In this work, we proposed a reappraisal of strong fluctuation theory (SFT) aimed at improving its capability in simulating snow backscattering. As conceived in the original form, SFT considers the air-snow and snow-soil interfaces as flat surfaces. This study aims at accounting for the roughness of the snow-soil interface by coupling SFT with suitable rough soil surface models, namely, Oh model and integral equation model (IEM), with the aim of increasing accuracy and reliability of SFT simulations. The accuracy of the reappraised SFT was evaluated in comparison with SAR data collected by Sentinel-1 (S-1) and COSMO-SkyMed (CSK) over two test areas located in the western part of Italian Alps, for which in situ measurements of the main soil and snow parameters were available. The reappraised SFT was able to significantly improve the simulation accuracy at both frequencies with respect to the original implementation. In the better case, we obtained$R^{2} =0.57$, RMSE =2.9 dB, and bias$= -0.17$dB. A comparison with dense media radiative transfer (DMRT) theory in single-layer configuration demonstrated that the reappraised SFT can obtain comparable results with a significantly smaller computational cost.

Data from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission have shown promise for the retrieval of soil moisture, and many soil moisture products using CYGNSS data have been developed. In this work, we present a merged product that combines several CYGNSS soil moisture products using a minimum variance estimator (MVE). The MVE identifies an optimal weighted averaging scheme based on the error covariance characteristics of the CYGNSS soil moisture products. The error covariance matrix is computed using two reference datasets: soil moisture data from the Soil Moisture Active Passive (SMAP) radiometer and in situ soil moisture data. The results from each of these provide insights into both the performance of the merged product and the individual input CYGNSS products. Overall, the merged product offers better performance than any individual CYGNSS product while also offering better temporal resolution than SMAP. The results of this work also demonstrate that the use of the MVE is a compelling technique for soil moisture applications.

In this paper the potential of integrating optical and microwave data to monitoring vegetation features has been exploited by using experimental data and models. The general idea was to cope the high sensitivity of radar data to water content of vegetation with the high sensitivity of optical data to pigments, thus producing more in-depth information on vegetation status. Two sorghum fields located close to Florence was taken under observation during summers 2022 and 2023, by gathering soil and vegetation parameters and collecting Sentinel-1 and Sentinel-2 images. Backscattering coefficient and some optical indices have been experimentally related to soil and vegetation water content and plant water status. The use of a simple e.m. model allowed estimating the plant water content in the canopy. The obtained results confirmed the validity of the followed approach, although further investigation is needed.

In this study, the retrieval of snow Liquid Water Content (LWC, %) from C- and X- band SAR data was based on Artificial Neural Network (ANN) and Random Forest (RF). Two approaches were explored for generating a sufficient amount of data to train and test the ANN and RF algorithms: the first strategy was defined as “model-driven”. The second one was defined as “data-driven”. The validation results showed that RF performs better than ANN in terms of correlation coefficient R, regardless of the selected approach ("model driven" RANN= 0.60, RRF= 0.68; “data-driven” RANN= 0.50, RRF= 0.88 at X-band). Moreover, the RF implementation trained with the “data-driven” approach outperformed the “model-driven” approach in terms of correlation coefficient R (RRF= 0.88 at X-band).

This study aims at improving the spatial resolution of snow depth (SD) products derived from microwave satellite radiometers by proposing a disaggregation method based on X-band SAR data. The method has been developed and tested in the Western part of Italian Alps, by involving Cosmo SkyMed (CSK) and AMSR-2 data. Machine learning methods play a twofold role in the proposed active/passive (A/P) implementation: the AMSR-2 data disaggregation process is indeed based on Artificial Neural Networks (ANN), while the SD retrieval using the disaggregated data is based on ANN and Random Forest (RF) algorithms. To assess the effectiveness of the proposed A/P technique, the SD retrievals have been compared with those obtained by estimating SD directly from CSK data. Taking advantage of the multifrequency information, the retrievals based on A/P method clearly outperformed those based on CSK data only: correlation increased from R=0.77 to R= 0.85 for the ANN based retrievals and from 0.76 to 0.86 for the RF based retrievals. The corresponding RMSE decreases from 34 cm to 28 cm and from 34 cm to 27 cm for ANN and RF, respectively, in a SD range between 0 and ≃ 220 cm.

This study aims at improving the accuracy of the Strong Fluctuation Theory (SFT) in simulating the backscattering from Alpine snowpacks, by simulating the roughness effect of the snow-soil interface through suitable models, as the Oh model and the Advanced Integral Equation Model (AIEM). As conceived in the original form indeed, SFT considers the air-snow and snow-soil interfaces as flat surfaces: such approximation can lead to inaccurate results under some observed conditions. The reappraised SFT was validated against Dense Media Radiative Transfer (DMRT) model simulations and experimental data available from Sentinel-1 (S-1) C-band and COSMO-SkyMed (CSK) X-band SAR in two alpine test sites located in the Northern Italy. The inclusion of rough soil contribution was found effective in improving significantly the SFT simulation in dry and wet snow conditions, with a significant improvement of correlation with SAR data: as an example, R2increased from 0.05 to 0.57 in the comparison with CSK. The comparison with DMRT pointed out a very good agreement between the two models, (R2=0.88 at C-band and 0.91 at X-band) with the not negligible advantage of an extremely reduced computational cost of the reappraised SFT with respect to DMRT.

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.

This study focuses on the retrieval of soil moisture (SMC) and forest Aboveground Biomass (AGB), and on the classification of fire disturbances in forests by using the NASA’s Cyclone GNSS (CyGNSS) data over land. Retrieval and classification algorithms, based on machine learning (ML) techniques, as Supported vector machines (SVM), Artificial Neural Networks (ANN) and Random Forests are implemented and validated against reference data from in-situ measurements and EO products.The research, which was carried out in the framework of two ESA project, has the twofold aim of further assessing the potential of GNSS-R for land applications and of defining retrieval concepts to be applied to the ESA’s SCOUT 2 HydroGNSS satellite mission.

