The objective of this work was to carry out a first evaluation of the PRISMA hyperspectral data potentialities for Burned Area (BA) mapping in an operational context related to wildfire risk management (i.e., simulating a civil protection scenario). To this aim, an automatic processing chain was developed to map the area burned by the wildfire that occurred in Pantelleria Island (Southern Italy) during the Summer of 2022. The differenced Normalized Burn Ratio (dNBR) index was used to perform this task and a simple, yet effective, criterion was defined to identify the spectral bands most suitable for the analysis. Results showed the potential retained by PRISMA data to map BA ca. one month after the fire outbreak, in an operational context (Dice Coefficient: 0.88). Their discussion also provided valuable insights related to the exploitation of hyperspectral remote sensing data for similar purposes.

Monitoring the "degree of filling" of Water Bodies (WB) is of paramount importance for Civil Protection (CP) applications: e.g., flood control and hydrological drought. Within this context, this work aims at prototyping a system to monitor the spatiotemporal variations of WB extent at high spatial resolution, to support the Italian Civil Protection Department (DPC) activities. The system is based on optical Sentinel 2 data, but it also exploits Sentinel 1 SAR data as a backup source of information to take into account S2 limitations linked to the presence of clouds and shadows. The system performances were validated in a pilot area (i.e., the San Giuliano reservoir, located in Southern Italy), for which reference data were available. Results showed that the presented system has a strong potential to be a valuable tool to be used for CP applications, especially if data are analyzed in terms of monthly anomalies.

This study investigates the relations between the dynamics of satellite-based indicators of reservoir water extent and the stress of the surrounding vegetation during dry periods and in drought conditions. A qualitative and quantitative comparison was performed between the monthly values of indices as NDVI, NDMI, fAPAR and the changes in the water extent of the San Giuliano Reservoir (Southern Italy), also in terms of anomalies. The indices and the water extent were derived from Sentinel-2 data, and the analysis was performed for a four-year time period (2019–2023). The results suggest that the dynamics of the reservoir water extent can be used to detect dry periods and/or drought conditions, and that the vegetation state indicators can complement the analysis and reduce the source of uncertainty. Being based on the satellite data only, the analysis has the potential to be replicated elsewhere in the World, including data scarce environments.

A big flood affected the Emilia-Romagna region (northern Italy) starting from 16 May 2023. During this event, more than 20 rivers burst their banks and about 15 people were killed. A persistent satellite-based mapping was requested by the Italian Department of Civil Protection (DCP) to monitor the time evolution of the Emilia Romagna flood. For this purpose, the X-band SAR data provided by the COSMO-SkyMed constellations (first and second generation) were exploited. This paper proposes a new procedure to map floods using SAR data acquired with different viewing geometries in order to fully exploit the on-demand capability of satellites like COSMO-SkyMed. The procedure includes the tasking of the satellites, the collection of a set of pre-flood images, the selection for each flood image of the most suitable pre-flood ones, and the generation of the flood maps using an automatic algorithm based on change detection. These activities were performed during the May 2023 Emilia-Romagna flood and allowed us to daily provide DCP with flood maps useful to support decision making and assess the damages.

The accuracy of soil moisture estimated from Synthetic Aperture Radar backscatter data at high resolution is limited by speckle. Common practice to mitigate speckle is to multilook the data prior to retrieving soil moisture. While multilooking indeed reduces speckle, it also decreases the spatial resolution and removes possibly useful high resolution information from the data. We therefore hypothesised that using higher resolution backscatter data for soil moisture retrieval would lead to higher retrieval accuracies. A high-resolution field study combined with a synthetic experiment showed that calculating soil moisture prior to multilooking to the final target resolution (calculate-then-average, CtA) has substantial advantages over the average-then-calculate (AtC) approach. Currently, the AtC strategy is most often applied in soil moisture studies, mainly due to its computational advantage compared to the CtA approach. We show that by making use of a higher source resolution backscatter data than the target resolution, we could improve the soil moisture retrieval over an agricultural field.

The accuracy of high resolution soil moisture estimated from SAR backscatter data is limited due to speckle in the native resolution backscatter data. However, reducing this speckly by means of spatial aggregation also removes useful information from the data. We therefore hypothesised that using unfiltered backscatter data in a soil moisture inversion model can be valuable in high resolution soil moisture applications. A field study combined with a synthetic experiment showed that calculating soil moisture prior to spatial averaging to the final target resolution (CtA) has substantial advantages over the average-then-calculate (AtC) approach. Currently, the AtC strategy is most often applied in soil moisture studies, mainly due to its computational advantage compared to the CtA approach. However, especially at high resolutions, using a slightly higher source resolution backscatter data than the target soil moisture resolution, can already improve accuracy of the soil moisture estimates.

Previous studies have shown that the decrease of temporal interferometric synthetic aperture radar (InSAR) coherence could be exploited to detect the appearance of floodwater in urban areas. However, as of today, approaches based on this principle only make use of single co-polarization images for identifying the presence of floodwater in the double-bounce feature. In this study, we take advantage of both co- and cross-polarization images to detect significant decreases of the multitemporal InSAR coherence in order to enhance the mapping of floodwater in urban areas. We consider that not only double-bounce scattering, but also multiple-bounce may occur in urban areas depending on how the building facades are oriented with respect to the synthetic aperture radar (SAR) sensor’s line of sight. The Sentinel-1 (S-1) mission is particularly well suited for applying and testing this kind of approach due to the systematic availability of dual-polarization data. Using as a test case, the widespread flooding in the city of Houston, USA, caused by Hurricane Harvey in 2017, we demonstrate that the proposed methodology leads to an increase of the accuracy of the urban flood maps from 75.2% when only using the VV polarization, to 82.9% when using the dual polarization information.

Space-borne SAR instruments can provide backscatter on a high spatial resolution, and with the introduction of the Sentinel-1 satellites, these can co-exist with relatively high temporal resolutions. Here, we use a combination of active microwave Sentinel-1 and optical Sentinel-2 data in the MULESME algorithm to estimate soil moisture on a field in Southeastern Luxembourg. Satellite data were compared to data gathered in the field and semi-continuous measurements from a nearby permanent station. Our results indicate that the accuracy of MULESME soil moisture estimates increases with a decrease in spatial resolution, but that this increase stagnates rather soon after the first few spatial aggregations, thus confirming the value of high resolution data. Future endeavours will focus on the analysis of soil moisture variation in time, compared to soil moisture measurements from a nearby permanent station.

In this paper, we present a fully automatic algorithm capable of mapping floodwater in urban areas using 20 m Sentinel-1 SAR data. It is composed of a two-steps approach that first uses the SAR data to identify buildings and then takes advantage of the Interferometric SAR coherence feature from both co- and cross-polarizations to detect the presence of floodwater in urbanized areas. The preliminary detection of buildings is a pre-requisite for classifying them as flooded based on the InSAR coherence temporal decrease when water is present in urban areas, given that in general buildings show a strong temporal coherence. In addition, the short temporal and perpendicular baselines of the intereferomeric Sentinel-1 image acquisitions is an advantage for this kind of approach. The algorithm is applied to Sentinel-1 images acquired during the major flood event that hit Jakarta (Indonesia) in January 2020.

