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 study explores the synergy of electromagnetic data modeling and Artificial Intelligence (AI) algorithms for soil moisture retrieval over agricultural fields using Polarimetric SAR (PolSAR) data. SAR acquisitions are considered as a valuable source for accurate estimation of soil moisture in agricultural areas. However, its retrievals are influenced by various factors, including vegetation cover. In this context, the polarimetric information of SAR acquisitions allows the interpretation of the occurring scattering processes. The proposed approach involves a physics-aware AI algorithm based on two Artificial Neural Networks (ANNs), trained on electromagnetic (EM) model simulations at L-band. Starting from a full polarimetric Covariance Matrix, the first AI model separates the different scattering contributions, estimating surface and double-bounce scattering mechanisms while minimizing the attenuation effects caused by the vegetation layer. Then, the estimated surface and double-bounce components are fed to an additional AI model to retrieve soil moisture. Field campaign data over actual corn fields were considered and ingested by the EM model to generate synthetic SAOCOM-like case studies which were used to validate the approach within the simulated domain.

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.

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.

Soil moisture represents a significant guiding factor for agricultural activities, especially for smart irrigation and crop yield estimation. In this context, SAR data is one of the most valuable sources of information for accurate and continuative estimation of soil moisture in agricultural areas. However, SAR-derived soil moisture retrievals are affected by several factors, including the vegetation cover, which is responsible for additional signal attenuation and scattering mechanisms. Concerning the algorithms for soil moisture estimation, Machine Learning (ML) has proved to be a valuable instrument for finding relations between SAR data and the soil dielectric properties. For this purpose, this study aims to synergically adopt electromagnetic data modelling and a ML algorithm to estimate the scattering contributions associated with the ground and demonstrate that they are more sensitive to soil moisture than the total received signal. To apply such approach to real SAR data, airborne acquisitions at L-band will be considered.

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

In this study, the spatial correlation between the vegetation optical depth at L-band (L-VOD) captured by the radiometer on-board the Soil Moisture and Ocean Salinity (SMOS) Satellite and the LiDAR products obtained by GEDI and ICESat-2 satellites is investigated at global scale. Suitable techniques, based on spatial and temporal averaging, are used to operate at the different spatial resolutions and to aggregate observations collected between May 2019 and April 2021. Then the correlation is investigated over four latitude ranges showing different peculiarities: in tropical latitudes, SMOS L-VOD is well correlated with the top of canopy height (RH100) recorded by both instruments and the plant area index (PAI) recorded by GEDI for all months of two years; in temperate northern latitudes the spatial correlations significantly decrease in cold months; in temperate southern latitudes, characterized by a high plurality of ecoregions, L-VOD proves to be better correlated with PAI than with RH100. Finally, the influence of forest intactness on L-VOD is also investigated. Intact forests markedly show the highest L-VOD values in tropical latitudes. The association disappears in boreal latitudes because of the extreme climatic factors that limit vegetation growing even in the case of intact landscapes.

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.

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 present a physics-based machine learning (ML) approach for estimating corn plant height from simulated synthetic aperture radar (SAR) data. The proposed study intends to demonstrate the physical awareness of datadriven approaches such as ML. In this regard, a multilayer perceptron (MLP) artificial neural network (ANN), designed for corn plant height estimation, was trained with simulated C- and L-band SAR data generated using a state of the art electromagnetic model for microwave backscattering from terrain covered with vegetation. Here we show how the most significant connections between the nodes composing the network and the most relevant input variables can be detected, demonstrating the physical meaning behind the mapping criteria of the network itself.

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.

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.

The NASA Soil Moisture Active Passive (SMAP) satellite mission aims to produce enhanced resolution surface soil moisture products by combining coincident but multiresolution L-band active and passive microwave measurements. Since the SMAP radar ceased operations early in the mission, Copernicus Sentinel-1 C-band radar observations are used in the combined product. The synergy is built on two basic foundations: first, active and passive signals covary in a known and systematic fashion, and second, measurements are available at multiple resolutions. In this study, we perform numerical simulations and assess global satellite observations to test the first foundation (covariation). Specific focus lies on the role of the vegetation canopy in modulating the active–passive relationship. We use a discrete radiative transfer model to simulate the slope$\beta $and coefficient of determination$R^{2}$of the relationship between active and passive signals, considering three vegetation types for which the model has been extensively assessed in previous experimental studies. We find that a linear relationship between backscatter and emissivity can be established over a range of vegetation conditions. The coupling between active and passive signals decreases with increasing vegetation water content, such that moderate or higher correlations (nonzero slopes) are retained up to 4 kg/m2(6.3 kg/m2) for L-band/L-band and 1.5 kg/m2(2 kg/m2) for the C-band/L-band configuration. We decompose the effects of different soil-vegetation scattering mechanisms, such as double-bounce, and different measurement error levels on the active–passive relationship. Comparisons with satellite data confirm that our simulations capture magnitudes and major trends found across global vegetated land masses.