In this study, a method for mapping the crop biomass, expressed as Plant Water Content (PWC in kg/m2), at high resolution is proposed. The method is based on SAR data at C and X bands and machine learning algorithms, and it is composed of some steps, including crop classification, soil moisture (SMC) retrieval and finally PWC retrieval. It has been developed and validated in an agricultural area located in Tuscany (Central Italy), for which timeseries of Sentinel-1 and COSMO-SkyMed images were available, along with in situ measurements of the main soil and vegetation parameters.The retrieval, so far limited to the wheat crops, resulted in correlation coefficient R=0.92 and RMSE=0.5 (kg/m2) between estimated and target PWC, by confirming the feasibility of using SAR for monitoring vegetation biomass at high resolution.

This paper deals with the analysis of the backscattering sensitivity at C and X bands to the agricultural crop characteristics and the implementation of a method for estimating crop biomass. The study areas were located in Tuscany (Central Italy) close to Florence. Series of Sentinel-1 and COSMO-SkyMed images have been collected for several years. An accurate crop classification method was first realized in order to separate crops characterized by different scattering behaviors, namely broad- and narrow-leaf crops. The backscattering trends have been simulated by using electromagnetic models based on radiative transfer theory. Algorithms based on Neural Network approaches have been implemented for estimating the crop biomass by using multi-frequency and multi-polarization SAR data at C and Xband.

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.

In this paper, an investigation on the agricultural management in semi-arid Mediterranean regions is presented. The selected test areas are located in Tunisia, near the Kairouan town. The agricultural fields are mainly cultivated with olive trees together with cereals, fruit trees and vegetables. The possibility to monitor this area by means of COSMO-SkyMed (CSK) data, thanks to the ASI Open Call initiative, is an added value to retrieve information concerning the temporal evolution of crop conditions and the use of water in semi-arid regions. The CSK images have been acquired in the period 2018–2019 and the spring 2021. The objectives of this research concern the use of CSK data to evaluate the correct growth of agricultural crop. The preliminary analysis shows that X -band backscatter is able to follow the seasonal moisture conditions and to identify different types of crops.

In this study, a disaggregation technique based on machine learning is proposed. The technique combines Sentinel 1 and AMSR2 data with the aim of enhancing the spatial resolution of the vegetation biomass, expressed herein as Plant Water Content (PWC), and Soil Moisture (SM) products generated from AMSR2 by the HydroAlgo algorithm developed at IFAC. Validation is still in progress; however, the results obtained so far demonstrated the effectiveness of the proposed disaggregation in mapping both PWC and SM at 100m resolution, thus overcoming the problem of coarse spatial resolution that hampers the potential of satellite microwave radiometers as the AMSR2 for operational applications in small scale basins.

Synthetic aperture radar (SAR) sensors, such as Advanced Land Observing Satellite-2 (ALOS-2) and Sentinel-1, provide significant opportunities for soil moisture content (SMC) retrieval with relatively high spatial resolutions (10~30 m). In this work, an artificial neural network (ANN) SMC retrieval algorithm combined with the water cloud model, the advanced integral equation model, and the Oh model database was proposed. The SAR copolarization backscatter, the local incidence angle (LIA), and the normalized difference vegetation index were used in input vectors for the ANN algorithm for the retrieval and mapping of the ALOS-2 and Sentinel-1 SMC at a 30-m resolution. The results of the comparison between the SMC retrievals and the measured SMC show that Sentinel-1 and ALOS-2 SMC retrievals with high accuracy correspond to low-vegetation areas (crop, grass, and shrub), with a root mean square error (RMSE) of 0.021 and 0.033 cm3/cm3, respectively. ALOS-2 SMC retrievals provide higher accuracy (RMSE = 0.076 cm3/cm3) than Sentinel-1 SMC retrievals at high vegetation (e.g., forest). However, it remains challenging for soil moisture retrieval in forest land. The C-band and L-band SMC retrievals have higher RMSE (up to 0.047 cm3/cm3) at low incidence angle (50°). In addition, by considering the impact of rainfall on the SMC, it appears that the Sentinel-1 and ALOS-2 SMC have a good response to the rainfall events. Finally, the results of the comparison between the SMC retrievals and the Soil Moisture Active Passive (SMAP) L2 SMC product show that the correlation coefficients between Sentinel-1, ALOS-2, and SMAP are higher in September when the vegetation is drying than in July when the vegetation is growing.

This study aims at relating the stickiness parameter (τ) of the Dense Media Radiative Transfer theory integrated with Sticky Hard Sphere (SHS) model (DMRT-QMS), to the physical parameters of the layered snowpack. A relationship has been derived to express τ, which modulates the attractive contact force between ice spheres, as a function of ice volume fraction (ϕ) and coordination number (nc). Since τ is not a measurable parameter, this is a step forward with respect to what is commonly made in literature, where τ is assumed as an arbitrary parameter, generally ranging between 0.1 and 0.3, to fit simulated backscattering data with those measured. As a first validation, DMRT-QMS was integrated with SNOWPACK model to simulate backscattering at X band (9.6 GHz) driven by nivo-meteorological data acquired on a test area located in Monti Alti di Ornella, Italy. The simulations were compared with Synthetic Aperture Radar COSMO-SkyMed (CSK) satellite observations. The results show a significant agreement (R2=0.68), although for a limited dataset of eight points in a unique winter season.

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.

In this paper the integration between the Dense Media Radiative Transfer (DMRT-QMS) model and the SNOWPACK model was investigated in order to simulate snow parameters and the backscattering at X band (9.6 GHz) from nivo-meteorological data. The role of the stickiness parameter ($\tau$) in DMRT-QMS was analyzed by using experimental data of backscattering collected from COSMO-SkyMed (CSK) and snow data generated by SNOWPACK. The relationships between$\tau$and both ice volume fraction ($\phi$) and coordination number ($n_{c}$) were assessed. The DMRT and SNOWPACK simulations were compared with CSK backscattering measurements showing a significant agreement, although for a limited dataset.

In this paper, an algorithm based on Convolutional Neural Networks (CNNs) was developed to correctly classify an agricultural area in central Italy, by using SAR images. This preliminary step is vital for mastering the different influence of crop types in SAR data before the implementation of algorithms devoted to estimate of vegetation biomass. In situ data collected on the test site were used for validating the CNN algorithm-based classification. After the agricultural species recognition, a sensitivity analysis between C-band Sentinel-1 and X-band COSMO-SkyMed backscatter coefficients and crop biomass was carried out, laying the foundation for the implementation of algorithms able to estimate the biomass of different crop types.

