This letter describes a procedure based on the radiative transfer theory to calibrate the scattering contributions from the Generalized Freeman-Durden (GFD) polarimetric decomposition over corn fields. The Tor Vergata electromagnetic model (TOV) is used to simulate canonical scattering mechanisms that are compared with those obtained applying GFD to both simulated and L-band SAOCOM-1A data. The proposed method first analyzes the error between the model and the GFD applied to the simulated data. A multivariate data fitting is then performed to derive a new expression of the GFD powers, which is tested on L-band real data. The GFD volume power obtains the greatest benefit from the calibration, reducing the Root Mean Square Error (RMSE) with respect to the corresponding TOV model contribution to 0.006 in linear units. To further test the procedure, a linear regression model is used to estimate soil moisture using the calibrated GFD powers from SAOCOM-1A real data. The retrieval performance, evaluated through a Leave-One-Out (LOO) cross-validation against in situ data, shows a significant improvement: the calibrated GFD powers leads to an increased linear correlation (0.32 to 0.57), while the RMSE is reduced (0.096 to 0.055 m³/m³).

This paper gives a summary of the HydroGNSS mission and status in the preparations up to launch. Some of the advancements in instrument are presented, including on-board gain adjustment, geolocation. Also the ground processing Payload Data Ground Segment is introduced and some of the processing advances, including antenna pattern modelling for EIRP estimation and bounding of measurement areas.

In view of the launch of the ESA HydroGNSS mission, whose receiver will measure both left and right-polarized global navigation satellite system reflectometry (GNSS-R) signal, this study analyses the features of dual-polarized signals by using simulations provided by the soil and vegetation reflection simulator (SAVERS) over both bare soil and forest. The reliability of GNSS-R dual-polarized simulations of SAVERS over land is first assessed by comparison with data collected in the frame of the GLObal navigation satellite system reflectometry instrument (GLORI) airborne campaigns. Then, the simulator is used to carry out a sensitivity analysis of left–right (LR) and right–right (RR) circularly polarized spaceborne GNSS-R signals to soil moisture (SM), soil roughness (SR), and forest biomass (BIO). The combinations of the two polarizations, such as ratio, difference, and normalized difference, are included in the analysis as well. The study evaluates also the SM effects on the horizontal-right (HR) and vertical-right (VR) polarized GNSS-R signal. The results show that the combination of the two circular polarizations can reduce the small-scale roughness effect in the SM monitoring as well as the effect of topography, and it can extend the sensitivity to large values of BIO. A critical point assessed by this study is the low value of the RR signal power, which may be difficult to detect over the noise floor, especially over land regions with low depolarization effects.

Phase altimetry has been recently introduced for high precision surface height measurements in satellite global navigation satellite system reflectometry. In the presence of coherent scattering the effectiveness of the technique has been demonstrated for sea surface at low grazing angle as well as for rivers and lakes. This study develops the concept of differential phase altimetry for land surfaces, provided that the surface roughness is small and the surface undulations are gentle. Differential phase altimetry is more tolerant with respect to atmospheric and systematic errors but its applicability is difficult in the presence of surface slope variations that may generate phase cycle slips that require complex post processing to unwrap the retrieved phase. To mitigate cycle slips an overlapping correlation process is introduced to generate phase values with high spatial resolution. A first evaluation of performance is presented using the CYclone Global Navigation Satellite System raw intermediate frequency acquisitions over land surface with gentle undulations.

In this paper, the results obtained by applying two polarimetric SAR decompositions to a time-series of L-band radar data, in terms of scattering contributions, are compared with the simulations of the Tor Vergata electromagnetic model. The objective was to evaluate the capability of polarimetric SAR decompositions to single out those scattering mechanisms mostly correlated to soil moisture or vegetation. We performed the analysis by using L-band full-polarimetric SAOCOM-1A data acquired over an agricultural region in the Monte Buey site (Córdoba Province, Argentina) and by considering five corn fields.

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.

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.

This letter describes a model-based algorithm for estimating tree height and other bio-physical land parameters from time series of synthetic aperture radar (SAR) interferometric coherence and backscatter supported by sparse lidar data. The random-motion-over-ground model (RMoG) is extended to time series and revisited to capture the short- and long-term temporal coherence variability caused by motion of the scatterers and changes in the soil and canopy backscatter. The proposed retrieval algorithm estimates first the spatially slow-varying RMoG model parameters using sparse lidar data, and subsequently the spatially fast-varying model parameters such as tree height. The recently published global Sentinel-1 (S-1) interferometric coherence and backscatter data set and sparse spaceborne GEDI lidar data are used to illustrate the algorithm. Results obtained for a small region over Spain show that the temporal coherence and backscatter time series have the potential to be used for global, model-based land parameter estimation.

In this paper, a joint analysis of multi-frequency SAR data has been performed to assess their sensitivity to soil moisture variations over an agricultural area. The main objective was evaluating the performances offered by C and L bands in terms of sensitivity to soil moisture. We used L-band quad-polarimetric data acquired by SAOCOM-1A and C-band dual-polarimetric data collected by Sentinel-1A over an agricultural area located in the Córdoba Province, Argentina. We analyzed the temporal evolution of the backscattering coefficient at different polarizations with respect to in-situ soil moisture measurements collected during a field campaign conducted by the Argentinian Space Agency as well as data recorded by a permanent network of ground stations. Sensitivity to other variables, such as the NDVI, is also discussed and analyzed.

Phase altimetry has been recently introduced for high precision measurements in satellite bistatic global navigation satel-lite system refiectometry in the presence of coherent scattering. This work introduces differential altimetry concept via a simulation study. The phase difference between two contigu-ous specular points paths is exploited to reduce atmospheric effects and systematic errors occurring over closely spaced paths. The work also introduces an oversampling technique to mitigate unwrapping errors.

HydroGNSS is the second mission supported by the European Space Agency (ESA) under the Scout program, which is a new framework (3 years from KO to launch, cost ≤ 30 M€) by which ESA aims to demonstrate disruptive sensing techniques or incremental science, while retaining the potential to be subsequently scaled up in larger missions or implemented in future ESA Earth Observation programmes. HydroGNSS consists of a scientific demonstrator that primarily addresses land bio-geophysical variables. The mission is comprised of one satellite (with an option on the second) flying at a low-Earth orbit to collect Global Navigation Satellite System reflections (i.e., Delay Doppler Maps, DDMs) near continuously over the globe. The DDMs are used to generate Level 2 products related to Essential Climate Variables (ECV s), whose estimation defines the primary scientific goal of the mission. In this contribution we outline a review of the ECVs targeted by HydroGNSS, showing some representative results achieved during preliminary studies about the mission. Special emphasis is given to the monitoring of soil freeze-thaw state and soil moisture. ECVs are of great interest and support the sustainable development agenda adopted by the United Nations members in 2015.

We propose an analytical solution of the scattering for the evaluation of the decorrelation time of reflected signals of opportunity. We consider spaceborne receivers and an approximated solution of the covariance of the scattered field under the Kirchhoff approximation. The case of an infinite illuminated surface showing gentle undulations is studied. It is discussed how the near-specular scattering, collected over land targets by a receiver from space, decorrelates as a function of the receiver movement and of the statistical parameters describing the illuminated surface. The study gives information of interest for the design of future bistatic missions, especially for GNSS reflectometry. The interpretation of data from space, which typically shows strong fluctuations, can also be supported.

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.

In this paper, we assess the estimation of bio-physical land parameters from time-series of interferometric SAR coherence supported by a physical model. The random-motion-over-ground model (RMoG) is revisited to partially capture the short- and long-term temporal variability of the coherence caused by motion of the scatterers and changes in their dielectric properties. The recently-published global Sentinel-1 interferometric coherence dataset is used to compare model predictions with observations and evaluate the need for additional model assumptions or ancillary data sets. Space-borne lidar data acquired by GEDI are also considered to further constrain the parameter estimation. This work is particularly relevant to upcoming SAR missions such as NISAR and ROSE-L that will generate global and dense time-series of interferometric temporal coherence at L-band.

The GNSS reflectometry (GNSS-R) potential for the monitoring of hydrological parameters as soil moisture (SM) and forest aboveground biomass (AGB) has been largely proved in recent years. In this study, algorithms based on Artificial Neural Networks (ANN) have been developed for the retrieval of both SM and AGB from GNSS-R observations. This activity has been carried out in view of the ESA's HydroGNSS mission. Waiting for HydroGNSS data, the algorithms have been implemented and validated by using the NASA's Cyclone GNSS (CyGNSS) land observations, confirming a promising potential of GNSS-R for the monitoring of both SM and AGB.

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.

A comparison of three different electromagnetic scattering models for land surface delay-Doppler maps (DDMs) obtained from global navigation satellite system reflectometry (GNSS-R) along a Cyclone Global Navigation Satellite System (CYGNSS) track in the San Luis Valley, Colorado, USA, is presented. The three models are the analytical Kirchhoff solutions (AKS), the Soil And VEgetation Reflection Simulator (SAVERS), and the improved geometrical optics with topography (IGOT). Common inputs to the three models were defined by using field samples of soil moisture and texture, soil surface roughness measurements, and a digital elevation model (DEM). The resulting peak reflectivity profiles of the models and the CYGNSS data all had a range of 10 dB along the selected track, mainly due to the influence of topography. The reflectivities obtained from all three models agreed with one another to within 2.4 dB along the full length of the track. The models also showed general agreement with the corresponding CYGNSS data, although the modeled profiles were higher than CYGNSS Science Data Record Version 3.1 by an average of 5 dB and also smoother. Additional characterization of fine-scale surface roughness is identified as an area for future work to improve model fidelity. An intercomparison of DDM structure for three selected acquisitions is also provided.

