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.

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.

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.

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.

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.

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 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 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 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.

The objective of this work is to assess the accuracy of LAI maps retrieved from ENVISAT MERIS data over wheat fields. The method consists of comparing, at catchment scale, the LAI maps retrieved from MERIS data to those retrieved from ASAR AP data. The latter were preliminary validated, at field scale, by means of in situ data. The experimental site is an agricultural area, mainly devoted to wheat cultivation, located in the Basilicata region, close to Matera city (Italy). On this area ENVISAT MERIS, ASAR AP and ground data were intensively acquired during the 2004 growing season. Results indicate that errors affecting wheat LAI estimations derived from optical and radar data are comparable.

This paper presents a retrieval algorithm that estimates spatial and temporal distribution of volumetric soil moisture content, at an approximate depth of 5 cm, using multitemporal ENVISAT Advanced Synthetic Aperture Radar (ASAR) alternating polarization images, acquired at low incidence angles (i.e., from 15/spl deg/ to 31/spl deg/). The algorithm appropriately assimilates a priori information on soil moisture content and surface roughness in order to constrain the inversion of theoretical direct models, such as the integral equation method model and the geometric optics model. The a priori information on soil moisture content is obtained through simple lumped water balance models, whereas that on soil roughness is derived by means of an empirical approach. To update prior estimates of surface parameters, when no reliable a priori information is available, a technique based solely on the use of multitemporal SAR information is proposed. The developed retrieval algorithm is assessed on the Matera site (Italy) where multitemporal ground and ASAR data were simultaneously acquired in 2003. Simulated and experimental results indicate the possibility of attaining an accuracy of approximately 5% in the retrieved volumetric soil moisture content, provided that sufficiently accurate a priori information on surface parameters (i.e., within 20% of their whole variability range) is available. As an example, multitemporal soil moisture maps at watershed scale, characterized by a spatial resolution of approximately 150 m, are derived and illustrated in the paper.

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.

The objective of this work is to investigate the synergistic use of leaf area index (LAI) retrieved by ENVISAT ASAR data and crop growth models, such as CERES-Wheat, to improve the accuracy of wheat yield predictions. The estimate reliability of CERES-Wheat strongly depends on the accuracy of its numerous inputs, which are not always available or accurate. As a consequence, the model would largely benefit from using updated information on the wheat status, provided by remote sensing at field scale. This work shows that the assimilation of ENVISAT ASAR AP data into the model lead to significant improvements in the wheat dry biomass and the grain yield model predictions.

In this work, the accuracy of soil moisture retrieved from ASAR data over bare or sparsely vegetated surfaces is investigated by means of a simulation study. The soil moisture retrieval method is based on an optimization algorithm that appropriately inverts theoretical direct models by assimilating a priori information on surface parameters. In order to account for a large variability of roughness conditions, two complementary models have been used, namely the integral equation method model and the geometrical optics model. The performance of the inversion method has been assessed on simulated noisy ASAR data, as a function of different a priori information quality level.

Based on recent experimental studies which have found a strong correlation between a multitemporal series of C-band HH/W backscatter ratios acquired at 40deg incidence angle and wheat biomass, this work investigates the effect of the assimilation of the radar retrieved information into CERES-Wheat crop model. A sensitivity analysis has shown that an inaccurate knowledge of some model inputs, concerning soil properties and crop management, can lead to erroneous predictions. However adopting a reinitialisation assimilation strategy, significant improvements in the model estimations have been obtained

In this work, the effects of SAR measurement errors as well as direct model errors on soil moisture retrieval from SAR data are investigated. In particular, the attention is focused on understanding under which conditions it is more convenient: a) feed the retrieval algorithm with accurate backscattering values (i.e. estimated at "field scale") then retrieve soil moisture estimate directly at "field" scale; b) use relatively noisy backscattering values, estimated at smaller scales (i.e. "segment scale"), to retrieve soil moisture estimates at "segment" scale and subsequently average the obtained soil moisture estimates at "field" scale. The adopted soil moisture retrieval algorithm is based on a regularized Neural Networks appropriately trained by IEM model. The SAR synthetic data simulates SAR data acquired by ERS and ENVISAT satellites. The performance of the inversion method is -given as a function of the SAR configuration and noise level.

In order to improve a better quantitative understanding of the effect of soil moisture of bare soils on C-band SAR data, five dedicated "weather" stations with soil moisture probes, rain gauges, and temperature thermometers have been installed through the Flevoland test site (The Netherlands) since April 2000. In addition to the in-situ data, spaceborne SAR data at C-band measured by ERS-2 are also collected (3 images every 70 days), taking advantage that the test site is close-by the locations of the ESA transponders used for calibration of the SAR data. In this paper, the variations of the backscattering coefficient due to soil moisture changes are shown as well as the effect of rain precipitation's on the soil moisture at different depths. Diurnal variations of the soil moisture have been observed during drying periods and soil temperature measurements allow a precise monitoring of this effect.