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.

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.

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.

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 latest developments in radar mission concepts suggest that subdaily synthetic aperture radar will become available in the next decades. The goal of this study was to demonstrate the potential value of subdaily spaceborne radar for monitoring vegetation water dynamics, which is essential to understand the role of vegetation in the climate system. In particular, we aimed to quantify fluctuations of internal and surface canopy water (SCW) and understand their effect on subdaily patterns of L-band backscatter. An intensive field campaign was conducted in north-central Florida, USA, in 2018. A truck-mounted polarimetric L-band scatterometer was used to scan a sweet corn field multiple times per day, from sowing to harvest. SCW (dew, interception), soil moisture, and plant and soil hydraulics were monitored every 15 min. In addition, regular destructive sampling was conducted to measure seasonal and diurnal variations of internal vegetation water content. The results showed that backscatter was sensitive to both transient rainfall interception events, and slower daily cycles of internal canopy water and dew. On late-season days without rainfall, maximum diurnal backscatter variations of >2 dB due to internal and SCW were observed in all polarizations. These results demonstrate a potentially valuable application for the next generation of spaceborne radar missions.

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.

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.

The objective of this article is a systematic investigation of the sensitivity of C- and X-band emissions to leaf shape and orientation for various growth stages of corn. To simulate these effects, we used the model developed at Tor Vergata University (TOV model), which is based on a matrix doubling algorithm considering multiple scattering. Corn leaves have specific properties of shape, curvature, and orientation. We have compared different approaches, including segmented elliptical disk oriented following leaf curvature, unique elliptical disk per leaf, and segmented circular disk with size determined by the shorter leaf dimension and following the leaf curvature. Moreover, widespread leaf inclination angle distribution functions combined with in situ measurements of leaf inclination angle are adopted. The scatterers' phase matrix calculations are based on the physical optics approximation. Simulations are conducted with the ground-measured soil and vegetation properties as inputs and evaluated against the corresponding ground-based, multifrequency radiometer observations carried out in four different years over Chinese sites. The investigations show that in most cases the segmented circular disk assumption shows the best correspondence to the measurements over intermediate growth stages when the vegetation heights lie between 50 and 200 cm, and the unique elliptical disk model achieves the best correspondence for the later growth stages when the vegetation heights are larger than 200 cm with prefer-erectophile distribution of leaf orientation. The use of in situ leaf inclination angle measurements can improve the model accuracy by up to 25 K for tall vegetation heights compared with random distribution assumption.

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 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.

In this letter, the results obtained with the last version (level 2, version 650) of SMOS retrieval algorithm are compared against independent measurements, over tropical forests. In particular, the climate research unit meteorological variables and data bases of forest height and forest biomass are considered. Comparisons with results obtained by AMSR-2 under similar conditions are also illustrated. Vegetation optical depth shows a generally good correlation with forest height and forest biomass, particularly in Africa and South America. Spatial and temporal trends of retrieved soil moisture follow trends of rainfall, particularly in regions of dry winter.

Data from the Soil Moisture and Ocean Salinity (SMOS) mission were exploited to explore its potential in providing ecosystem information. Here the strength of an innovative relationship, between SMOS V620 algorithm data and Ecosystem Functional Properties (EFPs) derived from flux towers data, was preliminary investigated for Africa and South America forests. Correlation was also explored for above ground forest biomass. High correlation values were found between SMOS vegetation optical depth (VOD) and a AGB reference map, and with EFPs at year level (2014). Different VOD-EFPs trends were found for different latitudinal belts, characterizing wet and dry forests. The results suggest that SMOS VOD data represent a tool able to provide repeated information on forest biomass and forest processes in tropical and subtropical ranges.

