The spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler map (DDM) data collected over ocean carry typical feature information about the ocean surface, which may be covered by open water, mixed water/ice, complete ice, etc. A new method based on Doppler spread analysis is proposed to remotely sense sea ice using the spaceborne GNSS-R data collected over the Northern and Southern Hemispheres. In order to extract useful information from DDM, three delay waveforms are defined and utilized. The delay waveform without Doppler shift is defined as central delay waveform (CDW), while the integration of delay waveforms of 20 different Doppler shift values is defined as integrated delay waveform (IDW). The differential waveform between normalized CDW (NCDW) and normalized IDW (NIDW) is defined as differential delay waveform (DDW), which is a new observable used to describe the difference between NCDW and NIDW, which have different Doppler spread characteristics. The difference is mainly caused by the roughness of reflected surface. First, a new data quality control method is proposed based on the standard deviation and root-mean-square error (RMSE) of the first 48 bins of DDW. Then, several different observables derived from NCDW, NIDW, and DDW are applied to distinguish sea ice from water based on their probability density function. Through validating against sea ice edge data from the Ocean and Sea Ice Satellite Application Facility, the trailing edge waveform summation of DDW achieves the best results, and its probabilities of successful detection are 98.22% and 96.65%, respectively, in the Northern and Southern Hemispheres.

The Sikkim–Bhutan seismic gap has witnessed a long earthquake quiescence since the 1714 M7.5~8.5 earthquake. The state of stress accumulation beneath the Sikkim–Bhutan Himalaya and its spatial correlation with seismicity remains unclear due to the lack of geodetic measurements and the low levels of seismic activity. We compile Global Positioning System (GPS) measurements in southern Tibet with the available velocities in the Sikkim–Bhutan Himalaya to reveal the characteristics of strain buildup on the Main Himalayan Thrust (MHT). We correct non-tectonic hydrological loading effects in a GPS time series to accurately determine the Three-Dimensional (3D) velocities of each continuous station. Extensive GPS measurements yield convergence rates of 16.2~18.5 mm/y across the Sikkim–Bhutan Himalaya, which is quite consistent with that observed elsewhere in the Himalaya. Based on a double-ramp structure of the MHT, a refined 3D coupling image is inverted using a dense network of GPS velocities. The result indicates significant along-strike variations of fault coupling beneath the Sikkim–Bhutan Himalaya. The locking width (coupling > 0.5) of western Bhutan reaches ~100 km, which is 30~40% wider than Sikkim and eastern Bhutan. An obvious embayment of decoupling zone near the border between Sikkim and western Bhutan is recognized, and coincides spatially with the rupture terminates of the 1934 Mw8.2 and the 1714 M7.5~8.5 earthquakes, indicating that the large megathrust earthquakes along the Sikkim–Bhutan Himalaya are largely segmented by the spatial variation of frictional properties on the MHT. Using a new compilation of seismic records in the Sikkim–Bhutan Himalaya, we analyze the spatial correlation between fault coupling and seismic activity. The result suggests that the seismicity in the Bhutan Himalaya is broadly distributed, instead of restricted in the lower edge of the interseismic locking zone. This implies that the seismic activity in the Bhutan Himalaya is not uniquely controlled by the stress accumulation at the downdip end of the locked portion of the MHT.

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The 2020 Mw 6.5 Monte Cristo earthquake occurred in the northeastern Mina deflection of the central Walker Lane Belt (WLB) in Nevada, USA. The Mina deflection represents a typical stepover zone that transfers the dextral slip in the northern Eastern California Shear Zone onto the dextral faults in the central WLB. The Monte Cristo earthquake provides a rare opportunity to investigate the strain accumulation and stress transition mechanisms in the WLB. In this study, ascending and descending Sentinel‐1 images were utilized to generate coseismic and early postseismic deformations associated with this earthquake. Combined with global positioning system measurements, these images were inverted for the coseismic slip and afterslip of the Monte Cristo earthquake. The preferred coseismic slip model suggests that the causative fault is characterized by two fault segments with southward dips of 64° and 79°. The coseismic rupture was dominated by sinistral slip with obvious normal slip components in the western segment of the source fault. The coseismic slip was mainly concentrated in the 3–12 km depth range and decreased at shallower depths, suggesting a moderate amount of shallow coseismic slip deficit. Rapid afterslip was mainly confined to the 0–3 km depth range, largely compensating for the shallow slip deficit caused by the mainshock. The widely distributed normal slips in the northeastern Mina deflection revealed by the Monte Cristo earthquake suggest the transtensional model is more applicable due to its ability to account for the slip transition kinematics in the Mina deflection.

Two effective machine learning-aided sea ice monitoring methods are investigated using 42 months of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data collected by the TechDemoSat-1 (TDS-1). The two-dimensional delay waveforms with different Doppler spread characteristics are applied to extract six features, which are combined to monitor sea ice using the decision tree (DT) and random forest (RF) algorithms. Firstly, the feature sequences are used as input variables and sea ice concentration (SIC) data from the Advanced Microwave Space Radiometer-2 (AMSR-2) are applied as targeted output to train the sea ice monitoring model. Hereafter, the performance of the proposed method is evaluated through comparing with the sea ice edge (SIE) data from the Special Sensor Microwave Imager Sounder (SSMIS) data. The DT- and RF-based methods achieve an overall accuracy of 97.51% and 98.03%, respectively, in the Arctic region and 95.46% and 95.96%, respectively, in the Antarctic region. The DT- and RF-based methods achieve similar accuracies, while the Kappa coefficient of RF-based approach is slightly larger than that of the DT-based approach, which indicates that the RF-based method outperforms the DT-based method. The results show the potential of monitoring sea ice using machine learning-aided GNSS-R approaches.