This work presents the results of an algorithm for the retrieval of snow surface albedo and grain size by exploiting Sentinel-3 OLCI data. The algorithm is a hybrid approach based on spectral indices and radiative transfer theory and it was tested and intercompared with ground data from 6 field stations and other sensors in the European Alps for the period 2017-2023. The results indicate that for albedo the algorithm agrees well with ground measurements showing an unbiased Root Mean Square Error (ubRMSE) between 0.05 and 0.13 and a correlation coefficient ranging from 0.63 to 0.78 depending on the locations. For grain size, even though a general underestimation is found, the estimates reflect well the typical grain size metamorphosis from winter to spring. To further test the algorithm, these results from Sentinel-3 data were also confronted with ECOSTRESS thermal data specifically useful to show the metamorphosis of the grain size. For albedo, the algorithm was further applied to PRISMA hyperspectral images, showing consistent results with the values obtained by multispectral Sentinel-3 imagery.

Snowmelt dynamics in Mediterranean mountains differs from the ones found in higher latitudes. This work assesses wet snow dynamics in semiarid environments combining proxy and remote sensing databases over a pilot area in the Sierra Nevada Mountain Range (southern Spain). A linear relationship was found between the minimum backscattering signal and the maximum snow depth achieved within a melting cycle. The slope of this relationship depends on the maximum snow depth reached and is linked to the contribution of the ground to the backscattering signal. These results delve into the understanding of wet-snow dynamics in Mediterranean mountains and can constitute the basis for an easy tool to compute maximum snowpack depth from the Sentinel 1 backscattering signal in these environments.

The THERESA (THErmal infRarEd SBG Algorithms) project aims to enhance algorithms for processing Thermal InfraRed data from the SBG-TIR (Surface Biology and Geology – Thermal InfraRed) mission. Starting from state-of-the-art algorithms, THERESA takes in account the mission's technical features to develop algorithms exploiting diverse spectral channels. During the two years lifetime of the project, THERESA will contribute to enhance the investigation of terrestrial phenomena by using both visible and thermal images. The thematic areas that will benefit from SBG-TIR data range from the vegetation analysis to the volcanic eruptions and fires monitoring. Several parameters will be achieved such as the estimation of ash and SO2emissions from volcanoes, the surface temperature, the detection of hotspots as well as the FRP (Fire Radiative Power) in case of HTE’s (High Temperature Events). THERESA's innovation lies in algorithms advancements; the project offers a 360-degree support to the SBG-TIR mission.

The main objective of the project SCIA (Sviluppo di algoritmi per lo studio della Criosfera mediante Immagini PrismA) is the development and optimization of methods for generating products related to the cryosphere. The project foresees the development of a robust processing chain of PRISMA hyperspectral data for the estimation of snow and glacier parameters in Alpine areas, through a combined use of satellite images, field data and radiative transfer models (RTMs). The image spectroscopy measurements provided by PRISMA will make possible to investigate radiometrically complex surfaces and obtain geophysical parameters currently only achievable through airborne hyperspectral sensors.

This paper presents data sets derived from MODIS imagery related to snow cover extent and phenology (duration, start and end of the season). The data are based on yearly information and can provide support for understanding the related changes in the last 23 years, and in correlation with changes in water resources and/or vegetation. The data set contains yearly averaged snow cover fraction (SCF), snow cover duration (SCD), first snow day (FSD) and last snow day (LSD).

This research aims to exploit the potentialities of multi-mission SAR data at X-, C- and L-band for the monitoring of snowpack and alpine soils. The snow parameters as snow water equivalent, snow liquid water content and snow metamorphism have been monitored and different methods are proposed for their retrieval. In order to gather consistent datasets, experimental activities have been conducted in two selected sites in Northern Italy, which are covered by alpine snow during winter and spring periods and are in some cases characterized by the presence of permafrost. Microwave responses of snow and soil have been then simulated by using electromagnetic (i.e., AIEM, Oh, SFT and DMRT-QCA), and physical models (SNOWPACK). Finally, machine learning approaches, as Artificial Neural Networks and Random Forest, were implemented for retrieving snow parameters; whereas interferometric techniques were used in case of snow and soil displacement as rock glaciers. Preliminary and consistent results have been obtained in terms of estimate of snow parameters and soil displacement. This multi-frequency/multi-mission approach enhances the ability of SAR sensors to monitor and analyze snow dynamics, contributing to improved decision-making in various domains.

The mapping of the snow cover from optical remote sensing is a largely investigated topic that still present some challenges especially in mountainous areas when high resolution time series are exploited. Providing reliable maps in all the acquisition conditions is limited by topographic effects such as shadows, sunglint on snow and atmospheric disturbances. If these effects are not corrected, the state of the art methods are generally failing. Support vector machine (SVM) proved to be an effective tool for dealing with classification problems, even when data are noisy and the classes are not linearly separable. In this work, we propose an unsupervised approach to snow cover fraction retrieval that collect "snow" and "snow free" samples to train a model tailored for the specific acquisition conditions of each image. The method has been applied in different test sites in the Alps. For a quantitative evaluation of the result, we used a snow maps derived by very high resolution images acquired in challenging situations of fractional snow cover and low sun elevation.

In this work, we tested two algorithms for the retrieval of snow surface albedo and grain size by exploiting Sentinel-3 OLCI data. The first algorithm is semi-empirical based on spectral indices and radiative transfer theory adapted from Painter et al. (2009, 2012) and the second is based on an approximation of the radiative transfer theory proposed by Kokhanovsky et al. (2019). Being interested mainly in mountain areas, we introduced adaptations to account for topography and heterogeneity of the area of interest. The algorithms were tested and intercompared in the European Alps for the period 2018–2021. The results indicate that for albedo both algorithms can follow the snow dynamics from winter to springtime. Both algorithms agree well with ground measurements showing a Root Mean Square Error (RMSE) between 0.05 and 0.15 and a correlation coefficient ranging from 0.71 to 0.81. As for grain size, a general underestimation is found for both methods, even though the semiempirical method follows better the typical grain size metamorphosis from winter to spring.

The research results described in this paper have been obtained in the framework of the 2019–2022 ALGORITMI project between the Italian Space Agency (ASI) and the Institute of Applied Physics of the National Research Council (CNR-IFAC). The focus of the research was the development of innovative algorithms for the estimation of geophysical parameters of soil, snow, and vegetation with the aim of monitoring soil, snow cover and agricultural crop conditions. The estimation of soil moisture, vegetation biomass, snow water equivalent, and crop classification was improved by using retrieval algorithms based on machine- learning approaches and temporal series of SAR images from COSMO-SkyMed (CSK) and Sentinel-1 (S-1) missions, along with optical images from Sentinel-2. This paper provides an overview of the most recent and valuable results obtained during the project. In particular, the validation of soil moisture provided R=0.89 and RMSE=0.025 m3/m3by integrating data from S-1 and CSK and that one of snow water equivalent gave R=0.85 with RMSE=86.24 mm (CSK HIMAGE) and R=0.86 with RMSE=71.59 mm (CSK PP). Early mapping results showed an almost monotonic progression in overall accuracy over time higher than 90% by increasing the available images.