Signal fluctuations at the receiving antenna have been studied from decades by the radar community, especially to understand the decorrelation of the scattering in radar interferometry. This was done assuming uncorrelated point-like scatterers, leading to a simple model for the geometric decorrelation. In this case, the scattering is certainly incoherent. The quasi-specular reflections gathered under the illumination of signals of opportunity can exhibit significant temporal fluctuations. They are related to the statistical features of the surface roughness and can be observed even in almost flat regions, where a predominant coherent reflection could be expected. The presence of gentle undulations, however, i.e., those showed by surfaces having variations of the profiles comparable with the wavelength over the vertical scale, but much longer over the horizontal one, can determine transition regions where the scattering is neither coherent nor completely incoherent. In these conditions, the nature of the fluctuations of the scattering is not well understood and it requires additional studies. A discussion about the dominance of coherent or incoherent reflection in the Global Navigation Satellite System Reflectometry (GNSS-R) community is presently ongoing. To describe the nature of the scattering, and to understand the decorrelation of the near-specular components in GNSS-R, we propose a numerical study of the field collected by a moving airborne receiver based on the Kirchhoff approximation. Our study demonstrates that the near-specular scattering collected over representative natural landscapes by a GNSS-R receiver is partially coherent and essentially incoherent in most cases. Its correlation time is a function of the roughness parameters, namely standard deviation and correlation length, as well as of the system parameters (i.e., spatial resolution and height). The analysis can provide useful information for the interpretation of GNSS data, which present intrinsic variability that can significantly affect the retrieval of the relevant bio-geophysical parameters.

To understand the temporal decorrelation of the near-specular component of land-scattered signals in global navigation satellite system reflectometry (GNSS-R) and describe the nature of the scattering considering spaceborne receivers at arbitrary altitudes, we propose here an analytical solution of the covariance of the field under the Kirchhoff approximation. Both cases of infinite illumination and finite illumination on the ground are studied. Surfaces with gentle undulations are considered, i.e., those having small slopes and showing slow variations of the profiles over the horizontal scale. This allows for investigating scattered fields that can be neither coherent nor completely incoherent over land surfaces that are nearly flat. In a recent work from the authors, an extensive numerical evaluation of the decorrelation of the near-specular land scattering was presented. The phenomenology of the problem was studied and discussed numerically, solving, for airborne receivers, the relevant scattering integral, both as a function of the geometry of the system and the statistical parameters of the illuminated surface. Such numerical results are used here to validate the proposed closed-form formulation. It is demonstrated how the near-specular scattering, collected over land targets by a GNSS-R receiver from space, decorrelates as a function of the receiver movement and the statistical parameters describing the illuminated surface (namely, height standard deviation and correlation length). The proposed analysis provides information of interest for the design of future GNSS-R missions. The interpretation of GNSS-R data from space, which typically shows strong fluctuations, can also be supported by this approximated analytical study.

Bistatic scattering from rough surfaces is typically approached through the analysis of the scattered field in the conventional H and V polarization basis, which coincides with the zenith and azimuth unit vectors in a spherical reference frame. This study delves into the impacts of different choices of the transmit and receive linear basis on the performance and design of a synthetic aperture radar (SAR) mission receive-only companion. This article formalizes the rotation of the scattered wave orientation at the antenna axes of the companion with respect to the transmitted one and introduces a novel set of linear polarizations, named principal polarizations, in transmit and receive, deemed more suited to represent the scattering mechanisms of rough surfaces. Such a set is defined by the polarization bases that maximize the radar cross section. It is shown that the theoretical estimates from the proposed geometrical framework provide a good agreement with analytical and numerical simulations, performed considering state-of-the-art numerical solutions. In addition, this article promotes the hypothesis that a bistatic radar configuration, defined through the conventional H and V linear basis, presents a strong similarity, from a target information retrieval standpoint, to a monostatic compact$\varphi $-pol mode, i.e., with the transmission of a linear polarization rotated by an angle$\varphi $. The rotation$\varphi $varies over the swath and as a function of satellite separation. For baselines of 250–300 km, such as those envisioned by the European Space Agency (ESA) Harmony Earth Explorer candidate, and for steep incidence angles, an equivalent$\pi /8$-pol can be achieved for rough surfaces.

Bistatic and multistatic radar observations are mostly conceived to exploit the signal phase, e.g., for interferometry, tomography, and ocean current applications. In these situations, the distance between the transmitting and receiving antennas (bistatic baseline) is generally small in order to, among other reasons, keep high the coherence between the backscattered (monostatic) and bistatic radar echoes. Less evidence can be found in the literature on the exploitation of the signal amplitude (i.e., the scattering coefficient) of monostatic observations combined with bistatic ones, thus implementing a multistatic observation. In this case, to increase the information content in the inverse problem (i.e., the retrieval of biogeophysical parameters), the observations to be combined must be sufficiently independent in order to improve the accuracy of the retrieval. This article aims at identifying suitable geometrical configurations of a passive satellite radar flying in convoy with an active spaceborne SAR at L-band. Few bistatic datasets exist to verify this concept experimentally, so that electromagnetic models can help understanding its potential in different fields. The applications foreseen in this article are the retrieval of soil moisture and vegetation biomass. A model-based investigation shows that retrieval performances can be improved by combining monostatic and bistatic measurements in geometric configurations requiring very large along track and across track baselines. In this way, the scattering mechanisms involved in the monostatic and bistatic geometry are sufficiently different so that their combination increases the retrieval performances with respect to a monostatic acquisition.

ALtimetry for BIOMass (ALBIOM) is a Permanent Open Call Project funded by the European Space Agency (ESA) to explore the possibility of forest biomass retrieval by using Copernicus Sentinel-3 (S-3) Synthetic Aperture Radar Altimeter (SRAL) in low- and high-resolution mode at Ku- and C-bands. It represents an original work in the research of new techniques for vegetation observation using altimetry data. Because of the complexity of the land surfaces, no algorithm has been developed for a specific retracking of the altimetric land waveform. This calls for the development of a model able to reproduce the acquisition system and the target scattering phenomena to simulate the interaction of the radar pulse with the land. In this first work we present the electromagnetic simulator of S-3 SRAL altimeter measurements over bare soil scenarios realized through a modification of the SAVERS (Soil And Vegetation Reflection Simulator) simulator developed by the team for GNSS-R reflectometry over land. The impact of topography has been also taken into account. We demonstrate that SAVERS for S-3 SRAL proved its capability to reproduce altimeter waveforms’ main attributes for both flat and topography scenarios.

Forests play a crucial role in the climate change mitigation by acting as sinks for carbon and, consequently, reducing the CO2 concentration in the atmosphere and slowing global warming. For this reason, above ground biomass (AGB) estimation is essential for effectively monitoring forest health around the globe. Although remote sensing-based forest AGB quantification can be pursued in different ways, in this work, we discuss a new technique for vegetation observation through the use of altimetry data that have been introduced by the ESA-funded ALtimetry for BIOMass (ALBIOM) project. ALBIOM investigates the possibility of retrieving forest biomass through Copernicus Sentinel-3 Synthetic Aperture Radar Altimeter (SRAL) measurements at the Ku- and C-bands in low- and high-resolution modes. To reach this goal, a simulator able to reproduce the altimeter acquisition system and the scattering phenomena that occur in the interaction of the radar altimeter pulse with vegetated surfaces has been developed. The Tor Vergata Vegetation Scattering Model (TOVSM) developed at Tor Vergata University has been exploited to simulate the contribution from the vegetation volume via the modeling of the backscattering of forest canopy through a discrete scatterer representation. A modification of the Soil And Vegetation Reflection Simulator (SAVERS) developed by the team for Global Navigation Satellite System Reflectometry over land has also been taken into account to simulate the soil contribution.

Monitoring freeze/thaw variations of the Earth surfaces is of paramount importance for the study of biogeochemical processes and climate change. At present, the use of passive sensors is well established, but, very recently, some studies demonstrated the potentialities of observations exploiting signals of opportunity. We propose here an advanced study to demonstrate the capability of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) to provide accurate and systematic information about the Earth-surface freeze/thaw state. Reflectivity values derived from TechDemoSat-1 (TDS-1) data are elaborated and compared against the Soil Moisture and Ocean Salinity (SMOS) freeze/thaw product, while state-of-the-art land cover data are used to select GNSS-R data within an estimated footprint. In spite of the limited data availability due to sparse spatial coverage and calibration issues of TDS-1 observations, the proposed analysis demonstrates the possibility of monitoring the freeze/thaw state by analyzing the calibrated reflectivity, including also the possibility of detecting the transition state between frozen and thawed conditions across seasonal variations. This feature makes the design of next-generation GNSS-R satellite missions a unique opportunity to achieve high-resolution freeze/thaw monitoring with small and low-cost platforms.