NASA's Soil Moisture Active-Passive (SMAP) mission aims at disaggregating L-band radiometer (36 km) with L-band radar (1-3 km) observations to obtain an intermediate resolution soil moisture product (1-9 km). Since SMAP's radar stopped operations in July 2015, a substitution with ESA's Sentinel 1 C-band radar is underway. For this purpose, the relationship of L-band radiometer and C-band radar observations needs to be determined, especially considering the frequency dependent influence of vegetation. This study investigates vegetation effects on covariations of backscatter and emissivity, considering the SMAP-Sentinel 1 (C/L-band) and original SMAP (L/L-band) frequency configurations. Covariations are expressed as the linear regression slope β between backscatter and emissivity signatures, which is simulated for corn and coniferous forest stands. Backscatter and emissivity signatures are obtained from Tor Vergata model simulations, whereas random signal disturbances are accounted for by an errors-in-variables model. As a general trend, we find that β tends to zero for increasing VWC, which is mostly explained by a decrease of radar sensitivity to soil moisture. For forest, which shows overall high VWC values (~5-15 kg/m2), we thus find low magnitudes of β in all cases. For corn (~0-7 kg/m2), we find considerable non-zero magnitudes of β for both frequency configurations, whereas the L/L-band case retains high magnitudes of β longer with respect to C/L-band.

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.

In this paper, retrieval results obtained using the last version (V650) of SMOS level 2 algorithms are tested considering pixels of Africa and South America. Yearly average values of vegetation optical depth are compared against forest height estimates at continental scale. For selected areas of African woody savannah, multitemporal trends of SM and VOD are compared against environmental variables available from Climatic Research Unit data base.

The NASA Soil Moisture Active Passive (SMAP) mission aims to disaggregate L-band microwave brightness temperatures (~40 km2) with finer resolution radar backscatter (1-3 km2) to obtain an intermediate resolution soil moisture product. The disaggregation is based on a linear functional relationship between backscatter and emissivity microwave observations that is captured by a covariation parameter β. Since SMAP's L-Band radar has stopped operations in July 2015, the substitution of Sentinel 1's C-Band radar for an operational soil moisture product is in preparation. However, while multiple studies have provided understanding of active-passive covariation for the L/L-Band case, little is known about the C/L-Band case. We utilize the Tor Vergata discrete backscatter and emission model to simulate growing wheat and corn stands and calculate the covariation parameter β for the L/L-Band and C/L-Band case. The study aims to provide insights into the strength, temporal dynamics and underlying scattering mechanisms of active-passive covariation for different vegetation types and frequency combinations. Our results indicate that for the C/L-Band case, vegetation cover limitations are generally more severe, and different β-dynamics and underlying scattering mechanisms are observed with respect to the L/L-Band case.

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

The main objective of SMOS over land is to retrieve soil moisture. Anyway, for soils covered by vegetation two model parameters, soil moisture and vegetation optical depth, are provided as outputs of the Level 2 algorithm. The precisions in the retrieval of the two parameters are linked, since improvements in soil moisture estimates can only be achieved if vegetation parameters are correctly estimated. The vegetation optical depth retrieval itself is also interesting since contains important information about vegetation, allowing us to open new perspectives for the monitoring of forests at global scale. In this work, vegetation optical depth products for July 2015, obtained over forests by the last 620 version of the SMOS retrieval algorithm, are considered and tested. The results of a comparison with three independent remote sensing datasets, containing forest height, forest biomass and Leaf Area Index (LAI) respectively, are presented. A further test is made considering AMSR2 data in order to evaluate the differences in the retrieval accuracy of the vegetation optical depth due to frequency. The analysis is conducted at global scale, and is focused on the pan-tropical biomes.

This paper reports model simulations of L band backscattering for dominant crops in Pampas region (Argentina). The objective is to exploit L band signatures that will be provided by SAOCOM SAR. For corn, sunflower, soybeans and wheat, a previously developed theoretical model has been run with input data sets representative of these crops. Results of parametric simulations, covering realistic ranges of crop height and soil moisture, are shown. Validation results, based on measurements in Italy and the US are also presented. The models were further run using ground measurements directly collected in Pampas, where radar experimental campaigns are in progress.

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).

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.