This study aims at estimating the dry snow water equivalent (SWE) by using X-band SAR data from the COSMO-SkyMed (CSK) satellite constellation. Time series of CSK acquisitions have been collected during the dry snow period in the Alto Adige test site, in the Italian Alps, during the winter seasons from 2013 to 2015 and from 2019 to 2021. The SAR data have been analyzed and compared with the in-situ measurements to understand the X-band SAR sensitivity to SWE, which has been further assessed by Dense Media Radiative Transfer (DMRT) model simulations. The sensitivity analysis provided the basis for addressing the SWE retrieval from the CSK data, by exploiting two different machine learning (ML) techniques, namely Artificial Neural Networks (ANN) and Support Vector Regression (SVR). To ensure a statistical independence of training and validation processes, the algorithms are trained and tested using SWE predictions of the fully distributed snow model AMUNDSEN as reference data and are subsequently validated on the experimental dataset. Due to its influence on the CSK estimates, the effect of forest canopy was accounted for in the analysis. Depending on the algorithm, the validation resulted in a correlation coefficient 0.78 ≤ R ≤ 0.91, and a Root Mean Square Error 55.5 mm ≤ RMSE ≤ 87.4 mm between estimated and in-situ SWE. Further analysis and validation are needed; however, the obtained results seem suggesting the Cosmo-SkyMed constellation as effective tool for the retrieval of the dry snow water equivalent in alpine areas.

The backscattered signal collected by space-born radars, such as Sentinel-1, for area covered by snow is affected by the condition of the snow itself, in particular during melting. However, the potential evidence of a relationship between the different melting phases and the amplitude of the backscattered signal still remains a difficult phenomenon to be described quantitatively. This paper proposes a preliminary approximated model, built upon i) a first-order simulation based on plane-wave incidence on stratified media that account for the bulky physical parameters of the snowpack, such as depth, liquid water content, density, and ii) a full-wave simulation to include the effect of the surface roughness. The model is tested against experimental data for a site in the Italian Alps (Malga Fadner), where data from Sentinel-1, as well as in-situ data about the composition of the snowpack, are available for the winter season 2017/18, showing good general agreement.

The monitoring of the snow cover area (SCA) from optical sensors on board of satellites is affected by the trade-off between spatial and temporal resolution provided by the current operational missions. This limits the possibility to exploit satellite SCA for hydrological purposes. In this paper, we propose a novel downscaling approach driven by the low resolution (LR) information that takes advantage of i) all the high resolution (HR) images acquired in the past over a catchment; and ii) the geomorphometric features that drive the snow redistribution process. Possible applications of the proposed method are time-series gap-filling and snow pattern detection. The downscaled scenes have been validated across existing HR scenes showing an accuracy of about 90%.

In this paper, we present a system for snow roughness measurement. The system is made up of digital camera and a black Forex panel. The whole system has been designed for being low cost and portable; and will be distributed as an open-source Python software. The system is based on the coregistration of the photos taken at the panel on the field with a reference scaled image with pixel size of 0.05 mm. In detail, the coregistration process is done by computing the homography, whose parameters are estimated exploiting automatic key-points matching. Among all methods forkey-points extraction, we used the scale invariant feature transform (SIFT) because of its robustness and recognized efficiency. After key-points extraction, the matching has been performed using a brute force matching (BFM) approach. The proposed system has been tested under different conditions, showing to be an effective and easy approach to snow surface roughness measurement, as well as robust.

The monitoring of snow conditions in Alpine areas to support water management and avalanche warning applications would require the estimate of snow parameters, such as the snow water equivalent (SWE). In this research, COSMO-SkyMed (CSK) X-band SAR data were exploited to estimate the SWE. In-situ snow measurements (depth, density, snow grain radius, temperature) collected in South Tyrol (Italy), were used to simulate the X-band backscatter with the Dense Medium Radiative Transfer (DMRT) electromagnetic model. Two SWE retrieval algorithms based on machine learning approach were implemented. The algorithms are based on Artificial Neural Networks (ANN) and Support Vector Regression (SVR) and have been trained with both experimental data and DMRT model simulations. These algorithms were applied to a selection of CSK StripMap HIMAGE HH-polarized scenes collected over the test area. The obtained results are promising and they confirm the potential of SAR data at X-band to retrieve snow parameters, although the algorithm validation should be improved in the future, with more consistent measurement dataset.

The characterization of snow conditions and the estimation of snow water equivalent (SWE) are the main goals of this paper, achieved through the exploitation of multi-frequency SAR data at both C- and X-bands from Sentinel-1 (S-1) and COSMO-SkyMed (CSK) satellites, respectively. Dry/wet snow conditions have first been assessed using C-band S-1 images. Subsequently, a sensitivity analysis was carried out by using datasets of in-situ snow measurements (i.e. snow depth, density, snow grain radius, temperature and wetness) collected in South Tyrol region, in north-eastern Italy. Simulations based on the Dense Medium Radiative Transfer (DMRT) forward electromagnetic model were considered to interpret and assess the experimental findings. Two retrieval algorithms for SWE estimation from X-band SAR data were implemented. These algorithms are based on machine learning approaches, i.e. Artificial Neural Networks (ANN) and Support Vector Regression (SVR). The training of the algorithms accounts for experimental data and DMRT model simulations and, then is applied to a selection of X-band CSK StripMap HIMAGE scenes collected over the test area. The results are promising, and pave the way for further analysis and validation to exploit the potential of SAR for snow parameter retrieval.

The present work exploits multi-frequency SAR and optical imagery in order to assess soil and vegetation water content values in agricultural areas in Italy and Argentina. Sentinel-1, Sentinel-2, ALOS-2, RADARSAT-2, and COSMO-SkyMed imagery were used in a retrieval algorithm based on Support Vector Machine (SVR) approach. Preliminary results indicate that the integration of optical and SAR data (C and L band) by the mean of machine learning techniques leads to an accurate retrieval of soil moisture (RMSE = 4%). Moreover, a preliminary test for the retrieval of Vegetation Water Content (VWC) indicates that ALOS-2 L-Band backscattering, combined with L-Band simulated backscattering, leads to a reliable model to compute VWC, without any optical information.

The characterization of snow conditions and the estimate of snowpack parameters have been investigated in this paper, by taking into account both C- and X-band SAR data collected from Sentinel-1 (S-1) and Cosmo-SkyMed (CSK) satellites, respectively. Although C- and X-band are not the most suitable frequency for the retrieval of snow parameters due to the high penetration power inside snowpack, some results concerning the wet/dry snow status and both Snow Depth (SD) and Snow Water Equivalent (SWE) estimate can be achieved by using appropriate algorithms and models.A sensitivity analysis was carried out by exploiting datasets of in situ measurements (snow depth, density, snow grain radius, temperature and wetness) collected on two test site in South Tyrol (Ulten Valley and Val Senales). This analysis provided indications on the sensitivity of C- and X-band backscattering to the target snow parameters. As a second step of the analysis, simulations based on the Dense Medium Radiative Transfer (DMRT) forward electromagnetic model have been considered for interpreting and assessing the experimental findings. Finally, an attempt of implementing a retrieval algorithm for estimating SWE from these frequencies is carried out. The algorithm is based on Artificial Neural Networks (ANN). The training of the algorithm accounts for experimental data and DMRT model simulations and, successively, it is applied to time series of CSK images collected on both test areas. The obtained results are encouraging, although more analysis and validation is needed for exploiting the potential of SAR in snow parameter retrieval.