The availability of many platforms carrying on board SAR payloads working at different frequency bands is paving the way to the development of new or improved products for different applications. Constellations of satellites also enable acquisitions close in time, thus not only improving the temporal resolution but also enabling almost coincident multifrequency observations of the same target. This work resumes previous investigations demonstrating the role of multifrequency data for the generation of thematic maps and the observation of flooded vegetated areas. The different signatures of the targets made it possible to improve the classification accuracy when SAR data, at different frequencies, were added to optical data. Although optical data still keep the best discrimination capability, multifrequency SAR data play a role to improve thematic accuracy. The higher penetration through the vegetation of L-band signal, with respect to C- and X-band, and the polarimetric mode made possible to clearly detect the enhancement of the double bounce scattering due to the presence of standing water under rice fields, although some evidence of that mechanism can be also detected even at X-band by change detection approaches.

In this work we present an automatic algorithm for providing probabilistic flood maps, not only on bare soils, but also within urban areas. The probabilistic flood mapping procedure is based on synthetic aperture radar (SAR) data and the Bayesian inference. Both intensity data and Interferometric SAR (InSAR) coherence feature are used. The approach improves the information content of a binary SAR-based floodwater map, which does not give any indication on the uncertainty in the pixel state.The proposed methodology is tested for the flood event that heavily affected the city of Houston (Texas) during the 2017 hurricane season. Data provided by the Sentinel-1 mission are used, with a geometric resolution of 20m. The algorithm takes fully advantage of the Sentinel-1 mission's repeat cycle of six days and narrow orbital tube to fully exploit the potentialities of InSAR coherence feature to detect floodwater in complex environments. The application of the proposed method to the Houston case study showed promising results.

In the framework of the E-SHAPE "EuroGEOSS Showcase: Applications powered by Europe" project, one of the pilot applications concerns the disasters in urban environment. One of the objectives of this application is the conceiving of innovative services for extreme-scale hydro-meteorological modelling that make use of Copernicus Earth Observation data. An example of innovative service is the ingestion of high-resolution Copernicus remote sensing products in numerical weather prediction (NWP) models. The rationale is that NWP models are presently able to produce forecasts with a spatial resolution in the order of 1 km, but unreliable surface information or poor knowledge of the initial state of the atmosphere may imply an inaccurate simulation of the weather phenomena. It is expected that forecast inaccuracies could be reduced by ingesting high resolution Earth Observation products into the models. In this context, the Copernicus Sentinel satellites represent an important source of data, because they can provide a set of high-resolution observations of physical variables (e.g. soil moisture, land/sea surface temperature, wind speed over sea, columnar water vapor) used in NWP model runs. The possible availability of a spatially dense network of Global Navigation Satellite Systems (GNSS) stations could also be exploited to allow NWP models to assimilate timely updated data about water vapor in the atmosphere. As a preliminary activity carried out in the frame of the E-SHAPE project, this paper presents the results of the experiments regarding the insertion/assimilation of surface information derived from Sentinel data into a NWP model. The experiments concern a flood event occurred in Italy in 2017 that affected an urban area, namely the Livorno city.

The utility of synthetic aperture radar (SAR) data to produce flood delineation maps is well established. However, flood mapping still represents a challenge in urban settlements, because the radar signatures of flooded urban pixels are generally ambiguous. As a matter of fact, flood mapping algorithms generally do not consider urban areas, thus producing a lot of missed detection errors. Recent studies demonstrated that SAR Interferometry (InSAR) represents a suitable tool to at least mitigate this problem. Following these studies, here we analyze time series of complex coherence data in stable scatterers, i.e., pixels exhibiting high backscatter combined with high temporal stability. Our idea is based on the fact the water surfaces show no coherence in a repeat-pass interferogram, so that a decrease of coherence may occur even for stable scatterers if floodwater is present in a resolution cell. The analysis was performed considering the floods that hit the city of Alicante (Spain) in March 2017. This event was observed by Sentinel-1 in Interferometric Wide Swath mode.

GeoSTARe is a proposed GEOsynchronous satellite mission designed to carry aboard a Synthetic Aperture Radar (GEOSAR) payload, which can provide radar images with unprecedented temporal resolution. The research activity described in this paper aimed at investigating the potentialities of such system for soil moisture (SM) monitoring finalized to hydrological applications. The objective was defining the GEOSAR requirements, in term of spatio-temporal resolution, for SM mapping. GEOSAR is capable to produce images with different sub-daily temporal resolution, with associated spatial resolution, according to the length of the synthetic antenna focused on ground. To this aim, three GEOSAR-derived SM products, characterized by different spatio-temporal resolutions (matching the expected performances of GEOSAR), were simulated. Then, these products were used in a synthetic hydrological SM data assimilation experiment to evaluate their impact on model discharge predictions. Results showed that, for such system, the best performances were obtained when the highest spatial resolution was used.

As of today, SAR imagery represents the most commonly used data source for remote sensing-based flood mapping. The data are characterized by a good sensitivity to water and are available day and night, regardless of cloud cover. Many studies have demonstrated that SAR systems are suitable tools for flood mapping on bare soils and scarcely vegetated areas. In spite of the progress in the development of Near Real Time SAR based flood mapping algorithms, the detection of inundation in urban areas still represents a critical issue. Here we propose a methodology for identifying floods that heavily affected the city of Houston (Texas) during the 2017 hurricane season. Our approach takes advantage of the Interferometric SAR coherence feature to detect the presence of floodwater in urbanized areas. In particular, data provided by the Sentinel-1 mission in both, Strip Map and Interferometric Wide Swath modes, have been used, with a geometric resolution of 5m and 20m, respectively. The algorithm takes fully advantage of the Sentinel-1 mission's repeat cycle of six days, thereby providing an unprecedented possibility to develop an automatic, high frequency flood mapping application that is suitable for complex environments. The test of the algorithm for the Houston case study showed promising results for mapping flood in urban areas.

The European Space Agency (ESA) STEAM (SaTellite Earth observation for Atmospheric Modelling) project aims at investigating new areas of synergy between high-resolution numerical atmosphere models and data from spaceborne remote sensing sensors, with focus on Copernicus Sentinels 1, 2 and 3 satellites. An example of synergy is the ingestion of surface information derived from Sentinel data in numerical weather prediction models. The rationale is that Sentinels 1, 2 and 3 are able to provide high spatio-temporal resolution information on the surface boundary (as well as the atmosphere column) and that an inaccurate representation of the boundary conditions represents a major source of uncertainty for weather forecasts. For a profitable ingestion of EO data in numerical weather prediction models, a critical aspect is the choice of a suitable model. Once the numerical model is chosen, the problem of the selection of the Sentinel-derived surface variables that have to be ingested in the model has to be tackled. While some data, such as sea and land surface temperature, are directly available, other surface data, such as soil moisture, have to be retrieved. Being STEAM currently in its initial phase, this paper gives a general overview of the project and focuses on the first activities performed in its framework. In particular, it describes the rationale behind the choice of the Numerical Weather Prediction Model and the multi-temporal approach designed to retrieve soil moisture from Sentinel-1 data. Moreover, the first results of the ingestion of Sentinel derived soil moisture, land surface temperature and sea surface temperature data into the selected model are shown. These results concern an extreme weather event that occurred in Tuscany (central Italy) in September 2017.

The objective of this research was to develop a method for water level retrieval in natural and artificial lakes. It was thought to be applied for monitoring purposes and flood control applications, especially in data-scarce environments. The method is based on a combined GIS, remote sensing and statistical modeling approach. It was tested on both optical (Landsat 8) and SAR (Cosmo-SkyMed®) data. The topographic information, required by the method, were obtained from freely available digital elevation models (SRTM and ASTER) to compare their performances. The Place Moulin Lake, an Alpine reservoir, was selected as study area since it represents a very challenging case study for developing the proposed methodology. The results showed that: i) the method provided reasonably accurate results when the degree of filling of the reservoir was high. ii) The accuracy of the results strongly relied on the accuracy of the topographic information. iii) The combination of Cosmo-SkyMed® and SRTM data provided more reliable results. Further analyses are required to evaluate the method in different environmental conditions.

