B. Ventura

Active shoreline of Ontario Lacus, Titan: A morphological study of the lake and its surroundings

Of more than 400 filled lakes now identified on Titan, the first and largest reported in the southern latitudes is Ontario Lacus, which is dark in both infrared and microwave. Here we describe recent observations including synthetic aperture radar (SAR) images by Cassinis radar instrument (λ = 2 cm) and show morphological evidence for active material transport and erosion. Ontario Lacus lies in a shallow depression, with greater relief on the southwestern shore and a gently sloping, possibly wave-generated beach to the northeast. The lake has a closed internal drainage system fed by Earth-like rivers, deltas and alluvial fans. Evidence for active shoreline processes, including the wave-modified lakefront and deltaic deposition, indicates that Ontario is a dynamic feature undergoing typical terrestrial forms of littoral modification.

Wet Snow Cover Mapping Algorithm Based on Multitemporal COSMO-SkyMed X-Band SAR Images

Multitemporal COSMO-SkyMed (CSK) images are exploited to map wet snow cover in a mountainous area in South Tyrol by using a ratio and a probability of error (POE) approach. Free water in the snowpack attenuates the X-band synthetic aperture radar (SAR) signal and wet snow can be classified by comparing images acquired under wet snow and snow-free conditions. The three steps of the algorithms are: preprocessing of SAR data with particular attention on the potential of speckle filtering to improve the classification, classification of wet snow and postprocessing of the snow cover area (SCA) map. Furthermore, the choice of the snow-free reference and wet snow images on the classification threshold and the SCA is assessed as well as the influence of different landcover classes (blocky scree, grassland, forest). Thresholds to distinguish snow-covered and snow-free pixels are - 2.6 dB for grassland and rocks. To quantify the accuracy of the ratio method, POE maps are calculated. The advantage of the POE method is its independency from auxiliary information on snow cover and the possibility to limit the maximum error. SCA maps derived with a maximum POE of 25% and ratio SCA maps show good overall agreement with total SCA of 66.8% (ratio) and 65.6% (POE) on 26th April 2010. A comparison to SCA derived from Landsat 7 ETM+ reveals that total SCA is similar to SAR SCA when a NDSI threshold of 0.7 is applied, but only 86% of the pixels are detected as snow from both sensors at the same time.

Cassini Radar Data: Estimation of Titan's Lake Features by Means of a Bayesian Inversion Algorithm

The analysis derived from the Cassini SAR imagery reflects the complex Titans surface morphology with a wide range of backscattering coefficients and peculiar features such as periodic structures and lakelike features, which were observed on July 22, 2006, when polar areas were first imaged, and are considered good candidates to be filled with liquid hydrocarbons. In this paper, the modeling description of lakes is addressed by means of a double-layer model which considers an upper liquid-hydrocarbon layer and a lower layer compatible with the radar response of the neighboring areas. This model is introduced into a Bayesian framework for the purpose of inferring the likely ranges of some parameters and, in particular, of the optical thickness of the hypothesized liquid-hydrocarbon layer and of the wind speed. The main idea is to use the information contained in the parameter probability density function, which describes how probability is distributed among the different values of parameters according to the various scenarios considered. The analysis carried out on lakes and surrounding areas on flybys T16 and T19 determines optical thickness values from 0.2 to 6. For T25 flyby, the inferred values of optical thickness indicate that a limit value of optical thickness may be 9. Considering that, beyond these values, the signal from the bottom layer is completely attenuated, information on the wind speed on the upper layer can be inferred. The found mean values of wind speed are around 0.2-0.3 m/s according to different hypotheses on the upper layer dielectric constant.

Inferring soil moisture variability in the Mediterrean Sea area using infrared and passive microwave observations