The possibility of improving the spatial resolution of Soil Moisture (SM) mapping from microwave satellite radiometers is extremely interesting for hydrological studies in small catchments as well as applications to precision farming. In this study, an algorithm based on Artificial Neural Networks (ANN) is proposed, with the aim of improving significantly the spatial resolution of the Soil Moisture Active Passive (SMAP) Enhanced 9 km Soil Moisture (SMC) product, by integrating SMAP and Sentinel 1 (S1) data. The ANN is trained with data at 9 km resolution, obtained by combining the Sentinel-1 data downsampled to the SMAP resolution and the corresponding SMAP SMC product. After training the ANN is applied pixel by pixel to the Sentinel-1 images at full resolution for generating the enhanced SMC maps. The method has been tested in an agricultural area located in Central Italy, for which in-situ SMC measurements were available: the Active/Passive synergy resulted in an appreciable improvement of both retrieval accuracy and spatial resolution.

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.

High-resolution soil moisture dataset is crucial for various application such as meteorology, climatology, hydrology and agriculture. Active microwave remote sensing sensors like radar provide earth observations at high spatial resolutions. This study based on physical model simulations (Advanced Integral Equation Method, AIEM, and Water Cloud Model, WCM) combined with the Artificial Neural Networks to investigate the potential of the ALOS-2 and Sentinel-1 radar images for estimating soil moisture at high spatial resolution. The results shows that the statistical parameters of the relationships between estimated and measured soil moisture, expressed in terms of R, bias, and RMSE, are 0.834~0.878, 1.59~3.65 vol% and 3.36~6.15 vol% for ALOS-2, and 0.722~0.896, 1.75~2.97 vol% and 3.24~6.86 vol%, for Sentinel-1. In densely vegetated area, RMSE significant increases, due to the limited penetration ability of L and C bands in high vegetation areas.

Modern agriculture is facing new challenges about food production for a growing population in a sustainable manner. Crop mapping at local and regional scale could provide valuable information in support of agricultural policy. This paper describes a field mapping investigation in a populated area in Tuscany (Italy). Satellite images from Sentinel-1 C-band and COSMO-SkyMed X-band SAR and Sentinel-2 optical sensors are input of classifiers based on deep learning and convolutional neural networks. Results pinpointed that the use of optical images allowed the best overall classification accuracy (99.7%), nevertheless X-band SAR imagery, providing an accuracy of 94.6%, could be a good substitute of optical indices in case of lack of cloud-free multispectral data.

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.

This study aims at assessing the potential of the NASA's Cyclone GNSS (CyGNSS) data for observing SM and forest biomass. As reference values for the comparison, global datasets of Vegetation Optical Depth (VOD) and SM derived from NASA's Soil Moisture Active and Passive mission SMAP have been considered. The results of the sensitivity analysis suggested exploiting the CyGNSS capabilities in estimating VOD and SM by setting-up prototype retrieval algorithms based on Artificial Neural Networks (ANN).

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.

The estimate of forest biomass on a global scale is of great relevance for many purposes related to the carbon cycle and the climate change.In this research work, the capability of GNSS sensors for evaluating forest biomass has been investigated by using data coming from two satellite sensors, i.e. TechDemoSat-1 (TDS-1) mission of Surrey Satellite Technology Ltd. and the NASA’s Cyclone GNSS (CyGNSS).Two reflectivity parameters were identified and compared to global forest biomass values obtained through ALOS2 and SMAP VOD. The sensitivity analysis provided interesting results with correlation coefficients (R) > 0.65, thus allowing the implementation of a retrieval algorithm based on a Neural Network approach. The results have been encouraging, showing R>0.8 and RMSE<0.2 on the area of Manaus.

This study aims at merging SMAP and Sentinel-1 (S-1) data for obtaining a surface soil moisture (SMC) product improved in accuracy, spatial and temporal resolution that can be used for hydrological modelling in small basins. A method based on Artificial Neural Networks has been developed and validated in a test area in central Italy. All the S-1 images available on the area between 2014 and 2017 have been considered for the analysis, along with the corresponding SMAP acquisitions. Distributed SMC values, to be used as reference for implementing and validating the algorithm, have been derived from the available in-situ data by using the well-assessed Soil Water Balance hydrological model (SWBM). The research is still ongoing; however, some preliminary results show that the merged ANN SMC product is improved in resolution and accuracy with respect to the SMC obtainable from a single sensor.The ANN SMC was successfully assimilated in the MISC hydrological model for improving the model predictions in small and medium basins.

The study addresses the problem of characterizing different seasonal conditions of snow cover by using ground-based and satellite radiometry. Experimental data collected with ground based radiometers to correlate microwave emission from snow to its physical conditions, and in particular to liquid water content were carried out by our group since '90s. Now we have reconsidered a long series of data collected during two winter-spring seasons on the Italian Alps with the aim of further studying the temporal evolution of single melting refreezing cycles. Moreover, looking at operational aspects, the potential of AMSR-E/2 radiometers in detecting the beginning of snow melting has been evaluated using data collected in both ascending and descending orbits. This investigation takes advantage of the two passes (in the morning and the afternoon), which correspond approximately to a minimum and maximum of surface temperature and therefore to the expected snow freezing and melting situations, respectively. In particular, a detailed study is in progress on the Mendoza River Basin in Argentina. Indeed, the melting of snow accumulated in the upper Mendoza river basin, during winter is the main water supply for agriculture, industry and human consumption in the area.

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.

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).

An investigation of snow characteristics in Alpine environment was carried out using COSMO-SkyMed and Landsat-8 images. After a check of the X band SAR data, together with simultaneous optical images in separating snow/no-snow areas and in detecting wet snow, the sensitivity of backscattering to snow depth was explored. A model based on Dense Media Radiative Transfer theory (DMRT-QMS) was applied for simulating the backscattering response at X band from snow cover in different conditions of grain size, snow density and depth. By using the model and snow data collected on the Cordevole basin in Italian Alps, the effect of grain size and snow density was pointed out, showing that the snow features affect the backscatter in different and sometimes opposite ways. Experimental values of backscattering were correctly simulated by using this model providing a relationship between simulated and measured backscattering with slope >0.9, determination coefficient, R2= 0.77, and root mean square error, RMSE = 1.1 dB.