As part of the Copernicus Programme, Sentinel 1 (S1) synthetic aperture radar (SAR) mission represents a unique monitoring tool whose potentialities for hydrological risk mitigation need to be evaluated. To this aim, S1-A derived soil moisture maps with high spatial resolution (100 m) and moderate temporal resolution (12 days) were assimilated within a time-continuous, spatially-distributed, physically-based hydrological model (Continuum) with the specific objective to evaluate the impact on discharge predictions and (flash) flood modelling. A Nudging assimilation scheme was chosen for the DA experiment due to its computational efficiency, particularly useful for operational applications. Results were evaluated in the Orba River catchment (Italy) in the time period October 2014 — November 2016, corresponding to the first two years of activity of the S1-A mission.

The triple collocation (TC) technique is being increasingly used to validate soil moisture retrievals derived from different sensors. In recent years, several extensions of this method were proposed in order to evaluate the error standard deviations of more than three systems. As the number of datasets grows the TC fundamental hypothesis, i.e. the absence of cross-correlation between the system errors, could be violated. In this paper, an Extended Quadruple Collocation (E-QC) is proposed to consider the presence of the error cross-correlation between soil moisture products, identifying automatically the couple of cross-correlated systems. The method is applied to soil moisture retrievals provided by satellite (SMOS, ASCAT, SMAP), model (ERAInterim) and in situ probes (ISMN). The method identified the presence of error cross-correlation between the satellite products and was able to correctly retrieve the system errors, otherwise biased if cross-correlation is not taken into account.

In this paper we investigate the double bounce enhancement due to standing water in flooded agricultural fields to assess the capability of an X-band radar to recognize the presence of floodwater under vegetation. The investigation was carried out by analyzing a polarimetric and multifrequency SAR dataset (COSMO-SkyMed, Alos-2, Radarsat-2) collected over the Vercelli district in North Italy, characterized by a widespread and intense cultivation of rice crop, were the fields were routinely artificially flooded and dried according to the agricultural practice. The investigation demonstrated that in July, when rice is well developed, high backscatter in X-band was observed in fields were the L-band polarimetric data recognized the double bounce return. The presence of a double bounce scattering enhancement at X-band was then established. At C-band the dihedral type of return was not clearly recognized because of the smaller incidence angle of Radarsat-2 acquisitions.

The utility of synthetic aperture radar (SAR) data to produce flood delineation maps is well established. However, for what concerns urban settlements, flood mapping still represents a challenge, because the radar signatures of flooded urban pixels are generally ambiguous. As a matter of fact, flood mapping algorithms generally do not consider urban areas, thus producing a lot of missed detection errors. To cope with this problem, this study proposes a new method that basically analyzes the complex coherence of urban pixels characterized by high backscatter combined with high temporal stability (stable scatterers). Contextual information is also used to reduce the noise of the maps. The rationale is that, since water surfaces show no coherence in a repeat-pass interferogram, a decrease of coherence may occur even for (some) stable scatterers if floodwater is present in a resolution cell. To develop the algorithm, the inundation that hit Houston (Texas, USA) in April 2016 was considered. This event was observed by Sentinel-1 in Interferometric Wide Swath mode. Results showed that looking at the coherence of stable scatterers could represent a reliable road to at least mitigate the problem of missed detection of flooded urban settlements.

First results of the assimilation of high-resolution Sentinel-1A based soil moisture products in a distributed, physically based, hydrological model are presented. A comprehensive evaluation of the assimilation's impact on discharge predictions is provided. Results are further compared to those obtained when assimilating the lower-resolution ASCAT-based soil moisture product. The exercise was carried out within the MIDA project framework (funded by the Italian Space Agency) aiming at producing root zone soil moisture maps useful for flood risk management applications. The experimental site is the Orba River Catchment (Italy). The period of investigation is October 2014-February 2015. Using a relatively simple data assimilation technique (Nudging) the results of our case study show that overall the assimilation of currently available Sentinel-1 data only marginally improves discharge simulations. However, the impact becomes more significant when specifically considering predictions of high flow. Further improvements are expected when both Sentinel-1A and B data will be available.

By providing high quality flood maps a spaceborne SAR can be an effective source of information. These maps support civil protection authorities for disaster risk reduction. Here we propose a methodology for identifying floods that occur on different types of land cover, such as urban areas, bare and poorly vegetated soil and vegetated areas. Our approach takes advantage of polarimetric SAR data and InSAR coherence to better characterize the landscape. Indeed, the Sentinel-1 repeat cycle of six days, and its systematic acquisition of dual-pol SAR data, provides an unprecedented chance to develop automatic, high frequency flood mapping algorithms for complex environments. The algorithm has been tested on two different Sentinel-1 datasets, acquired, respectively over Greece and Italy, showing promising results.

In this paper, we investigate the role of polarimetric features to improve flood mapping in agricultural areas. Considering that the double bounce enhancement due to standing water can increase the backscatter from flooded agricultural fields, polarimetry can potentially detect this mechanism and mitigate the misdetection of algorithms based on the identification of dark areas in the image. The investigation was carried out by analysing a polarimetric and multifrequency SAR dataset (COSMO-SkyMed, Alos-2, Radarsat-2) collected over the Vercelli district in North Italy, characterized by a widespread and intense cultivation of rice crop, were the fields were routinely artificially flooded and dried according to the agricultural practice. The investigation demonstrated that the polarimetric data are able to recognize the double bounce return in areas with high backscattering. They overcome the need of a pre-flood image, otherwise required to identify a sudden increase of backscatter to be ascribed to the standing water.

The use of synthetic aperture radar (SAR) data is presently well established in operational services for flood management. Nevertheless, detecting inundated vegetation and urban areas still represents a critical issue, because the radar signatures of these targets are often ambiguous. This paper analyzes the role of the interferometric coherence in complementing intensity SAR data for mapping floods in agricultural and urban environments. The advantages of the joint use of intensity and coherence are first discussed in a theoretical way and then verified on a case study, namely, the flood that hit the Emilia-Romagna region (Northern Italy) in January 2014. The short revisit time of the COSMO-SkyMed images, as well as a dedicated acquisition plan tailored to the requirements of the Italian Civil Protection Department, has allowed us to build a data set of radar interferometric observations of the event. Results show that the analysis of the multitemporal trend of the coherence is useful for the interpretation of SAR data since it enables a considerable reduction of classification errors that could be committed considering intensity data only. Interferometric data have permitted us to distinguish zones where water receded from areas where it persisted for a longer time and, in one case, to measure changes of water level.

The utility of the images supplied by synthetic aperture radar (SAR) systems for flood mapping was demonstrated by several literature studies and the use of SAR data is presently well-established in operational services for inundation management. However, because of the limited SAR image swath and the need of programming SAR acquisitions in advance, some events might be missed. Nowadays a number of satellite SAR instruments are operative and this allows for a combined use of different sensors in order to cover wide river basins and, when different instruments observe the same area, to potentially improve the accuracy of flood extent maps. To fully exploit these possibilities, the flood mapping algorithm should be able to process data provided by radars working at different frequencies and observing the Earth with different incidence angles, i.e., should be robust and transferable. In order to analyze the possible synergies between X-band and C-band sensors, in this work we present some results obtained through a joint use of an X-band instrument as COSMO-SkyMed and a C-band one as Sentinel-1 for flood monitoring performed during the Po River (Italy) flood occurred in November 2014. We focus on the modifications made to an automatic algorithm originally developed for a quick classification of COSMO-SkyMed in order to increase its reliability, as well as it robustness and transferability.

