A high-spatial-resolution hyperspectral image (HR-HSI) can be obtained by fusing a hyperspectral image and a multispectral image (HSI-MSI) since it takes the advantage of combing the low-spatial-resolution hyperspectral image (LR-HSI) and the high-spatial-resolution multispectral image (HR-MSI). Currently, most HSI-MSI fusion methods decompose HSIs in both spatial and spectral domains, which destroys the correlation of the two domains and results in poor fusion results. To further explore the correlation of spatial and spectral information, an unsupervised blind spectral-spatial cross-super-resolution network (CSRNet) is proposed for HSI-MSI fusion. The network transforms the fusion into two super-resolution reconstructions for a HR-HSI to avoid the decomposition of HSIs and achieves interaction with the two reconstructions to provide strong constraints for each other. The network uses the following procedure. First, the HSI-MSI fusion is transformed into a spectral-spatial cross-super-resolution model, which consists two branches: one for spatial and the other for spectral super-resolution reconstructions. As the basic blocks of the two branches, spatial super-resolution (SpaSR) and the spectral super-resolution (SpeSR) blocks in the new network are designed via iterations unfolding from half quadratic splitting (HQS). Then their corresponding spatial constraint (SpaC) and spectral constraint (SpeC) modules are established for the mutual constraints and interactions between the two branches. The SpaSR/SpeSR and SpaC/SpeC modules are alternately connected to form the above spatial/spectral super-resolution branches for reconstructing a HR-HSI. Both visual and quantitative results of experiments based on both simulated and real observation datasets showed that the proposed method outperformed seven commonly used methods, suggesting that the new method is effective for preserving the correlation of spatial and spectral information in HR-HSIs.

The multi-global navigation satellite system (GNSS) undifferenced and uncombined precise point positioning (UU-PPP), as a high-precision ionospheric observables extraction technology superior to the traditional carrier-to-code leveling (CCL) method, has received increasing attention. In previous research, only dual-frequency (DF) or multi-frequency (MF) observations are used to extract slant ionospheric delay with the UU-PPP. To reduce the cost of ionospheric modeling, the feasibility of extracting ionospheric observables from the multi-GNSS single-frequency (SF) UU-PPP was investigated in this study. Meanwhile, the between-satellite single-differenced (SD) method was applied to remove the effects of the receiver differential code bias (DCB) with short-term time-varying characteristics in regional ionospheric modeling. In the assessment of the regional real-time (RT) between-satellite SD ionospheric model, the internal accord accuracy of the SD ionospheric delay can be better than 0.5 TECU, and its external accord accuracy within 1.0 TECU is significantly superior to three global RT ionospheric models. With the introduction of the proposed SD ionospheric model into the multi-GNSS kinematic RT SF-PPP, the initialization speed of vertical positioning errors can be improved by 21.3% in comparison with the GRAPHIC (GRoup And PHase Ionospheric Correction) SF-PPP model. After reinitialization, both horizontal and vertical positioning errors of the SD ionospheric constrained (IC) SF-PPP can be maintained within 0.2 m. This proves that the proposed SDIC SF-PPP model can enhance the continuity and stability of kinematic positioning in the case of some GNSS signals missing or blocked. Compared with the GRAPHIC SF-PPP, the horizontal positioning accuracy of the SDIC SF-PPP in kinematic mode can be improved by 37.9%, but its vertical positioning accuracy may be decreased. Overall, the 3D positioning accuracy of the SD ionospheric-constrained RT SF-PPP can be better than 0.3 m.

The 2025 destructive Myanmar earthquake ruptured the previously recognized seismic gap along the Sagaing fault, with the longest ever recorded surface ruptures (500 km). Here, we use SAR and optical images to characterize coseismic and early postseismic deformation of this event. North‐south displacements delineate the extent of surface ruptures, which are confined between historically ruptured regions, supported by dramatic slip reductions along the strike near the two rupture terminations. Coseismic slip model suggests two high‐slip zones of distinct sizes separated by a low‐slip zone. Coseismic slip has peak values at the surface almost all along the rupture, suggesting an absence of shallow slip deficits. Early postseismic deformation over 1 month after the event is obtained by optical horizontal offsets. Rapid shallow afterslip (5 cm/day) is only found along the southern segment, with some spatial overlap with coseismically slipped zones indicating frictional heterogeneity of the Sagaing fault.

For high spatiotemporal resolution global navigation satellite system interferometric reflectometry (GNSS-IR) snow depth monitoring, addressing terrain effects and multi-GNSS data fusion together is essential due to their coupling. Analyzed multi-GNSS spatiotemporal availability to ensure coverage and revisit rate. Improved data usage and inversion accuracy through Complete Ensemble Empirical Mode Decomposition (CEEMD). Corrected anisotropic terrain errors using DEM to account for varied reflection footprints. A grid was established for data partitioning and fusion. Considering inter-system errors, the proposed signal peak ratio weighting (PRW) fusion of single-system inversions is based on signal quality. Then, by using inverse distance weighting, the multi-GNSS results were fused, achieving high-accuracy, hourly snow depth inversions with high spatial resolution. With terrain correction, the Correlation Coefficient (R) reached 0.984, Root Mean Square Error (RMSE) 0.136m, and Mean Error (ME) -0.060m, reduced by 9.05% and 24.84%. PRW further enhanced accuracy, increasing R to 0.985, reducing RMSE 14.6% to 0.128m, improving ME 40.57% to -0.047m. Grid fusion effectively integrated multi-GNSS data, showing daily R 0.865, RMSE 0.102m, ME -0.050m. Across season, R 0.984, RMSE 0.134m, ME -0.065m. Compared to equal weighting, R improved 4.72% and 3.05%, RMSE reduced 26.09% and 14.10%, ME decreased 28.57% and 16.44%. Hourly results achieved 94.44% coverage, averaging 5.19 usable tracks, demonstrating effectiveness. Overall, this paper presents an end-to-end solution for high spatiotemporal resolution snow depth inversion using GNSS-IR, and the methodology can be extended to other geophysical parameter retrievals.