In this paper, we study the effect of layered snow in alpine regions on microwave emission at Ku and Ka bands, using both experimental data and model simulations. A recent implementation of the multi-layer dense-medium radiative transfer model (DMRT) under the quasi-crystalline approximation (ML-QCA) was used to account for the effects of snow layers on the emission from dry snow covers. Model simulation have been compared with radiometric measurements, collected with ground based instruments during several long-term experiment carried out over three winter seasons between 2007 and 2011 in the Eastern part of Italian Alps. This comparison has the twofold purpose of validating the model and interpreting some particular aspects of snow microwave emission. The measured brightness temperatures at Ku and Ka bands were compared with those simulated through the ML-QCA model, by using the observed snow parameters as inputs. A direct comparison of measured and simulated data showed that the slope of correlation ranged between 0.7 and 1.0, with determination coefficients between 0.51 and 0.75 and Root Mean Square Error (RMSE) between 11 K and 15 K.

This paper will investigate the effect of layered snow in alpine regions on microwave emission at Ku and Ka bands, using both experimental data and model simulations. A multilayer dense-media radiative transfer model (DMRT), was implemented under the quasi-crystalline approximation (ML-QCA), to account for the effects of snow layers on the emission from dry snow covers. The model then evaluated the sensitivity of two microwave indices, based on frequency and polarization combinations, to snow parameters. Model simulations were compared to radiometric dual frequency/polarization measurements of snow covers, collected during long-term experiments carried out over three winter seasons between 2007 and 2011 in the Eastern Italian Alps. This comparison has the twofold purpose of validating the model with experimental data and verifying the influence of snow layering on microwave emission and related frequency and polarization indices. The wide variations in snow characteristics over several winter seasons allowed for an extended validation of the model, which was demonstrated to account for the complex stratigraphy (up to 15 layers) of snow. The measured brightness temperatures at Ku and Ka bands were compared to those simulated through the multi (ML-QCA) and single-layer (SL-QCA) models, by using the observed snow parameters as inputs. In the case of SL, we used the average value of all layers weighted for the layer thickness. The results showed that the ML-QCA model was better correlated to the radiometric measurements than the SL-QCA. A direct comparison of measured and simulated data showed that the slope of correlation for the single-layer ranged between 0.4 and 0.5, with determination coefficient lower than 0.3; whereas the slope in the multi-layer approach ranged between 0.7 and 1.0, with determination coefficients between 0.51 and 0.75 and Root Mean Square Error (RMSE) between 11K and 15K.

In this work, X and C band images acquired by COSMO-SkyMed (CSK) and Sentinel-1 (S1), respectively, on alpine environment have been compared for investigating snow characteristics. The specific capabilities of each sensor, involving also optical sensors (i. e., Landsat and Sentinel-2 satellites), have been exploited. Dense Media Radiative Transfer theory, with quasi-crystalline approximation (DMRT-QCA) was also considered, in order to simulate the snow feature behavior at X-band. Preliminary results show that the assimilation of different sensors returns more detailed information about the snow parameters in terms of spatial detail and physical parameter description.

Vegetation features have been assessed by using microwave radiometric data (AMSR-E/2, SMAP) in order to retrieve vegetation biomass maps on a global scale. The tau-omega model has been used for estimating the vegetation optical depth (tau, τ) from microwave data at different frequencies. An algorithm based on Artificial Neural Networks (ANN) and able to ingest data from different frequency channels has been implemented for the inversion of the model and the retrieval of vegetation biomass. The algorithm validation, carried out on the available experimental data, confirmed that microwave emission, and in particular the use of the two polarizations, H and V, can be legitimately used to produce vegetation maps on a global and local scale by separating several levels of biomass, without any need of further information from other sensors.

In this paper, the soil moisture content (SMC) estimated from Advanced Microwave Scanning Radiometer 2 (AMSR2) through the ANN-based “HydroAlgo” algorithm is firstly compared with the outputs of the Soil Water Balance hydrological model (SWBM). The comparison is performed over Italy, by considering all the available overpasses of AMSR2, since July 2012. The SMC generated by Hydroalgo is then considered as input for generating a rainfall product through the SM2RAIN algorithm. The comparison between observed and estimated rainfall in central Italy provided satisfactory results with a substantial room for improvement. The aim of this work is to exploit the potential of AMSR2 for hydrological applications on a regional scale and in heterogeneous environments characterised by different surface covers at subpixel resolution.

In this work, an integration of microwave data coming from different sensors (SMAP, Sentinel-1, AMSR2) has been attempted, in order to obtain an improved estimation of hydrological parameters and in particular of the Soil Moisture (SMC). The failure of radar sensor in SMAP satellite induced to look for other available microwave frequencies, both from active (e.g. Sentinel-1, C band) and passive sensors (e.g. AMSR2, from C to Ka bands).

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.

In this work, the surface soil moisture (SMC) derived from the AMSR-E acquisitions by using Artificial Neural Networks (ANN) is compared with simulated data obtained from the application of a soil water balance model in central Italy. All the overpasses available for the 9-years lifetime of AMSR-E have been considered for the comparison, which was carried out point by point over a grid of 91 nodes spaced at 0.1×0.1°, roughly corresponding to the Umbria region. The main purpose of this study is to exploit the potential of AMSR-E sensors for hydrological studies, and in particular, for SMC monitoring at regional scale in heterogeneous environments.

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.

In this paper, the effects of multilayer structure of snowpack and its temporal evolution on backscattering are investigated by model simulations. The study is focused on layering structures of dry snow that may represent a risk of avalanches in Alpine regions. The implemented model has been validated using X-band Cosmo SkyMed (CSK ®) acquisitions collected in the winters between 2009 and 2013 on a test area located in the Eastern part of the Italian Alps, and corresponding direct measurements of the main snow parameters. After the validation, the models are applied to simulate the backscattering from snow profiles typical of snow covers characterized by a high risk of avalanches.

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%.