This paper describes two examples of synergistic management of EO based services for flood emergency monitoring. The first relates to the benefits of collaboration between decision makers, end users and experts reporting the case of the November 2014 Liguria and Po River flooding, when early activation, prior to the occurrence of the flood, was made possible and the second shows the benefits of the synergy between Sentinel-1 (S-1) and COSMO-SkyMed (CSK) contributing missions for monitoring floods at national scale. The two examples are referred to flood monitoring but the conclusions can be easily extended to other risks.

Coastal zones are fragile and dynamic environments, most of the time largely urbanized and particularly vulnerable to natural hazards. Therefore, coastal areas are often exposed to high risk and shoreline position monitoring and modelling is required to mitigate it. In this context, satellite data are fundamental to provide synoptic and multitemporal information useful to map and model shoreline position through time. The aim of this work was to study the shoreline evolution of two selected areas, in Portugal and in Italy. Shoreline historical rates were obtained by analyzing Landsat images from mid-80's up to 2011. Subsequently, short-term scenarios (2014) were predicted and their accuracy was assessed by comparing 2014 modelled and observed shoreline positions. After that, Landsat 8 and Sentinel 1 images were exploited to extract and compare 2015 shoreline positions in a Data Fusion context. Finally, results were interpreted for their implications in the coastal risk reduction framework.

A multitemporal algorithm (MLTA) to retrieve soil moisture from radar data, originally conceived and validated for the C band radar aboard the Sentinel-1 satellite [1], has been modified/updated in order to ingest data provided by the SMAP mission. The implemented algorithm consists of integrating a dense time series of radar backscatter measurements within a multitemporal inversion scheme based on the Bayesian Maximum A Posteriori (MAP) criterion. Such estimator maximizes the probability density function of the vector of soil parameters (soil moisture and roughness) conditioned to the measurement vector. The calibration and validation tasks have been accomplished by using the data collected during the SMAP Validation Experiment 012 (SMAPVEX012). Preliminary results have assessed the potential of the algorithm at L-band.

Ka-band may lead to important applications for the next generation of SAR spaceborne sensors (e.g.: DEM, disaster management, GMTI, subsidence, ocean currents, vegetation height). Nevertheless, the lack of trusted references on backscatter at Ka-band revealed to be the main limitation for the investigation of future applications. The paper, supported by the ESA project “Ka-band SAR backscatter analysis in support of future applications”, is aimed at studying the wave interaction at Ka-band for a widely varying range of targets in order to define a set of well calibrated and reliable KA-band backscatter coefficients. Here, we propose several examples of backscatter data resulting from a critical survey of available datasets at Ka-band. The reliability of the results will be assessed via a preliminary comparison with Electromagnetic Models (EM).

Recent spaceborne polarimetric Synthetic Aperture Radars (SARs) enable the complete characterization of target scattering and extinction properties. Several missions are operating at X band while there are plans and analyses for systems operating at higher frequencies, such as Ka band. Systems operating at these frequencies have interesting and distinctive applications in the field of geosciences such as Cartography, Surface deformation detection, Forest cover mapping and many others. However, the detected ground surface response can be affected by atmospheric effects in both signal amplitude and phase, especially in presence of atmospheric precipitations. In this work we will introduce a simulation framework developed to characterize how precipitating clouds affect spaceborne X- and Ka-band SARs systems. The proposed framework is able to simulate the polarimetric SAR ground responses in terms of Normalized Radar Cross Sections (NRCS) and complex correlation coefficient, both for realistic atmosphere-ground scenarios and for synthetic canonical ones. Some preliminary results will be shown and discussed.

Spaceborne scatterometer observations, especially at Ku-band, are affected by rain in several ways and these effects need to be corrected to avoid errors in wind retrievals. In this work we propose a model to derive the surface backscattering coefficient in presence of both wind and rain. Our approach consists in the development of an ocean surface wind wave spectrum accounting for two effects due to raindrops impact on the surface: the rain-induced wave damping and the generation of ring waves. The results show that this extended spectrum is able to model the ocean surface wave modifications due to rain so that it can be used for further study in physically representing the scatterometer observations in presence of both wind and rain.

The latest generation of synthetic aperture radar (SAR) systems allows providing emergency managers with near real time flood maps characterized by a very high spatial resolution. However, mapping inundations in vegetated and urban areas still represents a critical issue, because the radar signatures of these targets are often ambiguous. This paper proposes a possible strategy to cope with flood mapping using SAR data in vegetated and urban areas. In particular, the use of the interferometric coherence is proposed to complement the information brought by intensity. The SAR images used for verifying the potentiality of the joint use of intensity and coherence data are the COSMO-SkyMed observations of the flood that hit the Emilia region (Northern Italy) in January 2014.

For validating remotely sensed products, the triple collocation (TC) is often adopted, which is able to retrieve the independent error variances of three systems observing the same target parameter. In this letter, three years of soil moisture data derived from the Advanced SCATterometer (ASCAT) aboard the MetOp satellite and the Soil Moisture and Ocean Salinity (SMOS) radiometer are analyzed and compared with the ERA Interim/Land model outputs and the ground measurements available from the International Soil Moisture Network. As we have four sources, a novel quadruple collocation (QC) approach is developed, which is more precise than TC since it uses the sources jointly. The results of QC show that the ERA model has the lowest error variance, while ground measurements are likely to be affected by the difficulty to represent a mean soil moisture within the satellite field of view by a limited number of stations. Moreover, the ASCAT retrievals outperform the SMOS ones if only anomalies with respect to the seasonal trend are considered, while the opposite occurs when the whole dynamic of soil moisture variation is considered.

Three years of soil moisture data derived from: the Advanced SCATterometer (ASCAT) and the Soil Moisture and Ocean Salinity (SMOS) radiometer are analyzed and compared. The comparison has been performed within the framework of an activity aiming at validating the EUMETSAT Hydrology Satellite Application Facility (H-SAF) soil moisture products derived from ASCAT over Europe and North Africa. A strategy has been set up in order to enable the comparison between products representing a volumetric soil moisture content, as those derived from SMOS, and a relative saturation index, as those derived from ASCAT. For specific sites, which belong to the International Soil Moisture Network, satellite products have been compared to the ground measurements of moisture gauges and to the ERA Interim/Land model output. The triple collocation technique has been exploited, which is useful to estimate the relative amount of error affecting different sources of soil moisture measurements. A preliminary approach with a quadruple collocation has been also investigated to jointly analyses the four sources of data.

The reliable estimation of soil moisture in space and time is of fundamental importance in operational hydrology to improve the forecast of the rainfall-runoff response of catchments and, consequently, flood predictions. Nowadays several satellite-derived soil moisture products are available and can offer a chance to improve hydrological model performances especially in environments with scarce ground based data. The goal of this work is to test the effects of the assimilation of different satellite soil moisture products in a distributed physically based hydrological model. Among the currently available different satellite platforms, four soil moisture products, from both the ASCAT scatterometer and the SMOS radiometer, have been assimilated using a Nudging scheme. The model has been applied to a test basin (area about 800 km2) located in Northern Italy for the period July 2012-June 2013.

