Sophie Allain-Bailhache

Retrieval of River Ice Thickness From C-Band PolSAR Data

River ice has an important effect on natural processes and human activities in northern countries. Current models for estimating river ice thickness are mostly based on environmental data. They require several inputs and yield only a global estimate of ice thickness for a large heterogeneous area. Attempts have been made intending to retrieve river ice thickness from remote sensing using monopolarized C-band radar data. No reliable maps of ice thickness have been produced. In this paper, the potential of polarimetric synthetic aperture radar (PolSAR) data for estimating river ice thickness is demonstrated, and a river ice thickness retrieval model is proposed. The C-band SAR images used in this paper were acquired by Radarsat-2 in the winter of 2009 over the Saint-François River (Southern Quebec), the Koksoak River (Northern Quebec), and the Mackenzie River (Northwest Territories) in Canada. Field campaigns were carried out to obtain ice thickness validation data at 70 locations. Polarimetric entropy was used to obtain ice thickness estimates. This approach results in spatially distributed ice thickness maps for selected ice types.

Interpretation of a RADARSAT-2 fully polarimetric time-series for snow cover studies in an Alpine context – first results

The aim of the SOAR #1341 project is to perform temporal analyses of changes in RADARSAT-2 full-polarimetry parameters on snow cover in a mountainous area. The objective of the present study was to determine whether there is a correlation between changes in radar statistics and changes in physical snow parameters during winter and spring. This paper focuses on the preprocessing of the images and presents the methodological steps and first results obtained in full polarimetry mode. Six RADARSAT-2 quad-pol images were acquired between January 2009 and January 2010, five in different snow conditions and one snow-free image in summer used as a reference. The fine acquisition mode was selected with a medium incidence angle (39°). A combination of LANDSAT-7 and SPOT optical images and field measurements was used for the validation step. First, RADARSAT-2 images had to be pre-processed due to the influence of high mountain topography on the polarimetric signal: the planned incidence angle was computed using a fine digital elevation model (DEM). Next, the DEM and optical dataset were reprojected onto the slant range mode of RADARSAT-2, the configuration required to preserve the phase signal and polarimetric statistics. Polarimetric analysis was performed using the PolSARpro software from ESA/IETR. The coherency matrices were calculated for each RADARSAT-2 image. Polarimetric descriptors based on the eigenvector–eigenvalue decomposition theorem of this coherency matrix were obtained. The behavior of the Single Eigenvalue Relative Difference, a polarimetric parameter that depends on scattering mechanisms, was analyzed. The polarimetric analysis showed an increase in the multiple scattering mechanism with a dry snow cover compared with the snow-free image. With a wet snow cover, there was an increase in contribution of the single scattering compared with the snow-free image. Our results showed that it was possible to identify temporal changes in dry, wet, or no snow characteristics throughout the winter season by analyzing primary polarimetric decomposition parameters and by comparing them with measurements made at 10 field sites.

Mapping dynamic wetland processes with a one year RADARSAT-2 quad pol time-series

Remotely sensed data are widely used to identify, delineate and characterize wetlands. Optical data provide interesting information on land-use and land cover but are limited to cloud-free periods and to a description of the top layer of the vegetation strata because penetration depth is very small. For these reasons, it is not possible to precisely inventory wetland vegetation and agricultural practices, as well as water cycles and water levels in these areas with passive remote sensing techniques. The objective of this article is to evaluate fully polarimetric RADARSAT-2 time-series datasets to determine the water cycle dynamics, in order to delineate precisely potential, effective and efficient wetlands. To that end, the development and validation of a supervised PolSAR segmentation including multi-temporal analysis of wetland evolution, and the investigation of polarimetric decomposition methods for quantitative physical parameter inversion algorithms are presented. The proposed methodology is based on the segmentation of a polarimetric descriptor, the Shannon Entropy, which has been shown to be a very sensitive parameter to the temporal variability of flooded areas. The results have been validated using soil moisture measurements in the field and a LiDAR image. They show that it is possible to produce detailed water feature maps useful for delineating water tables as well as water-saturated areas, and for monitoring water area dynamics. These products provide useful information to identify and delineate wetlands in order to support conservation and management in these ecosystems across large areas.

Spaceborne fully polarimetric time-series datasets for land cover analysis

The objective of this paper is to make a review of the current status of the project entitled Evaluation of RADARSAT-2 quad-pol data for functional assessment of wetlands (Id6842), developed in the frame of the CSA-ESA SOAR-EU (Science and operational applications research for Europe) program by a consortium comprising I.E.T.R at the University of Rennes 1 and COSTEL-LETG at the University of Haute-Bretagne. The main objective of this project concerns in evaluating fully polarimetric RADARSAT-2 time-series datasets to delineate precisely effective and potential wetlands, map detailed vegetation distribution, identify agricultural practices and determine water cycle and waterlevels.

