The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) has been operating continuously in orbit for nearly seven years. Its accuracy is crucial for ensuring the reliability of scientific applications. However, few external studies have been conducted to assess the long-term consistency of ICESat-2 elevation measurements. In this letter, we evaluate the consistency of elevation accuracy through footprint-level crossover observations. This approach first extracts crossovers by averaging elevations within each ~12m footprint, then analyzes their elevation differences using statistical and time-series approaches, and finally employs airborne LiDAR data for external validation. The results indicate that ICESat-2 elevation data exhibit excellent internal consistency over bare land areas from 2019 to 2024, with more than 40,000 footprint-level crossovers, a mean elevation bias of 0.02 m, and a standard deviation of 0.22 m. The long-term drift of the elevation data is approximately 1.1 mm/yr, well within the mission’s scientific requirement of 4 mm/yr. Compared with airborne LiDAR, ICESat-2 maintains high external accuracy over long-term observations, with an overall root mean square error less than 0.38 m across 377 beam tracks. Overall, this study provides new and independent assessment of the consistency of ICESat-2 elevation data to date.

Numerous studies have demonstrated the effectiveness of CYGNSS data for flood detection and mapping. However, the vast majority of the studies only focuses on flood extent detection, ignoring the importance of flood water levels in post-disaster relief. Meanwhile, most of the existing studies on inland water levels altimetry using CYGNSS data are based on the CYGNSS raw IF data using either the time-delay method or the phase method for altimetry. Although high accuracy can be obtained, at present the CYGNSS raw IF data is not a standard data, so it cannot be applied to the study of emergency flooding events. Given the above research gaps, this study proposes an effective method to combine CYGNSS L1 standard data with DTM for rapid estimation of flood water levels. The effectiveness of the proposed methodology is validated using the case of the 2022 Pakistan mega-flood. Comparison with ICESat-2 altimetry data gives encouraging results.

Interferometric synthetic aperture radar (InSAR) technology is the most advanced and effective method for monitoring large-scale slow-moving landslides. However, automatic landslide detection based on InSAR products regarding landslide samples and deep learning models is still challenging. Different InSAR products are inconsistent for slow-moving landslide detection due to the fuzzy boundaries of potential landslides and complex deformation characteristics. In addition, the accuracy of existing landslide detection models is not high because multiscale factors in feature abstract and feature fusion are rarely considered. This article proposes a multiscale Swin Transformer InSAR products detection network (MSIDNet) to detect slow-moving landslides automatically. First, we adopt an advanced Swin Transformer as the backbone to extract features and multiscale spatial-temporal attention blocks in the neck to improve the feature fusion capability. Meanwhile, to train the new model, we built a landslide dataset based on visual interpretation, including deformation rate, phase gradient, and C-index (GRCI). Experiments demonstrate that our proposed method outperforms the existing deep learning models such as Faster R-CNN and Yolov3. The GRCI dataset is more efficient and less biased than the traditional deformation rate and phase gradient datasets. The precision of detected landslides in a given testing area is 0.89, and it has good generalization ability. This study provides a new dataset and method for slow-moving landslide detection based on InSAR products.

Spaceborne global navigation satellite system reflectometry (GNSS-R) has shown potential for terrestrial applications. The feasibility of monitoring and mapping surface inundation using Cyclone Global Navigation Satellite Systems (CYGNSSs) has been demonstrated in literature. Nevertheless, most studies have only classified the surface into two states, inundated and noninundated, which do not meet the requirements of refined hydrological modeling. Here, we propose a new calculation flow to retrieve the surface water fraction (Wf) based on CYGNSS data by considering fractional and dynamic surface water monitoring for the first time. First, a newly proposed physics-based algorithm was utilized for coupling CYGNSS surface reflectivity (SR) and Soil Moisture Active Passive (SMAP) brightness temperature (Tb) to attenuate the effects of surface roughness and vegetation on CYGNSS observations. After removing the effects of surface roughness and vegetation, the SR was assumed to be equal to the sum of inundated SR and the noninundated SR multiplied by the corresponding fraction. Thus, Wf was obtained on a per-pixel basis. Next, Wf retrieval results were validated using the case of the catastrophic floods of 2022 in Pakistan. A comparison with the Wf obtained using SMAP Tb showed high spatial and temporal similarity; nevertheless, the CYGNSS results had a higher spatiotemporal resolution. A quantitative pixel-by-pixel comparison with the global flood monitoring (GFM) data derived from Sentinel-1 radar imagery, which had a finer resolution ($\sim $10 m) showed that the average overall accuracy of Wf retrieval was 64.44% and the average commission error was 17.78%. Finally, we quantified the impact of land use and land cover on the retrieval results while analyzing the advantages and limitations of the method. Thus, this study provides new insights for the future use of spaceborne GNSS-R data for monitoring inland water bodies.

Effective mitigation of atmospheric phase screen (APS) is crucial for synthetic aperture radar (SAR) interferometry in coastal regions. However, existing methods suffer from various limitations in estimating atmospheric delays, especially turbulent mixing components. As a solution, we propose a sparse-convolution-based attention deep residual U-shaped network (sARU-Net), a submanifold sparse convolutional neural network (SSCNN), for adaptive estimation of APS from InSAR coherent pixels. Moreover, we propose a straightforward and effective iterative processing strategy for generating sample datasets for model training. The results obtained from the TerraSAR-X and Sentinel-1 datasets in two coastal regions of China demonstrate a reduction in the standard deviation of the interferograms by 77.7% and 68.3%, respectively, after applying our correction method. In addition, the InSAR results align more closely with the leveling data. On the same dataset, our method shows superior performance compared with both the generic atmospheric correction online service (GACOS) for InSAR method and the linear model method. When compared with previous convolutional neural network (CNN), (i.e., attention-based deep residual U-shaped network, ARU-Net) method, our approach exhibits better accuracy and more detail and is 21% more computationally efficient for training, while requiring 24% less graphics processing unit (GPU) memory. The effectiveness of the proposed method shows that sparse convolution (SC) has great potential in mitigating the effects of APS in InSAR coherent pixels.

The Ice, Cloud and Land Elevation Satellite (ICESat-2) offers an unprecedented high density of laser data on the Earth’s surface. Evaluation and calibration of geolocation errors are important for the scientific application of laser data. In this study, a crossover method is proposed to evaluate and calibrate errors. This method mainly relies on small-scale reference data and consistency conditions at the trajectory crossovers. By matching the profile measurements of the ascending and descending altimeter tracks to find the location where the crossover discrepancy is minimized, the geolocation errors of the wide-area laser data are estimated. Three years of ICESat-2 measurements over the Ridgecrest (CA19) in the southern United States and McMurdo Dry Valleys (MDV) in eastern Antarctica were used to test the performance of the method. It was shown that the method can be used to estimate the geolocation error of the ICESat-2 laser data, which ranged from 4.65 m to 5.62 m for both sites. After calibration, the mean absolute error of the crossover discrepancies was reduced by approximately 50%. In addition, with the reference digital elevation model (DEM) as the true value, the elevation accuracy (RMSE) of the laser data at the CA19 site improved from 0.38 m before calibration to 0.34 m after calibration, and the improvement at the MDV site was even more significant, about 0.22m. In conclusion, the proposed method is effective and feasible and can improve the consistency of long-period laser data quality in large areas.