The latest generation of synthetic aperture radar (SAR) systems allows providing emergency managers with near real time flood maps characterized by a very high spatial resolution. Near real time flood detection algorithms generally search for regions of low backscatter, thus assuming that floodwater appears dark in a SAR image. It is well known that this assumption is not always valid. For instance, in urban areas, the double bounce backscattering involving ground and vertical walls produce high radar return that can be further increased by the presence of the highly reflective floodwater. In addition, even mapping bare or scarcely vegetated inundated terrains, or crops totally submerged by water can turn out to be a difficult task. In fact, in the presence of significant wind that roughens the water surface, floodwater can appear bright in SAR images. This paper proposes possible strategies to cope with flood mapping using SAR data in urban areas and in the presence of significant wind. In particular, the use of the interferometric coherence for floodwater detection in urban areas and the use of an electromagnetic model able to simulate the radar return from shallow water as function of the wind field are proposed.

The use of soil moisture maps derived from satellite remote sensing measurements in operational applications (e.g. hydrology) was generally limited to instruments providing low resolution data (scatterometers, microwave radiometers) so far, because only these instruments offered a suitable temporal resolution. However, with the launch, in 2014, of a new generation of synthetic aperture radar (SAR) systems working at L- and C-bands (i.e., SMAP, Sentinel-1), it is expected the availability of soil moisture maps characterized by high spatial resolution and short revisit time. This kind of data give also the opportunity to improve the quality of SAR-derived soil moisture maps by applying multitemporal inversion techniques that, using a time series of SAR data, allows for dealing with the well-known ill-posedness of the retrieval problem. In anticipation of the availability of Sentinel-1 images, this study uses a dataset of ERS-2 SAR data with a revisit time of only three days to test a multitemporal soil moisture retrieval algorithm, already designed within the framework of project funded by European Space Agency, when vegetation is well-developed. As validation data, the in situ measurements of a set of stations belonging to the International Soil Moisture Network are used. To provide further insights on soil moisture retrieval from satellite microwave data, SAR retrievals and stations measurements are also compared with the estimates provided by ASCAT and SMOS.

The availability of the data provided by present and future constellations of Synthetic Aperture Radar (SAR) sensors and the development of reliable flood mapping algorithms allows producing frequent flood maps characterized by high spatial resolution. Progresses have been also achieved in flood modeling, so that a joint use of SAR-derived and model-derived inundation maps seems to be very promising to improve flood mapping accuracy. This paper presents the major outcomes of a combined use of a multi-temporal series of COSMO-SkyMed observations and of a hydrodynamic model, accomplished within the framework of an activity aiming at the interpretation of the dynamics of the flood that hit Albania in January 2010. By calibrating the model with the COSMO-SkyMed derived maps, a number of products, such as a map of the maximum water depth, can be generated. Results show a good agreement between SAR-derived and model-derived flood extents. Moreover, the maximum water depths are found in the areas where floodwater was present for the longest period of time, according to COSMO-SkyMed observations.

An automatic method to distinguish water surfaces (either flooded or permanent water bodies) from artifacts caused by heavy precipitation and wet snow is designed to improve flood detection accuracy in X-band synthetic aperture radar (SAR) images. The algorithm implementing the proposed method, mainly based on image segmentation techniques and on the fuzzy logic, consists of two principal steps: 1) detection of regions (or segments) of low-radar backscatter that appear dark in a SAR image, and 2) classification of each detected segment. Ancillary data, such as a local incidence angle map, a land cover map, and an optical image (helpful to detect wet snow), are also used. Through the fuzzy logic, the algorithm integrates different rules for the detection of dark areas, as well as for their classification based on radiometric, geometrical and shape features extracted from the segmented SAR image and on the ancillary data. The algorithm is tested on the COSMO-SkyMed imagery of the severe weather event that hit Northwest Italy on November 2011. A comparison with measured data, provided by the weather radars belonging to the Italian radar national network, and with the ground precipitation, forecasted by a numerical weather prediction model routinely used within the framework of the EUMETSAT Hydrology Satellite Application Facility project, indicates that the algorithm produces reliable classification maps, being able to distinguish the rainfall signature on X-band SAR images from that of flooded areas.

This paper presents an investigation on the rainfall signature for two COSMO-SkyMed (CSK) satellite case studies. Both of them are relative to a severe precipitation weather event, occurred in northwestern Italy (close to Liguria region) on November 3-8, 2011. This event was monitored by using a number of CSK images provided by the Italian Space Agency (ASI). In this case CSK X-SAR data have been compared with the weather radar (WR) Italian Radar National Mosaic. A third case study is relative to Hurricane “Irene” event, occurred in Eastern United States (close to Delaware) on late August 2011. CSK X-SAR images are compared with respect to concurrent ground-based S-band NEXRAD weather radar reflectivities. The correlation of the precipitating cloud fields between CSK X-SAR and WR images is significant in all case studies. An application of a refined XSAR-based precipitation retrieval method is presented. The X-SAR surface response is estimated using ancillary data, such as land cover maps and a digital elevation model (DEM).

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 describes the first outcomes of an activity aiming at validating the H-SAF soil moisture products derived from Metop-ASCAT data. For this purpose, an extensive comparison between SMOS and ASCAT derived soil moisture retrievals has been accomplished by considering the 25 km resolution ASCAT products and the SMOS L2 products. Both Europe and Northern Africa have been considered and data acquired during 2010 have been used. The procedure that has been followed to accomplish the comparison is described together with the first results. The way the ASCAT soil moisture relative index has been converted into a volumetric moisture content, which represents a critical aspect of the comparison, is also described. Results have demonstrated that, after the conversion of the H-SAF estimates into absolute volumetric soil moisture, the two products show a relatively good degree of correlation. Additional factors, such as spatial property features are also preliminary investigated.

In this work, an automatic method to distinguish, in X-band SAR images such as those supplied by Cosmo-SkyMed, water surfaces (either flooded, or permanent water bodies) from artifacts due to heavy precipitation, is designed to improve flood detection accuracy. The method, mainly based on the fuzzy logic, consists of two main steps, i.e., the detection of low backscatter areas and the classification of each dark object present in the considered SAR image. The algorithm uses ancillary data, such as a local incidence angle map and a Land Cover map. Through the fuzzy logic, it integrates different rules for the detection of low backscatter areas (based on the standard deviation of the backscattering coefficient and on a well-established radar backscattering model), as well as different rules for the classification of the low backscatter (dark) areas (i.e., to distinguish water surfaces from artifacts) based on their geometrical and shape features and on both land cover and local incidence angle.

The major outcomes of the analysis of the COSMO-SkyMed (CSK) synthetic aperture radar (SAR) observations of the area hit by the 2011 Japan tsunami are presented. The height of the tsunami waves was such as to cause a widespread inundation of the coastal area. The SAR acquisitions have been performed on March 12 (i.e., one day after the tsunami occurred) and March 13, 2011 in interferometric mode, so that not only the information on the intensity of the radar signals, but also the complex coherence has been used. The interpretation of the available data has allowed us to detect the flooded areas, as well as the receding of the floodwater from March 12 to March 13, 2011 and the presence of the debris floating above the water surface. Moreover, thanks to the high spatial resolution of the CSK images, the presence of floodwater in some urban areas in the Sendai harbor has been revealed by exploiting the information on the coherence. Our interpretations have been confirmed by a couple of optical images used as benchmarks.