This research aimed at exploiting the joint use of machine learning and polarimetry for improving the retrieval of surface soil moisture (SMC) from SAR acquisitions at C- and X-band.The study was conducted on an alpine test area in Italy and two agricultural areas in Canada, for which series of Radarsat-2 (RS2) and COSMO-SkyMed (CSK) images were available along with direct measurements of SMC from in-situ stations. The analysis confirmed the sensitivity of SAR backscattering (σ°) from both sensors to the SMC variations, with similar correlations (R ≃0.5). The comparison of SMC with the Compact Polarimetric (CP) parameters, computed from the RS2 acquisitions by Radarsat Constellation Mission (RCM) data simulator pointed out that the right and left polarized signals and the Shannon entropy intensity also have some sensitivity to SMC variations, with R ≃0.4 for all the three parameters.Based on these results, two different machine learning (ML) algorithms, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN) have been implemented and tested on the available data. On the South Tyrol test area, both SVR and ANN tested with different combinations of RS2 and CSK data were able to retrieve SMC with a RMSE between 4% and 6% of SMC and R between 0.78 and 0.88, depending on the combination of inputs. The ANN algorithm based on CP data was tested on the Canada areas, being able to estimate SMC with a RMSE between 2% and 5% of SMC and R between 0.85 and 0.96.

In this work, we present a framework to integrate physically based hydroclimatological models and remote sensing products, by exploiting their advantages and overcoming their limitations, for an improved understanding and estimation of the alpine glacier accumulation and ablation processes. The capability of remote sensing to well represent the spatial variability of the snow cover over the glaciers is used to correct possible errors in the model simulations, thus obtaining a more reliable estimation of annual glacier mass balance. The proposed approach is tested on the glaciers in the Rofen Valley (Austria) by employing the AMUNDSEN model for accumulation and ablation processes simulation and Landsat-5/7/8 data for glacier zone mapping from 1998 to 2016.

The main idea of this study is the development of an innovative data fusion method through which state-of-the-art remotely sensed products and hydrological modelling simulations can be integrated to improve the retrieval and the reliability of snow cover and snow water equivalent mapping. The proposed method is based on a machine learning technique, Support Vector Machine (SVM), and on exploitation of two well-instrumented test-sites in EUREGIO region for the validation. Results show an improvement of performances with respect to single products from remote sensing and model. On EUREGIO scale the accuracy of snow cover mapping obtained from fusion reaches 0.95.

Evolution in the Copernicus Space Component (CSC) is foreseen in the mid-2020s to meet priority Copernicus user needs not addressed by the existing infrastructure, and/or to reinforce services by monitoring capability in the thematic domains of CO2, polar, and agriculture/forestry. This evolution will be synergetic with the enhanced continuity of services for the next generation of CSC. The “High Spatio-Temporal Resolution Land Surface Temperature Monitoring (LSTM) Mission”, identified as one of the CSC Expansion High Priority Candidate Missions (HPCM), currently undergoes an ESA preparatory phase (phase A/B1) study to establish mission feasibility. The LSTM mission shall provide enhanced measurements of land surface temperature with a focus responding to user requirements related to agricultural monitoring.

In this paper, a novel approach for the retrieval of snow wetness is presented for Sentinel-1 (S-1) data. The approach uses the information on snow proprieties provided by the hydroclimatological model AMUNDSEN and confirmed by comparisons performed at different sematic level to train a regressor that is able to exploit the dual-polarimetric information provided by S-1. The preliminary results obtained for the Rofental in Austria are discussed.

In this work, the characterization and extraction of snowpack parameters from X-band SAR imagery is investigated. X-band is not yet the most suitable frequency for the retrieval of snow parameters, namely Snow Depth (SD) and Snow Water Equivalent (SWE); however, it is the higher frequency available in the existing SAR systems. Previous research demonstrated that retrieval of SD/SWE can be achieved at this frequency too for . A sensitivity analysis is carried out by exploiting datasets of in situ measurements (snow depth, density, snow grain radius, temperature and wetness) collected on two test sites in the Eastern Italian Alps: the Cordevole plateau (Dolomites) and the Ulten valley (South Tyrol). This analysis provides indications on the sensitivity of X band backscattering to the target snow parameters. As a second step of the analysis, simulations based on a forward electromagnetic model, namely the Dense Medium Radiative Transfer (DMRT), are considered for interpreting and assessing the experimental findings. Finally, an attempt of implementing a retrieval algorithm for estimating SWE from X-band data is carried out. The algorithm is based on machine learning approaches, namely Supported Vector Regressions (SVR) and Artificial Neural Networks (ANN). The training of both algorithms accounts for experimental data and DMRT model simulations. These algorithms are applied to time series of COSMO-SkyMed (CSK) images collected on both test areas. The obtained results are encouraging, although more analysis and validation is needed for exploiting the potential of SAR X band in snow parameter retrieval.

This research aims at exploiting the integration of C- and X-band SAR data for the monitoring of Soil Moisture Content (SMC). Time series of Radarsat2 (RS2) and COSMO-SkyMed (CSK) images are collected on two test areas, located in Italy and in Canada. The backscattering sensitivity to SMC measured by in-situ stations is investigated considering the available sensor frequencies and polarizations. In addition, for exploiting the potential of fully polarimetric acquisitions of RS2, simulated Compact Polarimetric (CP) data are computed by using a Radarsat Constellation Mission (RCM) data simulator, and their sensitivity to the target SMC is examined. Based on the experimental findings, two machine learning (ML) approaches to the SMC retrieval, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN) are implemented and tested on the two areas. Looking at the preliminary results, the integration of X- and C-band images does provide valuable information for the retrieval of SMC, while the simulated CP parameters exhibit a certain sensitivity to SMC. On the South Tyrol test area, both SVR and ANN tested with different combinations of RS2 and CSK data were able to retrieve SMC with a RMSE between 2% and 4% of SMC and correlation coefficient R between 0.85 and 0.97, depending on the combination of inputs. The application of the ML algorithms to the other available images on the Mazia test area and the implementation of the ML retrieval algorithm using CP data are still under investigation.

InSAR coherence is a promising parameter for land-cover classification and mapping. The ESA SEOM SInCohMap project is devised to test and analyze multi-temporal InSAR coherence potentialities exploiting dense multitemporal data from the Sentinel-1 constellation. In the framework of the project, this paper shows the first classification results using machine learning algorithms over a two-year period of InSAR coherence data. The evaluation is performed on the test site of Doñana (Seville, Southwestern Spain), mainly an agricultural area where different land covers can be identified. Classification results exploiting InSAR coherence shows accuracies around 80 % for this site.

This research aims at analyzing the integration of C and X band data collected from Radarsat2 (RS2) and COSMO-SkyMed (CSK) systems on two Italian test areas, located in South-Tyrol and in Tuscany, close to Florence, to estimate soil moisture (SMC, in %) and vegetation biomass (PWC, in kg/m2). Two retrieval approaches based on Support Vector Regression (SVR) and Artificial Neural Network (ANN) have been applied to these areas. Looking at the preliminary results, it has been noted that the integration of X and C band images could provide valuable information for the retrieval of SMC, even though further investigations should be carried out on a larger time-series and set of samples. On the South Tyrol test area, SVR methods provided an accuracy in the estimate of SMC with determination coefficient, R2> 0.85 and root mean square error, RMSE2from 0.35 to 0.8 and RMSE= from 6 to 2 (% SMC), depending on the polarization combinations considered as input. X band allowed instead retrieving the PWC of cereal fields with a satisfactory accuracy (R2=0.94 and RMSE=0.35 Kg/m2).

