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Spaceborne bistatic synthetic aperture radar (BiSAR) systems utilize an intersatellite link to achieve phase synchronization. However, radio frequency interference (RFI) from communication satellites and ground-based radars often contaminates the synchronization signal, leading to inaccuracies in the inverted digital elevation model (DEM). Therefore, this article puts forward an advanced phase-preserving synchronization RFI suppression method and validates it using data from an $L$-band BiSAR system, LuTan-1 (LT-1). The method involves detecting and locating RFI within a monopulse synchronization signal, and the signal at the estimated RFI position is removed to obtain a preprocessed signal. Then, based on the preprocessed signal and the RFI model, RFI is estimated using a gradient-based approach. Finally, the estimated RFI is subtracted from the monopulse signal to obtain the desired signal. In addition, synchronization RFI suppression and DEM generation experiments are performed on the LT-1 data to verify the proposed method. Experimental results demonstrate that the method effectively suppresses synchronization RFI and improves DEM accuracy, and it has extensive application prospects in future low-band distributed interferometric synthetic aperture radar missions.

Accurate estimation of forest aboveground biomass (AGB) is crucial for research on terrestrial carbon cycling and global climate change. In this study, we introduce an improved approach for estimating forest AGB combining P-band and X-band interferometric synthetic aperture radar (InSAR) data. Forest AGB was estimated by combining unbiased forest height and volume backscatter intensity. For forest height, a multilayer model and subaperture decomposition technology were used to remove the penetration bias of the X-band and reduce the effects of forest scatterers on the extraction of a pure understory terrain phase based on P-band, respectively. For volume backscatter intensity, a ground cancellation algorithm based on P-band InSAR was used to eliminate ground scattering contributions unrelated to forest AGB. The proposed method was validated using airborne P-band InSAR data and spaceborne X-band InSAR data gathered over the study area on the Saihanba Forest Farm in Hebei, China. The unbiased forest height and volume backscatter intensity had stronger correlations with forest AGB than estimates derived from unimproved features. The proposed method returned high-precision estimates of forest AGB with an accuracy of 83.73%, an improvement of 8.80% over an estimate derived from unoptimized features. Additionally, AGB estimates combined with forest height and backscatter intensity were greater than those based on a single feature, with the contribution of the former is greater than that of the latter.

Current interferometric wide area ground motion services require the estimation of the coherence magnitude as accurately and computationally effectively as possible. However, a precise and at the same time computationally efficient method is missing. Therefore, the objective of this article is to improve the empirical Bayesian coherence magnitude estimation in terms of accuracy and computational cost. Precisely, this article proposes the interferometric coherence magnitude estimation by machine learning (ML). It results in a nonparametric and automated statistical inference. However, applying ML in this estimation context is not straightforward. The number and the domain of possible input processes is infinite and it is not possible to train all possible input signals. It is shown that the expected channel amplitudes and the expected interferometric phase cause redundancies in the input signals allowing to solve this issue. Similar to the empirical Bayesian methods, a single parameter for the maximum underlaying coherence is used to model the prior. However, no prior or any shape of prior probability is easy to implement within the ML framework. The article reports on the bias, standard deviation and RMSE of the developed estimators. It was found that ML estimators improve the coherence estimation RMSE from small samples ($2 \leq N < 30$) and for small underlaying coherence compared to the conventional and empirical Bayes estimators. The developed ML coherence magnitude estimators are suitable and recommended for operational InSAR systems. For the estimation, the ML model is extremely fast evaluated because no iteration, numeric integration or bootstrapping is needed.

A flood event induced by heavy rainfall hit the Taiyuan basin in north China in early October of 2021. In this study, we map the flood event process using the multitemporal synthetic aperture radar (SAR) images acquired by Sentinel-1. First, we develop a spatiotemporal filter based on low-rank tensor approximation (STF-LRTA) for removing the speckle noise in SAR images. Next, we employ the classic log-ratio change indicator and the minimum error threshold algorithm to characterize the flood using the filtered images. Finally, we relate the flood inundation to the land subsidence in the Taiyuan basin by jointly analyzing the multitemporal SAR change detection results and interferometric SAR (InSAR) time-series measurements (preflood). The validation experiments compare the proposed filter with the Refined-Lee filter, Gamma filter, and a statistically homogeneous pixels selection based multitemporal SAR filter. The results demonstrate the effectiveness and advantage of the proposed STF-LRTA method in SAR despeckling and detail preservation, and the applicability to change scenes. The joint analyses reveal that land subsidence might be an important contributor to the flood event, and the flood recession process linearly correlates with time and subsidence magnitude.