In this work, the outcomes of a research activity, that lasted approximately seven years (2003-2010), in which soil moisture was monitored on a test site in Northern Italy by collecting a series of SAR images and in situ data are presented. Radar data were provided by the C-band ENVISAT/ASAR instrument. The research activity aimed at calibrating and validating a pre-operational algorithm, conceived to be used by the Italian Civil Protection, for high resolution soil moisture mapping from SAR data. The algorithm is focused on the Bayesian theory of parameter estimation. The Maximum A Posteriori (MAP) probability criterion or the Minimum Variance one are used to retrieve soil moisture by inverting a forward scattering model. Ancillary data such as optical images and land cover data are also used. The results of the validation activity have confirmed the validity of the proposed mapping approach. In particular, the algorithm allowed us to retrieve soil moisture with a R2coefficient of 0.77 and with a root mean square error in the order of 0.07 m3/m3.

The Cosmo-SkyMed mission offers a unique opportunity to obtain radar images useful for flood mapping, being characterized by high revisit time, thanks to the four satellites that form its constellation. In the context of a study aiming at evaluating the usefulness of Earth Observation data for managing flood events, particularly focused on Cosmo-SkyMed, an algorithm to map flooded areas from synthetic aperture radar imagery has been developed. It is based on methods developed in previous studies and aims at combining an image segmentation technique based on mathematical morphology and the fuzzy logic that allows us to label the identified objects as flooded or non-flooded. The default parameters of the fuzzy classifier are derived from the outputs of well-established electromagnetic scattering models.

A numerical simulator of satellite microwave radiometric observations of mountainous scenes, developed in a previous study, has been used to predict the relief effects on the measurements of a spaceborne radiometer. For this purpose, the trends of the error due to topography, i.e., the difference between the antenna temperature calculated for a topographically variable surface and that computed for a flat terrain versus the parameters representing the relief, have been analyzed. The analysis has been mainly performed for a mountainous area in the Alps by assuming a simplified land-cover scenario consisting of bare terrain with two roughness conditions (smooth and rough soils) and considering L- and C-bands, i.e., those most suitable for soil moisture retrieval. The results have revealed that the error in satellite microwave radiometric observations is particularly correlated to the mean values of the height and slope of the radiometric pixel, as well as to the standard deviations of the aspect angle and local incidence angle. Both a regression analysis and a neural-network approach have been applied to estimate the error as a function of the parameters representing the relief, using the simulator to build training and test sets. The prediction of the topography effects and their correction in radiometric images have turned out to be feasible, at least for the scenarios considered in this study.

A multilayer neural network has been developed to retrieve IPWV over land from brightness temperatures measured by the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) on board the Aqua satellite. Our study was optimized over two areas in Northern and Central Italy. Good agreements on the order of 0.24 cm and 0.33 cm rms, respectively, were found between neural network retrievals and ECMWF IPWV data for clear-sky. In the presence of clouds, an rms of the order of 0.38 cm was found for both areas.

The Cosmo-SkyMed mission offers a unique opportunity to obtain radar images useful for flood mapping, being characterized by high revisit time, thanks to the four satellites that form its constellation. In the context of a study aiming at evaluating the usefulness of Earth Observation data for managing flood events, particularly focused on Cosmo-SkyMed, an algorithm to map flooded areas from synthetic aperture radar imagery has been developed. It employs also ancillary data as a land cover map and a digital elevation model. The approach is based on the fuzzy logic because such a theory allows us to exploit the theoretical knowledge about the radar return from inundated areas and to account for simple hydraulic considerations and contextual information.

A numerical simulator of satellite microwave radiometric observations of mountainous scenes, developed in a previous study, is used to predict the relief effects on the measurements of a spaceborne radiometer. For this purpose, the trends of the error due to topography versus the parameters representing the relief have been analyzed for a test case concerning a mountainous area in the Alps by assuming bare soil and considering L- and C-bands. The results have revealed that the error in satellite microwave radiometric observations is particularly correlated to the mean value of the height and of the slope of the radiometric pixel, as well as to the standard deviation of the aspect angle and of the local incidence angle. A regression analysis has been applied to estimate the error as a function of the parameters representing the relief, using the simulator to build training and test sets.

A numerical simulator of satellite microwave-radiometric observations of orographically complex scenes, at various frequencies and observation angles, has been developed. The Simulator of Topographic Artefacts in MIcrowave RAdiometry (STAMIRA) exploits the information on the relief, extracted from a digital elevation model, and has been applied to a test case concerning a mountainous area in the Alps by assuming a simplified land-cover scenario consisting of bare terrain with two kinds of roughness (smooth and rough soils). The 1-10-GHz range has been considered to determine scattering and emission of soil and a nonscattering atmosphere has been supposed. The simulations have shown the large impact of the rotation of the polarization plane and of the brightness-temperature enhancement occurring for facets illuminated by radiation from the surrounding elevated terrain with respect to flat surfaces which scatter atmospheric downward radiation only. By considering also the antenna-pattern integration and the dependence of surface emissivity on the local observation angle, we have found that, for our case study, the brightness temperature is larger than that measured observing a flat terrain at horizontal polarization. At vertical polarization, the opposite occurs. These differences are analyzed and quantified.

This paper investigates the sensitivity of backscattering coefficient to variations of soil moisture and flooding for two kinds of crops, such as wheat and maize, and for deciduous forests. Investigations are based on model simulations at L and C band, VV and HH polarization. At L band, a significant sensitivity to flooding effects is observed for all vegetation covers. At C band, the sensitivity is still acceptable for wheat, while for maize it is present only in case of non uniform cover. For forests, the performance of C band is poor.

The COSMO-SkyMed mission is expected to give a fundamental contribution for flood mapping, because of the high revisit time and throughput achieved by the four satellites that form the constellation. To study the potentiality of COSMO-SkyMed radar data for this purpose, two inundation events are analyzed in this paper, namely the flood occurred in Myanmar in May 2008 and the event that took place in the city of Alessandria (Italy) in April 2009. For the first event, two radar images were considered, one temporally close to the peak of the event, and the other one acquired one week later. As for the Alessandria flood, a time series of images was available, so that an attempt to monitor the temporal evolution of the inundation was accomplished.

Two different approaches to deal with the problem of estimating soil moisture content from SAR data in the presence of vegetation are presented. They exploit also the information about the biomass provided by ancillary optical data. The first method is suitable for sparse vegetation and is founded on the application of the well-known water cloud model. As for dense vegetation canopy, we have designed a model that expresses the variation of the component of the backscattering coefficient due to the soil characteristics as a function of the variations of the measured backscattering coefficient and of the biomass, assuming the availability of a time series of radar and optical data. To carry out the soil moisture retrieval, a multi-temporal inversion algorithm, based on the Bayesian MAP criterion, has been developed. It integrates all the samples of the time series of SAR data corrected for the vegetation effects. The approaches were evaluated on two case studies; the first one concerning an ENVISAT/ASAR observation of an agricultural site located in Northern Italy. The second test was performed on the AirSAR data collected during the SMEX02 experiment. The comparison between the estimated soil moisture contents and the in situ measurements has given encouraging results.