On the use of fully polarimetric RADARSAT-2 time-series datasets for delineating and monitoring the seasonal dynamics of wetland ecosystem

The project entitled Evaluation of RADARSAT-2 quad-pol data for functional assessment of wetlands (Id6842), developed in the framework of the CSA-ESA SOAR-EU (Science and operational applications research for Europe) aims to contribute to the application development in demonstrating the exploitation of fully polarimetric time-series datasets for the functional assessment of wetlands. The objective of this paper is to address the issue of evaluating fully polarimetric RADARSAT-2 time-series datasets to determine the water cycle dynamics, in order to delineate precisely potential, effective and efficient wetlands.

Retrieving soil moisture below a vegetation layer using polarimetric tomographic SAR data

This paper proposes to use polarimetric tomographic synthetic aperture radar (PolTomSAR) data to spatially discriminate and characterize the ground signature under a vegetation layer. The influence of the different scattering mechanisms on the ground response is analyzed. The importance of the double-bounce is a limitation for the soil response characterization. A new algorithm is proposed to use the double bounce response to characterize the soil response and to estimate the soil moisture.

Study of soil respons under a vegetation layer using TomSAR data and ground-based TomSAR data

This paper proposes to first use simulated Polarimetric Tomographic Synthetic Aperture Radar (PolTomSAR) data to spatially discriminate and characterize the ground signature under a vegetation layer. The ground signature estimated from the tomogram will then be analyzed to and the influence of the different scattering mechanisms on the entropy and SPAN simulated values at soil height, then simulated data are compared to measurements acquired by a ground based SAR system.

Wetland water segmentation using multi-angle and polarimetric Radarsat-2 datasets

The segmentation of open water areas plays an important role in the wetland monitoring. Moreover, a veil of mist often covers this kind of land and the use of optical sensors is quite limited. In this paper, the open water segmentation is applied over Radarsat-2 images acquired in the north west of France. Results obtained for different incidence angle are presented and validated using meteorological data.

One year spaceborne full polarimetric time-series for mapping wetland processes

The project entitled Evaluation of RADARSAT-2 quad-pol data for functional assessment of wetlands (Id6842), developed in the framework of the CSA-ESA SOAR-EU (Science and operational applications research for Europe) by a consortium comprising I.E.T.R at the University of Rennes 1 and COSTEL-LETG at the University of Haute-Bretagne, aims to contribute to the application development in demonstrating the exploitation of fully polarimetric time-series datasets for the functional assessment of wetlands. The objective of this article is to address the issue of evaluating fully polarimetric RADARSAT-2 time-series datasets to determine the water cycle dynamics, in order to delineate precisely potential, effective and efficient wetlands. To that end, the development and validation of a general PolSAR segmentation including multi-temporal analysis of wetland evolution, and the investigation of polarimetric decomposition methods for quantitative physical parameter inversion algorithms are presented.

Agricultural vegetation classification with SVM and polarimetric SAR data

Polarimetric SAR data at L-band are known to be particularly well adapted for estimating moisture content and roughness. However, many agricultural fields are generally covered by a short vegetation layer that hampers this analysis. In fact, many applications of surface parameter retrieval methods using polarimetric SAR data over agricultural sites revealed that parameters are underestimated over most of the fields covered by short vegetation (e.g. grass, clovers, winter wheat). This bias is due to the electromagnetic contribution of the vegetation which significantly modifies the polarimetric response. An identification of different kind of vegetation is necessary in order to determine the feasibility to estimate soil moisture. The AgriSAR campaign, Agricultural Bio-/Geophysical Retrievals from Frequent Repeat SAR and Optical Imaging, was conducted for ESA in 2006 in order to study the agricultural vegetation. The multi-temporal datasets were acquired with the DLRs E-SAR sensor in Görmin (Germany). From this campaign, many ground measurements were obtained: Leaf Area Index (LAI), wet and dry biomass and soil moisture. Thus, using all information, eight agricultural vegetation classes could be characterized independently of soil moisture. This paper presents this identification necessary to elaborate an original mapping technique allowing localizing agricultural fields having a vegetation layer. A classification based on the support vector machine (SVM) and on the analysis of polarimetric parameter behavior is developed using multi-temporal images over fields covered by vegetation. The obtained vegetation maps allow the analysis of the temporal evolution of plants. This classification has high product and user accuracy which are presented. The technique is shown to perform well over the AgriSAR dataset.