In this work, the sensitivity to soil moisture of different scattering mechanisms observed by an airborne polarimetric radar operating at L-band has been investigated. The main objective was assessing the capability of a fully polarimetric radar system to disentangle the change in soil moisture under different vegetation covers. We used polarimetric data collected by the NASA UA VSAR airborne radar flying over the Yucatan Lake site in Louisiana. Six overflights were analysed, and six regions of interest characterized by different vegetation covers were selected. The temporal trends of the magnitude of different scattering mechanisms, according to the Freeman-Durden and the Nonnegative Eigenvalue decompositions, as well as of the NDVI and a nearby precipitation gauge are analysed and discussed with reference to the theoretical expectations.

A comparison of three different electromagnetic scattering models for delay-Doppler maps (DDMs) of global navigation satellite system reflectometry (GNSS-R) from land is performed along a Cyclone Global Navigation Satellite System (CYGNSS) track over a validation site in the San Luis Valley, Colorado, USA. The peak reflectivity profiles of all three models and of the corresponding CYGNSS data are found to be in general agreement and are strongly influenced by topography. An intercomparison of DDM structure for one acquisition is also included. Efforts to refine the model results using a high resolution lidar survey are ongoing.

The Global Navigation Satellite System Reflectometry (GNSS-R) is an emerging remote sensing technique based on the exploitation of scattered navigation signals for monitoring bio-geophysical parameters of the Earth surface. The receiver is placed onboard of airborne or spaceborne platforms, whose movement can affect the feature of the gathered signals. These can present strong fluctuations, which depend on the electromagnetic parameters of the illuminated surface and on its statistical features. Fluctuations have been studied from decades by the radar community, especially to understand the decorrelation of the scattering in radar interferometry. It has been done, however, by only considering uncorrelated point-like scatterers, leading to a simple model. To characterize the temporal decorrelation of the near-specular scattering in GNSS-R systems, we describe here some numerical results collected by a moving receiver changing the random surface parameters and accounting for the coherent/incoherent nature of the scattered field.

ALtimetry for BIOMass (ALBIOM) is a research project funded by the European Space Agency to study the possibility of estimating above ground biomass by means of low- and high-resolution Sentinel-3 altimetry data. We present preliminary results of a sensitivity analysis, developed to assess in what extent waveforms and altimetry observables are affected by the presence of forests. Ku- and C-band altimetry data have been collected and processed to properly select well-tracked waveforms over land, which are then related to collocated biomass data. A statistical analysis has been performed, highlighting a sensitivity of the estimated normalized radar cross section with respect to forest biomass, even though it is strongly disturbed by the soil topography within the radar footprint. Additionally, the inaccurate positioning of the time-tracking window limits the number of useful data.

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.

Monitoring freeze/thaw variations of the Earth surfaces is of great value for the study of biogeochemical processes and climate changes. Over the last decade, the use of passive sensors has been established and, more recently, some researches demonstrated the potential of exploiting observations based on signals of opportunity. We propose an advanced study to assess the capability of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) to give systematic information about the soil freeze/thaw state of high-latitude regions. To this aim, reflectivity values derived from TechDemoSat-1 (TDS-1) data are elaborated using state-of-art land cover data.

In recent years, the global climate change increased significantly the occurrence and severity of forest disturbances due to fires, causing important alterations in forest ecosystems that also impact on climate and affecting the forest capability of providing resources for human needs. This paper aims at exploiting the potential of Global Navigation Satellite System Reflectometry (GNSS-R), based on L band signals, for the detection of forest disturbances due to fires. The study focused on the forested part of Angola that was largely affected by fires during the summer 2019 and exploited the data collected by the NASA Cyclone GNSS (CyGNSS) constellation. As reference data for developing and testing the method, the ESA CCI decadal burned areas maps have been considered. A simple approach based on the temporal gradient of the GNSS-R observables, namely Signal to Noise Ratio (SNR) and Equivalent Reflectivity ($\Gamma$), allowed identifying satisfactorily the burned areas with respect to the reference data, by enabling the generation of maps every ten days.

The aim of this study is to assess the potential use of data recorded by the Global Navigation Satellite System Reflectometry (GNSS-R) CYGNSS constellation to characterize desert surface roughness. The study is applied over the Sahara, the largest non-polar desert in the world. This is based on a spatio-temporal analysis of variations in Cyclone Global Navigation Satellite System (CYGNSS) data, expressed as changes in reflectivity (Γ). In general, the reflectivity of each type of land surface (reliefs, dunes etc.) encountered at the studied site is found to have a high temporal stability. A grid of CYGNSS Γ measurements has been developed, at the relatively fine resolution of 0.03° x 0.03°, and the resulting map of average reflectivity, computed over a 2.5-year period, illustrates the potential of CYGNSS data for the characterization of the main types of desert land surface (dunes, reliefs, etc.). A discussion of the relationship between aerodynamic roughness and CYGNSS reflectivity is reported. An aerodynamic roughness (Z0) map of the Sahara is proposed, using four distinct classes of terrain roughness.

The objective of this study is to analyze the potential of global navigation satellite system reflectometry (GNSS-R) data from the Cyclone Global Navigation Satellite System (CYGNSS) constellation to map the subsurface of a desert environment, investigate fossil river systems, and identify geological structures hidden beneath dry sand. This analysis is based on reflectivity estimates considering the main component of the coherent signal using an average of the signal over a period of 2.5 years on a grid with a resolution of 0.03°. Two sites are analyzed: Kufrah in the Libyan Desert and the Bir Safsaf region located in the southern Egyptian Desert. In both cases, we observe strong similarity with past observations derived from L-band synthetic aperture radar (SAR) data. Although the spatial resolution of CYGNSS data is lower than that of SAR data, different structures of the subsurface can be identified by the former.

Approximately, 30% of the Sentinel-1 (S-1) swath over land is imaged with incidence angles higher than 40°. Still, the interplay among the scattering mechanisms taking place at such a high incidence and their implications on the backscatter information content is often disregarded. This article investigates, through an experimental and numerical study, the S-1 sensitivity to the surface soil moisture (SSM) over agricultural fields observed at low (~33°) and high (~43°) incidence angles and quantifies the impact of the incidence angle on the SSM retrieval accuracy. The study sites are the Apulian Tavoliere (Italy) and REd de MEDición de la HUmedad del Suelo (REMEDHUS) (Spain), which are both instrumented with a hydrologic network continuously measuring SSM. At low incidence angles, results confirm that for crops such as wheat and barley, dominated in C-band by surface scattering, there exists a good sensitivity of S-1 VV to SSM. At high incidence angles, the sensitivity to SSM holds through the combination of the soil attenuated and double bounce scattering. Conversely, over crops dominated by volume scattering, such as sugar beet, the S-1 VV signal is not correlated with the in situ SSM observations, neither at low nor at high incidence. For all the crops, the sensitivity of S-1 to SSM in VH is found significantly lower than in VV. The impact of the incidence angle on the SSM retrieval has been studied with a recursive algorithm based on a short-term change detection approach. An upper and lower bounds for the worsening of the S-1 VV retrieval performance at far versus near range observations have been estimated. In the worst-case scenario, the root mean square error (RMSE) increases from ~0.056 m3/m3, at low incidence, to ~0.071 m3/m3, at high incidence. The mechanism that lowers the retrieval accuracy at high incidence angles is further investigated in the synthetic experiment and its impact on the RMSE is estimated in terms of the volume scattering contribution.

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 monitoring of the freeze/thaw dynamic of high-latitude Earth regions is of paramount importance for the study of the carbon cycle and the climate changes. Current approaches essentially rely on the use of active and passive microwave remote sensing, while limited work has been dedicated to study the potential of the Global Navigation Satellite System Reflectometry technique based on spaceborne platforms. In this contribution, reflectivity values derived from the TechDemoSat-1 data have been collected and elaborated, to be compared against the SMAP freeze/thaw product. The proposed analysis indicates a significant seasonal cycle of freeze/thaw state in the calibrated reflectivity, thus opening new perspectives for the bistatic L-band high-resolution satellite monitoring of the freeze/thaw state.

The quasi-specular reflection collected under the illumination of signals of opportunity can exhibit temporal fluctuations related to the characteristics of the surface roughness. Even in quite flat regions, gentle undulations, i.e., those comparable with the wavelength over the vertical scale and much longer over the horizontal one, can exist and can significantly affect the temporal pattern of the signal at the receiver. In this contribution, a full-wave model based on the Kirchhoff approximation is adopted to study and characterize the fluctuation of the scattered field. Numerical simulations are developed, while measurements based on an airborne experiment are introduced to corroborate the concept in realistic conditions. The analysis can give interesting information to help the interpretation of GNSS data and their intrinsic variability, especially those collected by means of satellite platforms.

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.

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).