Soil moisture (SM) inversions based on active microwave remote sensing have shown promising progress but do not easily meet expected application requirements because a number of inversion algorithms can only produce point estimates of SM and cannot quantify the uncertainty of SM inversions. Although previous studies have reported Bayesian maximum posterior estimations that are capable of retrieving SM within a probabilistic framework, they have primarily focused on the optimal estimators of SM and have typically ignored the uncertainty of SM inversions. This paper presents an SM probabilistic inversion (PI) algorithm based on Bayes' theorem and the Markov Chain Monte Carlo technique and capable of revealing the uncertainty of SM inversions and obtaining highly accurate SM estimates via maximum likelihood estimations (MLEs). The algorithm is implemented based on the advanced integral equation model, water cloud model simulations, and dual-polarized TerraSAR-X observations. The ground SM and vegetation water content (VWC) measurements from the Heihe watershed allied telemetry experimental research experiments are applied for validation. The results show that: 1) uncertainties in SM inversions, defined with respect to the measures of dispersion of SM posterior probability distribution, are approximately 0.1-0.12 m3/m3and 2) an acceptable inversion accuracy is obtained via MLEs, which present an SM Root Mean Square Error (RMSE) of 0.045 and 0.047 m3/m3for bare and vegetated soils, respectively, and a VWC RMSE of 0.45 kg/m2. The presented PI can quantify the uncertainty in SM inversions; therefore, it should be useful for improving active microwave remote sensing estimations of SM.

In this paper, we address the problem of retrieving soil moisture over a grassland alpine area from Synthetic Aperture Radar (SAR) data using a statistical algorithm trained by simulations of a physical model. A time series of C-band VV-polarized Wide Swath images acquired by Envisat Advanced SAR (ASAR) in the snow-free periods of 2010 and 2011 was simulated using a discrete radiative transfer model (RTM). The test area was located in the Mazia valley, South Tyrol (Italy), where the main land types are meadows and pastures. Soil moisture was collected from five meteorological stations, two of which situated in meadows and the rest in pastures. The smallest and the highest RMSEs of the RTM simulations were 0.78 dB and 1.91 dB, respectively. After backscattering simulation, the top soil moisture was estimated using Gaussian Process Regression (GPR). GPR was trained with the backscatter model simulations (including terrain features) for 2010, and then used to predict moisture from radar observations acquired in 2011. The relative importance of different input features was also assessed. The RMSE of the predicted soil moisture for the largest training data set (including aspect as a terrain feature) was 5.6% Vol. and the corresponding correlation coefficient was 0.84.

This paper presents a novel hybrid method to the estimation of bio/geophysical parameters, which models and corrects deviations from correct target values when theoretical electromagnetic models are used for the inversion process. The proposed hybrid method integrates theoretical models with empirical observations associated to a few field reference samples. This is achieved based on two steps. In the first step, deviations between estimations obtained by a theoretical model and empirical observations are initially computed. Then, deviations associated to unlabeled samples (for which reference measures are not existing) are characterized based on two different strategies: 1) the global deviation bias strategy (which assumes that the deviations of samples are constant within the input space); and 2) the local deviation bias strategy (which assumes that the deviations of samples are variable within different portions of the input space). In the second step, the theoretical model estimates of unlabeled samples are corrected based on the estimated deviations. The experimental analysis carried out in the context of soil moisture content retrieval from microwave remotely sensed data confirms the effectiveness of the proposed hybrid estimation method.

This paper presents the Radar for Icy Moon Exploration (RIME) that is a fundamental payload in the Jupiter Icy Moon Explorer (JUICE) mission of the European Space Agency (ESA). RIME is a radar sounder aimed to study the subsurface of Jupiter's icy moons Ganymede, Europa and Callisto. The paper illustrates the main goals of RIME, its architecture and parameters and some recent advances in its design.

With this work, we present a method for the detection of alpine permafrost surface deformations by using DInSAR (Differential SAR Interferometry) technique, integrating RADARSAT-2 and COSMO-SkyMed data through Support Vector Machine (SVM). On our test dataset, the combination of the two sensors produces an increase of classification accuracy equal to 8.5% with respect to the case in which only one sensor is employed, leading to an overall accuracy equal to 86.9%. We are also showing here how COSMO-SkyMed data time series acquired in the snow-free period are well suited to estimate surface deformations on some particular alpine rock glaciers. The velocities estimated with the SBAS (Small BAseline Subset) algorithm are well correlated with velocity measurements obtained by means of a ground based total station, showing a root mean squared error (RMSE) and R square value equal to 3.5 cm and to 0.67 respectively.

This study aims at presenting the results of a correlation analysis between the COSMO-SkyMed X-band backscattering coefficients (σ0) at VV and VH polarization and the DEIMOS-based Normalized Differential Vegetation Index (NDVI), carried out over carrot and pea fields. The analysis shows a significant higher correlation at VH (R=0.70 and R=0.65 resp.) than at VV polarization (R=0.15and R=0.32 resp.) for both the crop species analyzed. The results seem to suggest the possibility of using SAR X-Band VH data for crop status monitoring, at least for carrot and pea fields.

In this work the possibilities of combining modelled (GEOtop, Hydrological model) and remotely sensed (ENVISAT ASAR WS) soil moisture content (SMC) values were investigated introducing a novel approach for data fusion on a product level. Data fusion was performed through the definition of a correction term for the modelled SMC dataset. For the determination of this term machine learning (Support Vector Regression) was used. As a reference dataset in-situ SMC measurements were considered. The benefit of the proposed method was successfully shown as R2between modelled and measured SMC values was improved from 0.11 to 0.61.

This research aims at analyzing the integration of C and X band data collected from Radarsat2 (RS2) and COSMO-SkyMed (CSK) systems on some test areas in Italy, in order to estimate the main geophysical parameters of soil and vegetation, such as soil moisture and vegetation biomass. A check of the sensitivity of SAR signal to the soil parameters was first carried out on both test sites. Over the South-Tyrol area a retrieval approach based on the Support Vector Regression methodology, which was already tested in this area using C-band data from ENVISAT/ASAR data, was carried out. From these preliminary results it can be concluded that X-band images combined with C-band images could provide valuable information for the retrieval of SMC, even though further investigations should be carried out on a larger time-series and larger set of samples.

An Artificial Neural Network (ANN) algorithm for the Soil Moisture Content (SMC) retrieval from the C-band EPS-SG SCA scatterometer, which will replace the Metop ASCAT, was implemented and tested with real data and model simulations. The main aim of this activity was in understanding the potential of VH channel, which inclusion on the mid-beam antenna of EPS-SG SCA is currently being considered, for improving the retrieval accuracy respect to the existing SMC product derived from ASCAT.

The prediction of monthly mean discharge is critical for water resources management. Statistical methods applied on discharge time series are traditionally used for predicting this kind of slow response hydrological events. With this paper we present a Support Vector Regression (SVR) system able to predict monthly mean discharge considering discharge and snow cover extent (250 meters resolution obtained by MODIS images) time series as inputs. Additional meteorological and climatic variables are also tested as inputs for the SVR approach. The prediction system has been evaluated on 14 catchments in South Tyrol (Northern Italy). Considering as a reference the estimates based on the average discharge computed on the past 10 years, which is a common practice for water resources management in the study region, the percentage root mean square error (RMSE%) is reduced of 11% and 6% for a prediction lag of 1 and 3 months respectively.

