In this study, we develop an original method for segmenting Sentinel-1 SAR images as a discrete-time dynamical system for estimating the extent of wet snow. The system considers different variables and parameters, including radar amplitude images and terrain information (altitudes, slopes, orientations). The dynamical variable is the SAR amplitude ratio (ratio between a SAR and reference image without snow), whose evolution follows a law described by a function applied to a set of connected pixels. The set of corresponding pixels and the strength of their connection are determined from a digital terrain model. The application of the dynamical system is nonlinear and is inspired by models of interacting particles from biology (neural network models). As the iterations increase, the dynamics leads to a segmentation of the image taken as an initial condition. A significant advantage of our system is that it considers the physical information of the terrain to determine couplings between neighbouring pixels. This algorithm has been tested over the season 2017-2018 and provides estimates of wet snow maps that are perfectly consistent with reference products, opening up new prospects for their use in different contexts (for example, for improving existing segmentation methods and for enhancing the quality of gaps filling approaches in case of missing data, etc.).

In this study, we investigated the ability of deriving cyclic/annual motion information from Persistent Scatterers Interferometric (PSI) Time Series. One of the potential applications of such information is to characterize and map motion related to clay Swelling/Shrinking phenomena. Such an application could become particularly interesting with the development of nation/continent wide service based on PSI (such as EGMS from Copernicus). We identified biases affecting the PSI data limiting the approach that must be investigated in further works.

We present a novel iterative method for segmenting Sentinel-1 SAR images to estimate the extent of wet snow in the mountains. The algorithm consists of a discrete-time dynamical system fed by various variables, including radar amplitude images and terrain information (elevations, slopes, orientations), and controlled by different parameters. The dynamical system uses the SAR amplitude ratio (ratio between an SAR and a reference image without snow) as an initial condition and makes it evolve iteratively toward a segmented image. A digital terrain model modulates the connection between pixels in the discrete-time dynamical system, thereby ensuring a physical consistency in the iterative algorithm. This algorithm is tested over the 2017–2018 season and its outputs are compared with the Copernicus state-of-the-art snow/wet snow products and with expert estimates of snow elevations at the scale of the Grandes-Rousses alpine French massif. We show that the dynamical system can be used to derive wet snow maps in very good agreement with independent data. Furthermore, the elevations and dates of snow retreat estimated by the dynamical system are found to be in much better agreement with estimates obtained from optical satellites and forecaster expertise when compared with the SAR-based Copernicus products for southern facing slopes.

In this study, we develop a novel method to automatically detect areas of snow avalanche debris using a color space segmentation technique applied to synthetic aperture radar (SAR) image time series through January 2018 in the Swiss Alps. Debris avalanche zones are detected assuming that these areas are characterized by a significant and localized increase in SAR signal relative to the surrounding environment. We undertake a sensitivity study by calculating debris products by varying the D-M reference images (a stable reference image taken several weeks before the event). We examine the results according to the direction of the orbit, the characteristics of the terrain (slope, altitude, orientation), and also by evaluating the relevance of the detection with the help of an independent SPOT database by Hafner and Buhler[1]including 18 737 avalanche events. Small avalanches are not detected by SAR images, and depending on the orientation of the terrain some avalanches are not detected by either the ascending or the descending orbit. The detection results vary with the reference image; best detection results are obtained with some selected individual dates with almost 70% of verified avalanche events using the ascending orbit.

This article discusses the issue of snow avalanches monitoring at regional scale in the French Alps using SAR observations from Sentinel-1. A color space segmentation technique applied to SAR image time series has recently been used to detect areas of avalanche debris and successfully evaluated in the Swiss Alps. The objective of this paper is to generalize the detection of avalanche debris to all the French massifs and to develop indicators derived from our satellite product Sentinel-1 that can provide information about the avalanche activity at the spatial scale of the massifs. Evaluations are also planned using model outputs, in-situ measurements and very high resolution optical satellite observations.

We develop and evaluate a new methodology of wet snow detection over the French mountains using a colour space segmentation technique applied to SAR image time series during the period ranging from August 2016 to August 2020. Wet snow products are evaluated against snow products from optical measurements and snow simulations from a state-of-the-art snowpack model. The variability of wet snow, in space and time, is examined to infer knowledge about the seasonal and inter-annual variability of wet snow at the scale of the French mountain massifs.

The interest for maritime surveillance and ship detection in particular has been growing during the last years. In this context, spaceborne SAR systems may contribute to the improvement of security and safety at sea. As such, to allow observation of non-cooperative boats, the revisit times of such systems must be compatible with the objectives of reactivity of maritime surveillance. Under CNES (French Space Agency) initiative, an airborne campaign using the ONERA Airborne SAR SETHI took place over the Mediterranean Sea. The main objective of this dedicated campaign of acquisition was to perform very precise measurements of sea clutter and ship Radar Cross Sections (RCS) for various conditions of acquisition (sea state, observation angle, boat type). This paper is focused on X-band dual-polarized SAR data acquired at 50° incidence angle and we show that such polarimetric configuration provides significant gain on ship detection compared to a mono-polarized SAR without degrading the revisit time like that would be the case with quad-pol SAR data.

In the frame of SAMSON study (a Feasibility study of a space-based maritime surveillance system), funded by CNES, several concepts for a dual sensor RADAR + AIS (Automatic Identification System) constellation have been addressed. One of them supported by Thales Alenia Space is described in this paper. Its interest for global maritime surveillance is discussed. The constellation performance is presented in terms of revisit time and detection probability for each sensor before data fusion processing. Instruments are described as well as the proposed satellite concept.

The paper describes an alternative concept to conventional SAR instruments for ship detection over all ocean surfaces. The concept is specifically oriented for ship detection, and not for land or sea imaging. It allows wide swath coverage (as high as 1000 km). It exhibits high detection performances of small ships even in adverse sea states conditions. Its power consumption is reduced allowing a permanent operation all along the orbit. At least, it uses already developed and low cost technologies.

This paper presents the use of TerraSAR-X images m the context of the Kalideos programme, which aims at providing the user community with time series of multi-spectral (optical and radar) and multi-resolution remote sensing imagery. 120 TerraSAR-X acquisitions are scheduled for three distinct thematics: volcano monitoring (Reunion island site), sugarcane crop monitoring (Reunion island site) and forest monitoring (Arcachon/Landes forest site). We describe here the advancement of these studies, focusing on sugarcane crop monitoring which is the most advanced one. These studies will serve as typical examples of multi-thematic use of TerraSAR-X imagery and demonstrate the relevance of TerraSAR-X imagery for the development of scientific reference datasets.

An important question with respect to the performance of the interferometric cartwheel and similar bistatic SAR systems, is whether the ambiguities combine coherently and introduce artifacts in the resulting interferograms. We here review the theoretical behavior of range and azimuth ambiguities and extend the description to the case of non-zero Doppler processing. Simulation results confirm that ambiguities cannot significantly degrade the performance of an L-band interferometric cartwheel

Clarifies the use of interferometry on Radarsat-1 in mid- and long-term intervals between two acquisitions, taking into account changes in the evolution of the azimuth Doppler since April 1999 to maximize coherence and signal to noise ratio. This study is based on fine mode acquisition over Piton de la Fournaise volcano (La Re/spl acute/union island) and shows examples of displacement fields of the July 1999 eruption.