# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 5 Cryosphere Applications
### Sea Ice and Snow over Sea Ice  
Using TDS-1 data, a number of studies [129; 130] successfully matched the incoherent returns from the ocean and the coherent returns from sea ice cover, based on different algorithms and theoretical models, to obtain ice-ocean returns. These results were compared to ice concentration maps derived from passive microwave sensors. Two studies showed advancements when using a neural network (NN) approach [131] and a convolutional neural network (CNN) approach [132] in TDS-1 measurements for sea ice detection and concentration prediction. The CNN approach improved the accuracy of estimations and simplified the network training. The three studies had success rates of over 95% in identifying sea ice from the ocean. Due to increased interest in bistatic radar reflections for polar regions and successful results from these studies, a mission concept was developed to derive thin ice thickness using phase information from collected raw data, which are complementary to thickness derivations from the L-band SMOS mission [57; 133].  
A study conducted over the Arctic Sea [134] used GNSS-R data from the TDS-1 mission to classify sea ice types during the sea ice formation period. This study, which focused on the fall period of October 2015 in the Beaufort and Chukchi seas region, examined considerable expanses of young ice, first-year ice (FYI), and multi-year ice (MYI). The authors implemented a sea ice multi-step classification, and the results were validated against SAR-derived sea ice-type maps produced at the US National Ice Center. The sea ice type classification results showed that the L-band GNSS bistatic radar signals were highly sensitive to the different surface scattering properties of primary ice types, and identified FYI, MYI, and young ice with success rates of 70%, 82%, and 81%, respectively. The research described in [12] utilized ground-based GNSS-R data to retrieve thickness measurements of snow and ice during the FSSCat mission calibration and validation efforts and the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) polar expedition. The study employed a four-layer model to simulate the interference pattern created by combining the GNSS direct and reflected signals over the sea ice. The findings indicated that multi-angular and multifrequency data could effectively retrieve measurements of sea ice and snow thickness and resolve thickness ambiguities. In [135], a study demonstrated the use of GNSS interferometric reflections to observe long-term snow height variations in Antarctica. The authors utilized eight antennas from the Polar Earth Observing Network (POLENET) and the ROB1 antenna, which was deployed by the Royal Observatory of Belgium in the eastern part of Antarctica. The study reports a decrease of more than 4 m in snow height on the Flask Glacier in the Antarctic Peninsula between 2012 and 2014, with an uncertainty of 2.5 m. On the Lower Thwaites Glacier, the authors observed a snow surface drop of 10 m, with a conservative uncertainty of 1 m, between 2010 and 2020. Lastly, on the West Antarctic Ice Sheet (WAIS) divide, the authors noted an upward motion of 1.2 m with an uncertainty of 0.4 m from 2005 to 2019. In [136], researchers utilized neural networks to create an algorithm for sea ice concentration and extent by combining L-band microwave data and GNSS-R data from the FSSCat mission in both the Arctic and Antarctic oceans. The accuracy of the algorithm was compared to that of the OSI SAF products, and the sea ice extent map's overall accuracy was found to be over 97% using L-band microwave data, and up to 99% when using both GNSS-R and L-band microwave data. For sea ice concentration, the absolute errors were below 5% when using L-band microwave data and below 3% after combining it with GNSS-R data. The total extent computed area using this methodology was similar, with a difference of 2.5%, to those calculated using well-established algorithms such as OSI SAF or NSIDC.