In this paper the results obtained during the CATARSI project have been shown. The aim was to implement an algorithm capable to extract soil moisture and vegetation biomass from SAR data at both L and X bands. An algorithm based on Artificial Neural Networks was tested on SAR images collected in 2010 during the BioSAR (L-band) campaign in Sweden for the retrieval of forest biomass and in 2010-2012 in Italy by using COSMO-SkyMed (X-band) data for the retrieval of agricultural crop biomass. The results obtained demonstrated a clear sensitivity of backscattering at these frequencies to the biomass of both agricultural crops and forests. The retrieval algorithm was able to identify different levels of biomass with a notable accuracy.

In recent decades, with the development of low-frequency missions such as SMOS and Aquarius, which have a large antenna, the need has arisen to find stable areas for the external calibration of L-band radiometers. “Cold sky” and “calm ocean” are routinely used as low reference temperatures, and Antarctica, in particular the East Antarctic Plateau, has been investigated in recent years as a potential candidate for higher reference temperatures. The reason for this interest lies in its geographical location (it can be seen several times a day by polar-orbiting satellites), as well as in the size, structure, spatial homogeneity, and thermal stability of this area. In particular the area of Dome-C, where the Italian-French base is located, was monitored in the past years using ground based radiometer and satellite data. Data acquired in new experiment, started in 2012, are described in the present study together to an analysis of SMOS data collected in the same area. The results pointed out that the brightness temperature over that region is very stable both in space and time and have individuated a large area able to contain several footprints of space-borne radiometers and thus is suitable for cross calibration between the sensors.

In this paper an experiment concerning the capabilities of GNSS-R sensors for land applications was described. An airborne campaign was performed in summer and fall 2011 over two areas close to Florence (Italy): an agricultural zone and a forest plot of poplars. A detailed comparison of the GNSS-R signals with ground truth data was performed. Both LR and RR reflection coefficients have been found to be sensitive to changes in the surface soil moisture, with a total variation of about 6 dB between dry and wet conditions. Regarding the sensitivity to vegetation, it was observed that the measured LR coefficients have a moderate power variation due to the presence of woody vegetation. It was observed that the LR coefficient experienced a monotonic decrease with increasing biomass, up to an estimated forest dry biomass of more than 150 t/ha.

The analysis of the relationships between FI & SPD and SWE/SD was carried out using experimental data and simulations obtained using the DMRT-QCA Multilayer model. The comparison of experimental results and model analyses made it possible to investigate the polarizing effect of snow layering and to better assess the sensitivity of FI and SPD to the snow accumulation.

Monitoring of snow cover is crucial to the study of global climate changes for water resource management, as well as for flood and avalanche risk prevention. The sensitivity to snow characteristics of X-band backscattering of COSMO-SkyMed mission has been analyzed in the framework of experimental and model activities. X-band data have been found to contribute to the retrieval of the snow water equivalent (SWE), provided that the snow cover is characterized by a snow depth (SD) of roughly 60-70 cm (SWE >; 100-150 mm) and with relatively large crystal dimensions. Subsequently, an algorithm for retrieving SD or SWE has been developed and tested with experimental data collected on several ground stations.

In recent years, there has been growing interest on the part of the remote sensing community in using the Antarctic area for calibrating and validating data of low-frequency satellite-borne microwave radiometers. In particular, the East Antarctic Plateau appears to be suited for this purpose. The reasons for this interest are the size, structure, spatial homogeneity, and thermal stability of this area. This is particularly interesting for low-frequency microwave radiometers since, due to the low extinction of dry snow, the upper ice-sheet layer is almost transparent and the brightness temperature variability is therefore extremely small. In the context of calibration and validation activities of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite, an experiment called DOMEX-2, which included radiometric L-band measurements, was carried out at the Italian-French base of Concordia located at Dome C in the East Antarctic Plateau from December 2008 to December 2010. Ground measurements (i.e., snow temperature at different depths, snow structure, meteorological data, etc.) were also collected during the experiment. This paper presents information on the experimental campaign, the characteristics of the radiometric measurements, and the main results. A comparison with SMOS data is also presented.

Monitoring of snow cover is crucial in water resource management and hydrological risk prevention. Experiments have shown the ability of C-band SAR in mapping the extent of wet snow. But, detection of dry snow at this frequency is difficult due to the high transmissivity of the snowpack. A model sensitivity study, corroborated by experimental data, has demonstrated that COSMO-Skymed X-band data can give significant information for generating maps of SWE for snow depth higher than about 50-60 cm.

The backscattering coefficient variations of Cosmo-SkyMed and TerraSAR-X SAR sensors have been investigated. When possible, the data of the two sensors have been compared and a quantitative analysis was carried out. The comparison of SAR data has been also performed taking into account the temporal variations, in order to quantify potential changes of surface parameters. A series of both Cosmo-SkyMed (CSK) and TerraSAR-X (TSX) images were collected on both mountain and agricultural areas. The potentials of X-band backscattering in estimating hydrological parameters of the surface were investigated.

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.

The capability of COSMO-SkyMed in estimating vegetation biomass has been investigated in this paper. SAR data from COSMO-SkyMed were collected on two agricultural areas in Italy in 2010 at different dates during the vegetation cycle. The performances of X-band data have been compared with accurate ground truth measurements of soil and vegetation carried out simultaneously to satellite passes. Experimental data have been compared with model simulations obtained with a discrete element radiative transfer model. Moreover, an inversion algorithm, based on an Artificial Neural Network and trained by using AIEM and the radiative transfer model, has been applied to retrieve the plant water content of wheat and sunflower crops and to generate the corresponding plant water content maps.

This paper describes an algorithm for retrieving the snow depth from the data acquired by the microwave radiometers operating from space. The algorithm is based on Artificial Neural Network techniques and has been developed and tested using a large dataset of AMSR-E acquisitions and corresponding direct measurements of snow depth and air temperature collected over Siberia within the framework of the GCOM/AMSR2 mission. The algorithm has been subsequently applied to the AMSR-E acquisitions collected during the winter seasons between 2002 and 2011 on Alpine regions, setting up a procedure for evaluating and correcting the effects of the orography and the forest coverage.

