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

Doppler scattering simulations from sea-like surfaces are sensitive to non-linear hydrodynamic effects in wave propagation. Two commonly considered approaches to introduce such non-linearities – the Creamer transform and the Choppy wave model – are examined in this paper. It has been observed that the two models yield simulated Doppler spectra with conspicuously different centroids. Based on the modulation transfer function theory for the Doppler moments of sea backscatter, it seems plausible that such a behavior could result from model differences in the hydrodynamic modulation of smaller, scatter-producing ripples by larger waves. We perform numerical experiments to measure the corresponding hydrodynamic modulation transfer functions (HMTF). An established approach uses a single-harmonic large-scale surface. A correlation-based technique is also proposed that allows HMTFs to be estimated in Monte Carlo simulations of full-spectrum ocean-like surface profiles. Both methodologies show the Creamer HMTF values to be significantly larger than their Choppy-wave counterparts. Estimates made with ocean-like surfaces are also found higher than those based on the solitary-wave approach, particularly for the Creamer model. Application of the first-order Romeiser-Thompson theory with the estimated HMTF values produces Doppler shifts that are in general agreement with those observed in exact scattering simulations using either surface representation. The results of the study reveal considerable differences between the hydrodynamic modulation effects of the two surface models and demonstrate that those disparities are important contributors to the observed deviations in Doppler centroids. More work is required to conclusively determine a preference of one model over the other.

Doppler spectra obtained from simulations of ocean-like surface bistatic scattering are shown under the first order small slope approximation of rough surface scattering. Spectral properties are examined as a function of wind speed, polarization, and observing geometry in order to assess the potential for the bistatic remote sensing of various sea surface properties.

The spectral description of the wind-roughened ocean surface usually involves an omnidirectional spectral model and an azimuthal spreading function that describes the fraction of the waves of a certain length propagating at a given angle with respect to the wind direction. A number of such functions have been introduced and refined over the years. We use the 2nd-order Small Slope Approximation (SSA2) to calculate and compare the backscattered normalized radar cross sections resulting from several of them. In the process, the same Hwang omnidirectional spectrum is used. The SSA2 allows evaluating both co-polarized and cross-polarized backscattering cross sections. This study focuses on L and Ku radar bands and wind speeds between 5 and 9 m/s and covers a wide range of incidence angles. The results are compared with geophysical model functions where available.

A recent ship-based field campaign demonstrated the use of a maritime multi-channel reflectometer using Sirius-XM broadcast radio downlinks as Signals-of-Opportunity. This campaign involved the collection of a large data set of bistatic ocean surface scattering measurements, which coincided with ground truth data detailing local oceanographic parameters. The experiment was unique in that most of its measurements were taken well outside the plane of incidence, with illumination from an oblique angle. This study offers a novel opportunity to explore the fidelity of electromagnetic models (coupled with oceanographic models) to predict radar scattering in these unique geometries. Match-ups are shown between measured and modeled bistatic normalized radar cross sections and range profiles, with the intention of both 1) supporting shore- and pier-based reflectometry through bistatic surface scattering model validation and 2) demonstrating the utility of using reflectometry for oceanic remote sensing from a sea-faring vessel.

A method for determining the cutoff wavenumber used in the geometrical optics theory of scattering from a random rough surface is presented. The method is based on the “alpha stable distribution” approach described in previous studies, and yields a cutoff wavenumber that depends on the spectrum model applied, the angle of incidence, and the frequency of interest. Use of the cutoff wavenumber so determined is found to improve agreement between predictions of the geometrical optics and physical optics theories of near specular scattering from the sea surface in some situations.

Simulation results are used to assist algorithm development for extracting the ocean surface significant wave height (SWH) from microwave scattering of satellite broadcast signals from the ocean surface. The analysis shows that the Doppler frequency spectrum width is a reliable parameter for extracting the SWH. The numerical experiments include both deep and shallow water conditions. The correlation between Doppler frequency spectrum width and SWH is robust and applicable to all wind speeds, inverse wave ages, and water depths tested in this study (5–21 m/s, 0.8–2.0, and 3 m–$\infty $, respectively).

This work presents data from a recent field campaign in which an S-band passive radar using noncooperative satellite-based illumination was used to measure bistatic radar scattering from the sea surface. Significant wave height (SWH) information from a nearby Waverider buoy was reported concurrent with passive radar measurements. These simultaneous data collections fostered a study of the sensitivity of passive radar amplitude and Doppler measurements to SWH. This work demonstrates that Doppler and amplitude measurements with passive radar using satellite-based illumination can provide a promising approach to coastal remote sensing of SWH and mean wave direction, particularly when the scene is illuminated by multiple transmitters with a large physical separation between them.