We present and discuss an electromagnetic model for the description of the coherent scattering from bare soils illuminated by a radar system under arbitrary bistatic geometries. The scattering problem is solved under the Kirchhoff approximation (KA) accounting for both the sphericity of the wavefront of the incident wave and the radiation pattern of the transmitting and receiving antennas. We propose here a general formulation and solution of the scattering problem applicable to an arbitrary bistatic geometry. We discuss and demonstrate the importance of our extension for the characterization of the coherent scattering generated in bistatic radar systems, both inside and outside the plane of incidence. The model is validated against the numerical solution of the Kirchhoff integral and, in the case of the perfect plane conductor, by comparison with the image theory. The work is intended to provide a simple methodology to characterize the coherent normalized radar cross section (NRCS) of a rough surface to be used within the radar equation for extended targets, similarly to what is done for the incoherent component. It aims at enabling a local characterization of the coherent scattering in realistic conditions (e.g., in the presence of inhomogeneous and mountainous surfaces), a feature that is particularly important for practical applications, such as the modeling and understanding of the bistatic scattering generated by sources of opportunity and specifically for Global Navigation Satellite System Reflectometry (GNSS-R) related applications.

The estimation of aboveground biomass is commonly recognized for global relevance because of the vegetation role in the carbon cycle. Both active and passive microwave sensors can significantly contribute to this goal because of their high sensitivity to water content and high penetration at lower frequencies (L-/P-bands). In particular, Global Navigation Satellite Systems (GNSSs) are recently receiving increasing interest as source of opportunity to be employed as illuminator for L-band remote sensing, since they could provide low-cost sensors for nondestructive forest biomass estimation over large areas. In this article, we suggest a method to extract forest information using the GNSS direct signals collected in clear sky and below the vegetation canopy at both circular polarizations. An experimental campaign, carried out in the framework of an European Space Agency (ESA) project, was conducted over three poplar forests with different biomass to verify the feasibility of this technique. The relationships between the GNSS measurements and the tree parameters were first assessed and then interpreted and supported by statistical analysis and a theoretical model. The signal collected under the canopy is affected by attenuation and depolarization with respect to the one collected in open air, and this article demonstrated that both direct line-of-sight propagation and volume scattering play a role in the signal magnitude and its fluctuation in time. Although the experimental data set is limited in size and environmental conditions, two inversion algorithms were also tested with the encouraging retrieval results.

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.

The reflection of radar echoes collected under both monostatic and bistatic configurations generally undergoes losses of coherence. These can be affected by many factors, depending on both the system features and the illuminated surface, and it should be properly considered for an accurate characterization of the scattering phenomenon. In this contribution, we investigate and discuss the spatial coherence of the bistatic signal scattered by a rough soil when illuminated by sources of opportunity. A simple analytical model accounting for the essential geometry of the system and of the sphericity of the illuminated wavefront is presented, and the spatial decorrelation of the signal collected by a moving receiver is analyzed. The analysis can be of great interest for the design of next-generation satellite bistatic missions and, in particular, for the assessment of the potentialities of earth observation based on Global Navigation Satellite System Reflectometry.

With the recent launch of TechDemoSat-1 and CYGNSS missions, spaceborne GNSS-R data are now available. In order to understand the involved scattering mechanisms, potentialities and limitations of GNSS-R measurements over land, an electromagnetic simulator represents a powerful tool. In this paper, the SAVERS simulator, that was developed and validated on ground based and airborne data, is now upgraded to take account for the important role of the topography in the satellite acquisitions. The Delay Doppler Maps acquired by satellite sensors show a clear topography effect. The reliability of the simulator is here tested over a volcanic area of Chad and compared with the TechDemoSat data.

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.

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.

The main objective of this study is to propose an inversion algorithm for the estimation of soil moisture from CYGNSS data. The algorithm based on the change detection technique is applied to CYGNSS data after several corrections taking in account the incidence effects as well as different noises. The algorithm is validated on a one study site in North Africa. Comparisons with field data and ASCAT products illustrate a strong potential for CYGNSS products.

The electromagnetic scattering generated by agricultural tilled soils can be affected by a strong anisotropic component of the rough-surface profile. An accurate and reliable modeling of the normalized radar cross section, under both monostatic and bistatic geometries, is particularly important and desirable, especially for the correct estimation of the soil moisture content by means of satellite-based observations. In this paper, moving from the modeling so far proposed in the literature, we present and discuss a novel, more general (i.e., 2-D), spectral representation of an agricultural tilled soil, implementing a solution of the scattering based on the first-order small-slope approximation. Comparisons are given with a well-established model based on a 1-D representation of the soil correlation function, accounting for the radiation pattern of the illuminating antenna. The investigation gives new insight on the phenomenology of the bistatic scattering from the anisotropic soil, providing interesting information for the next generation of satellite missions, which foresees the possibility of launching companion satellites carrying aboard a passive receiver collecting the signal transmitted by active SAR-based platforms.

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.

An analysis of CYGNSS data is presented, with the objective of assessing the potentials of these data for land applications. The GPS-Reflections over land acquired by the CYGNSS observatories are exploited to detect properties of the land soil moisture. A land reflectivity observable, derived from CYGNSS Delay/Doppler Maps, is computed using a calibration approach suitable for land reflections. The sensitivity of the reflectivity observable to the soil moisture parameter is investigated through a comparison with soil moisture data from the SMAP satellite. Some preliminary results on the correlation between the CYGNSS reflectivity and the SMAP soil moisture are presented. This work is being conducted within the framework of the European Space Agency Project “Potential of Spaceborne GNSS-R for Land Applications”.

In this contribution, we analyze the bistatic scattering generated by agricultural soils, which can be characterized by a strong anisotropic component of the rough surface profile. A novel spectral representation is proposed in conjunction with a numerical solution of the scattering based on the well-known small-slope approximation. Comparisons are given with different modeling. The investigation provides interesting and novel information on the phenomenology of the bistatic scattering from the anisotropic soil. This is of great interest for the next generation of satellite missions, which foresees the possibility of launching companion satellites carrying aboard a passive receiver collecting the signal transmitted by an active SAR-based platform.

An essential step in earthquake risk assessment consists in collecting information regarding buildings at risks in order to evaluate their vulnerability, and, ultimately, their expected damages for events of specific intensity. A number of building features can be retrieved by means of remote sensing, either directly (through direct observations of features such as their size or footprints) or indirectly (by appraising the expected building types, given features such as the shape of roads or roofs, and an a-priori knowledge of the urban landscape of interest). Here, we evaluate to which extent the accuracy of mean damage grade can be improved by means of remote sensing using the RISK-EU (2004) [1] assessment grid. The results show that if nothing is known regarding the building stocks in the city of interest, the uncertainty in average vulnerability can be reduced by half using remote sensing observations if adequate indirect proxies can be found.

This paper presents an experimental sensitivity analysis of Sentinel-1 (S-1) backscatter to soil moisture (SM) content observed at low (i.e., ~33°) and high (i.e., ~45°) incidence angles over five agricultural fields of an experimental farm located in the Puglia region (Italy). The analysis focuses on the period from March to June 2017 during which 38 S-1 images along ascending orbits were acquired over the site. Results indicate a slight decrease in the radar sensitivity to SM going from low to high incidence with an impact on SM retrieval error that increases from ~5.65 m3/m3% to ~7.63 m3/m3%.

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 applications of spaceborne GNSS reflectometry data over land are investigated in this work using the data collected by the UK TechDemoSat experimental mission. In particular, the sensitivity of the reflection (including specular coherent reflection and to some extend diffuse incoherent scattering) to soil moisture and forest biomass are preliminary considered. In order to quantify the biomass and moisture sensitivity it is necessary to extract a quantity, like the surface reflectivity, as much as possible independent from the system parameters. We have tried to exploit the direct signal from the uplooking antenna for this purpose and we show differences with respect to other approaches. To understand the scattering mechanisms and potentialities and limitations of GNSS-R over land, an electromagnetic simulator is used and compared to the experimental data. Although the simulator was tuned on ground based and airborne data, the satellite platform poses additional problems due to the low magnitude of the reflected signal and to topography effects. These are discussed in the paper.

This work investigates the capability of Interferometric SAR (InSAR) technique to monitor the atmosphere. Changes in the Atmospheric Phase Screen (APS) derived by InSAR are strictly related to changes in the atmosphere stratification and to the 2-D distribution of the water vapor columnar content. High spatial/temporal resolution APS maps provide by a SAR geosynchronous satellite (GEO-SAR) can be assimilated into limited area models (LAMs) for a better characterization of the local scale phenomena. As the observation of GEO-SAR are taken with high incidence angles, the path delay is measured along the slant direction (STD) and thus both the observation operator and the Global Navigation Satellite System (GNSS) products used for calibrating the APS must be considered along that direction. In this work, we compare the STD predicted by the Weather Research and Forecasting Model (WRF), and retrieved by a network of GNSS stations with the APS derived from Sentinel-1.

The GLORI Campaign performed in June-July 2015 to investigate the sensitivity of airborne GNSS-R measurements to land parameters is presented. In this paper data obtained on forest areas are analyzed. Ground truth measurements of tree height, density and diameter at breast height, AGB etc were measured over 100 maritime pine forest plots of various ages. The correlation between forest parameters and GNSS reflectivity in LHCP polarization (ΓLR) or polarization ratio (PR) yields to high sensitivity for high elevation angles (70°-90°). Results show that PR illustrates highest potential than the reflectivity in LHCP.