The objective of this study was to infer soil moisture variability from a combination of passive microwave and infrared satellite observations. The proposed approach is mainly based on the concept of apparent thermal inertia (ATI) and makes use of the daily gradient in brightness temperature from MODIS AQUA to infer soil moisture at moderate spatial resolution. Soil moisture retrievals from optical polar orbiting satellites are affected by discontinuities due to the presence of clouds and spurious fluctuations because of low temporal sampling, which is not sufficient for a reliable daily cycle sampling. To mitigate these limitations, we propose using soil moisture temporal trend derived from passive microwave based product, namely the NASA AMSR-E soil moisture product, to filter estimates from MODIS observations. Passive microwave-based soil moisture products exhibit less fluctuation because of their coarse resolution and lower sensitivity to atmosphere. They can therefore be considered as natural “low pass filters” thus reducing the effect of noise in the infrared based estimates. A sensitivity test was conducted to identify to determine the contribution of various factors to the inferred soil moisture from ATI and the error that they may introduce in the estimates. The ATI-based approach was then applied to qualitatively describe the spatial distribution of soil moisture. The algorithm was validated over two different test areas in Italy and France where reference measurements are available. For the test site in Italy, the obtained ATI values were clustered around four different values corresponding to different levels of wetness. The determined four classes of soil moisture (low, medium, medium-high, and high) were compared to available in situ observations. An agreement with in situ observations of 81% was obtained. In densely vegetated areas, only three classes of soil moisture were instead distinguishable. The obtained agreement between observed and inferred soil moisture values was 88%. Also, in the second study area in France, where vegetation is more dominant, only three classes of soil moisture were determined with a lower agreement of 73%. In addition, the ATI trends are in agreement with thermal inertia values determined from physics-based formulation. This study showed that a combination of infrared and passive microwave observation may lead to a better mapping of soil moisture at the regional scale.

Exploitation of Cosmo-Skymed image time series for snow monitoring in alpine regions

The main aim of this work is to adapt the ratio-technique for snow cover mapping developed for C-band to the X-band and high resolution COSMO-SkyMed images. This algorithm, aimed at detecting wet snow, is based on the difference in backscattering coefficients between snow-covered areas in winter images and snow-free summer images. For these purposes, a series of COSMO-SkyMed acquisitions (Stripmap PingPong mode, dual polarizations VV-VH) has been planned and acquired over the test site located in South Tyrol (Northern Italy) in correspondence of the melting and winter season. Contemporary to radar passes field campaigns have been performed. The objective is to test the sensitivity of X-band data to different snow conditions. An analysis has been carried out to find the most suitable filtering technique which allows a clearer distinction of distributions of backscattering coefficients of snow-covered and snow-free areas. Based on this analysis a first map of snow from the images acquired on 26–27 April 2010 (wet snow) was derived and compared with snow cover area derived from LANDSAT ETM+ of 20-04-2010 based on NDSI. Further statistical analysis will be carried out also considering the new acquisitions.

Neural network adaptive algorithm applied to high resolution C-band SAR images for soil moisture retrieval in bare and vegetated areas

In general algorithms for soil moisture retrieval from high resolution satellite data cannot be easily extended to areas where they have not been calibrated and validated. This paper presents the application of an innovative approach for the detection of soil moisture from high resolution SAR images in order to overcome this main limitation by introducing a priori information. During the training phase, extensive data sets of SAR images and related ground truth on four areas characterized by very different surface features have been analyzed in order to understand the ENVISAT/ASAR responses to different soil, environmental and seasonal conditions. From preliminary analyses, the comparison of the backscattering coefficients in dependence of soil moisture values for all the analyzed datasets indicates the same sensitivity to soil moisture variations but with different biases, which may depend on soil characteristics, vegetation presence and roughness effect. These bias values have been used to introduce an adaptive term in the electromagnetic formulation of the backscattering responses from natural bare surfaces. The simulated data from this new model have been then used to train a neural network to be used then as an inversion algorithm. Preliminary results indicate an improvement in the accuracy of soil moisture retrieval with respect to the use of a traditional neural network approach. The results have been also compared with the estimates derived from the application of a Bayesian approach.

Time series analysis of dual-pol COSMO-SkyMed images for monitoring snow cover in alpine areas

Time series of dual-polarized COSMO-SkyMed (CSK) images are exploited for detection of seasonal snow cover in Alpine areas. For the first time a complete time series of CSK images acquired during snow fall and melt period in winter 2010-2011 is addressed to verify the snow cover mapping capabilities of X-band radar images under different conditions (from dry to wet snow). The algorithm for snow detection is based on a multi-temporal approach with the concept that free water in the snowpack attenuates the X-band synthetic aperture radar (SAR) signal and wet snow can be classified by comparing images acquired under wet snow and snow-free conditions. Thresholds to make this distinction are compared across all the images to check sensitivity to different winter conditions and land-use classes. The impact on the snow cover area (SCA) detected is verified by also exploiting both polarizations in the form of Cross-pol ratio, ratio of VH channel with the reference image, and Depolarization factor, ratio between VH and VV channel of the same image. Snow maps from CSK images compared with LANDSAT ETM+ snow maps indicate a constant underestimation in the detection of snow extent especially during winter season thus showing a scarce sensitivity of X-band signals to snow in dry conditions. The presence of VH polarization indicated, however, an increase in the snow detection variable between 10 and 15%.