In this paper, we used an improved version of the Tor Vergata radiative transfer model to simulate the backscattering coefficient for the L-band SAR signals over areas covered with vegetation. Fields of winter wheat, maize and sugar beet observed during the AgriSAR2006 campaign were investigated. For maize field, the presence of periodic soil surface profiles played an important role in determining the total backscattering. Soil moisture was also estimated using an inverse algorithm based on a supervised, non-parametric learning technique, v-SVR. v-SVR proved good generalization properties even with a limited number of training samples available. Dependence to the origin of training samples, as well as the influence of different features, was thoroughly considered.

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.

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.

This study investigates the potentialities offered by active and passive simultaneous acquisitions at L band for monitoring of soil moisture in forested areas. Airborne data, acquired over the moderately dense Gillenbah forest in the framework of SMAPEx-3 project, have been analyzed to derive the sensitivity of emissivity and backscattering coefficient to soil moisture variations during the campaign, considering a full set of ground measurements characterizing the forest environment.

The 12th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad 2012) was held at Villa Mondragone, University of Rome "Tor Vergata," near Frascati, Italy, on March 5-9, 2012. The objective of MicroRad 2012 was to provide an open forum to report and discuss recent advances in the field of microwave radiometry, particularly with application to remote sensing of the environment. The meeting was highly successful, with more than 120 attendees representing 20 countries. There were 76 oral presentations and more than 40 posters. From the papers presented at MicroRad 2012 and others submitted specifically for this special issue, 12 were selected for inclusion in the special issue. The papers were carefully peer reviewed with the usual standards of the IEEE TGRS. As is evident from the table of contents, these papers span a broad range of microwave radiometry and remote sensing applications and reflect the interest in MicroRad and the vitality of research in this area.

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.

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 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.

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.

Using Microwave Remote Sensing techniques represents an important contribution to forecasting skill of regional scale flooding events occurring over time scales of days to weeks. Furthermore, it could add skills to predictions of flood peak timing and magnitude, besides climatic change predictions. In this paper, the temporal trends of Polarization Ratio at some AMSR-E bands (C, X, and Ka) will be studied in order to show its potential in flood and rainfall monitoring. To this end ground truth data concerning rainfall and water level measurements in the lower Bermejo Basin in Argentina have been used, and a general correlation between variations of the Polarization Ratio and ground parameters has been found at the various AMSR-E frequencies.

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.

The synergy between active and passive observations is becoming an attractive subject, particularly stimulated by the SMAP program. This paper investigates the potential advantage of the synergy related to the decoupling between the influence of soil and vegetation variables. If both the emissivity e and the backscattering coefficient σ° are available, the ratio between σ° and the reflectivity 1-e can be computed. This ratio is scarcely influenced by soil moisture while, assuming soil roughness to be almost constant with time, the ratio is mostly influenced by vegetation biomass.

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.

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

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.

This paper shows model simulations of forest emissivity at L-band and at global scale. The electromagnetic model developed at Tor Vergata University has been combined with information available from forest literature. Using allometric equations and auxiliary information, the geometric and dielectric inputs required by the model have been related to global variables available at large scale, such as Leaf Area Index. Simulations indicate that, at L-band, leaves are almost transparent, attenuation is mostly due to branches, and soil contribution can be still appreciable, unless the forest is dense. The model is being refined, to consider seasonal variations of foliage cover, subdivided into arboreous foliage and understory contribution. Parametric simulations, as well as comparisons with experimental data, are shown.

This paper analyses the SAR response of wetland ecosystems under different environmental conditions and at two different frequencies. We exploited the opportunity of observing the same inundation phenomena by two currently available SAR systems, such as ENVISAT ASAR (C band) and ALOS PALSAR (L band). The results obtained for C band are similar to the ones reported previously in the same area. Increasing water level in marshes is characterized by an increase and then a decrease in the backscattering coefficient of vegetation. An increase when water level changes the soil from saturated to flooded condition and a decrease when the water covers the vegetation. The new ALOS PALSAR L band results shows that in marshes, the increase in water level is seen as a decrease in the backscattering coefficient, since the reduction of emerged biomass reduces the available matter for the wave to interact with.

