In this paper, we assess the flood mapping capabilities of the X-band Synthetic Aperture Radar (SAR) imagery acquired by the bistatic pair TanDEM-X/TerraSAR-X (TDX/TSX). The main objective is to investigate the added value of the bistatic TDX/TSX Interferometric Synthetic Aperture Radar (InSAR) coherence in addition to the SAR backscatter in the context of inundation mapping. As a classifier, we consider a Random Forest (RF) classification scheme using TDX/TSX SAR intensities and their bistatic InSAR coherence to extract the flood extent map. To evaluate the classification results and as no “ground truth” was available at the SAR data acquisition time, we set up a LISFLOOD-FP hydraulic model for simulating the temporal evolution of the flood water. The flood map simulated by the model shows good performances with an Overall Accuracy (OA) of $97.92\%$ and a Critical Success Index (CSI) of $94.01\%$ . The SAR-derived flood map is then compared to the LISFLOOD-FP extent map simulated at the SAR data acquisition time. As a test case, we consider the flooding event of the Richelieu River that occurred in the Montérégie region of Quebec (Canada) from April to June 2011. Experimental results highlight the potential of the bistatic InSAR coherence for more accurate flood mapping in a complex landscape with urban and vegetation areas. The classification results of the SAR-derived flood map with respect to the LISFLOOD-FP flood map reach an OA of $78.65\%$ and a Precision of $82.08\%$ when integrating the bistatic InSAR coherence. These classification OA and Precision values are $69.63\%$ and $64.52\%$ , respectively, using only the TDX/TSX SAR intensity.

The phenomenon of soil salinization in semi-arid regions is getting amplified and accentuated by both anthropogenic practices and climate change. Land salinization mapping and monitoring using conventional strategies are insufficient and difficult. Our work aims to study the potential of synthetic aperture radar (SAR) for mapping and monitoring of the spatio-temporal dynamics of soil salinity using interferometry. Our contribution in this paper consists of a statistical relationship that we establish between field salinity measurement and InSAR coherence based on an empirical analysis. For experimental validation, two sites were selected: 1) the region of Mahdia (central Tunisia) and 2) the plain of Tadla (central Morocco). Both sites underwent three ground campaigns simultaneously with three Radarsat-2 SAR image acquisitions. The results show that it is possible to estimate the temporal change in soil electrical conductivity (EC) from SAR images through the InSAR technique. It has been shown that the radar signal is more sensitive to soil salinity in HH polarization using a small incidence angle. However, for the HV polarization, a large angle of incidence is more suitable. This is, under considering the minimal influence of roughness and moisture surfaces, for a given InSAR coherence.

The problem of interferometric SAR (InSAR) image co‐registration is addressed. Classical InSAR matching considers only the translation shift between two complex images. However, for accurate radar image matching (different swaths, paths, or aspects angles), other shift parameters would be useful. For two‐dimensional images, the application of the Symmetric Phase Only Matching Filtering (SPOMF) to the Fourier–Mellin Invariant (FMI) descriptors allows an accurate and efficient registration of translated, rotated and scaled images. This paper discusses the utility and feasibility of the SPOMF technique on the FMI descriptors of two interferometric SAR images. The FMI‐SPOMF technique is applied on different pairs of InSAR data corresponding to areas with different relief types, in France, Nepal and Tunisia. The results are compared with those of classical cross‐correlation registration techniques. We find that the FMI‐SPOMF descriptors of an interferometric pair of images allows more precise co‐registration and moreover could decide on the exploitability of a given interferometric pair.

The recent advancement in synthetic aperture radar (SAR) technology has enabled high-resolution imaging capability which calls for efficient speckle filtering algorithms to pre-process radar imagery. Since the introduction of Lee sigma filter in 1980, the various versions of the minimum mean square error (MMSE) filter were developed, focusing essentially on how to estimate the processed pixels. For instance, the iterative MMSE (IMMSE) filter which is commonly initialized by the boxcar filter maintains the initially filtered homogeneous areas and corrects the initially blurred spatial details after few iterations. In this study, an effort is made to enhance the performance of the IMMSE filter in terms of speckle reduction and spatial detail preservation by refining the choice of the initial filter, optimizing its parameters and improving the estimation of local statistics. Compared to the basic version, results showed that the improved IMMSE considerably enhanced the filtering criteria. When the IMMSE filtering process was initialized by the Non-Local Mean NLM filter, for few iterations, the IMMSE improved the filtering performances. Simulated, Airborne (ESAR, Oberpfaffenhofen Germany) and Spaceborne (Sentinel 1, Palm Jumeirah Dubai UAE) SAR data were used to assess the filtering performances of the studied filters.

Soil salinization is one of the most hazardous phenomenon accelerating the land degradation processes. Map ping and tracking soil salinity changes is fundamental for anticipating natural disaster, such as desertification, in arid and semi-arid regions. In this work, we establish an empirical model for soil salinity mapping based on a gaussian mixture and using field electrical conductivity (EC) measures. The developed model is tested on saline soil samples collected from the semi-arid region of Kairouan located in central Tunisia. It is based on statistical moments derived from multiband (HH and VV) intensity synthetic aperture radar (SAR) data of the Envisat satellite. The resulting salinity map is composed of three classes of salinity (Low, Medium and High) with respect to the EC measurements. The developed model is validated for low salinity distribution, whereas, it needs more samples to be generalized for medium and high soil salinity content.

Speckle filtering in synthetic aperture radar (SAR) images is essential for the extraction of significant information for homogeneous extended targets. In this letter, we exploited the minimum mean square error to establish a linear rule between the values of the filtered pixels and their variances. Then, the filtered pixel for infinite number of looks (INL) was predicted by linear regression of means and variances for various window sizes. The results show that the proposed INL-prediction filter improved the filtering performances of the original filters. Simulated and real SAR data were used for validation.