In this study we evaluate five different retrieval algorithms, applied on MetOp-A ASCAT backscatter data, in their ability to retrieve soil moisture on a global scale. Correlation and triple collocation analysis are performed using in situ and land surface model data as a reference. Results do not clearly identify one best algorithm. We therefore conclude that future work should focus on the exploitation of the strengths and weaknesses of different modelling approaches in a synergetic way rather than trying to find one model that suits every possible situation.

The COSMO-SkyMed®X-band SAR satellites constellation, due to its capability to acquire dense temporal data series at very high to high ground resolution and in co and cross-polarizations, is a promising system for crops monitoring and management. Its use, in combination with other SAR and optical systems in a virtual constellation, can result in a very effective tool for agricultural practice monitoring, management and control. This work intends to show a preliminary analysis of a dataset of COSMO-SkyMed®products acquired at HH, VV and VH polarizations over the Marchfeld agricultural area in Austria. The analysis has been carried out at a regional scale by taking into consideration the temporal behavior of two different clusters of vegetation macro-classes distinct for their different Normalized Differential Vegetation Index (NDVI) temporal signatures. Preliminary results show a significant correlation of the NDVI values with the COSMO-SkyMed®HH backscattering coefficients for both the clusters of classes as well as a significant one with VV backscattering coefficients for NDVI values lower than 0.7.

On mountain areas, one main important pre-processing step for optical satellite imagery is the topographic correction. The illumination condition strongly depends on the area topography, sun elevation and azimuth and acquisition geometry of the sensor. These elements generate unequal light distribution over the observed surface. This effect needs to be corrected to improve classification and parameters retrieval performances. In this paper, a novel empirical approach for topographic correction is presented using the combined (direct and diffuse) solar radiation. The combined solar radiation has the advantage to take into account the brightening effect of topography on scene radiance. The approach aims at defining correction coefficients depending on the different land cover. We tested our approach using both high and medium resolution images and terrain effects in both data sets were considerably reduced.

In mountain areas, soil moisture is a key parameter for both agricultural management and natural hazard support. This paper presents an approach for retrieval of soil moisture content (SMC) from different satellite sensors with a specific focus on mountain areas. The experimental analysis was carried out on images acquired over the Südtirol/Alto Adige Province (Italy) during 2010-2011 from the RADARSAT2 in quad-pol mode and Envisat ASAR in Wide Swath mode in VV polarization. The methodology for soil moisture retrieval is based on the Support Vector Regression (SVR) method specifically trained to be able to consider topographic effects of the mountain areas. The comparison with ground measurements collected during field campaigns indicates an RMSE value of around 5% of SMC% while the comparison with fixed ground stations reports an error of around 9% of SMC%. Comparing RADARSAT2 and ASAR SMC, both datasets reveal very similar distributions of SMC values. The cumulative histogram curve for the two datasets shows a slight underestimation of SMC in the ASAR product. This could be ascribed to the reduced resolution of ASAR WS and the use of VV polarization.

This paper presents the results concerning the application of COSMO-SkyMed SAR images to mountain areas for the challenging retrieval of some key parameters of the hydrological cycle: soil moisture content (SMC), snow depth (SD), and snow water equivalent (SWE). The results obtained so far are encouraging. Regarding SMC, the results indicate that, in spite of the low penetration capability of X-band wavelength, the SAR signal is well related to soil moisture variations, even in presence of vegetation. As far as snow cover is concerned, from the analysis of data it has been observed that the backscattering coefficient remains almost constant until the SD of dry snow accumulated on soil is higher than 50-60 cm and increases rapidly as SD rises up to 150 cm. The use of an inversion algorithm for the retrieval of SWE, based on Artificial Neural Networks, showed a determination coefficient higher than 0.8, with an associated probability value of 95%.

A Bayesian approach for soil moisture change detection under different roughness conditions is proposed in this letter. The main objective of this approach is to exploit the changes in backscattering signals and relate them to soil moisture variations over agricultural fields by considering also the possible changes in the radar signal due to roughness variability. The method is trained and tested on two data sets acquired during SMEX'02 experiment. One data set considers AirSAR P-band data for which the soil can be considered bare and the second data set considers the correspondent L-band data for which the influence of vegetation cannot be considered negligible. The results indicate that the approach is able to detect soil moisture changes both for P-band and L-band data. In case of L-band one main problem is indeed the presence of vegetation which reduces the backscattering coefficients dynamics.

This paper presents an approach to improve the capability to retrieve soil moisture information from SAR data. More in details the proposed approach consider different inversion approaches and outlines a procedure how to combine the results derived from these regressors with the main aim to improve the accuracy in the estimation of the target variables. The approach was tested in the case of fully polarimetric AirSAR images acquired over agricultural fields covered with soybean and corn crops. The single regressors were an empirical and a Bayesian approach. The approaches were applied to C and L band images and also to a combination of both frequencies. The results indicate that when the retrieved information from the regressors are properly combined based on the select figures of merit such as R2and RMSE the accuracy can improve up to around 30%.

A comparison among multi-source data with the main aim to detect soil moisture dynamics in an Alpine catchment is presented. The data sources are: ground measurements derived from field campaigns and meteorological stations, simulations from a hydrological model and estimates derived from SAR images. The test site is located in the north-western part of South Tyrol, mainly covered by pastures and meadows. The analysis indicates that the diverse sources are able to detect soil moisture dynamics at different spatial and temporal scales. Remote-sensing observations show consistent patterns through the summer season. Major control is land-use, with irrigated meadows in the bottom of the valley being the moister areas, and pastures along the upper hillslope the driest areas. Secondary control is topography, with increased moisture in convergent locations. Model simulations better reproduce the temporal trends as also detected by the ground stations; however, spatial patterns are quite different, with models results showing a much more uniform distribution.

The aim of this research is to examine the ability of an approach based on Bayesian inference to retrieve surface soil moisture in an experimental agricultural area located in the province of Córdoba, Argentina. Radar images from SARAT sensor were used, as well as measurements of biophysical parameters in the field. Several implementations of the main algorithm were designed to evaluate their different capability to reproduce the ground data. The Bayesian inversion was performed based on electromagnetic model: the Integral Equation Model (IEM) for bare soil, and the Water Cloud Model (WCM) for vegetated fields. For bare soil, the results showed high sensitivity of the algorithms to the different roughness conditions of each plot, while for vegetated areas, the availability of field measurements limited the comparisons between the obtained maps and the in situ data.

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.

The potential of SAR data in discriminating vegetation/forest types it is here explored using Neural Networks (NN) in an Alpine environment. Amplitude data from two SAR polarimetric sensors, namely RADARSAT2 Standard Quad Polarization (SQP) and ALOS PALSAR Fine Beam Dual (FBD), were used separately and in conjunction to discriminate four vegetation types: conifer forest, broadleaved forest, riparian vegetation, and dwarf pine and shrubs (mainly composed by Pinus mugo species). Results indicate successful separation of needle-leaved from broadleaved and/or riparian vegetation, but scarce ability to discriminate the other two types. ALOS PALSAR produced better results in separating vegetation types with respect to RADARSAT2 reaching in the best case a K Cohen's coefficient equal to 0.88. Results obtained from combination of the two SAR data were successful, but still in the range of those obtained by single scene usage.