This letter is focused on the microwave signature characterization of the Greenland ice sheet. Such characterization is carried out by exploiting the S- and Ku-band brightness temperatures measured by the radar altimeter RA-2 when it operates as a radiometer during the ENVISAT Commissioning Phase for the purpose of calibrating the receiver. Despite the poor radiometric resolution and the calibration issues, this activity represented a unique opportunity to gather brightness temperatures at frequencies that are not available from current spaceborne microwave radiometers. The analysis of the passive RA-2 data investigates the influence of terrain height and of the temperature of the snow layers on the brightness temperatures at RA-2 bands. The effect of the different penetration depths of the electromagnetic radiation at S- and Ku-bands is also pointed out. Measurements from the Advanced Microwave Scanning Radiometer for the Earth Observing System are used to complement the data provided by RA-2 and to verify their reliability.

A simulation study to quantify the influence of the topography on the measurements performed by a satellite microwave radiometer is accomplished in this work. The Northern Italy region (including Alps) is considered and the information on the relief, extracted from a digital elevation model (DEM), is exploited. We have developed a simulator of satellite microwave radiometric observations of mountainous scenes operating at different frequencies and observation angles. The simulation shows that the changes in the local observation angle generally tend to decrease the antenna temperature. The effect of the rotation of the polarization plane attenuates (for horizontal polarization) or enlarges (vertical polarization) this decrease. Facets illuminated by radiation from surrounding elevated terrain enhance their brightness temperature with respect to surfaces which scatter atmospheric downward radiation. At horizontal polarization, this results in a general overestimation of the brightness temperature with respect to that measured observing a flat terrain. At vertical polarization, both under and overestimation may occur.

A unified approach to simulate both microwave radiometric and scatterometric observations over a sea surface based on the two-scale electromagnetic scattering model has been implemented. The simulations have been compared with empirically derived geophysical model functions (GMFs) for the purpose of tuning the electromagnetic forward model and the embedded sea surface characterization and wave spectrum. The comparison has concerned a large number of wind speeds, frequencies, and observation angles for both the passive and active cases. Two widely adopted literature spectra have been used as benchmarks. A new expression for the hydrodynamic modulation function has been developed too. The proposed new spectrum has allowed us to reproduce both the passive and active GMFs' behaviors fairly well, generally improving the agreement yielded by literature models, particularly for scatterometric simulations.

The possible contribution of bistatic radar measurements for bare soil moisture retrieval is investigated in this paper. A simulation study based on well-established electromagnetic models of rough surface scattering (both coherent and incoherent components) has been accomplished for this purpose. The retrieval accuracy has been evaluated by using both the Cramer–Rao lower bound and the error variance of a linear regression estimator, thus considering slightly different assumptions on retrieval conditions. Both methods have allowed us to identify the optimal system configurations in terms of observation directions, polarizations, and frequency. This identification has been carried out for single-polarization and multipolarization receivers and for the case in which bistatic measurements are complemented by monostatic ones, which are expected to be available through already-existing spaceborne synthetic aperture radars. The optimal systems have first been singled out by considering a Gaussian autocorrelation function (ACF) and a constant value of correlation length. Successively, the simulations for an exponential ACF and a variable correlation length have been analyzed, demonstrating that the results substantially remain the same. The comparison between the soil moisture estimation accuracy yielded by the optimal configurations and that provided by the standard monostatic radar has shown that a significant improvement in the quality of retrieval can be achieved by complementing bistatic and monostatic measurements.

The possible contribution of bistatic radar measurements to estimate bare soil moisture and vegetation biomass is investigated by a simulation study based on well established electromagnetic models (both coherent and incoherent components). The best system configuration, in terms of observation directions, polarisations and frequency has been singled out by predicting the retrieval accuracy. This has been evaluated using the Cramer-Rao lower bound to identify optimal configurations for single polarisation and multipolarisation receivers, as well as in case the bistatic measurements are complemented by monostatic ones.

A simulation study to understand the influence of the topography on the land emissivity estimated by a satellite microwave radiometer is accomplished in this work. A mountainous area in the Alps (Northern Italy) is considered and the information on the relief, extracted from a digital elevation model (DEM), is exploited. We have simulated an observation of the area of interest performed by a satellite radiometer flying at 800 km of altitude and conically scanning the area with an angle of 53deg. We have considered the following frequencies: 23.8, 36.5 and 90.0 GHz, with the following spatial resolutions: 60times40, 37times29 and 15times13 km for the, 23-, 36-, and 90-GHz bands, respectively. The results indicate that the effect of the topography tends to lower the antenna temperature, thus implying an underestimation of the surface emissivity. This effect is larger at 90 GHz for which the maximum underestimation of the antenna temperature is in the order of 5 K.

The two-scale electromagnetic model is a well-established theory for simulating microwave polarimetric passive observations of a sea surface. A critical aspect is the long computational time that is required to run the forward model, which hampers the creation of large training databases or iterative simulations within retrieval algorithms. To tackle this problem, a neural network (NN) technique is proposed in this paper. In particular, we have adopted NNs to emulate a simulator named SEAWIND, which implements the two-scale model and was validated in previous works. Two training algorithms, including a regularized approach, have been considered and compared. The assessment of the proposed approach has been carried out by statistically comparing neural-network-derived simulations with SEAWIND-derived ones for two validation data sets comprising different climatic conditions, as well as by computing the azimuthal Fourier harmonic coefficients versus wind speed and atmospheric transmittance. Regressive model functions have also been used as benchmarks. This paper demonstrates the feasibility of an NN approach to efficient and effective modeling of sea-surface thermal emission and scattering.

A research activity aiming to assess the potential of bistatic measurements of scattered radiation from the land surface is presented. The purpose is to identify the best configuration of a passive system measuring the signal originated by sources of opportunity, like GNSS or radars aboard a satellite. The preliminary result of the study consists of the validation of the electromagnetic model simulating bistatic scattering from bare soil, including the coherent component. A very preliminary sensitivity analysis to soil moisture is also presented.

The passive calibration of the Radar Altimeter (RA) consists of characterizing the receiver gain by observing natural surfaces with known emission in the so-called noise-listen mode. It is based on the comparison between the simulated values of the brightness temperature impinging on the altimeter antenna, and the digital counts at the output of the altimeter receiver in the absence of echo. The proposed method aims to calibrate measurements of the backscattering coefficient performed by a spaceborne altimeter based on the assumption that the receiver gain is the main source of uncertainty. This paper focuses on the general approach undertaken to characterize the receiver and to simulate the brightness temperature at the top of the atmosphere observed by the Envisat RA-2. The simulations rely on emissivity models for land and sea, as well as on atmospheric radiation models supported by a continuous flow of online data used as model inputs. To assess the accuracy, the model outputs are compared with observations from calibrated radiometers, namely the Special Sensor Microwave/Imager and Tropical Rainfall Measuring Mission Microwave Imager, with particular attention to the low-frequency channels (10 and 19 GHz). The new method has been first tested on European Remote Sensing satellite data and has been subsequently adopted for Envisat RA-2 in the framework of the Envisat Calibration and Validation activities managed by European Space Agency. The evaluation of the receiver gain at both Ku-band and S-band is presented and compared to the preflight values, as well as to transponder calibration done for Ku-band. An error budget for the final estimates is also presented and discussed

A procedure to estimate atmospheric and sea surface parameters in the Mediterranean area from satellite microwave radiometric measurements is described. The method is founded on a simulator of brightness temperatures at the top of the atmosphere. The simulator is based on microwave sea emissivity and scattering model functions, derived from the outputs of the SEAWIND software, which implements a two-scale microwave sea surface model and a radiative transfer scheme in a nonscattering atmosphere. The development of the model functions aims to reduce the SEAWIND computational time, still maintaining its sensitivity to the main geophysical variables. Different adaptations of the simulation model have been performed to better reproduce the radiometric data in the region of interest. A comparison between the simulations and the Special Sensor Microwave/Imager (SSM/I) observations acquired throughout year 2000 over the Mediterranean Sea has permitted us to refine the model functions as well as to assess the whole simulation procedure. As for the inversion problem, a regression analysis has been applied to two different synthetic datasets to retrieve integrated precipitable water vapor, liquid water path and wind speed. The first dataset simulates the observations of SSM/I, whilst the second one concerns the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). Both have been generated by using the ECMWF atmospheric profiles and the measurements of the SeaWinds scatterometer aboard QuickSCAT. The SSM/I data have been used to carry out a statistical validation of the estimators. AMSR-E observations of a Tramontane-Mistral event, typical of the Mediterranean Sea, have been analyzed to evaluate the benefits of its expanded channel capability.