Exploitation of C- and X-band SAR images for soil moisture change detection estimation in agricultural areas (Po Valley, Italy)

This paper presents the analysis of C and X band images in the scope of soil moisture detection in agricultural fields. Archived data have been analyzed in order to understand the SAR signal behavior of vegetated fields in comparison to bare soils. The results indicate that the sensitivity to bare fields of C and X band signatures is very close, while it changes in presence of vegetation. In particular the effect is directly proportional to amount of vegetation that in this preliminary analysis has been evaluated through the NDVI variable. After this analysis, a statistical approach has been applied to SAR images to infer the information on the soil moisture values. Several experiments have been carried out by considering only C band data, only X band data and a combination of C and X band data. For bare soils, C and X band data determine very similar results and in good agreement to ground measurements. For vegetated fields, C band data tend to underestimate soil moisture due to the vegetation attenuation, while X band data, mainly influenced by vegetation, determine very poor results. Encouraging results are obtained by the combination of C and X band data, thus indicating that X band data can be used in combination to C band data in order to compensate the effect of vegetation.

Soil moisture retrieval from SAR images as a calibration tool for soil moisture index derived from thermal inertia with MODIS images

This paper aims at identifying an operational methodology to derive soil moisture status from optical images by using soil moisture values derived from SAR images as a calibration tool . In the first part of the paper, an algorithm based on Bayesian techniques for the retrieval of soil moisture from C-band SAR images is presented. The algorithm is composed of two modules, one for bare soil and the other for vegetated soil which includes also the use of optical images in order to take into account the vegetation contribution. soil moisture values retrieved from images are then used as a calibration tool for a soil moisture index derived from MODIS images. In this case, the method to estimate soil moisture index from optical and thermal images is based on the calculation of the Apparent Thermal Inertia (ATI). ATI is considered as an approximate (apparent) value of the thermal inertia and is obtained from spectral measurements of the albedo and the diurnal temperature range. soil moisture estimated from SAR images and the ATI are compared in order to find a calibration curve which should cover the entire soil moisture values from saturation to residual moisture values. For the calibration experiment, three main sites were chosen which exhibit different landscape and climatic characteristics. The Basento basin is located in Southern Italy and is characterized by long period of droughts. The Scrivia valley is flat alluvial plain measuring situated close to the confluence of the Scrivia and Po rivers in Northern Italy. The Cordevole watershed, located at the foothill of Mount Sella in Northern Italy is mainly covered by grassland and it was selected because of its relatively smooth topography. The first results indicate a good correlation between ATI and the soil moisture values derived both from measurements and estimated from SAR images.

Seasonal Snow Cover Mapping in Alpine Areas Through Time Series of COSMO-SkyMed Images

A time series of COSMO-SkyMed (CSK) images is exploited for detection of seasonal snow cover in alpine areas. For the first time, a complete time series of CSK images acquired during snow fall and melt periods in winter 2010-2011 is addressed to verify the snow cover mapping capabilities of X-band radar images under different conditions (from dry to wet snow). The algorithm for snow detection is based on a multitemporal approach with the concept that free water in the snowpack attenuates the X-band synthetic aperture radar signal and wet snow can be classified by comparing images acquired under wet snow and snow-free conditions. Thresholds to make this distinction are compared across all the images to check sensitivity to different winter conditions and land-use classes. The impact of variable and fixed thresholds on the retrieved snow-covered areas is assessed. Snow maps from CSK images compared with Landsat Enhanced Thematic Mapper Plus snow maps indicate a constant underestimation in the detection of snow extent, particularly during winter season, thus showing a scarce sensitivity of X-band signals to snow in dry conditions. Probability of error maps are also calculated for each CSK snow map, thus providing information on the classification error associated to each pixel labeled as snow. The analysis of the snow line variation during spring determines good time consistency in the determination of snow maps from CSK images.