Observations of the 3D ice velocity field are important for studies of glacier hydraulics and for modeling the dynamic response of glaciers to changing boundary conditions. A method for 3D ice velocity retrieval from repeat pass SAR data of crossing orbits applying offset tracking in amplitude images is presented. In contrast to the conventional technique for ice motion mapping which assumes surface-parallel flow, this method delivers the true velocity vector. The procedure is validated using in-situ GPS data on an outlet glacier of the Vatnajökull ice cap in Iceland.

A new change detection algorithm based on a Bayesian approach is developed and tested. The main objective of this approach is to exploit the changes in backscattering signals and relate them to soil moisture variations over agricultural fields under the hypothesis of both constant and variable roughness. The proposed methodology overcomes the limitations of the some change detection methods because it takes into account also possible changes in the radar signal due to roughness variability. The method is trained and tested on two data sets considering both C and L-band backscattering coefficients in relation to soil moisture and roughness measurements. The C-band dataset was acquired over bare soils while the L-band data set was acquired on vegetated fields and was exploited to understand the impact of vegetation in such approach. The results indicate that the approach is able to detect soil moisture changes both for C-and L-band data. In case of L band data, the presence of vegetation seems to determine backscattering dynamics reduction with respect to soil moisture changes.

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%.

The main objective of this paper is to assess the capability of a soil moisture (SMC) algorithm adapted to the GMES Sentinel-1 characteristics, developed within the framework of an ESA project (SMAD-1). The SMC product shall be generated from Sentinel-1 data in near-real-time and delivered to the GMES services within 3 hours from observations. Two different complementary approaches were proposed: the first approach was based on Artificial Neural Networks (ANN), which represented the best compromise between retrieval accuracy and processing time, thus being compliant with the timeliness requirements. The second approach was based on a Bayesian Multi-temporal method, allowing an increase of the retrieval accuracy, especially in case of few ancillary data available, at the cost of computational efficiency, taking advantage of the frequent revisit time achieved by Sentinel-1. The algorithm was validated in several test areas in Italy, US and Australia, and finally in Spain by performing a `blind' validation.

This paper presents a study on the retrieval of snowpack biophysical parameters in mountain areas from satellite remote sensing imagery. More in detail, the integration of new generation X-band Cosmo SkyMed SAR imagery and land surface temperature (LST) information derived from optical thermal remote sensing is investigated. First, a sensitivity analysis is carried out, in order to understand whether and to what extent the investigated remote sensing signals are sensitive to variations in different snowpack target parameters. Then, an advanced retrieval system based on the Support Vector Machine approach in a multilevel architecture is developed. Experiments carried out in a small valley in the eastern Alps during the winter 2010/11 point out the effectiveness of the combined use of X-band SAR and thermal satellite imagery for the characterization of snowpack parameters in terms of both accuracy on reference point measurements and capability to reproduce spatial patterns of the target variables.

The Italian Space Agency (ASI Agenzia Spaziale Italiana) funded 27 scientific projects in the framework of COSMO-SkyMed (CSK) program. A subset of them focused on the improvements of the quality and quantity of information which can be extracted from X-SAR data if integrated with other independent techniques (GPS, data and imagery in other bands and wavelengths). The paper summarizes the results obtained from same of these projects and, in particular, regarding: the use of GPS observations and Numerical Weather Models (NWM) to remove atmospheric artifacts from InSAR imagery so improving the CSK potentialities in the field of topographic mapping; the integration of SAR data in X, L and C bands to improve snow cover monitoring and glaciers detection; the use of X-SAR data to retrieve rain precipitation and its validation with C-band radar observations; the improvements of the focusing techniques.

The objective of this study is to infer a soil moisture index from an approach mainly based on the concept of apparent thermal inertia (ATI). To reduce the effect of spurious variability and cloud presence, soil moisture temporal trend derived from passive microwave based product, namely the NASA AMSR-E-soil moisture product, are used as a tool to filter the data. The AMSR-E data due to their coarse resolution can be considered as natural “low pass filter” thus reducing the effect of noise. Furthermore, the approach considers the soil moisture estimates derived from SAR sensors and use them to spatially calibrate the information coming from the optical data. The algorithm has been validated over two different test areas in Italy and France where ground truth measurements were available. Four main clusters of ATI have been identified and classified into 4 different levels of wetness. In densely vegetated areas, only three classes of soil moisture were distinguishable. The comparison with ground measurements indicates an accuracy of around 88% on the Italian test sites and of 73% on the French test sites, the last mainly characterized by densely vegetated fields.

This paper presents a novel hybrid approach to the estimation of biophysical parameters from remotely sensed data. This approach integrates theoretical analytical models and empirical models based on field reference samples to increase the reliability and the accuracy of the estimation. The estimation process is modeled by two terms: the first one expresses the relationship between the input features and the target biophysical variable according a theoretical model based on the physics of the considered problem; the second one corrects the deviation between theoretical model estimates and true target values according to an empirical data-driven model. The latter is derived by exploiting the available (typically few) field reference samples. In this way the robustness and generality of theoretical model based estimates, which stem from the rigorous theoretical foundation, is preserved, while the bias and imprecision (due to simplifications in the analytical formulations of the model with respect to the real estimation process) are reduced. Results achieved for the specific application of soil moisture estimation from microwave remotely sensed data with two different correction strategies are reported. These results show the effectiveness and the potentiality of the proposed integration approach.

In this work, fully polarimetric RADARSAT2 SAR images and advanced feature extraction strategies are investigated for improving the retrieval of soil moisture content in Alpine meadows and pastures. More in detail, standard Intensity & Phase polarimetric features, polarimetric decompositions and general purpose feature extraction strategies are exploited in combination with a sequential forward selection to increase the accuracy of the system. The capability of the system to provide spatially and temporally distributed estimates of soil moisture is also addressed by using different satellite acquisitions. The achieved results indicate that the polarimetric information in the SAR data, if properly exploited, allows one to improve the estimation of soil moisture content in the investigated mountain area. Concerning the mapping of the target variable, the analysis of the results suggest that the proposed estimation system is promising and effectively maps the soil moisture status both in time and space.

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.

This article presents an object-oriented classification approach to identifying orchards, vineyards and agricultural fields. This approach uses texture-related parameters obtained from very high spatial resolution data, in particular Quickbird images and orthophotos. A multi-resolution segmentation procedure was applied to delimit individual agricultural plots as segments. Textural information of the generated segments was then used to classify orchards, agricultural fields (grassland) and two wine cultivation systems (trellis and pergola). In this article three different texture-based approaches are compared to correctly classify the given plots: (1) a ‘zonal mean maximum’ of texture measurements, which consider the maximum value of four directions of texture measurements related to plots; (2) a relational sub-object approach based on a thematic derived texture filter technique that reflects individual row structures; and (3) a hybrid approach combining the two previous ones. In order to identify relevant parameters for each approach, the data mining software See5 is used. The hybrid approach increased overall accuracy by 8% and 6% for Quickbird (92% accuracy) and orthophotos (88% accuracy), respectively. The application of the same methodology to the orthophotos alone results in a lower accuracy but still one that is acceptable. This offers the possibility of also considering orthophotos for this kind of detection, especially when Quickbird data are not available. In this sense, the developed methodology can be considered as a new object-based landscape analysis technique suitable for the provision of accurate maps able to fulfil the requirements of scientists, planners and other end-users.