The capabilities of bistatic radar observations to estimate the wind field over the ocean are investigated in this paper. The work is based on the analysis of simulated data obtained through a well-established electromagnetic model, which accounts for the anisotropy of the ocean's spectrum and of second-order effects of the scattering phenomenon. Both co-polarized and cross-polarized C-band numerical data, obtained considering monostatic and bistatic configurations, are exploited to investigate on the existence of optimal configurations able to minimize the wind vector error estimation. To this aim, the sensitivities of the bistatic normalized radar cross section with respect to both wind speed and direction are accurately investigated and exploited to evaluate the minimum achievable error standard deviation of the estimation. Small and large baselines are analyzed, giving particular emphasis to bistatic geometries constituted by one or two passive receivers aligned along the track defined by the active system. This investigation, originally performed in the framework of the SAOCOM-CS scientific satellite mission, is conceived to accurately assess the potentiality of bistatic observations of the ocean over variable baselines and to gather valuable information for the design of future bistatic satellite missions.

The assessment of satellite image classifications is usually carried out using a test sample assumed as the ground truth, from which a confusion matrix is derived. There are cases where the reference data, even those coming from a ground survey, are affected by errors and do not represent a reliable truth. In the field of geophysical parameter retrieval, the triple collocation (TC) technique is applied for validating remotely sensed products when the source of test data (e.g., ground data) does not represent a reliable reference. TC is able to retrieve the error variances of three systems observing the same target parameter, assuming that their errors are independent. In this paper, we exploit the same idea to test the classification accuracy in cases where the ground truth is not available. We extend the TC approach to the classification problem for a general number of classes, but we solve it numerically for a two-class problem (i.e., collapsed and noncollapsed buildings). The specific case refers to the detection of L'Aquila 2009 earthquake damage from very high-resolution optical data. The image classification, performed by exploiting an object-based analysis, is compared with those from two different ground surveys carried out after the earthquake by different teams and with different purposes. This paper demonstrates the power of the TC approach for assessing the classification accuracy with no reliable ground truth available, and provides an insight into the problem of assessing damage, from satellite and on ground, in a very critical and unsafe situation, like the one occurring after an earthquake. Moreover, it was found that the remotely sensed product can have an order of accuracy comparable to that of at least one of the ground surveys.

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.

The interest on aboveground biomass measurements is raised from its relation with the understanding of the carbon cycle. This paper proposes a method for the estimation of aboveground biomass (AGB) exploiting the interaction of L-band electromagnetic waves with forest vegetation. The proposed method uses the Global Navigation Satellite Systems (GNSS) direct signals in clear sky and below the vegetation to extract the attenuation and de-polarization versus satellite elevation. An experimental campaign was conducted over three forests with different level of biomass. The proposed inversion algorithm is based on artificial neural networks showing a correlation with ground truth above 96%.

This work aims at defining applications, products and user requirements, as well as the hardware and ground processing design of a companion satellite mission which shall carry aboard a “passive” radar working in tandem with the Argentinian L-band radar developed by CONAE and denoted as SAOCOM. The primary objective (i.e., science driver) of the SAOCOM companion satellite mission (SAOCOM-CS) is forest tomography, which will be carried out by exploiting small baselines between active and passive systems (order of km) changing with time. Conversely, this paper summarizes the investigation carried out for different bistatic radar configurations that are characterized by much larger spatial baselines (up to hundreds of km) and bistatic angles with very large components both in azimuth and in elevation. Soil moisture and vegetation biomass retrieval takes advantage from the combined exploitation of monostatic and bistatic measurements. The retrieval ambiguity related to target azimuthal anisotropy could also be reduced by bistatic observations, like in the case of the ocean surface. The bistatic system shall collect data with suitable directions and polarizations. The expected performances of a multistatic system have been predicted using electromagnetic models.

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.

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.

GEROS-ISS (GEROS hereafter) stands for GNSS REflectometry, Radio Occultation and Scatterometry onboard the International Space Station. It is a scientific experiment, proposed to the European Space Agency (ESA) in 2011 for installation aboard the ISS. The main focus of GEROS is the dedicated use of signals from the currently available Global Navigation Satellite Systems (GNSS) for remote sensing of the System Earth with focus to Climate Change characterisation. The GEROS mission idea and the current status are briefly reviewed.

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 paper describes the preliminary results of the January-August 2015 multi-frequency EO data acquisition campaign conducted over the Maccarese (Central Italy) farm. From January to May radar Cosmo SkyMed Ping-Pong (HH-VV), RapidEye and ZY-3 multispectral VHR optical images, as well as in situ data, have been acquired to retrieve biophysical and/or bio-chemical characteristics of soil and crops. LAI trend has been analyzed and compared by using both polarimetric and optical retrieval algorithms while soil moisture measurements have been compared with the radar backscattering.

In this work, the land surface temperature (LST) retrieval using Landsat Thematic Mapper (TM) data over the city of Florence, Italy, characterized also by the presence of rural and vegetated zones, was compared with a high-resolution (1 m ground pixel size) thermal image provided by an airborne survey made on July 18, 2010. Two Landsat scenes were processed before and after the flight, with the aim to evaluate the impact of the Landsat TM resolution of the thermal channel (120 m) on the LST estimation over an urban texture. The thermal data were downscaled at 30 m using a statistical approach employing spectral indices: such a method highlights the potentials and limits of the LST downscaling performed over an heterogeneous urban area.

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.

Nowadays very high resolution (VHR) Synthetic Aperture Radar (SAR) systems can provide near real time earthquake damage maps with an high degree of details to stakeholders in charge of managing the emergency phase. However, the increased resolution introduces new challenges to interpret and detect changes in urban areas caused by seismic events. In metric resolution SAR sensors a building appears as a complex of image structures associated to different scattering mechanisms, preventing the use of pixel-based algorithms. In this paper we propose an object oriented approach, focusing the attention on the double-bounce return from buildings, trying to detect damages looking at changes of these particular image patterns. The identification of double-bounce regions is performed using open and close morphological filters and assuming linear structuring elements with different orientation and length. The change detection analysis based on a pre- and a post-event image is carried out using four change detection indicators, such as: intensity ratio, interferometric coherence, intensity correlation and Kullback-Leibler divergence. All change features are extracted using all pixels within each identified object, i.e., double-bounce regions. The test case is the earthquake that hit L'Aquila city (Italy) on April 6, 2009, while the dataset is composed of two X-band COSMO-SkyMed SAR images acquired before and after the event. A macro-seismic survey map was available to evaluate the obtained results.

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.

SAOCOM is an Argentinian L band system formed by two satellites (SAOCOM-1A and SAOCOM-1B). ESA is investigating the possible applications of a companion satellite (SAOCOM-CS) carrying a passive receiver working in concert with one of the SAOCOM-1 satellites. During the mission there will be cycles with a long along-track bistatic baseline, suitable for deformation monitoring. Together with the combination of ascending and descending orbits, this geometry will produce four measurements from different viewing geometries, enabling us to estimate the 3D motion vector. Here we investigate the sensitivity of such a configuration, and the opportunities for increasing the density of persistent scatterers.

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.

This paper aims at identifying suitable geometrical configurations of a passive satellite radar flying in convoy with an active spaceborne SAR. The work has been performed in the frame of the SAOCOM-CS scientific investigations. It is a small satellite that ESA is conceiving to fly in convoy with the Argentinian SAOCOM 1B to acquire bistatic radar data at L-band. The applications foreseen in the paper are the retrieval of soil moisture and crop biomass. It is shown by a model based investigation that retrieval performances can be improved by combining monostatic and bistatic measurements in geometric configurations requiring very high along track and across track baselines.

This paper reviews the simulation tool developed to predict the GNSS-R signal over land (bare soil and vegetation). The tool is presently being used to test the capability to retrieve the main land parameters, namely soil moisture and vegetation biomass. An ongoing research is using the simulations to assess a multistatic concept which aims to take advantage of the combination of GNSS-R and radar backscattering data provided by a SAR or the GNSS receiver itself. Preliminary performance assessments are presented.

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.

This paper investigates the algorithms that are used to predict the full polarimetric bistatic normalized radar cross-section of rough surfaces. These include small perturbation method (SPM), the physical optics (PO) approach, the small slope approximation (SSA) and the integration equation method (IEM) and its derivatives improved IEM and advanced IEM. The methods are then compared to ground truth values obtained from multiple Monte Carlo runs a numerical Method of Moments (MOM) code using the same surface statistics. Effects of using band-limited exponential instead of a true exponential correlation function for surface statistics are also explored. Mean L1-norm error values integrated over the hemisphere are given between AIEM and MOM and SSA and MOM.

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.

The vertical structure of the atmospheric water vapor induces phase errors in interferometric synthetic aperture radar (SAR) data. This paper presents a simulation study to investigate whether spaceborne submillimeter radiometric observations, which can be realized with fairly high spatial resolution, are able to derive the vertical structure of the atmospheric wet delay. The accuracy of the retrieved zenith wet delay (ZWD) trend as a function of surface height is assessed in order to correct the associated height dependence of the interferometric phase error in a SAR interferogram. Using a simulated benchmark, we evaluate the errors associated with the use of both a linear and an exponential model of the behavior of ZWD as a function of the surface height. This paper shows a fairly accurate reconstruction of the trend parameters estimated from radiometer brightness temperature images, with respect to realistic atmospheric profiles provided by radiosounding observations (RAOBs). The trend parameters that we consider in this paper are the slope K for the linear model and scale height H for the exponential one. An overall better accuracy is found for the exponential model, which is more representative of the actual behavior of ZWD with height, resulting in a residual uncertainty in the path delay due to the atmospheric stratification of approximately 0.2-0.3 cm and nearly zero bias, as compared to RAOBs.