Antarctica is an important part of the Earth ecosystem. Due to its temperature, mass of stored water (and related thermal inertia) and position, it influences the atmosphere and marine circulations. Despite of its importance, Antarctica is the most unexplored region of Earth. The knowledge about the Antarctic environment is very limited due to its impervious conditions which hamper the human exploration. In this context remote sensing techniques helps in filling the gap. As pointed out in some preliminary works [1][2], microwave brightness temperature data can be useful for estimating snow sub-surface and air temperature. The aim of this paper is to extend previous studies by using AWS and AMSR-E data collected daily for 6 years for estimating snow and air temperature over the East Antarctic plateau. We assessed that the snow sub-surface temperature can be estimated by using linear regressions with a R between satellite and ground data greater than 0.9 (the retrieval RMSE obtained is around IK), while the determination coefficient of the relationships between the air and brightness temperatures was found to be around 0.7 (the associated retrieval RMSE varies between 2K and 7K).

We present in this paper an empirical approach for the characterization of the internal layering of dry snow masses by means of GNSS-R. A forward model has been designed for reconstructing reflected waveforms given a dry snow profile and geometry (elevation and elevation-rate), as a sum of multiple responses from different layers. To extract the internal information, Fourier transforms of time series of waveforms are computed to generate lag-holograms. The frequency stripes that appear are related to the depths of the contributing snow layers. The same analysis has been done with real data, showing with agreement with the models.

In recent years there is growing interest, on the part of the remote sensing community, in using the Antarctic area for calibrating and validating data of the low-frequency satellite-borne microwave radiometers. In particular, the East Antarctic Plateau appears to be suited for this purpose. The reason of this interest lies in the size, structure, spatial homogeneity and thermal stability of this area. This is particularly interesting for low-frequency microwave radiometers since, due to the low extinction of dry snow, the upper ice sheet layer is almost transparent and the brightness temperature variability is therefore extremely small. In preparation for the November 2009 launch of the ESA's SMOS satellite, an experiment called DOMEX, which included radiometric L-band measurements was started in the Austral summer 2009. Data acquired in the campaign confirmed the temporal stability of the site. Comparison between ground and satellite data at L-band are also presented here. Moreover multi-frequency analysis of data collected over this area is performed by using SMOS and AMSR-E data.

In this paper, temporal trends of brightness temperature and related microwave indexes from AMSR-E and SMOS satellites were analyzed. Datasets were collected on three forest areas characterized by different climatic conditions and tree species. The test plots are: a deciduous forest of Larix in China; an evergreen Spruce forest in Russia; the mixed "Foreste Casentinesi" in Central Italy. The availability of different frequencies allowed us to investigate various canopy and soil effects. At the higher frequencies, the frequency index is sensitive to the snow cycle, and the polarization index is sensitive to the leaf cycle. At L band, a clear decrease of emissivity, at both polarizations, is associated to the snow melting process.

Monitoring of snow cover is crucial to the study of global climate changes, for water resource management, as well flood and avalanche risk prevention. The sensitivity of X band backscattering of Cosmo-Skymed mission has been first exploited by using model simulation and experimental data. An algorithm for retrieving snow depth or snow water equivalent has been then developed and test with experimental data.

The results of an experiment carried out in Italy for exploiting the capabilities of X-band SAR in the monitoring of soil and vegetation characteristics are summarized in this paper. Data from X-band Cosmo-Skymed mission have been collected in two agricultural areas and compared with C-band data of ENVISAT/ASAR and with ground truth measurements. In general, a certain sensitivity to vegetation biomass and to moisture of bare soils has been found.

GPS reflected signals have become a source of opportunity for remote sensing of the Earth's suface. In this work, we present several capabilities of this technique in two different polar environments: Greenland and Antarctica. The first part is dedicated to the retrieval of sea-ice properties, giving emphasis to the study of the coherent phase for altimetric and roughness estimations, and polarimetric measurements for the determination of the ice salinity variation. The results show good agreement with a tide model and daily ice charts. On the second part, some preliminary results and analysis strategies to retrieve dry snow signatures are presented.

This work deals with mapping snow water equivalent as well as soil moisture at low resolution from multifrequency microwave radiometers. The algorithm developed and implemented in this work produces the spatial distribution at regional scale of snow depth (SD) and of soil moisture (SMC) of snow free areas by using the brightness temperatures of the Advanced Multifrequency Scanning Radiometer (AMSR-E).

This paper shows a model study about the emissivity of forests. Model outputs are compared with multifrequency airborne measurements carried out over five broadleaf forests in Italy. Two flights took place, in summer 1999 and winter 2002. Available ground truth included important variables, such as biomass, tree density, and average trunk diameter. This data set, in conjunction with allometric equations and information taken from the literature, is used to give inputs to the model. A general agreement between simulated and measured data is observed at L-, C-, and X-bands. The same model is used to investigate the sensitivity of forest emissivity to soil moisture, woody volume, and average diameter. As expected, a moderate effect of soil moisture is observed only at L-band and for forests with a lower woody volume. At L-band, the model predicts a general increase of emissivity with woody volume but indicates that also the trunk diameter exerts an important influence, since it is a variable which controls several geometrical properties. These results allow us to single out the influence of soil moisture, woody volume, and geometrical properties at L-band. The increase of emissivity with frequency, observed in experimental data, is interpreted by means of electromagnetic considerations about branch scattering.

The possibility of a global monitoring of hydrological parameters in Africa was endeavored by using both active and passive microwave sensors. Two ALOS/PALSAR images of Ethiopia were compared with optical data and ground information collected on site by the Istituto Agronomico per l'Oltremare, in Florence. Moreover, AMSR-E data were used as a reference for investigating soil moisture and vegetation conditions. The brightness temperature and the backscattering coefficient values have been related to land features, obtained from ground data and cartographic and meteorological information.

An operational algorithm to produce snow cover maps from remote sensing data in the Italian Alps has been implemented in the framework of the Italian national project PROSA to contribute timely information to civil protection from floods and landslides. The algorithm can generate maps in presence of cloud cover by combining optical data from MODIS and SAR data from ENVISAT/ASAR. It has been validated on a wide area in North Italy by comparing the algorithm output with ground measurements.

Experimental campaigns with airborne and satellite microwave sensors have been carried out on an agricultural area in Northern Italy with the main purpose of gathering a suitable set of data to validate two operational algorithms developed to retrieve soil moisture from passive and active microwave sensors at different spatial scales. The algorithms will be used in a pilot project based on the use of Earth observation data in forecasting and monitoring the risk of floods and landslides. Radiometric data have been collected with the airborne IFAC instruments and the AMSR-E, while ENVISAT/ASAR images have been acquired for high resolution estimate of soil moisture at field scale.