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

The objective of this paper is to carry out a systematic investigation about the sensitivity of radar to maize crop growth and soil moisture by considering a wide range of frequencies and angles and all linear polarizations. We show the results of a correlation study carried out on the data collected on a maize field at Suberg, in the Swiss region named Central Plain, by the multifrequency RAdio ScAtteroMeter (RASAM). This agricultural field was monitored over a long period of time at a wide range of frequencies and observation angles so that the correlation between the backscattering and crop height and the biomass and soil moisture was studied under several plant and observation conditions. Moreover, we describe some recent refinements applied to the vegetation scattering model developed at Tor Vergata University, Rome, Italy, and we evaluate the accuracy of extended comparisons between model outputs and RASAM signatures. The Tor Vergata model is finally applied to give a theoretical basis to the experimental correlation findings.

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.

In this work, we show the correlation study carried out on the data collected on a maize field in the Swiss region named Central Plain, by the multifrequency RASAM scatterometer. This agricultural field was monitored over long periods, at a wide range of frequencies and observation angles, so that the correlation between backscattering and crop height, biomass and soil moisture could have been studied under several plant and observation conditions. Moreover, we describe some recent refinements applied to the vegetation scattering model developed at Tor Vergata University, and we evaluate the accuracy of extended comparisons between model outputs and RASAM signatures. The Tor Vergata model is finally applied to give a theoretical basis to the experimental correlation findings.

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.

This paper describes a model which simulates the emission of forests at L band. In particular, the problem of soil emission attenuated by vegetation is considered. Results of comparisons with experimental data collected by the upward looking ELBARA radiometer are presented and discussed.

A discrete model, based on the radiative transfer theory, is used to simulate coniferous forest emissivity at L-band. Inputs to the model are given by using a detailed geometrical representation of Les Landes forest. Simulated emissivities are compared against EuroSTARRS campaign measurements, which were made over the same forest at nominally vertical polarization and several angles. The model has also been used to investigate the sensitivity of L-band radiometers to soil moisture under forests. Results of this investigation indicate that the soil contribution to emission is potentially appreciable, even under developed forests. This may be a useful result, in view of future satellite missions, such as SMOS and HYDROS

This paper assess the possibilities of the synthetic aperture radar (SAR) sensors currently in orbit for the maize monitoring defining the configurations (polarization and incidence angles at C-band) maximizing the sensitivity to plant growth and reducing the impact of the soil moisture on the signal. Temporal evolution of the signal was simulated in all the possible configurations using the radiative transfer model developed by the University of Rome "Tor Vergata." The input parameters came from an intensive field campaign providing a detailed description of maize crop over the Belgian Loamy site all along the 2003 growing season. The model was validated for vertical (VV) and horizontal (HH) polarization using ERS, ENVISAT, and RADARSAT observations. The C-band SAR signal in single polarization was found to be sensitive to crop growth till the leaf area index (LAI) reached 4.6 m/sup 2//m/sup 2/, while the soil moisture influenced the signal for sparsely vegetated fields (LAI<2.7 m/sup 2//m/sup 2/). Dual-polarizations indexes were found sensitive to maize growth and less sensitive to soil moisture variations. The VV/VH polarization ratios computed from signal recorded at high incidence angle (35/spl deg/ to 45/spl deg/) could be considered to assess the crop growth till LAI reached 4.9 m/sup 2//m/sup 2/ with low sensitivity to soil moisture. At the beginning of growth, the emergence of maize plants could be detected using the copolarized ratio (VV/HH) computed at low incidence angle. These indexes allow discriminating various crop conditions at a given date between fields of a same region.

Several efforts, aimed at developing and refining crop backscattering models, have been done during the last years. Although important advances have been achieved, it is recognized that further work is required, both in the electromagnetic characterization of single scatterers and in the combination of contributions. This work is focused on the description of leaf geometry and of the internal structure of stems. Recently developed routines, able to model the scattering cross sections of curved sheets and hollow cylinders, are adopted for this purpose and run within the multiple-scattering model developed at the University of Rome "Tor Vergata". Input parameters are taken from experimental campaigns. In particular, ground data collected over a maize field at the Central Plain site in 1988, over wheat and maize fields at the Loamy site in 2003, and over wheat fields at the Matera site in 2001 and 2003 are considered. The multitemporal backscattering coefficients at C-band are simulated. The results obtained under different assumptions are compared to each other, and with C-band radar signatures collected over the same fields. The influence of some critical factors, affecting crop backscattering, is discussed. It is demonstrated that a more detailed scatterer characterization may improve the model accuracy, especially in the case of hollow stems.

