Persistent scatterer interferometric synthetic aperture radar (PSInSAR) technology provides a powerful tool for detecting ground surface displacements. However, one of its major limitations is an insufficient number of coherence points due to decorrelation. In this article, we propose an effective ground surface displacement monitoring approach based on the Stanford method for persistent scatterer (StaMPS). This approach dramatically increases the density of coherence points by jointly exploiting persistent scatterer (PS) and distributed scatterer (DS) points. In particular, we develop a new DS phase estimation method based on nonlinear optimization estimation (NLE) and exploit it in our approach. The NLE can obtain better DS phase noise reduction, can detect more coherence points, and requires slightly less computational time than conventional maximum-likelihood estimation (MLE). To demonstrate the effectiveness of our new approach, we apply it over 24 Envisat SAR images acquired over Tucson, Arizona in the USA. The coherence pixel density is increased substantially (> 6 times) compared with the conventional StaMPS-based PSInSAR approach. As for the improvement of our NLE over MLE, it can detect 11.7% more coherence pixels, can achieve better phase noise reduction, and needs approximately one third less computational time. These results validate the effectiveness of our new approach and suggest its great potential for land displacement measurements.

Snow density is an important variable in snowpack research. The comprehensive applicability evaluation of the snow density datasets is a prerequisite of these datasets for their applications in hydrology processes and climate change, as well as in snow equivalent water retrieval algorithms. In this letter, the applicability of three snow density datasets, including European ReAnalysis (ERA)-Interim, ERA5, and the newly released ERA5-Land datasets, was first assessed using two ground evaluation datasets with different land covers from seven snow survey courses and four densely sampled networks in China. The results show that the ERA-Interim dataset significantly overestimates snow density during the entire snow season, with an overall root mean square error (RMSE) larger than 112 kg/m3, and lacks temporal dynamics. The ERA5 and ERA5-Land datasets are generally in good agreement with the ground measurements in China. The averaged RMSEs of the ERA5 dataset are 56.2 kg/m3 against snow course sites and 28.3 kg/m3 versus the densely sampled measurements, and those of the ERA5-Land dataset are 56.6 and 28.4 kg/m3, respectively. However, the ERA5 and ERA5-Land datasets still underestimate snow density over time, especially for the middle and late snow seasons. These new findings are expected to provide valuable feedback to model developers to further enhance the accuracy of snow density datasets.

Distributed scatterer interferometry (DSI) is a well-known technique for ground surface deformation monitoring. Central to this process, phase estimation reconstructs a consistent phase series from all interferometric combinations. In theory, the maximum likelihood estimator (MLE) is the optimum approach for phase estimation. However, in practice, its performance is often compromised. Previous studies have demonstrated that the coherence magnitude bias is a source of error. However, other sources of error in the MLE processing remain unclear. This study systematically assesses the sources of error in phase estimation and develops a hybrid approach that corrects three identified sources of error: 1) To address the error from inhomogeneous pixels, an algorithm based on the covariance matrix preestimation and general likelihood ratio test (CMGLR) is developed to select more accurate homogeneous pixels; 2) to mitigate the bias from coherence magnitude matrix, we apply the oracle approximating shrinkage (OAS) algorithm to estimate the precision matrix with higher accuracy; and 3) to tackle the noise from interferometric phase matrix, the filtering principles are defined and the covariance matrix filtering (CMF) algorithm is designed to suppress the noise. A series of simulated experiments demonstrate the effectiveness of the proposed approach. Additionally, a real TanDEM-X experiment shows that the proposed approach can reconstruct the time series phase with reduced noise. Furthermore, the estimated deformation exhibits improvement with significantly increased measurement points MPs (>2.4 times) and higher accuracy compared to the traditional method based on the Kolmogorov–Smirnov (KS) test and sample covariance matrix (SCM). Particularly, it exhibits exceptional performance in monitoring fine structures, while the traditional method usually fails with very few MPs. These results underscore the significant potential of this approach in the realm of ground surface deformation monitoring.

Pixel similarity measurement is a critical step in distributed scatterer (DS) interferometry, directly affecting DS phase estimation. Despite considerable efforts to improve its accuracy, existing methods still suffer from unsatisfactory performance, especially with small stack sizes. In recent years, deep neural networks have achieved remarkable breakthroughs in interferometric synthetic aperture radar (InSAR) processing. However, their potential for measuring pixel similarity in multitemporal InSAR remains unexplored. This article proposes a neural network-driven pixel similarity measurement approach, termed PSMNet. To address the challenge of accurately defining true data, a supervised learning strategy is designed. The proposed network consists of two main modules: 1) a feature extraction module that generates high-level feature images with enhanced representation and reduced noise, and 2) a similarity measurement module that evaluates pixel similarity without relying on assumptions about data distribution. The network is trained on synthetic data, enabling it to generalize for different stack sizes and target characteristics. Extensive experiments on simulated and real TanDEM-X images demonstrate a significant accuracy improvement of the proposed approach, highlighting its robust performance for varying stack sizes and computational efficiency advantage compared to traditional methods. The proposed approach further enhances DS phase estimation and increases the number of measurement points, showing great promise for ground surface deformation monitoring.

Feature matching is a core step in multi-source remote sensing image registration approaches based on feature. However, for existing methods, whether traditional classical SIFT algorithm or deep learning-based methods, they essentially rely on generating descriptors from local regions of feature points, which can lead to low matching success rates due to various challenges, including gray-scale changes, content changes, local similarity, and occlusions between images. Inspired by the human approach of finding rough corresponding regions globally and then carefully comparing local regions, and the excellent global attention property of transformers, the proposed feature matching network adopts a coarse-to-fine matching strategy that utilizes both global and local information between images to predict corresponding feature points. Importantly, the network has great flexibility of matching corresponding points for any feature points and can be effectively trained without strong supervised signals of corresponding feature points and only require the true geometric transformation between images. The qualitative experiment illustrate the effectiveness of the proposed network by matching feature points extracted by SIFT or sampled uniformly. In the quantitative experiments, we used feature points extracted by SIFT, SuperPoint, and LoFTR as the keypoints to be matched. We then calculated the mean match success ratio (MSR) and mean reprojection error (MRE) of each method at different thresholds in the test dataset. Additionally, boxplot graphs were plotted to visualize the distributions. By comparing the MSR and MRE values as well as their distributions with other methods, we can conclude that the proposed method consistently outperforms the comparison methods in terms of MSR at different thresholds. Moreover, the MSR of the proposed method remains within a reasonable range compared to the MRE of other methods.