The mean power of the reflected Global Navigation Satellite System (GNSS) signals acquired by a GNSS-Reflectometry (GNSS-R) receiver can be modeled through the integral bistatic radar equation by weighting the contributions of all scatterers on the surface by the system impulse response. The geophysical properties of the scattering surface affect the magnitude of the reflected navigation signals through the bistatic scattering coefficient which, in case the observed surface is land, is a function of the soil dielectric properties, surface roughness, and vegetation cover. In this paper, the GNSS-R signal simulator developed in the framework of the Land MOnitoring with Navigation signal (LEiMON) Project, supported by European Space Agency, is presented. The simulator is able to predict the power reflected by land, taking as input the system and observation parameters, as well as the land surface parameters. The latter are used to simulate both the coherent and the incoherent scattering, taking advantage of widely used theoretical models of bistatic scattering from bare soils and vegetated surfaces. First, the geometrical formulation is discussed, and then, the problem of polarization mismatch due to real antennas at circular polarization is faced following the polarization synthesis approach. Finally, a comparison with some experimental data collected during the LEiMON campaign is presented. The simulations display the same trend of the experimental data, thus showing that the simulator can be used as an efficient tool for the interpretation of GNSS-R measurements.

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.

Very recently, it has been observed that GNSS-R can provide a significant contribution to agricultural and forestry applications, since the use of GNSS signals as sources of opportunity enables bistatic radar measurements at L-band, which showed to be sensitive to soil moisture and vegetation parameters. This perspective has been investigated in two experimental activities funded by the European Space Agency: the LEiMON and GRASS campaigns. This work has been carried out with the aim of interpreting the data collected during the two campaigns over land. This requires to model the coherent component associated to the mean surface, but at the same time the diffuse incoherent component due to roughness at wavelength scale. In presence of vegetation, both components must be taken into account. The paper presents the approach followed to develop a simulator of GNSS-R data over land, aiming to support potential applications of GNSS-R for soil moisture and biomass retrieval.

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 authors propose an inversion scheme devoted to the retrieval of the atmospheric specific absorption coefficient based on data collected by a ground-based microwave radiometer. The technique processes elevation scans. The vertical plan is modelled by allocating M bins in the vertical directions. The forward modelling is defined by linearization of the radiative transfer equation around a reference model of the atmospheric parameter pattern. The goal is to estimate the atmospheric attenuation along the vertical path 3 km away from the radiometer location, where a transponder calibration device for the nadir looking RA-2 altimeter on board of the ENVISAT satellite is positioned. Estimation is performed through inversion of the brightness temperature values observed at different elevation angles, provided a proper parameterization of the model space. According to the agreement of the reference model to the actual conditions two cases are given: either the solution is acceptable or it is not reliable and a different inversion scheme is to be implemented. © 2012 IEEE.

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.

As far as Ku band is concerned, the backscatter coefficient (sigma naught) of the radar altimeter (RA-2) aboard the ENVISAT satellite has been calibrated using a transponder (TPD) developed at ESA/ESTEC. The transponder has been exploited during the 6 month Commissioning Phase to generate early calibration results. In order to consolidate this calibration results and to check the altimeter during the satellite lifetime, a continuous monitoring was performed by operating the transponder as much as possible. The first calibration campaign dates back to February 24th, 2004. The last campaign occurred on October 5th, 2010. Since then the change of the ENVISAT orbit hampered the prosecution of the activity. This paper aims to review the entire effort for calibrating the RA-2 sigma naught measurements, which lasted for almost seven year. It presents the final outcomes of the activity, providing the users with the correction (bias) to get the calibrated sigma naught and analyzing its stability during almost the entire ENIVISAT lifetime.

The work presented in this paper has been carried out with the aim of interpreting the data of a GNSS Reflectometer (GNSS-R) over land. The problem involves the analysis of bistatic scattering of the incoming signal collected around the specular direction. This requires to model the coherent component associated to the mean surface but at the same time the diffuse incoherent component due to roughness at wavelength scale. In presence of vegetation, both components will be affected, the former mainly because of the canopy attenuation and the latter for the combined effect of attenuation and volume scattering. The paper reviews the problem and presents the approach followed to develop a simulator of GNSS-R data over land, aiming to support potential applications of GNSS-R for soil moisture and biomass retrieval.

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.

Atmospheric water vapor is a crucial constituent affecting both climate change and hydrological cycle processes, whereas on the other hand, it has a significant impact on the electromagnetic signal propagation. Since the distribution of atmospheric water vapor strongly varies with time, location, and altitude, it is necessary to monitor it at high spatial and temporal resolution. Unfortunately, mapping its spatial distribution is difficult due to the lack of meteorological instrumentation at an adequate spatial and temporal observation scale. For many geophysical applications, there is also the need to reconstruct spatial details of integrated precipitable water vapor from information available only at coarser spatial scales. Spatial downscaling approaches can play a significant role when high-resolution water vapor retrievals from relatively new sensors, like synthetic aperture radars, or from conventional sensors, like the infrared radiometers MEdium Resolution Imaging Spectrometer (MERIS) or Moderate Resolution Imaging Spectroradiometer (MODIS), are used in synergy to enhance the accuracy of integrated water vapor retrievals. In this context, this paper introduces some new methodological aspects to increase the spatial resolution of integrated precipitable water vapor observations using a statistical downscaling spectral approach. To highlight the potential and the usefulness of the proposed downscaling estimation procedure, collocated 250-m MERIS and 1-km MODIS acquisitions are used. Results reveal the ability of spectral downscaling to reproduce quite well the second-order statistical variability of the water vapor field at small spatial scales with a root-mean-square error comparable with conventional interpolation techniques.

The detection of the land surface scattering of the signal radiated by navigation satellites may help estimating geophysical parameters such as soil moisture and vegetation biomass. In fact, the modulation of the GNSS signal and its frequency (L band) are particularly effective to sense vegetation attenuation and change of soil permittivity due to moisture. An experiment has been carried out in Italy using a GNSS reflectometer (GNSS-R) developed by STARLAB, mounted on top of a crane and looking toward a couple of agricultural fields by two downlooking antennas operating at right and left circular polarization. The data collected during the experiment have been interpreted by comparing them to the output of a theoretical simulator, with the purpose of interpreting from an electromagnetic point of view the scattering mechanisms involved in the experiment. A summary of the simulator main feature and some comparison results is presented in this paper.

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.

This work aims to assess the potential of Synthetic Aperture Radar (SAR) data combined with optical data to support local administrations in the knowledge of the land use and land cover at regional scale. In particular, the contribution of data available in the future through the SIASGE project, combining L-band and X-band radar imagery, is assessed in order to produce thematic maps. Moreover, the further contribution brought by C-band has been evaluated. The classification, focused on two regions in the north side of Italy, is driven by the legend of already existing maps tackling the real needs of the land managing authorities. As the combination of data from optical imagery is fundamental to achieve good thematic accuracy, the work has exploited the Support Vector Machine learning technique, which is more suitable than standard statistical parametric approaches in this respect. Concerning the classification step, some algorithmic issues has been faced to improve the results, such as training set selection strategy and data fusion techniques. The work has proved as the multi source data set (SAR and optical) is fairly suitable to produce thematic maps comparable to what already in use at local administrative level, allowing to obtain reliable maps with a classification accuracy in the order of 90%.

Spaceborne Interferometric Synthetic Aperture Radar (InSAR) is a well established technique useful in many land applications, such as tectonic movements, landslide monitoring and digital elevation model extraction. One of its major limitation is the atmospheric effect, and in particular the high water vapour spatial and temporal variability which introduces an unknown delay in the signal propagation. This paper describes the general approach and some results achieved in the framework of an ESA funded project devoted to the mapping of the water vapour with the aim to mitigate its effect in InSAR applications. Ground based (microwave radiometers, radiosoundings, GPS) and spaceborne observations (AMSR-E, MERIS, MODIS) of columnar water vapour were compared with Numerical Weather Prediction model runs in Central Italy during a 15-day experiment. A dense network of GPS receivers was deployed close to Como, in Northern Italy, to complement the operational network in order to derive Zenith Total Delay as well as Slant Delay which can support InSAR processing. A comparison with Atmospheric Phase Screens (APS) derived from a sequence of Envisat multi pass interferometric acquisitions processed using the Permanent Scatters technique on the two test sites has been also performed. The acquired experimental data and their comparison give a valuable idea of what can be done to gather information on water vapour, which, besides InSAR applications, plays a fundamental role in weather prediction and radio propagation studies. The work has been carried out in the framework of an ESA funded project, named "Mitigation of Electromagnetic Transmission errors induced by Atmospheric Water Vapour Effects" (METAWAVE). This paper presents the general approach an the various methodologies exploited in the project, together with the overall intercomparison of the results. In deep details on the comparison with the InSAR APS maps derived by the PS technique, as well as on GPS receiver processing and water vapour tomography are reported in two companion papers.