In this work, multitemporal synthetic aperture radar (SAR) data in conjunction with an electromagnetic (EM) model and a vegetation growth model were used to monitor and explain burn-regrowth events of junco vegetation in a wetland environment. The data used were from Radarsat-1, ENVISAT Advanced Synthetic Aperture Radar (ASAR), and European Remote Sensing 2 (ERS-2) temporal series. The EM model is based on radiative transfer theory and describes junco vegetation as a set of vertical dielectric cylinders on a flat flooded surface. It was used, together with the vegetation growth model, to predict the temporal evolution of the radar response during a burn-regrowth event. This simulation was compared with the ERS-2 vertical (VV) data. It was observed a "bell-shaped" temporal trend that was confirmed by the simulated data with a mean error of 2.5 dB. Additionally, in view of current and future ENVISAT ASAR Alternating Polarization Mode Precision data, the horizontal (HH) SAR temporal response was also simulated giving as a result strong differences between simulated HH and VV temporal trends. These differences are in good agreement with the ones observed between Radarsat-1 HH and ERS-2 VV SAR data acquired at close dates and also with the same differences observed between HH and VV ENVISAT ASAR data. Electromagnetic modeling results provide a sound theoretical interpretation of these observations.

Currently, there is a strong interest in studying the L-band emission of forests to exploit the sensitivity of the microwave signature to surface soil moisture and forest biophysical parameters such as biomass or LAI. In this paper we contribute to the understanding of the forest emission problem through simulations of the L-band brightness temperature based on a discrete radiative transfer model. Prior to L-band simulations, a geometric model of a maritime pine tree forest has been developed to get a close description of a real forest. L-band simulations are then analysed and compared to airborne radiometric data over the Les Landes (SW France) Maritime pine tree forest.

This paper shows some recent advances acquired in the crop backscattering model developed at Tor Vergata University. In particular, new routines, able to compute cross sections of curved leaves and hollow cylinders, are described

This paper describes an algorithm aimed at monitoring the soil moisture and the growth cycle of wheat fields using radar data. The algorithm is based on neural networks trained by model simulations and multitemporal ground data measured on fields taken as a reference. The backscatter of wheat canopies is modeled by a discrete approach, based on the radiative transfer theory and including multiple scattering effects. European Remote Sensing satellite synthetic aperture radar signatures and detailed ground truth, collected over wheat fields at the Great Driffield (U.K.) site, are used to test the model and train the networks. Multitemporal, multifrequency data collected by the Radiometer-Scatterometer (RASAM) instrument at the Central Plain site are used to test the retrieval algorithm.

This contribution discusses some relevant features of bistatic scattering from vegetation, based on a simulation analysis using a theoretical model developed at Tor Vergata. This paper is focused on land cover monitoring by a combination of monostatic and bistatic radar. The main mechanisms contributing to bistatic scattering are highlighted and its exploitation in monitoring vegetation covered surfaces is discussed.

This paper describes an algorithm aimed at monitoring the soil moisture and the growth cycle of wheat fields using radar data. The algorithm is based on neural networks trained by an electromagnetic model and multitemporal ground data measured on fields taken as a reference. The retrieval procedure is tested using mutitemporal signatures collected at a test site.

The use of satellite technology in monitoring environmental conditions of coastal waters for an early detection of pollution and eutrophication phenomena is of increasing importance. The daily monitoring of Sea Surface Temperature utilizing low-resolution multispectral sensors, especially designed for atmospheric and oceanographic studies, requires a complex procedure to compare the remote sensed data, which represent values averaged over each large pixel size, with the point like calibration data collected in situ. In this experiment, we have studied three small shallow water basins. In order to compare point like in situ measurements with Sea Surface Temperature values derived from AVHRR sensor, a bi-dimensional dynamic model of the two basins of the Taranto gulf, has been developed. Through this model, space and ground/sea data, at different spatial and temporal resolution and spacing, have been integrated. The comparison between in situ and RS data for the year 2000 will be shown as well as the temporal and spatial temperature dynamics.

This article describes a model based on radiative transfer theory, where the leaves geometry is represented by a curved rectangular dielectric sheet.

