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 article 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 m 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 (Cir Cor) 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 multifeature 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.

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. Comparing 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.

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. 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 canceling 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 canceling 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 in 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 canceling 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 AF-FOV (EAF-FOV) in Earth’s remote sensing, as expected.

The use of delay-Doppler maps (DDMs) measured by Global Navigation Satellite System Reflectometry (GNSS-R) for target detection and location is a hot issue because of the potentially global coverage and short revisit periods of GNSS-R satellite missions. Existing researches have explored the detection of oil rigs, oil spills, sea ice, and other targets with spaceborne GNSS-R. However, these researches may not fully consider the advantages of multiple GNSS-R satellites, for example, Cyclone Global Navigation Satellite System (CYGNSS) which has eight satellites. Two problems of using multiple GNSS-R satellites for detection and location are how to estimate the sea clutter that is used to cancel the clutter in DDMs and how to remove location ambiguity caused by the transformation from the delay-Doppler (DD) domain to the spatial domain. In this article, an oil rig is taken as an example for detection and location with multiple GNSS-R satellites. To distinguish the oil rig from sea clutter, this article uses subspace projection to estimate the clutter and subtract the estimation from the DDMs. Moreover, after the clutter cancellation, this article projects the potential oil rig coordinates in the DD domain into a uniform coordinate system and removes the location ambiguity based on multisatellites fusion. The measured DDMs collected by CYGNSS are employed to conduct experiments to validate the feasibility of the proposed methods for detection and location with DDMs. By subtracting the estimated sea clutter from DDMs, the potential oil rig can be indicated in the DD domain. Two location experiments can calculate the unambiguous coordinate of the oil rig with a deviation less than 4 km.

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.

Beidou geostationary earth orbit (GEO) satellite-based global navigation satellite system reflectometry (GNSS-R) technique has been developed for measuring surface deformation in a carrier-phase-based, cost-effective, and continuous manner. However, several improper assumptions in the original system and method, such as neglecting the slight GEO satellite movement during angle determination and deeming inter-channel phase difference as time-invariant, can introduce certain errors in estimating the surface deformation. In this work, these issues were analyzed and improved for more accurate estimations. Utilizing a Wilkinson power divider, the inter-channel phase error is estimated and calibrated with direct signal in the master channel. The GEO-motion phase error is theoretically modelled and compensated for based on precise ephemeris. A compensating algorithm of GNSS-R deformation retrieval technique utilizing Beidou GEO satellites is presented, by which encouraging results are yielded from field experiments emulating long-term monitoring. On artificial targets, the retrieval root mean square (RMSE) is better than 8 mm.

In this letter, we proposed a method based on cyclone global navigation satellite system (CYGNSS) for improving the accuracy of soil moisture (SM) estimation through the selection of auxiliary data and a four-step quality control. We investigate the impact of elevation and vegetation, as well as evaluating the quality of Doppler delay map (DDM) and different ground terrains. The program adopts the support vector machine (SVM) algorithm, input CYGNSS, and auxiliary data. With the hourly SM data on Wuhan Baoxie site from January 2020 to August 2020 as an example, the effectiveness of quality control was verified. A substantial improvement in correlation coefficient of ~0.46 for average between CYGNSS reflectivity andin situSM was obtained compared with ~0.03 before quality control, resulting in better SM estimation accuracy compared with that ofin situmeasurements (from$R = 0.67$to 0.87).

The Sensor Web is a coordinated observation infrastructure composed of distributed resources that can behave as a single, autonomous, task-able, reconfigurable observing system that provides observed and derived data along with the associated metadata by using a set of standards-based service oriented interfaces. But these resources, including sensor, data, platform etc., with different characteristics are hard to be fused well. This paper analyzes concepts of various types of sensor web resources for atmospheric observing, focusing on how to access different types of resources expediently, abstract essential features of these sensor web resources, and construct a Sensor Web Resources Ontology for Atmospheric Observation (SWRO-AO) represented by Web Ontology Language. The SWRO-AO could serve as a knowledge repository of sensor web resources for the research and application community in atmospheric science.

Bi-static remote sensing with GPS signal (direct and reflect signals) has supported some researches in soil moisture measuring, sea state estimating and sea altimetry monitoring. In these researches the special designed receivers are generally required, such as the "delay-mapping" GPS receiver or open loop differential real-time receiver, because they can find the characteristics of the reflected electromagnetic wave affected by the object. Now the commercial single frequency GPS receiver becomes more lighter, smaller and lower power, it is potentially capable of sea remote sensing. However it is ignored. In this paper, a ground based GPS-R technology with single frequency receiver is introduced. The technology is based on both the scatter theory from the rough surface and the GPS double difference theory of carrier phase. This paper describes the GPS-R instrument, introduces the basic method and shows the result of GPS-R experiments on bridge. These results provide good initial conditions for widespread sea state monitoring if using GPS-R receiver network . this paper also discusses the results under the influence of the satellite elevation and single difference .