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 method based on neural networks is proposed to retrieve integrated precipitable water vapor (IPWV) over land from brightness temperatures measured by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). Water vapor values provided by European Centre for Medium-Range Weather Forecasts (ECMWF) were used to train the network. The performance of the network was demonstrated by using a separate data set of AMSR-E observations and the corresponding IPWV values from ECMWF. 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 during clear-sky conditions. In the presence of clouds, an rms of the order of 0.38 cm was found for both areas. In addition, results were compared with the IPWV values obtained from in situ instruments, a ground-based radiometer, and a global positioning system (GPS) receiver located in Rome, and a local network of GPS receivers in Como. An rms agreement of 0.34 cm was found between the ground-based radiometer and the neural network retrievals, and of 0.35 cm and 0.40 cm with the GPS located in Rome and Como, respectively.

This paper illustrates a procedure for the retrieval of tropospheric profiles (temperature, pressure, and humidity) using only refractivity profiles coming from Global Positioning System (GPS)-low-Earth-orbit radio occultation, without the constraint of independent knowledge of atmospheric parameters at each GPS occultation. In order to achieve this goal, we have used an approach based on neural networks (NNs), exploiting a data set of 1106 occultations collected over the Arctic region during the winter season of 2007 and 2008. Total refractivity (N) profiles from Formosa Satellite 3 (FORMOSAT-3)/Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) satellites have been used as input for training the NNs, whereas the target profiles of dry and wet components (Ndand Nw) were derived using prior information on dry and wet fractions of the total refractivity provided by the analysis of the European Centre for Medium-Range Weather Forecast (ECMWF). Once we have retrieved Ndand Nwby the trained networks, the other atmospheric parameters (pressure, temperature, and vapor) can be computed, and we have done so relative to colocated ECMWF data, which we have assumed as atmospheric truth. Finally, some comparisons with radiosonde observations (RAOBs) are shown, and performances and potential of the proposed approach are discussed. Profiles computed using 1-D variational retrieval by the COSMIC Data Analysis and Archive Center have also been considered as a benchmark in the RAOB comparison.

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.

Spaceborne interferometric synthetic aperture radar (SAR) (InSAR) imaging has been used for over a decade to monitor tectonic movements and landslides, as well as to improve digital elevation models. However, InSAR is affected by variations in round-trip propagation delay due to changes in ionospheric total electron content and in tropospheric humidity and temperature along the signal path. One of the largest sources of uncertainty in estimates of tropospheric path delay is the spatial and temporal variability of water vapor density, which currently limits the quality of InSAR products. This problem can be partially addressed by using a number of SAR interferograms from subsequent satellite overpasses to reduce the degradation in the images or by analyzing a long time series of interferometric phases from permanent scatterers. However, if there is a sudden deformation of the Earth's surface, the detection of which is one of the principal objectives of InSAR measurements over land, the effect of water vapor variations cannot be removed, reducing the quality of the interferometric products. In those cases, high-resolution information on the atmospheric water vapor content and its variation with time can be crucial to mitigate the effect of wet-tropospheric path delay variations. This paper describes the use of a ground-based microwave radiometer network to retrieve 3-D water vapor density with fine spatial and temporal resolution, which can be used to reduce InSAR ambiguities due to changes in wet-tropospheric path delay. Retrieval results and comparisons between the integrated water vapor measured by the radiometer network and satellite data are presented.

When considering a bistatic system made up of GNSS satellites and a receiver, the power at the receiver is modeled taking into account the matched filtering of the incoming signal with the PRN code modulation and Doppler filtering. In this paper, the simulator developed in the framework of the LEIMON Project supported by ESA, will be presented. The simulator is able to predict the power reflected by land taking as input the system and observation parameters, as well as the land surface parameters. The earth surface (represented by bare and vegetated soils) leaves its signature through the bistatic scattering coefficient which has been modeled by means of well established electromagnetic theories applicable at L-band. Experimental data collected during the LEIMON campaign will be compared with simulated data.

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 illustrates some applications of COSMO-SkyMed (CSK) observations for rapid mapping of flooded areas and damages in small to medium size catchments. The results presented here have been obtained within the framework of the project "OPERA Civil protection from floods" funded by the Italian Space Agency and run by a team of scientific research centres and private companies. The project aims to the systematic evaluation of the added value of the use of Earth Observation techniques into operational flood prediction chains. Due to the specific geomorphology of Italy, the focus is mainly on flash floods on small sized river catchments. Monitoring and modelling processes at proper space-time scales in this environment raise several issues to be solved, compared to applications in larger river basins. Here we address some related to the suitable use of CSK imagery.

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.

Spaceborne Interferometric Synthetic Aperture Radar (InSAR) is a well established technique useful in many land applications, such as monitoring tectonic movements and landslides or extracting digital elevation models. One of its major limitations is the atmospheric variability, and in particular the high water vapor spatial and temporal variability, which introduces an unknown delay in the signal propagation. On the other hand, these effects might be exploited, so as InSAR could become a tool for highresolution water vapor mapping. This paper describes the approach and some preliminary results achieved in the framework of an ESA funded project devoted to the mitigation of the water vapor effects in InSAR applications. Although very preliminary, the acquired experimental data and their comparison give a first idea of what can be done to gather valuable information on water vapor, which play a fundamental role in weather prediction and radio propagation studies.

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.

Using satellite sensors to detect urban damage and other surface changes due to earthquakes is gaining increasing interest. Optical images at different resolutions and radar images represent useful tools for this application, particularly when more frequent revisit times will be available with the implementation of new missions and future possible constellations of satellites. Very high resolution (VHR) images (on the order of 1 m or less) may provide information at the scale of a single building, whereas images at resolutions on the order of tens of meters may give indications of damage levels at a district scale. Both types of information may be extremely important if provided with sufficient timeliness to rescue teams. The earthquake that hit the city of Bam, Iran, has been taken as a test case, where QuickBird VHR optical images and advanced synthetic aperture radar data were available both before and after the event. Methods to process these data in order to detect damage and to extract features used to estimate damage levels are investigated in this paper, pointing out the significant potential of these satellite data and their possible synergy.

Remote sensing sensors for detecting urban damage and other surface changes due to earthquakes is gaining increasing interest. To this aim optical images can represent useful tools for this application thanks to their very high ground geometric resolution, especially when more frequent revisit times will be feasible to the implementation of new missions and future possible constellations of satellites. Sub-meter resolution images at visible frequencies are able to provide information at the single building scale. This kind of information is extremely important if provided with sufficient timeliness to rescue teams. In this work, the December 26th, 2003, earthquake that hit the ancient city of Bam (Iran) has been investigated. The urban area was very close to the epicenter of the seism thus causing strong damage to the urban structures. Pre- and post-earthquake QuickBird panchromatic images have been used to show the capability of this data to map damage at building scale by means of segmentation approach based on the application of morphological operators. A validation process has been performed by comparing the map of damage levels at single building scale with a detailed ground-based damage map provided byinsitusurvey.

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.

Parametric and nonparametric approaches to evaluate land-cover change detection using very high resolution (VHR) satellite imagery are applied to the analysis of the demolition of the Rocky Flats nuclear weapons facility located near Denver, CO. Both maximum-likelihood and neural network classifiers are used to validate a new parallel architecture which improves the accuracy when applied to VHR satellite imagery for the study of land-cover change between sequential satellite acquisitions. An enhancement of about 14% was found between the single-step classification and the new parallel architecture, confirming the advantage and the robust improvement obtained with this architecture regardless of the classification algorithm used. In this paper, we demonstrate and document the demolition and removal of hundreds of buildings taken down to bare soil between 2003 and 2005 at the Rocky Flats site.

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 paper focuses on the multiple roles and advantages of the use of remotely sensed, especially COSMO/SkyMed, imagery in the context of flooding-event prevention and management, describing a support system for civil protection from floods and some of the image processing and analysis techniques involved in.

Numerous space-borne X-band Synthetic Aperture Radars (X-SAR) systems will be launched by European agencies in the coming decade commencing this year. Those X-SARs can measure precipitation over land, thereby significantly augmenting the sensors that comprise the Global Precipitation Mission (GPM). This will incur relatively little incremental cost because they have already been funded. X-SAR measurements are especially beneficial over land where rainfall is difficult to measure by means of microwave radiometers that depend on scattering by frozen hydrometeors associated with that rain. The improved horizontal resolution of the retrievals will match the higher spatial resolution of mesoscale and general circulation models that will become available in the coming decade.

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.

This paper deals with a feasibility analysis of spaceborne bistatic missions for Earth Observation, with signals at microwave bands. The analysis, performed in the frame of a wider study financed by ESA, considers as input bistatic configurations, investigated in another paper, suitable for typical land applications i.e. soil moisture and vegetation biomass retrieval. Signal sources are GPS constellation and spaceborne SAR’s. This analysis mainly encompasses the design of possible orbits implementing the desired bistatic configurations, and the characterization of the system performances. The latter are evaluated through several different parameters, grouped in two classes: target observation parameters, strictly related to the illuminator-target-receiver geometry at given epoch (e.g. signal to noise ratio, spatial resolution, observation angles) and system parameters such as: spatial coverage (measuring whether or not a point is accessible by the satellites in bistatic configuration), number of bistatic accesses to the target area, revisit time (indicating the delay between two successive bistatic acquisitions of the same area).

