The recent access to GNSS (Global Navigation Satellite System) phase observations on smart devices, enabled by Google through its Android operating system, opens the possibility to apply precise positioning techniques using off-the-shelf, mass-market devices. The target of this work is to evaluate whether this is feasible, and which positioning accuracy can be achieved by relative positioning of the smart device with respect to a base station. Positioning of a Google/HTC Nexus 9 tablet was performed by means of batch least-squares adjustment of L1 phase double-differenced observations, using the open source goGPS software, over baselines ranging from approximately 10 m to 8 km, with respect to both physical (geodetic or low-cost) and virtual base stations. The same positioning procedure was applied also to a co-located u-blox low-cost receiver, to compare the performance between the receiver and antenna embedded in the Nexus 9 and a standard low-cost single-frequency receiver with external patch antenna. The results demonstrate that with a smart device providing raw GNSS phase observations, like the Nexus 9, it is possible to reach decimeter-level accuracy through rapid-static surveys, without phase ambiguity resolution. It is expected that sub-centimeter accuracy could be achieved, as demonstrated for the u-blox case, if integer phase ambiguities were correctly resolved.

The discontinuity surface between Earth crust and mantle, the so-called Moho, is commonly estimated by means of seismic or gravimetric methods. Usually these methods do not yield the same result since they are based on different geological and geophysical hypotheses, as well as different data types, also in terms of quality and spatial distribution. In particular, global crust models based only on seismic data (e.g. the CRUST2.0 model) can be locally very accurate since seismic profiles give an almost direct observation of the actual crust structure, but can be quite uninformative in large regions where no data are available or they are too inhomogeneous. On the contrary, when using satellite gravity observations like those provided by the ESA mission GOCE, information on the Moho can be inferred from a uniform and global data set. However, Moho models estimated by gravity data are in general characterized by simplified hypotheses to guarantee the uniqueness of the solution of the inverse gravitational problem. The aim of this work is to attenuate these drawbacks by combining the seismic global model CRUST2.0 with gravity observations from the GOCE satellite mission. More specifically, the used GOCE data are grid values at mean satellite altitude estimated by the so-called space-wise approach. After reducing the data to a two-layer model by removing the effect of topography, bathymetry and sediments, a combined inversion driven by a priori information on the CRUST2.0 accuracy and by the error covariance structure of the GOCE grids is performed. In addition, the observation errors as well as the error due to the data reduction are tentatively taken into account to estimate the accuracy of the final Moho model. The result is an update of the CRUST2.0 Moho model with a 0.5° × 0.5° resolution, which at the same time contains seismic and geological information and it is consistent, at 20 mE level, to the actually observed gravity field. A first comparison with the CRUST2.0 Moho shows that in the continental crust the mean difference between the two models is of the order of 1.5 km with standard deviations depending on the considered region. As expected, the main variations (standard deviation of the order of 7 km) are located in South America, Africa and Antarctica where very few data in the CRUST2.0 were originally used. In the rest of the world, differences have a standard deviation of about 4 km. As for the oceanic crust, it can be noted that the corrections to the CRUST2.0 model are of the order of 3 km (mean value) with a standard deviation of 6 km. Finally, the solution computed in this paper has been compared with a set of Moho models at different scales from global to local ones showing that it is reasonably consistent (differences of about 5 km standard deviation) also with seismic observations.

Flight height is a fundamental parameter for correcting the gamma signal produced by terrestrial radionuclides measured during airborne surveys. The frontiers of radiometric measurements with UAV require light and accurate altimeters flying at some 10 m from the ground. We equipped an aircraft with seven altimetric sensors (three low-cost GNSS receivers, one inertial measurement unit, one radar altimeter and two barometers) and analyzed ~3 h of data collected over the sea in the (35–2194) m altitude range. At low altitudes (H < 70 m) radar and barometric altimeters provide the best performances, while GNSS data are used only for barometer calibration as they are affected by a large noise due to the multipath from the sea. The ~1 m median standard deviation at 50 m altitude affects the estimation of the ground radioisotope abundances with an uncertainty less than 1.3%. The GNSS double-difference post-processing enhanced significantly the data quality for H > 80 m in terms of both altitude median standard deviation and agreement between the reconstructed and measured GPS antennas distances. Flying at 100 m the estimated uncertainty on the ground total activity due to the uncertainty on the flight height is of the order of 2%.