This contribution discusses some relevant features of bistatic scattering from vegetation. Based on a simulation analysis using a theoretical model developed at Tor Vergata, it is shown that specular scattering of coniferous forests present a better sensitivity to biomass with respect to monostatic case. The performances of a non-specular bistatic radar are also simulated.

An algorithm, based on an electromagnetic model and a neural network, aimed at monitoring the multitemporal evolution of wheat fields, is described. Three different sites are used to validate the model, provide reference ground data, and test the algorithm.

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 microwave model developed at the Tor Vergata University is used to simulate the emissivity of forests, in order to study the performance of an L-band spaceborne radiometer, similar to that carried by the Soil Moisture Ocean Salinity mission. The model is first validated, and the importance of a correct vegetation growth parametrization in the modeling procedure is pointed out. This model is also used to calibrate a simple zero-order radiative transfer model, since simple models have been recognized to be useful in retrieval applications at a global scale. We show that although a zero-order approximation cannot be directly used for forests, a simple formulation may be applied, provided the albedo and the optical depth are defined as equivalent parameters.

The microwave brightness temperatures of a wheat field were measured in 1993, during the whole growth cycle by the six-frequency PORTOS radiometer, at two polarizations and several angles. In this paper, the emissivities measured at L-, C-, X-, and K-band are compared with those simulated by a discrete multiple scattering model based on the radiative transfer theory. The agreement between experimental and simulated data is generally good at all frequencies, although a unique set of input parameters has been used. It is demonstrated that the model simulations lead to a reliable identification of the radiometric configurations most sensitive to ground variables. Moreover, they aid the development of a soil moisture inversion algorithm that performs well even with soils covered by developed vegetation.

The capability of multifrequency polarimetric synthetic aperture radar (SAR) to discriminate among nine vegetation classes is shown using both experimental data and model simulations. The experimental data were collected by the multifrequency polarimetric AIRSAR at the Dutch Flevoland site and the Italian Montespertoli site. Simulations are carried out using an electromagnetic model, developed at Tor Vergata University, Rome, Italy, which computes microwave vegetation scattering. The classes have been defined on the basis of geometrical differences among vegetation species, leading to different polarimetric signatures. It is demonstrated that, for each class, there are some combinations of frequencies and polarizations producing a significant separability. On the basis of this background, a simple, hierarchical parallelepiped algorithm is proposed.

A model, based on the radiative transfer theory and the matrix doubling algorithm, is described and used to compute the emissivity e of forests. According to model simulations, the L-band emissivity trend versus forest biomass is more gradual than that of the backscatter coefficient. This gradual behavior is observed, in absence of leaves, also at C- and X-bands, while leaves anticipate saturation and make e higher in coniferous forests and lower in deciduous forests. Model results are successfully validated by some available experimental data. Operational aspects, concerning the potential of airborne and spaceborne radiometers in identifying forest type and estimating biomass, are discussed.

A model, based on the Radiative Transfer Theory and the Matrix Doubling algorithm, is described and used to compute the backscatter coefficients σo's of forests. The objective is to investigate the radar sensitivity to woody biomass and some geometrical tree properties like branch dimensions and orientation. Model computations show that the backscatter coefficient is sensitive to the woody volume, particularly at HV polarization, P and L band; however, the radar response is appreciably influenced also by branch dimensions and orientation. Comparisons with experimental results available in the literature are shown. The correspondence between predicted and measured σo's is generally good, although a slight overestimation is noted at L band, while uncertainties about data calibration and soil properties make the comparisons more difficult, in some cases, at P band.

Polarization characteristics of centimetric microwave emission from canopy-covered fields are investigated. Experimental data are compared against theoretical predictions obtained by two different models. In a simple and direct approach, vegetation is considered as a uniform absorbing and scattering slab with plane parallel boundaries, while in a more realistic description, plants are modeled as an ensemble of lossy dielectric disks and thin cylinders (needles). A parametric analysis, carried out to assess the sensitivity of the polarization index (PI) to the most significant parameters of vegetation shows that, although the PI is mainly influenced by global parameters such as leaf area index and plant water content, morphological parameters such as disk or needle dimensions and orientation distribution may play a relevant role. In particular, a model composed of a mixture of disks and needles is able to represent the negative value of PI sometimes measured over fully grown vegetation.>