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.

An inundation event occurred in Italy on November 1994 has been analysed in order to asses the capability to map the flooded areas by radar (SAR) images in support to civil protection interventions. ERS images collected before and after the inundation have shown the presence of different scattering mechanisms occurring in the flooded areas and originating either an increase or a decrease of the backscattering coefficient, depending on the land covers type. This analysis has allowed us to develop a flood discrimination procedure based on a fuzzy approach able to integrate all the possible sources of available data (SAR images, land cover and a DEM) and prior information (cover dependent backscattering behaviour). A comparison with a ground survey providing the maximum flood extension has given encouraging results.

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.

Bistatic radars have been recently proposed as an alternative to conventional monostatic radars since they can provide additional information in many fields of remote sensing applications. However, up to now, no bistatic radar campaigns, nor laboratory experiments, having vegetation as the target have been set up. This paper presents theoretical simulations of the bistatic scattering coefficient of crop and forest canopies. The electromagnetic model developed at Tor Vergata has been used to analyse scattering as a function of the observation angle, both in azimuth and elevation, and it will be shown that biomass monitoring can be optimized at out-of-incidence scattering planes.

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.

The 8th Specialist Meeting on Microwave Radiometry and \nRemote Sensing Applications (MicroRad04) was held on \nFebruary 24-27, 2004 in Rome, Italy. The coorganizers of the \nConference were Prof. N. Pierdicca of The University of Rome \n“La Sapienza” and Prof. F. S. Marzano of CETEMPS, University \nof L’Aquila. The meeting was an overwhelming success and \ncan be summarized by 162 submitted abstracts, 152 participants, \n85 oral presentations, and 42 interactive posters during four full \ndays of sessions. \nMicroRad04 was held at the Engineering College of the University \nof Rome “La Sapienza” situated in one of the most beautiful \nsites of the Eternal City, between the Colosseum, the archaeological \narea, and the early Christian Basilicas. It was the \nlatest of a series focusing on Microwave Radiometry and Remote \nSensing of the Environment. The very first one dates back \nto March 1983, when itwas organized and supported by the University \n“La Sapienza” of Rome, Italy, as a result of the initiative \nof Prof. G. d’Auria. The satisfactory outcome of the first \nmeeting stimulated an agreement among the participants to ensure \nthe continuity in the form of a periodic meeting, the second \nof which, supported by IROE-CNR in Florence, Italy, occurred \nin 1988. Since then, more regular meetings, every 30 months \napproximately, were scheduled and held in the U.S. (Boulder, \nCO, 1992; Boston, MA, 1996) and in Italy (Rome “Tor Vergata,” \n1994; Florence, 1999), alternately. In 2001, the meeting \nwas hosted by NOAA in Boulder, CO. \nThe MicroRad04 Meeting was organized by the Department \nof Electronic Engineering of the University “La Sapienza” of \nRome and was created as an open invitation to convene again \nin Rome, 20 years after the first meeting. The objective of MicroRad04 \nwas to set up a common forum to report and discuss \nrecent advances in the specific field of microwave radiometry, \nthus to gather all parties belonging to the research and industrial \ncommunity, active in projects and studies in microwave radiometry \nof atmosphere, ocean, and land. \nContributions on topics of primary interest were received, and \nthe papers were separated into both oral and interactive sessions. \nThe 15 sessions of the meeting were focused on classical \nand new advanced topics of environmental remote sensing \nby microwave radiometry, emphasizing the methodological, instrumental, \nand application point of views. Interdisciplinary and \nsensor synergy issues were also stimulated.

The prompt detection, mapping and assessment of urban damages due to earthquakes is a key point, particularly in remote areas or where the infrastructures are not well developed to ensure the necessary communication exchanges or where their operability has strongly decreased as a consequence of the event. The combination of Synthetic Aperture Radar (SAR) data and optical images is a promising and suitable approach. We propose two test cases, the 1999 Izmit (Turkey) and the 2003 Bam (Iran) earthquakes where we investigate the capability to detect urban changes and classify them. Moreover, a comparison with ground based data is also shown.

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.

This paper deals with a procedure to generate maps of the integrated precipitable water vapor (IPWV) over the Mediterranean area by using estimates from a global positioning system (GPS) network over land and from the Special Sensor Microwave/Imager (SSM/I) over sea. In particular, we investigate the application of the kriging geostatistical technique to obtain regularly spaced IPWV values. The horizontal spatial structure of water vapor retrieved by SSM/I is explored by computing variograms that provide a measure of dissimilarity between pairs of IPWV values for the region of interest. Because the water vapor density decreases with height, the GPS station elevation is accounted for in the interpolation procedure. In this respect, the potential of the kriging with external drift relative to the ordinary kriging is evaluated by applying a test based on the cross-validation approach. Case studies are presented and qualitatively compared to the corresponding Meteosat infrared images. A quantitative comparison with an independent source of information, such as IPWV computed from radiosonde observations and from European Centre for Medium-Range Weather Forecasts analysis, is also performed.

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.

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

Estimates bare soil geophysical parameters (i.e. standard deviation of roughness height s, length of correlation l and soil moisture m/sub v/) using polarimetric SAR data. Two direct models have been implemented to simulate the radar measurements and were introduced in an inversion scheme. The methods have been tested on simulated data where speckle effects and model errors were taken into account. A correlation between the roughness parameters, as it can be expected in real measures, has been imposed in the simulation in order to evaluate its effect on the estimation accuracy. Different inversion algorithms based on a Bayesian approach, developed with simple optimization-integration procedures have been compared to neural networks. The comparison has assessed the accuracy achievable by different radar system configurations. Polarimetric data acquired during MAC Europe and SIR-C campaigns, over selected bare soil fields, has been processed to show preliminary estimation results.

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.

A new approach is presented to determine atmospheric temperature profiles by combining measurements coming from different sources and taking into account evolution models derived by conventional meteorological observations. Using a historical database of atmospheric parameters and related microwave brightness temperatures, the authors have developed a data assimilation procedure based on the geostatistical Kriging method and the Kalman filtering suitable for processing satellite radiometric measurements available at each satellite pass, data of a ground-based radiometer, and temperature profiles from radiosondes released at specific times and locations. The Kalman filter technique and the geostatistical Kriging method as well as the principal component analysis have proved very powerful in exploiting climatological a priori information to build spatial and temporal evolution models of the atmospheric temperature field. The use of both historical radiosoundings (RAOBs) and a radiative transfer code allowed the estimation of the statistical parameters that appears in the models themselves (covariance and cross-covariance matrices, observation matrix, etc.). The authors have developed an algorithm, based on a Kalman filter supplemented with a Kriging geostatistical interpolator, that shows a significant improvement of accuracy in vertical profile estimations with respect to the results of a standard Kalman filter when applied to real satellite radiometric data.

The objective of this paper is to evaluate the potential of a Bayesian inversion algorithm using microwave multisensor data for the retrieval of surface rainfall rate and cloud parameters. The retrieval scheme is based on the maximum a posteriori probability (MAP) method, extended for the use of both spaceborne passive and active microwave data. The MAP technique for precipitation profiling is also proposed to approach the problem of the radar-swath synthetic broadening; that is, the capability to exploit the combined information also where only radiometric data are available. In order to show an application to airborne data, two case studies are selected within the Tropical Ocean-Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA-COARE). They refer to a stratiform storm region and an intense squall line of two mesoscale convective systems, which occurred over the ocean on February 20 and 22, 1993, respectively. The estimated rainfall rates and columnar hydrometeor contents derived from the proposed algorithms are compared to each other and to radar estimates based on reflectivity-rainrate (Z-R) relationships. Results in terms of reflectivity profiles and upwelling brightness temperatures, reconstructed from the estimated cloud structures, are also discussed. A database of combined measurements acquired at nadir during various TOGA-COARE flights, is used for applying the radar-swath synthetic broadening technique in the case of an along-track radar-failure countermeasure. A simulated test of the latter technique is performed using the case studies of February 20 and 22, 1993.

A retrieval technique for estimating rainfall rate and precipitating cloud parameters from spaceborne multifrequency microwave radiometers is described. The algorithm is based on the maximum a posteriori probability criterion (MAP) applied to a simulated data base of cloud structures and related upward brightness temperatures. The cloud data base is randomly generated by imposing the mean values, the variances, and the correlations among the hydrometeor contents at each layer of the cloud vertical structure, derived from the outputs of a time-dependent microphysical cloud model. The simulated upward brightness temperatures are computed by applying a plane-parallel radiative transfer scheme. Given a multifrequency brightness temperature measurement, the MAP criterion is used to select the most probable cloud structure within the cloud-radiation data base. The algorithm is computationally efficient and has been numerically tested and compared against other methods. Its potential to retrieve rainfall over land has been explored by means of Special Sensor Microwave/Imager measurements for a rainfall event over Central Italy. The comparison of estimated rain rates with available raingauge measurements is also shown.