Interferometric SAR (InSAR) is a practical technique to derive 3-D structure of the earth's surface. This article conducts a comparative study of digital elevation model (DEM) reconstruction using the single-baseline and multibaseline InSAR techniques. To improve the accuracy of the DEM generated from the single-baseline InSAR, we improve the weight definition approach according to the coherence magnitudes for the fusion of single-baseline InSAR DEMs. For multibaseline InSAR, we adopt a method based on the maximum-likelihood estimation (MLE) algorithm, which uses external DEM as a constraint. A TerraSAR-X dataset comprising four SAR images acquired over Barcelona is used for experimental investigation. The accuracies of four types of DEM are comparatively studied, including DEM by single-baseline InSAR, DEM by the fusion of single-baseline InSAR DEMs, DEM by unconstrained multibaseline InSAR, and DEM by constrained multibaseline InSAR. The results show that the proposed weighted fusion of single-baseline InSAR performed slightly better than the constrained multibaseline InSAR method, and both of them performed better than the single-baseline results, demonstrating that multiple observations can effectively improve the accuracy of the reconstructed DEM.

The Source Physics Experiment (SPE) Phase I conducted six underground chemical explosions at the same experimental pad with the goal of characterizing underground explosions to enhance the United States (U.S.) ability to detect and discriminate underground nuclear explosions (UNEs). A fully polarimetric synthetic aperture RADAR (PolSAR) collected imagery in VideoSAR mode during the fifth and sixth explosions in the series (SPE-5 and SPE-6). Previously, we reported the prompt PolSAR surface changes cause by SPE-5 and SPE-6 explosions within seconds or minutes of the underground chemical explosions, including a drop of spatial coherence and polarimetric scattering changes. Therein it was hypothesized that surface changes occurred when surface particles experienced upward acceleration greater than 1 g. Because the SPE site was instrumented with surface accelerometers, we explore that hypothesis and report our findings in this article. We equate explosion-caused prompt surface expressions measured by PolSAR to the prompt surface movement measured by accelerometers. We tie these findings to UNE detection by comparing the PolSAR and accelerometer results to empirical ground motion predictions derived from accelerometer recordings of UNEs collected prior to cessation of U.S. nuclear testing. We find the single threshold greater than 1 g hypothesis is not correct for it does not explain the PolSAR results. Our findings show PolSAR surface coherence spatial extent is highly correlated with surface velocity, both measured and predicted, and the resulting surface deformation extent is corroborated by accelerometer records and the predicted lateral spall extent. PolSAR scattering changes measured during SPE-6 are created by the prompt surface displacement being larger than the spall gap.

This article compares the performance of four different interferometric synthetic aperture radar (SAR) techniques for the estimation of rice crop height by means of bistatic TanDEM-X data. Methods based on the interferometric phase alone, on the coherence amplitude alone, on the complex coherence value, and on polarimetric SAR interferometry (PolInSAR) are analyzed. Validation is conducted with reference data acquired over rice fields in Spain during the Science Phase of the TanDEM-X mission. Single- and dual-polarized data are exploited to also provide further insights into the polarization influence on these approaches. Vegetation height estimates from methodologies based on the interferometric phase show a general underestimation for the HH channel (with a bias that reaches around 25 cm in mid-July for some fields), whereas the VV channel is strongly influenced by noisy phases, especially at large incidences [root-mean-square error (RMSE) = 31 cm]. Results show that these approaches perform better at shallower incidences than the methodologies based on coherence amplitude and on PolInSAR, which obtain the most suitable results at steep incidences, with RMSE values of 17 and 23 cm. On the contrary, at shallower incidences, they are highly affected by very low input coherence levels. Hence, they tend to overestimate vegetation height.

The distribution of free and open access radar satellite datasets, like those of Sentinel-1, has provided new opportunities for monitoring archaeological sites and monuments in a systematic way, and especially after earthquake events. While optical sensors are established in the scientific literature and radar sensors are lately introduced in the relevant literature, the role of satellite-driven ready products is still limited discussed. With the continuous improvement of remote sensing satellite data quality and accuracy, high-resolution data needs to be processed, while at the same time, this required high computational complexity. In respect to this, over the last years, various efforts have been made to support high-performance cloud-based processing, providing to the end-users ready products in a short time. This study presents the results from the exploitation of a relevant new cloud platform, namely the Hybrid Pluggable Processing Pipeline (HyP3) system that integrates GAMMA software, for detecting ground displacement within archaeological sites in Cyprus, after a 5.6 magnitude scale earthquake in 2015. Ascending and descending pairs of Sentinel-1 images, acquired before and after the event, were processed through the HyP3 platform, revealing small relative ground displacements in the area under study. The processing chain was performed in less than 1 h -per pair- on the HyP3 system, indicating that similar approaches could be beneficial in the future to support cultural heritage management of large areas.