Long-term microwave and infrared radiometric measurements of snowpack were carried out with ground-based sensors in winter 2006-2007 and 2007-2008, together with conventional measurements of snow-cover profiles. The first experiment focused on the behavior of snow emission during the destructive and constructive metamorphisms. The second involved a correlation analysis of the small fluctuations related to diurnal solar cycle in order to obtain the time delay of microwave brightness temperatures Tb with respect to the snow surface temperature. From this analysis, it was possible to estimate an effective (weighed average) temperature and the thickness of the layer that mostly contributed to microwave emission at 19 and 37 GHz. The ratio of the brightness temperature to the effective temperature can be assumed to be an equivalent emissivity of the snowpack. Data collected in both years have been compared with simulations carried out using the advanced Institute of Applied Physics (IFAC) Radiative Advanced Dry Snow Emission (IRIDE) model driven by data collected on ground. The model is based on the advanced integral equation method to represent soil, coupled to a layer of dry snow whose electromagnetic properties are described by the dense medium radiative transfer theory with quasi-crystalline approximation applied to a medium (air) filled with sticky particles. Simulations performed by using ground data as inputs to the model have been found to be well in agreement with experimental data. Moreover, the comparison of model simulations with experimental data allowed one to understand some peculiar characteristics of microwave emission from the snowpack related to its physical conditions.

In this paper, we report the results of an experimental study aimed toward investigating microwave emission from forests. The experiment was carried out in 2006 on two forest stands of poplar (Populus alba)and pine(Pinus italica), using ground-based microwave radiometers at the L-, C-, X-, Ku-, and Ka-bands, in H and V polarizations. Measurements on poplar were performed on different dates and at different incidence and azimuth angles, looking downward (from the top of trees and from below the crown) and upward (from the soil level). Only one downward-looking measurement was carried out over a pine plot with dry soil in April. All the remote sensing measurements were complemented with ¿ground-truth¿ data. The collected experimental data made it possible to quantify the spectral signatures of poplar, as well as the variation of angular trends of brightness temperature in different seasons of the year. The sensitivity of L-band emission to soil properties and leaf biomass was also investigated. Moreover, the measurements on poplar, combined with a simple radiative transfer model (the so-called omega-tau equation), allowed estimating the transmissivity of the canopy with and without leaves. The analysis of data has shown that for the observed forest type, the sensitivity to soil moisture under defoliated trees can be noted at both the L- and C-bands.

Microwave radiometric measurements of snow pack were carried out with ground based sensors in winter 2007-2008. Data collected on dry snow, showed small fluctuations related to diurnal solar cycle and presented a time delay of microwave brightness temperatures with respect to the snow surface temperature. The measurement of these delays, together with a correlation analysis of the brightness and physical temperature of snow, made it possible estimating the thickness of layers that mostly contributed to microwave emission at 19 and 37 GHz. Simulations performed with IRIDE model were consistent with experimental data.

The Antarctic plateau is a part of Antarctic extending for a few hundred kilometers around the South Pole with an average elevation close to 3000 m a.s.l.. This area provides unique opportunities for various scientific disciplines including Glaciology, Atmospheric and Earth Sciences. In this paper temporal and spatial variability of multi-frequency microwave emission from the East Antarctic plateau by using AMSR-E data collected from 2005 to 2007 is analyzed.

In preparation of the satellite mission CoReH2O, one of the six missions which has been selected for scientific and technical feasibility studies within the Earth Explorer Programme of the European Space Agency, experimental and theoretical studies started in order to investigate backscatter properties and improve the methods for retrieval of snow physical properties from SAR data. The aim of this paper is to investigate the impact of vegetation in the retrieval of snow parameters from backscattering measurements at the X- and Ku-bands. First the key vegetation types found in snow covered regions where identified on the basis of available global scale data base. A model able to simulating scattering from a vegetated snow-covered terrain was then developed and implemented. Lastly, a sensitivity analysis to vegetation parameters was conducted on sparse vegetation and coniferous forest.

In this paper an overview of the main microwave characteristics observed on three forest areas selected in different areas worldwide is given. Microwave parameters were analyzed for a yearly cycle (2007-2008), paying particular attention to the seasonal variations forest leaf biomass, expressed as leaf area index (LAI), and climatic conditions. Some microwave indexes of polarization and frequencies are in good agreement with the seasonal variations of vegetation. The emission at C-band was found to be related to the moisture conditions of the area.

This paper describes a microwave model which simulates the emissivity of forests, including litter effects. Detailed input data about forest geometry are obtained by direct measurements and/or by allometric equations. Model outputs have been compared with multifrequency measurements carried out over five broadleaf forests in Italy. Fundamental information about forest properties was available. A general agreement between simulated and measured data is observed. Some discrepancies require further investigation. Also a component analysis has been done. The contribution of soil emission is very low for higher forest volumes, but is appreciable for lower forest volumes.

In this paper, we present an intercomparison of algorithms for retrieving soil moisture content (SMC) from ENVIronmental SATtellite (ENVISAT)/Advanced Synthetic Aperture Radar images. The algorithms taken into consideration were a feedforward artificial neural network (ANN) with two hidden layers, a statistical approach based on Bayes' theorem, and an iterative algorithm based on the nelder-mead direct-search method. The comparison was carried out by using both simulated and experimental data. Simulated data were obtained by means of the integral equation model (IEM). Experimental data were collected in an agricultural area in Northern Italy during 2003-2005; they included backscattering coefficient at HH and HV polarizations and at an incidence angle of thetas = 23deg, as well as detailed ground truth measurements of SMC, surface roughness, and vegetation parameters. HH-polarized data were related to SMC, whereas the information of the cross-polarized channel was used to correct the backscatter for the effects of surface roughness. A comparison of the algorithms with experimental data showed that all the tested approaches produced SMC values that are very close to the measured ones. However, the predictions of the ANN were slightly more suitable than the other methods for generating maps in reasonable time. The production of moisture maps carried out at different dates using this algorithm pointed out the feasibility of separating up to six levels of spatial/temporal variations of SMC in the range of 10%-35%.

