Imen Gherboudj

Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10): Overview and Preliminary Results

The Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) was carried out in Saskatchewan, Canada, from 31 May to 16 June, 2010. Its main objective was to contribute to Soil Moisture and Ocean Salinity (SMOS) mission validation and the prelaunch assessment of the proposed Soil Moisture Active and Passive (SMAP) mission. During CanEx-SM10, SMOS data as well as other passive and active microwave measurements were collected by both airborne and satellite platforms. Ground-based measurements of soil (moisture, temperature, roughness, bulk density) and vegetation characteristics (leaf area index, biomass, vegetation height) were conducted close in time to the airborne and satellite acquisitions. Moreover, two ground-based in situ networks provided continuous measurements of meteorological conditions and soil moisture and soil temperature profiles. Two sites, each covering 33 km × 71 km (about two SMOS pixels) were selected in agricultural and boreal forested areas in order to provide contrasting soil and vegetation conditions. This paper describes the measurement strategy, provides an overview of the data sets, and presents preliminary results. Over the agricultural area, the airborne L-band brightness temperatures matched up well with the SMOS data (prototype 346). The radio frequency interference observed in both SMOS and the airborne L-band radiometer data exhibited spatial and temporal variability and polarization dependency. The temporal evolution of the SMOS soil moisture product (prototype 307) matched that observed with the ground data, but the absolute soil moisture estimates did not meet the accuracy requirements (0.04 m3/m3) of the SMOS mission. AMSR-E soil moisture estimates from the National Snow and Ice Data Center more closely reflected soil moisture measurements.

Validation of SMOS Data Over Agricultural and Boreal Forest Areas in Canada

This study was conducted as part of the Soil Moisture and Ocean Salinity (SMOS) calibration and validation activities over agricultural and boreal forest sites located in Saskatchewan, Canada. For each site covering 33 km × 71 km (i.e., about two SMOS pixels), we examined the SMOS brightness temperature (L1c) and soil moisture (L2) products from May 1 to September 30, 2010. The consistency of these data with respect to theory and to the temporal variation of surface characteristics was first discussed at both sites. Then, the SMOS L1c (prototype 346) and L2 (prototypes 305-309) products were evaluated using the Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) ground measurements and L-band passive microwave airborne measurements, in addition to AMSR-E soil moisture estimates and simulations from the zeroth order τ- ω radiative transfer model. For both study sites, the model underestimated SMOS brightness temperatures in V polarization, whereas an overestimation was observed in H polarization. The data sets showed that both the SMOS and AMSR-E soil moisture values were underestimated compared with ground measurements collected during CanEx-SM10 but less so for the AMSR-E estimates. The SMOS soil moisture product was underestimated with a RMSE varying from 0.15 to 0.18 m3/ m3. Furthermore, the overall results showed that errors in the soil moisture estimates increased with the absolute value of soil moisture.

A Backscatter Modeling for River Ice: Analysis and Numerical Results

A microwave backscatter model was developed to help provide an understanding of the interaction of a radar signal with the different ice types formed on natural freshwater bodies. This model was based on the radiative transfer theory, which is solved by the doubling matrix method. This numerical method provides an explanation for scattering effects due to volume, boundaries, boundary-volume interactions and interactions between layers. Three ice types were analyzed: columnar ice, frazil ice, and snow ice. Simulations with the model proved that the radar response from river ice cover depends on both ice-cover boundaries. The shape and distribution of air inclusions within the different ice types seem to have a significant impact on their contributions to the total response. The presence of tubular air inclusions within columnar ice causes an increase in the total response as a result of a double-bounce scattering. Small spherical and closed air inclusions within snow ice and frazil ice generate significant backscattering at high frequencies due to volume and surface-volume scattering. A further increase in the ice-cover thickness with air inclusions also causes increased scattering. Superposing two or more of these ice types causes considerable multiple scattering between layers. Finally, radar ice measurements collected over the Athabasca River were employed to further validate the model, and satisfactory results were obtained.

Classification of river ice using polarimetric SAR data

Ice jams are a major cause of river flooding in Canada. These events can be devastating for the environment, human infrastructure, and population. Although methodologies have been developed to discriminate ice types using single-polarization synthetic aperture radar (SAR) data, SAR polarimetry has not yet been used. In this paper a polarimetric SAR airborne image of the Saint-François River, Quebec, has been analyzed. Complementary data about the characteristics of the ice cover were obtained simultaneously with the image acquisition. The usefulness of each polarimetric parameter is explored to obtain realistic ice type classifications. We propose to compute a rule-based hierarchical classification and compare it with a Wishart classification. A single-polarization-based classification is also used to show the limits of this approach in discriminating water from ice. The hierarchical classification more accurately separates areas of ice from areas of open water (81% producer’s accuracy). Both classifications show good results, with few ambiguities in detection of the consolidated ice class. Detection of the thermal ice class is not highly accurate. Thermal and frazil ice classification is performed better when hierarchical classification than when Wishart classification is used. Lastly, the hierarchical classification is better adapted to river ice than Wishart classification, and fully polarimetric data are significantly better than single-polarization data for discriminating water from ice.

Investigating polarimetric SAR data for cryospheric monitoring in a Canadian environment

Since the early 90s, INKS has been developed tools for snow monitoring, river ice characterization and seasonal frost mapping in Canada. The focus to date has been on the use of monopolarized or multipolarized SAR data. With the forthcoming of RADARSAT-2, we have undergone a series of studies to asses the potential information gain from polarimetric SAR data. Airborne polarimetric SAR data from the Canadian CV-580 have been acquired over three different Canadian sites in winter: 1) a boreal forest 2) an agricultural watershed and 3) the Saint-Francois River. This paper presents the preliminary results obtained from the polarimetric data set over the Saint-Francois River in February 2003.