An electromagnetic forward model originally developed to simulate the sea emission at microwave bands is extended to the active case. This work focuses on the tuning of the model to reproduce the behaviour of the geophysical model functions of ERS, NSCAT and QuikSCAT scatterometcrs (C and Ku band). In order to better match the model functions, we have modified some assumptions within the model concerning both the isotropic and anisotropic components of the sea wave spectrum and the hydrodynamic modulation. The preservation of the ability, to simulate passive radiometric data trough the same assumptions is also assessed.

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 analysis of a large set of data, collected from the Special Sensor Microwave/Imager (SSM/I) radiometer and from the NASA SeaWinds scatterometer over the Mediterranean Sea is presented, with the aim of studying the mean behavior of various geophysical parameters, such as water vapor, surface rain rate, wind speed and Ekman pumping for the years 2000 and 2001. This paper presents the results of the study of two different meteorological situations occurring in the Mediterranean basin. Moreover, ability of these instruments for analyzing extreme events is shown through examples.

A reliable estimate of atmospheric and sea surface parameters from satellite microwave radiometric measurements requires the availability of a large dataset of simulated brightness temperatures to train the retrieval algorithms. The validity of the radiative forward model adopted to generate the synthetic brightness temperatures, for known atmospheric and surface conditions, represents a crucial point of any retrieval method. This work proposes an approach based on microwave sea emissivity and scattering model functions, derived from the two-scale sea surface model and on a radiative transfer scheme in a nonscattering atmosphere. For the latter, the author investigate about the possibility to adopt a simple cloud model derived from the outputs of a simulation of a stratiform event occurred in the Mediterranean basin. The simulation has been carried out by using a detailed microphysical time-evolving cloud model.

The capability of some inversion algorithms to estimate surface rain rate at the midlatitude basin scale from the Special Sensor Microwave Imager (SSM/I) data is analyzed. For this purpose, an extended database has been derived from coincident SSM/I images and half-hourly rain rate data obtained from a rain gauge network, placed along the Tiber River basin in Central Italy, during nine years (from 1992 to 2000). The database has been divided in a training set, to calibrate the empirical algorithms, and in a validation one, to compare the results of the considered techniques. The proposed retrieval methods are based on both empirical and physical approaches. Among the empirical methods, a regression, an artificial feedforward neural network, and a Bayesian maximum a posteriori (MAP) inversion have been considered. Three algorithms available in the literature are also included as benchmarks. As physical algorithms, the MAP method and the minimum mean square estimator have been used. Moreover, in order to test the behavior of the algorithms with different kinds of precipitation, a classification of rainy events, based on some statistical parameters derived from rain gauge measurements, has been performed. From this classification, an attempt to identify the type of event from radiometric data has been carried out. The purposes of this paper are to determine whether the use of an extended training set, referred to a limited geographical area, can improve the SSM/I skill in rain detection and estimation and, mainly, to confirm the validity of the physical approach adopted in previous works. It will be shown that, among all the estimators, the neural network presents the best performances and that the physical techniques provide results only slightly worse than those given by empirical methods, but with the well-known advantage of an easy application to different geographical zones and different sensors.

An accurate retrieval of atmospheric and sea surface parameters from spaceborne microwave radiometers requires to validate the radiative forward model capable to simulate the brightness temperatures for known atmospheric and surface conditions. Matching the forward model and the retrieval algorithm to the climatological condition of the area of interest is also essential to obtain better retrieval accuracy. This work focuses on the Mediterranean Sea and aims to set up a consistent and accurate forward model using different sources of geophysical quantities and a large set of SSM/I data for validation purposes.

The skill of some algorithms, based on both a physical and an empirical approach, to estimate surface rain-rate in the Mediterranean region from the special sensor microwave imager (SSM/I) data is analysed. A reliable validation set consisting of rain-gauge data collected throughout 9 years is used to perform such analysis. We aim to confirm the validity of the physical approach adopted in previous works, showing that its behaviour is similar to the one presented by the algorithms based on an empirical approach, but with the well-known advantage of an easy extension to zones different from the calibration one and to sensors operating at other frequencies. We also evaluate the improvement that can be obtained using local scale algorithms rather than global scale ones.

This paper concerns the remote sensing of atmospheric integrated precipitable water vapour (IPVW) using a Global Positioning System (GPS) network and the Special Sensor Microwave Imager Radiometer (SSM/I) in the Mediterranean area. A comparison of IPWV maps from the two different techniques is presented. Some preliminary attempts to develop a data assimilation method, are also proposed.

The passive calibration of the radar altimeter consists in characterising the receiver by observing natural surfaces with known emission in the so-called noise-sensing mode. The paper focuses on the general approach undertaken to simulate the brightness temperature at the top of the atmosphere observed by the Envisat Radar Altimeter (RA-2). It is based on emissivity models for land and sea as well as atmospheric radiation models supported by a continuous flow of on-line data used as model inputs.

The capability of some empirical algorithms to estimate surface rain-rate at mid-latitude basin scale from the Special Sensor Microwave Imager (SSM/I) data is analyzed. We propose three retrieval techniques based on a multivariate regression, a Bayesian maximum a posteriori inversion and on an artificial feed-forward neural network. Three algorithms available in literature are also included as benchmarks. The training data set is derived from coincident SSM/I images and half hourly rain-rate data obtained from a rain-gauge network, placed along the River Tiber basin in Central Italy, during 9 years (from 1992 to 2000). The work points out that an algorithm based on regression or a neural network is a good estimator of low precipitation, while it tends to underestimate high rain rates. The best results have been achieved with the Bayesian method.

A physical-statistical approach to simulate cloud structures and their upward radiation over the Mediterranean is described. It aims to construct a synthetic database of microwave passive observations matching the climatological conditions of this geographical region. The synthetic database is conceived to train a Bayesian maximum a posteriori probability inversion scheme to retrieve precipitating cloud parameters from spaceborne microwave radiometric data. The initial microphysical a priori information on vertical profiles of cloud parameters is derived from a mesoscale cloud-resolving model. In order to complement information from cloud models and to match simulations to the conditions of the area of interest, a new approach is proposed. Climatological constraints over the Mediterranean are derived on a monthly basis from available radiosounding profiles, rain-gauge network measurements, and colocated METEOSAT infrared measurements. In order to introduce the actual surface background in the radiative-transfer simulations, a further constraint is represented by the monthly average and variance maps of surface emissivity derived from Special Sensor Microwave Imager (SSM/I) clear-air observations. A validation of the forward model is carried out by comparing a large set of brightness temperatures measured by the SSM/I with the synthetic cloud radiative database to asses its representativeness and range of variability.