Signals from global navigation satellite systems (GNSS) can be utilized as signals of opportunity in remote sensing applications. Geophysical properties of the earth surface can be detected and monitored by processing the back-scattered GNSS signals from the ground. In the literature, several airborne GNSS-based passive radar experiments have been successfully demonstrated. With the advancements in small unmanned aerial vehicles (UAVs) and their applications for environmental monitoring, we want to investigate whether GNSS-based passive radar can provide valuable geospatial information from such platforms. Low-cost GNSS reflectometry sensors, developed using commercial of the shelf components, can be mounted onboard UAVs and flown to sense environmental parameters. This paper presents the results of a preliminary study to investigate the feasibility of utilizing data collected by UAV-based GNSS-R sensors to detect surface water for a potential application in supporting flood monitoring operations. The study was conducted in the area surrounding the Avigliana lakes in Northern Italy. The results show the possibility of detecting small water surfaces with few tens of meters resolution, and estimating the area of the lake surface with 92% accuracy. Furthermore, it is proved through simulations that the use of multi-GNSS increases this accuracy to about 99%.

GNSS Reflectometry (GNSS-R) is a consolidated remote sensing technique that exploits the back-scattered GNSS signals to retrieve information about the Earth surface. By the use of GNSS-R sensors onboard UAVs, this tool can bring significant advantages in agriculture, especially for the detection of the presence of water/moisure in specific rural areas. However, to perform this detection, GNSS-R needs a priori calibration of a threshold on the received reflected power in order to distinguish the presence/no prence of water. This is a limitation for the adoption of this approach at large scale. The work presented in this paper aims at overcoming such a limitation, proposing a novel approach based on artificial intelligence for the automatic water detection on the Earth surface, avoiding a priori, empirical, thresholding.

Signals transmitted by Global Navigation Satellite Systems can be exploited as signals of opportunity for remote sensing applications. Satellites can be seen as spread sources of electromagnetic radiation, whose signals reflected back from ground can be processed to detect and monitor geophysical properties of the Earths surface. In the past years, several experiments of GNSS-based passive radars have been demonstrated successfully, mainly from piloted aircraft. Then, the proliferation of small UAVs enabled new applications where GNSS-based passive radars can provide useful geospatial information for environmental monitoring. Thanks to the availability of commercial Radio Frequency front ends and the enhanced processing capabilities of embedded platforms, it is possible to develop GNSS-based passive radars at moderated cost. These can be mounted on Unmanned Aerial Vehicles, and be used to support the sensing of environmental parameters. This paper presents the results of an experimental campaign based on the use of a UAV for GNSS reflectometry, tailored to the detection of the presence of water on ground after floods. The work is part of wider project, which intends to develop solutions to support rescuers and decision makers to manage operations after natural disasters, through the integration and modelling of geospatial data coming from multiple sources.

Ionospheric scintillations, which usually manifest as sudden, rapid fluctuations in radio wave signal phase and amplitude, challenge the reliability of satellite communication and navigation. Based on the single phase screen assumption, this study uses the one‐dimensional (1D) in‐situ plasma density data of ESA's Swarm constellation data to develop a three‐dimensional (3D) power spectrum of electron density perturbation and construct a model to estimate scintillations caused by small‐scale ionospheric plasma density irregularities. By deriving the turbulence strength () and calculating the amplitude scintillation index S₄, the global distribution of ionospheric scintillation is derived. Scintillation from our model shows typical seasonal variations, with peaks during equinoxes at both high and low magnetic latitudes. For local time (LT) dependence, the scintillation at low magnetic latitudes peaks around 21:00 LT, while at high magnetic latitudes, the maximum occurrence appears around noon, with an asymmetry between the northern and southern hemispheres. In addition, positive correlations between scintillation occurrence and solar activity, as well as geomagnetic storms are observed, with higher magnetic latitudes more being affected by geomagnetic disturbances. These features of our model‐estimated scintillations agree well with the occurrence of small‐scale plasma density irregularities at different magnetic latitudes as reported by previous studies. Our study introduces a way to estimate the global coverage of ionospheric scintillation from in‐situ satellite measurements, which cannot be achieved by the ground‐based GNSS networks due to the lack of coverage in the ocean regions.

Software-Defined Radio (SDR) Global Navigation Satellite System (GNSS) receivers for operational Ionospheric Scintillation Monitoring (ISM) have faced challenges and were deemed failures by many research projects. The need for a highly stable oscillator and a robust real-time signal-tracking capability have been the main challenges. The Istituto Nazionale di Geofisica e Vulcanologia (INGV) led a project called DemoGRAPE (Demonstrator of GNSS Research and Application for Polar Environment) to deploy an SDR GNSS Receiver in SANAE IV Antarctica station, which has been continuously operating since January 2016. The SDR receiver was designed by the Navigation Signal Analysis and Simulation (NavSAS) group in collaboration with the Joint Research Centre (JRC) of the European Commission. The system deploys the SDR in parallel to a Septentrio PolaRxS ISM receiver. The two receivers are fed by the same receiving antenna (i.e., they share the same field of view and, hence, cross the same portion of the ionosphere) such that they receive quasi-identical signals except for the independent effects of their front-ends. The SDR produces as output a log file, equivalent to the ISM record produced by PolaRxS, that contains the amplitude and phase scintillation indexes. In addition to this, the SDR system records the raw digital samples of the GNSS signal when the internal algorithm of the receiver detects scintillation presence. These data can be used to replicate the scintillation event in the laboratory and perform dedicated post-processing of the raw signal itself. The system has been in operation for over seven years now, spanning more than half a solar cycle. It has recorded several geomagnetic storm events as well as abundant data in quiet conditions. To the best of our knowledge, this is the longest dataset of this nature (co-located PolaRxS and SDR receivers). We compare the performance of the two receivers by analyzing the scintillation climatology maps obtainable from both receivers' data. Here, the climatology is obtained using the well-established Ground Based Scintillation Climatology (GBSC) technique from the data provided by the two different receivers. The study shows that the SDR climatology maps under disturbed geomagnetic conditions are equivalent to those obtained from the PolaRxS receiver. Under quiet conditions, the SDR shows more scintillation events than the PolaRxS. Carefully inspecting the differences in the scintillation occurrence between the two receivers, they seem not to happen randomly but mainly concentrated along the expected (climatological) position of the auroral oval. This means that the SDR application for space weather monitoring could be possible and that the SDR could be utilizable as an informative tool by the ionospheric scintillation community.