This letter presents an experimental analysis of the application of the ε-insensitive support vector regression (SVR) technique to soil moisture content estimation from remotely sensed data at field/basin scale. SVR has attractive properties, such as ease of use, good intrinsic generalization capability, and robustness to noise in the training data, which make it a valid candidate as an alternative to more traditional neural-network-based techniques usually adopted in soil moisture content estimation. Its effectiveness in this application is assessed by using field measurements and considering various combinations of the input features (i.e., different active and/or passive microwave measurements acquired using various sensor frequencies, polarizations, and acquisition geometries). The performance of the SVR method (in terms of estimation accuracy, generalization capability, computational complexity, and ease of use) is compared with that achieved using a multilayer perceptron neural network, which is considered as a benchmark in the addressed application. This analysis provides useful indications for building soil moisture estimation processors for upcoming satellites or near-real-time applications.

This work aims at deriving a methodology for calculation of a soil moisture index based on the apparent thermal inertia (ATI) approach. For the processing, MODIS images have been exploited which have a higher resolution (1 km) if compared with METEOSAT images and are suitable for the ATI calculation. Furthermore, the approach considers the soil moisture estimates derived from SAR sensors and use them to calibrate the information coming from the optical data. The main advantage of this approach is to transform a soil moisture index derived from optical images in soil moisture values by using a comparison between spatial distributed data. In order to make the calibration more robust and consider the variability from different areas, three main test sites have been chosen located in Italian regions with different meteorological and landscape characteristics. In case of anomalous values due to the not appropriate acquisition time, AMSRE soil moisture data are used as prior information in order to improve the estimates.

This paper proposes to model the critical issue of the choice of the free parameters of a supervised non-linear regression technique (the so called model selection issue) as a multiobjective optimization problem. In this framework, the multi-objective function is made up of a set of two or more quality metrics (e.g., MSE, R2, etc.) computed on the test (or validation) samples. A set of solutions is derived according to the concept of Pareto optimality. The advantages of the proposed approach with respect to the traditional ones (which typically optimize a single scalar metric) are mainly two: (1) the capability to derive solutions which jointly optimize the set of metrics considered and represent different possible optimal tradeoffs among them; and (2) the possibility for the user to effectively select the model that optimizes the requirements of the specific retrieval problem. Results achieved for the specific application of soil moisture estimation from microwave remotely sensed data with the Support Vector Regression (SVR) technique are reported. These results show the effectiveness of the proposed approach.

This paper presents a semi-automatic procedure for the classification of high resolution images in order to obtain a fast update of land cover - land use maps. The adopted methodology can be considered as both recursive and hierarchical. It is hierarchical since it is developed as a top-down procedure starting from the main classes and moving down towards subclasses in three levels. It is recursive because in each level the classification procedure is repeated until a fixed accuracy threshold is reached by considering the results of different classifiers. Innovative aspects of the classification procedure are the use of an existing land-use map as a-priori information and an integrated land use model which indicates the probability of a certain class according to the landscape characteristics (slope, altitude and aspect). The study is carried out on six test sites located in South Tyrol, northern Italy. The procedure is applied to 6 SPOT V images (10-meter resolution. The achieved accuracy in all classes was higher than 93%.

This paper presents a cross-comparison of the data acquired by the MODIS, CLOUDSAT and CALIPSO sensors in order to understand the limit of the developed cloud-mask algorithm and to provide a quantitative validation assessment by using exclusively remotely sensed data. The comparison has been carried out by considering both the cloud mask and the intermediate levels such as the brightness temperatures and the reflectance values for different channels from which the cloud mask is derived. The preliminary analysis indicates a general good agreement among the different sources. A main underestimation of cloud cover is present on the sea and especially for high thin clouds. First results indicate that in order to increase the cloud cover accuracy the threshold for the intermediate levels (brightness temperature and reflectance values) may be changed by taking into account also the cloud vertical profiles.

This paper focuses on the Titan surface parameters retrieval with emphasis on a combination of passive and active microwave measurements from Cassini spacecraft on the areas characterized by large liquid surfaces and neighboring land areas. The methodology consists of a combination of direct modeling and inversion algorithms. First, these surfaces have been described by means of a double layer model which considers an upper liquid hydrocarbons 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, in particular the optical thickness of the hypothesized liquid hydrocarbons layer and the wind speed. Second, the optical thickness information is used as an input to a forward radiative transfer model calculation to obtain simulated brightness temperatures. Comparison of the observed and computed brightness temperatures allows addressing the consistency of the observations from the two instruments.

The analysis derived from the Cassini SAR imagery reflects the complex Titan's 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.

Neural network (NN) approaches and statistical methods, based on a Bayesian procedure, are applied and compared in soil moisture (SM) retrieval from remotely sensed data. The principles and the practical implementations of Bayesian procedures and NNs are briefly discussed in terms of the advantages and disadvantages of each. Experimental tests are carried out by using the same set of training and test data for each method. The methodologies have been applied to two sets of data to retrieve SM from bare soils and to verify their accuracy. One data set contains scatterometer and radiometer data acquired on a variety of agricultural fields in different polarizations, frequencies, and incidence angles. The other is made up of five experiments carried out with a C-band scatterometer on rough and smooth soils at different polarizations and incidence angles. There are significant similarities in the performance of each method; they both retrieve the same features and trends in the analyzed data sets. Algorithm performances change according to SM level and data configuration. The main difficulties are found in retrieving low SM values, and in this case, the error on estimates is reduced when the data with two polarizations or two incidence angles are inserted in the inversion procedure. One major difference between the methodologies is that the NN performance improves, with respect to the Bayesian method, when more inputs are presented as two polarizations or two incidence angles in the training phase.

This paper proposes an alternative inversion algorithm for extracting soil and vegetation parameters from multi-frequency, multi-polarimetric SAR data. The alternative approaches aim at identifying a useful modelization of the soil and vegetation response to radar signal and then indicate a possible solution to the extraction of soil moisture and vegetation water content. The core of the algorithm is based on the determination of probability density functions (pdfs) through a Bayesian methodology and has been initially developed for bare soils and tested on numerous data sets. The purpose is to apply this inversion algorithm to fields that have different levels of vegetation cover and considering different theoretical and empirical approaches. In fact, as already stated, a single approach, theoretical or empirical model, cannot be often applied on a wide number of cases. This paper addresses a possible solution based on two alternative different modelizations.

During the first two years of the Cassini mission, a great amount of data dealing with Titan's surface has been collected. In particular, the analysis derived from the SAR imagery reflects the complex Titan's surface morphology. In fact, in the different Cassini radar images a certain number of areas with peculiar features has been identified, such as: dark and bright areas (Ta, T3), periodic structure ("sand dunes") and, above all, hydrocarbon lakes [2],[11]. The proof for the presence of hydrocarbons lakes on Titan has been obtained during the T16, the radar pass performed on Titan by the Cassini spacecraft on 22 July 2006 [12]. In this paper, the investigation of Titan's surface parameters (physical and morphological) has been carried out by the means of Bayesian inversion technique, and simulations of the wave motion for the hypothesized hydrocarbons liquid surfaces has been performed.

