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.

Adaptive multilooking applied to multiple synthetic aperture radar (SAR) observations has been proven to be an effective process to improve the quality of multitemporal interferometric SAR (InSAR), in which the key task is to select the statistically homogeneous pixels (SHPs). The existing algorithms are mainly based on time-series information from the same position and perform unsatisfactorily when image number is small. In this study, we propose an adaptive multilooking approach based on the context covariance matrix for SHP selection, named CCM-SHPS. The core idea is to exploit spatially adjacent pixels to enhance the information volume. The context covariance matrix is constructed and the Wishart statistic test is employed to measure the similarity. The proposed CCM-SHPS is validated by a simulated stack on the filtered InSAR results, including the amplitude, interferometric phase, and coherence, demonstrating its advantage over five representative algorithms in speckle suppression and edge preservation.

Phase linking is a key step in distributed scatterer interferometry (DSI), which can significantly reduce decorrelation by retrieving a consistent phase series. The performance of phase linking can be severely degraded by the inaccurate coherence magnitude matrix. Recently, some studies proposed to mitigate the problem by employing the regularization methods, e.g., adding a quantity to the diagonal or shrinking to the identity matrix. However, the correction is insufficient due to the simple structure assumption. In this study, we propose a new phase linking approach based on a powerful regularization method. Specifically, it achieves the maximum likelihood estimation of the coherence matrix under the structural constraint of total positivity. A simulated stack is exploited to test the performance of the proposed approach. The qualitative and quantitative evaluations demonstrate its superiority over the existing regularization methods.

With the launch of a number of multipolarimetric synthetic aperture radar (SAR) satellites, many multipolarimetric phase estimation algorithms have been introduced to reduce the decorrelation of distributed scatterers. They typically perform the traditional phase estimation on complex coherence matrix. Thus, the primary focus lies in accurately estimating the complex coherence matrix to achieve precise phase estimation. In this paper, we propose a multipolarimetric phase estimation approach with improved coherence matrix estimation, termed MPPE-CME. It includes two major steps. The first step is to select the polarimetric interferometric pairs using our proposed selection algorithm, which is adaptive and without setting any empirical parameters. In the second step, based on the selected polarimetric interferograms, we develop a dominant scattering mechanism (SM) extraction algorithm to estimate the complex coherence matrix with enhanced accuracy. The simulated experiment validates the effectiveness of the proposed polarimetric interferometric pair selection and dominant SM extraction algorithms. The real data experiment conducted at the Chengdu Tianfu International Airport demonstrates that MPPE-CME outperforms other multipolarimetric phase estimation algorithms with significantly reduced reconstructed phase noise, increased measurement point density, and improved deformation accuracy.

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.

Distributed scatterers (DSs) should be included in multitemporal interferometric synthetic aperture radar to improve the spatial density and quality of monitoring points. As a key step, phase estimation can significantly reduce the decorrelation of DSs by exploiting all available interferograms. The current phase estimation algorithms are known to be affected by the coherence bias. In this study, we propose an improved DS phase estimation approach, which uses a refined coherence bias correction algorithm. To demonstrate the effectiveness of the proposed approach, we apply it over 50 simulated synthetic aperture radar images. The coherence bias can be significantly reduced by the proposed approach, including an average coherence bias reduction of more than 35% over the existing bias correction algorithm. The reconstructed phase series obtained by the proposed approach have higher accuracy than the current methods.

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.

Sensitivity analysis (SA) of model parameters is of great importance for understanding, development, and application of models. However, the influence of snow microstructure variability on snow water equivalent retrieval from passive microwave measurements is still unclear. This article explores the parameter sensitivity of the microwave emission model of layered snowpacks (MEMLS) with improved born approximation (IBA) by using a quantitative global SA method, the extended Fourier amplitude sensitivity test (EFAST) algorithm. A deep analysis is conducted, including the sensitivity of passive microwave emission to snow parameters, the sensitivity variation analysis for different snow conditions, and the temporal properties of the parameter sensitivity. The results show the exponential correlation length, snow depth, and snow density are the three most sensitive parameters for snow without salt in the MEMLS model for the brightness temperature gradient at 18.7 and 36.5 GHz. For snow with a small salt content, the exponential correlation length, snow depth, snow temperature, and snow density are the four most sensitive parameters. Second, snow parameter variability highly affects the microwave radiation. The sensitivity values of microwave brightness temperature to snow depth gradually increase when the exponential correlation length is less than 0.25 mm and then slightly decreases with the increase of exponential correlation length and decreases along with the increase of snow density. Finally, our analysis highlights the importance to include the snow density, especially for deep snow depth, in the combination of sensitive factors in future multiparameter retrievals.