effective FOV of PIMSs has never been studied for target detection before. In this article, the effective FOV of PIMSs is first studied for target detection. The effective FOV of PIMSs is analyzed and discussed for target detection in theory, which is based on the FOV of PIMSs for Earth’s remote sensing and the background cancelling method. Numerical simulations are performed to analyze the effective FOV of PIMSs in terms of the metallic with a “cold” TB characteristic and the island with a “hot” TB characteristic. A series of experiments have been performed to verify the effective FOV of the PIMSs for target detection that ships, islands, and aircrafts. Both numerical and experimental results have demonstrated that the effective FOV of the PIMSs for target detection based on the background cancelling method can be extended to the central hexagonal regions (H), and it exhibits a wider FOV than alias-free FOV (AF-FOV) and/or Extended alias-free FOV (EAF-FOV) in Earth’s remote sensing, as expected.

The use of global navigation satellite system reflectometry (GNSS-R) to detect and locate maritime targets is currently attracting considerable attention from researchers. However, some difficulties in detecting and locating maritime targets based on GNSS-R remain. First, targets are difficult to detect due to the influence of sea clutter; second, the low resolution of the delay-Doppler Map (DDM) leads to low location accuracy. For maritime target detection, this study proposes a DDM symmetry-based target detection algorithm that can be used to supplement previous target detection methods. For location, this study proposes a maritime target location method using geometric semantic constraints based on spaceborne GNSS-R. Compare the results of direct location and linear semantic constraint location of the South China Sea No. 4 (SCS-4) shows that after using geometric semantic constraints, the absolute accuracy increased by an average of 2.73 km, and the relative accuracy increased by an average of 32.32%. This method can help improve the location accuracy of CYGNSS for maritime targets.

Airborne passive interferometric microwave sensors (PIMSs) were proposed as a powerful complementary tool for ship detection, especially in low visibility and high sea clutter environments. At present, there is only one algorithm for ship detection by airborne PIMSs, and this algorithm is only based on the “cold” brightness temperature (TB) characteristic of metal ships with wakes in passive interferometric microwave images (PIMIs). Moreover, it has been found that ship wakes always exhibit a “hot” TB characteristic in PIMIs. In this article, an improved algorithm is innovatively proposed for ship detection by airborne PIMSs to improve detection performance, and it is simultaneously based on the “cold” and “hot” TB characteristics of metal ships with wakes. The microwave TB model of metal ships with wakes is established to analyze its TB characteristics. Given the TB model and its characteristics, an improved algorithm is introduced, which comprises five steps: searching targets, improving TB maps, clustering targets, estimating ship headings, and estimating ship trajectories. The improved algorithm can detect ships with a low false-alarm probability (FAR) and accurately estimate ship headings using only a single snapshot and the ship trajectory. The practicability and detection performance of the improved algorithm is verified by airborne experiments using an X-band PIMS. The experimental results demonstrate that the improved algorithm can accurately detect ships, estimate ship headings using only a single snapshot, and estimate ship trajectories. The impact of the algorithm parameters on the detection performance is analyzed and discussed. Moreover, the detection performance of the improved algorithm is analyzed and compared with the performance of the only existing algorithm, and the experimental results indicate that the improved algorithm has a better detection performance with a lower FAR value than the only existing algorithm, as expected.

Owing to the global coverage and high visibility of global navigation satellite systems (GNSS), passive radar using GNSS satellites as transmitters of opportunity has shown great potential for moving target detection, and its possibility to detect short-range maritime targets has been verified. Nevertheless, the existing methods always require tens of seconds for echo integration, which is not ideal for the detection of targets with high maneuverability. To solve this problem, a bistatic configuration using BeiDou geostationary earth orbit (GEO) satellites as illuminators of opportunity, which is suited for short-time coherent integration of echo signals, is presented. In addition, the corresponding signal processing method is introduced. To achieve a more continuous trajectory of the target, a method used to fuse range-Doppler maps obtained individually from multiple BeiDou GEO satellites is also described. The effectiveness and functionality of our proposed algorithms are demonstrated with an experiment in which a ferry was used as a moving target and its real-time location data were adopted as a reference.