The sensitivity of bistatic scattering coefficient sigmadeg to soil moisture (smc) is investigated on the whole upper half space by means of model simulations of the incoherent scattered fields. The achieved results, represented as maps of sigmadeg as a function of azimuth and zenith angles, are evaluated by means of a quality index which takes into consideration the effect of roughness on smc measurement.

In this paper, the result obtained on two forest stands of poplar (Populus alba) and pine (Pinus italica) in Italy, by using multi-frequency microwave radiometers, are described. Measurements were performed at L, C, X, Ku and Ka bands at different incidence angles, both in H and V polarizations, by using microwave radiometers mounted on an hydraulic boom. The sensitivity of L-band emission to woody volume was confirmed, although the effect of soil moisture is significant, especially at low values of forest biomass. Measurements carried out in upward direction gave the possibility of separating the contributions of crowns, trunks and soil and, by using a simplified model based on the radiative transfer theory, measuring consequently the forest transmissivity at different frequencies.

The importance of microwave sensors in monitoring snow parameters is well recognized. However, several problems are still open regarding the reliability of remote sensing for operational use. In 2002-2005 a series of ERS SAR and ENVISAT ASAR images were collected on the Italian Alps to monitor the temporal evolution of snow cover. In the same time a long sequence of multi-frequency radiometric data was collected with ground based sensors. The measurements confirmed the potential of microwave active and passive sensors in monitoring the extent of wet snow cover and in estimating the liquid water content of wet snow and the snow water equivalent of refrozen snow.

A Neural Network algorithm was tested in two Italian sites for producing multi-temporal soil moisture maps starting from ENVISAT/ASAR images, collected in 2003 and 2004. Several SAR images were analyzed for a flat agricultural area located close to Alessandria in North-west Italy, and a mountainous site on the Italian Alps. The obtained results showed a reasonable agreement with ground truth data and meteorological conditions, and maps with 4-5 levels of soil moisture of both the test sites were generated from the available ENVISAT ASAR images. A further validation of the retrieval algorithm was carried out by using two ENVISAT images collected form the Kemjoki River beat bog area in Finland, on May 7, 2004 and July 27, 2005. In spite of the problems related to the wideness and non-homogeneity of the area, and the lack of detailed ground measurements, the results obtained by using the Artificial Neural Network can be considered satisfactory. Soil moisture is a key state variable that influences the redistribution of the radiant energy and the runoff generation and percolation of water in soil. We know that local measurements of soil moisture content (SMC) are strongly affected by spatial variability, besides being time-consuming and expensive. Moreover, the use of hydrological models for extending the forecast of soil moisture over larger areas is not easy, and depends on the homogeneity of the selected areas and the information available on them (soil properties, i.e. hydraulic characteristics, and permeability, together with meteorological and climatological data, etc.). The possibility of measuring soil moisture on a large scale from satellite sensors, with complete and frequent coverage of the Earth's surface, is, therefore, extremely attractive. However, up to now, the only available frequency from space is C band, operational on ERS-2, RADARSAT, and ENVISAT satellites, which is not the optimal for this aim. The retrieval of soil moisture maps at C-band is, in fact, still challenging, since the effects of soil surface roughness and vegetation cover on the backscattering coefficient at this frequency is high, and needs the use of correcting procedures (1-3). In spite of these problems, multi-temporal soil moisture maps have been already produced in two Italian sites starting from ENVISAT/ASAR images, collected in 2003 and 2004, by using an algorithm based on Neural Networks. In this case several SAR images were analyzed for a flat agricultural area, located in North-west Italy, and a mountainous site on the Italian Alps. The obtained results showed a satisfactory agreement with ground truth data and meteorological conditions, and enabled us to generate maps with 4-5 levels of soil moisture of both the test sites from the available ENVISAT ASAR images (4). Using ENVISAT data collected on the Kemijoki area in Finland, a further validation of the retrieval algorithm was carried out. In spite of the problems related to the difference in polarization configuration, the wideness and non-homogeneity of the area, and the lack of detailed ground measurements, the results obtained by using the Artificial Neural Network were satisfactory.

The standard method for retrieving snow-covered area with C-band SAR is based on thresholding the ratio between a wet snow SAR image and a dry snow reference scene. A new approach is suggested here, where the snow cover fraction is retrieved by using a gradual transition between snow free and snow covered conditions. The paper discusses the method and applies it on a set of Radarsat data acquired in Norway in May 2003. A near simultaneous aerial optical image and data from field campaigns are used to verify the results.

In this paper the actual capabilities of ENVISAT/ASAR images in providing soil moisture maps have been tested. Several SAR images were collected on two test areas in Italy: a flat agricultural region in the Alessandria area and a mountain zone on the Cordevole watershed (Arabba). An inversion algorithm based on artificial neural networks (ANN) to retrieve soil moisture from backscattering data was tested and successfully compared to ground measurements.

This work present some experimental campaigns aiming to assess the potential of monitoring soil moisture by ENVISAT ASAR (Advanced Synthetic Aperture Radar). Soil moisture measurements were carried out on November 2003 and June 2004 close to Alessandria (Northern Italy) by using a TDR probe simultaneously to ENVISAT overpasses. In situ measurements had been collected in agricultural fields that were subsequently identified on ENVISAT ASAR images by means of a geo-coding process. Pixels of each area have been averaged in order to compute the mean backscattering coefficient. Results have been derived concerning the sensitivity of the backscattering coefficient to soil-moisture compared to what is predicted by a semi-empirical scattering model.

An experiment aimed at investigating the potential of ENVISAT/ASAR in measuring soil moisture is described in this paper. Two test areas were chosen as test sites: Montespertoli and Alessandria, in Central and Northern Italy, respectively. After a preliminary analysis of the direct relationship between the backscattering coefficient at C-band and the soil moisture content of individual fields, a Bayesian approach was attempted for retrieving soil moisture. To obtain a statistically significant data set, simulations performed with the integral equation model were added to experimental data. Moreover, an artificial neural network was tested on Alessandria area

We demonstrate a fully automatic flood detector for non-forested areas on an ERS SAR image of the 1994 flooding incident in Alessandria, Italy. The flood detector is capable of detecting the majority of the flooded area. However, there are some undetected flooded areas which exhibit high backscatter caused by double bouncing from flooded vegetation.