The number of Android devices enabling access to raw GNSS (Global Navigation Satellite System) measurements is rapidly increasing, thanks to the dedicated Google APIs. In this study, the Xiaomi Mi8, the first GNSS dual-frequency smartphone embedded with the Broadcom BCM47755 GNSS chipset, was employed by leveraging the features of L5/E5a observations in addition to the traditional L1/E1 observations. The aim of this paper is to present two different smartphone applications in Geoscience, both based on the variometric approach and able to work in real time. In particular, tests using both VADASE (Variometric Approach for Displacement Analysis Stand-alone Engine) to retrieve the 3D velocity of a stand-alone receiver in real-time, and VARION (Variometric Approach for Real-Time Ionosphere Observations) algorithms, able to reconstruct real-time sTEC (slant total electron content) variations, were carried out. The results demonstrate the contribution that mass-market devices can offer to the geosciences. In detail, the noise level obtained with VADASE in a static scenario—few mm/s for the horizontal components and around 1 cm/s for the vertical component—underlines the possibility, confirmed from kinematic tests, of detecting fast movements such as periodic oscillations caused by earthquakes. VARION results indicate that the noise level can be brought back to that of geodetic receivers, making the Xiaomi Mi8 suitable for real-time ionosphere monitoring.

The release of Android GNSS Raw Measurements API, (2016) and the growing technological development introduced by the use of multi-GNSS and multi-frequency GNSS chipsets – changed the hierarchies within the GNSS mass-market world. In this sense, Android smartphones became the new leading products. Positioning performances and quality of raw GNSS measurements have been studied extensively. Despite the greater susceptibility to multipath and cycle slip due to the low cost antenna used, a positioning up to sub-meter accuracy can be achieved. Among the improvements in positioning and navigation, the availability of GNSS measurements from Android smartphones paved new ways in geophysical applications: e.g. periodic fast movements reconstruction and ionospheric perturbances detection.  In fact, considering the number of Android smartphones compatible with the Google API, additional costless information can be used to densify the actual networks of GNSS permanent stations used to monitor atmospheric conditions. However, an extensively analysis on the reconstruction of ionospheric conditions with Android raw measurements is necessary to prove the accuracy achievable in future ionosphere monitoring networks based on both permanent GNSS station and Android smartphone.The aim of this work is to assess the performance of multi-frequency and multi-GNSS smartphone – in particular, Xiaomi Mi 8 and Huawei Mate 20 X – in the reconstruction of real-time sTEC (slant Total Electron Content) variations meaningful of ionospheric perturbations. A 24-hour dataset of 1Hz GNSS measurements in static conditions was collected from the two smartphones in addition to data collected from M0SE, one of the EUREF/IGS permanent stations. The VARION (Variometric Approach for Real-time Ionosphere Observations) algorithm, based on the variometric approach and developed within the Geodesy and Geomatics Division of Sapienza University of Rome, was used to retrieve sTEC variations for all the observation periods.The results, although preliminary, show that it is possible to study also from the smarthphone the trend of sTEC variations with elevation: lower elevation angles cause noisier sTEC variations. RMSE of the order of 0.02 TECU/s are found for elevation angles higher than 20 degrees as it happens for permanent stations. At the same time, the sTEC variations were compared to the overall measurements noise, due to both environmental and receiver noise, in order to statistically define the correlation between RMSE and derived sTEC variation.Although the results obtained in this work are encouraging, still further analyses need to be carried out especially at latitudes where ionosphere conditions and perturbations play a major role. However, the possibility to perform such analyses on datasets collected worldwide is prevented from their availability. The last part of this work is therefore focused on the identification of a methodology to share with the GNSS community to collect, store and share GNSS measurements from Android smartphones to enable the researchers to enlarge the spatial and temporal boundaries of their research in the field of ionosphere modelling with mass-market devices.