This paper addresses the capability of synthetic aperture radar and optical images in combination with theoretical models to detect the vegetation water content (VWC) at field level. In this paper, a retrieval algorithm for the estimation of VWC from AirSAR acquired on vegetated fields during the SMEX'02 experiment is addressed. The aforementioned campaign has been chosen because, along with sensor observations, extensive ground truth measurements were acquired. The retrieval procedure, which is based on a Bayesian approach, has been initially developed for soil moisture extraction. It consists of two modules: one is pertinent to bare soils and the other one has been modified for vegetated fields. The last one uses the synergy with optical images to correct for the contribution of VWC. The VWC, a variable in the inversion procedure, as well as soil moisture can be estimated. The results indicate a good correlation with both ground measurements and VWC calculated from Landsat images through the use of normalized difference water index (NDWI). Furthermore, in the inversion procedure, the introduction of the dependence on roughness improves the estimates. This indicates that, even for dense vegetation, the contribution from bare soil greatly influences the radar signal. Three main levels of VWC are discriminated in the inversion procedure: values below 1 kg/m2, values between 1 and 3 kg/m2, and values greater than 3 kg/m2.

This work assesses the possibility of obtaining soil moisture maps of vegetated fields using information derived from radar and optical images. The sensor and field data were acquired during the SMEX'02 experiment. The retrieval was obtained by using a Bayesian approach, where the key point is the evaluation of probability density functions (pdfs) based on the knowledge of soil parameter measurements and of the corresponding remotely sensing data. The purpose is to determine a useful parameterization of vegetation backscattering effects through suitable pdfs to be later used in the inversion algorithm. The correlation coefficients between measured and extracted soil moisture values are R=0.68 for C-band and R=0.60 for L-band. The pdf parameters have been found to be correlated to the vegetation water content estimated from a Landsat image with correlation coefficients of R=0.65 and 0.91 for C- and L-bands, respectively. In consideration of these correlations, a second run of the Bayesian procedure has been performed where the pdf parameters are variable with vegetation water content. This second procedure allows the improvement of inversion results for the L-band. The results derived from the Bayesian approach have also been compared with a classical inversion method that is based on a linear relationship between soil moisture and the backscattering coefficients for horizontal and vertical polarizations.

In the context of the project HYDRO-POL, a study was carried out to test the efficiency of two different approaches: the use of L band active and passive data or the use of L-C-X bands passive data to retrieve soil moisture of bare soils. Simulated data are generated implementing classical superficial scattering models: IEM model for active L-band and L-C-band passive data, GO model for X-band passive data. Data are simulated considering different roughness conditions and moisture content. As the inversion problem is very complex, artificial feedforward backpropagation neural networks (NN) were employed. The best performing NNs are chosen to simulate a retrieval with a dataset artificially added with noise. In each case, the best retrieved parameter is the real part of the dielectric constant, while roughness parameters, especially autocorrelation length, is not very well retrieved. In many cases, retrieved values are out of range, so that the simulated values and targets appear unrelated. Applying a very generic filter that eliminates values very far from the proper range, correlation coefficients grow up. This filter cleans up the resulting data removing a small part of them. After this filtration, correlation coefficients relative to the real part of the dielectric constant surpass 0.82. In spite of the filtering process, roughness parameters retrieval is of inferior quality. On smooth soil, the three considered configurations work in an equivalent way, excellently retrieving the real part of the dielectric constant, without a need for filtration. On medium and rough soil, inversion results generally more difficult, so that performance gets worse. Active-passive approach results more efficient than the L-C-X one

Six remote sensing experiments are analyzed in order to study the feasibility of soil parameters extraction from active and passive microwave data. The inversion process has been carried out through two methodologies: a Bayesian and a neural network approach. Two different sets of data have been analyzed: one experiment with active and passive data on a smooth soil and five experiments carried out with a C-band scatterometer on rough and smooth soils at different polarizations and incidence angles. In the case of active and passive data, using a Bayesian algorithm, the correlation coefficients between the extracted and the measured values of soil moisture are R=0.83, R=0.84 and 0.72 for the three analyzed data configurations. In the neural network approach, the correlation coefficients are R=0.72, R=0.83 and 0.79. The best performance is achieved when two different frequencies, 4.6 GHz for active data and 2.5 GHz for passive data are employed where the neural networks produce the lowest errors in the estimates. For the second group of data, the neural network makes fewer mistakes and overestimates only the values of epsiv that originated from backscattering coefficients acquired on the rougher field. The Bayesian approach tends to overestimate the values of epsiv with an average bias of 5%

An inversion technique based on the merging of microwave remotely sensed data is applied to ground-based radiometer and scatterometer data acquired for the same area. The purpose of this technique is to retrieve the dielectric constant of bare soils. The algorithm is based on a Bayesian approach and combines prior information on the dielectric constant and surface roughness with observed data, in order to obtain a marginal posterior probability density function. The function describes how the probability is distributed within the range of the dielectric constant values, given the measured values of emissivity and backscattering coefficient. The algorithm allows for the incorporation of all the available sources of information, such as multipolarization and multifrequency data. Several criteria, which have been used to compare the predicted and the observed values, show that for dielectric constant values higher than 10 the best performance is achieved when data with one polarization and one or two frequencies are exploited. For dielectric constant values of less than 10, the configuration with two polarizations produces the best estimates.

An algorithm is presented for retrieving soil parameters using microwave remotely sensed data. The algorithm is based on Bayes' theorem of conditional probability and combines prior information on soil moisture and surface roughness with remote sensing measurements. In the Bayesian inference, the key point is the evaluation of a joint density probability function based on the knowledge of data sets consisting of soil parameters measurements and of the corresponding remote sensing data. The calculation of the marginal distribution has been obtained by a numerical integration known as Markov Chain Monte Carlo. This method is especially useful when the posterior density function has not a standard form. Furthermore, it is possible to obtain, at the same time, the distribution for all the parameters included in the process.

This paper investigates the relationship between C-band backscatter measurements and wheat biomass and the underlying soil moisture content. It aims to define strategies for retrieval algorithms with a view to using satellite C-band synthetic aperture radar (SAR) data to monitor wheat growth. The study is based on a ground-based scatterometer experiment conducted on a wheat field at the Matera site in Italy during the 2001 growing season. From March to June 2001, eight C-band scatterometer acquisitions at horizontal-horizontal and vertical-vertical polarization, with incidence angles ranging from 23/spl deg/ to 60/spl deg/, were taken. At the same time, soil moisture, wheat biomass, and canopy structure were collected. The paper describes the experiment and investigates the radar sensitivity to biophysical parameters at different polarizations and incidence angles, and at different wheat phenological stages. Based on the experimental results, the retrieval of wheat biomass and soil moisture content using Advanced Synthetic Aperture Radar data is discussed.

This work addresses the possibility of estimating soil moisture values starting from remotely sensed data in the microwave domain. The inversion approach is developed in a Bayesian framework, in order to merge point measurement derived from different sensors. The results indicate that the best combination to obtain reliable estimates of soil moisture is the case when backscattering coefficients and brightness temperature are considered with different polarisations. Furthermore, the introduction of prior information helps the inversion procedure to resolve the unavoidable ambiguities present in such problems.

Microwave sensors are of primary importance in mapping surface states and measuring some significant quantities which affect the hydrological cycle. A space mission aiming at monitoring soil moisture and surface salinity at a global scale is suggested. The mission is based on a combination of polarimetric active and passive microwave sensors.