Numerical experiments are carried out to explore the retrieval of ocean surface significant wave height (SWH) with the Doppler spectrum width (DSW) of microwave bistatic scattering from the ocean surface. Variations of DSW with incidence and scattering angles, wave height, wind speed, wave age, local water depth, and swell condition are described. Physical interpretation is offered on the relationship between SWH and DSW through the surface wave orbital velocity. Among the various environmental factors, the swell exerts the largest influence on the resulting relationship between DSW and SWH. The analysis considers a stationary transmitter and receiver only in order to provide insight into the impact of wave motions on the Doppler signatures.

Currently, sea-ice thickness is often estimated using microwave radar altimetry. It is well known that radar measurements are highly dependent on the roughness characteristics of the observed scene and can have a profound effect on altimetry-based thickness estimates. This simulation-based study highlights the importance of sea-ice roughness parameterization in the context of radar altimetry. This work provides a new sea-ice roughness parameterization in the form of an elevation spectrum developed from field campaign data. The formulation of this elevation spectrum is partly based on the generalized power law. Simulations have been developed to predict electromagnetic scattering from surfaces described by the new spectrum, with a focus on the X-band (10 GHz) scattering with nadir incidence. In this work, both the analytical small slope approximation and numerically exact Method of Moments are applied to various sea-ice surface spectra, representing a wide range of surface roughness conditions. The results show that the normalized radar cross section of the sea-ice interface varies significantly depending on the surface elevation spectrum and the statistics of the rough interface. Comparisons with numerically exact methods show that analytical approximations to rough surface scattering work reasonably well for the cases considered here, except at large scattering angles.

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.

This study implements direct numerical simulations of in-plane bistatic scattering in 2-D space to mimic the setup of an actual wavetank experiment. The latter investigated feasibility of passive sensing of water surface conditions using commercial geostationary satellites as transmitters of opportunity. While range resolution afforded by the signal bandwidth and sensing geometry is very coarse (nearly a hundred meters at best), finer spatial sensitivity can still be obtained due to narrow pattern of the receiving antenna located close to surface. Variations of the signal power and short-term Doppler shifts with time – all observed in the field data – can be used as additional sources of remote sensing information. Therefore the numerical simulations took account of antenna patterns and locations, while using a representative measured wave spectrum. We demonstrate that undulations in power and spectrogram centroids are indeed present in the modelled data and can be related to the dominant wave period. Critically, the numerical experiment allows considering a broad range of scattering angles (receiver antenna orientations) to indicate when such wave period retrievals are feasible.

Doppler characteristics are intrinsic to scattering from ocean surface and can be used for sea state retrievals. In this study, bistatic reflections of time-harmonic S-band signal from evolving sea-like surface are simulated in 2-D space using an exact technique based on the first-principles boundary integral equation formulation. Large Monte Carlo ensembles allow obtaining well-averaged Doppler spectra. The behavior of basic spectral parameters (mean shift and width) at both VV and HH polarizations is analyzed across a broad range of scattering angles (the incident angle is kept at 45°). Changes in those parameters with the sea state (wind speed) are examined. The simulations are also repeated with the two approximate scattering models: the coherent Two-Scale Model and the second-order Small Slope Approximation, providing rigorous assessment of their ability to predict Doppler properties of sea surface scatter.

The systematic retrieval of near surface soil moisture (SSM) fields at high resolution (e.g., 0.1-1.0 km) is a challenging task that requires the exploitation of new retrieval algorithms and SAR data with advanced observational capabilities (in terms of spatial/temporal resolution, radiometric accuracy, very large swath, long-term continuity and rapid data dissemination). The launch of the Sentinel-1 (S-1) constellation provides these capabilities and calls for the development and validation of pre-operational SSM products at high resolution. The objective of this paper is to present and initially assess a SSM retrieval algorithm developed in view of S-1 data exploitation. The activity is supported by a large scientific community engaged in fostering a more effective interaction between researchers working in the field of high and low resolution SSM retrieval.

Many previous studies have shown the sensitivity of radar backscatter to surface soil moisture content, particularly at L-band. Moreover, the estimation of soil moisture from radar for bare soil surfaces is well-documented, but estimation underneath a vegetation canopy remains unsolved. Vegetation significantly increases the complexity of modeling the electromagnetic scattering in the observed scene, and can even obstruct the contributions from the underlying soil surface. Existing approaches to estimating soil moisture under vegetation using radar typically rely on a forward model to describe the backscattered signal and often require that the vegetation characteristics of the observed scene be provided by an ancillary data source. However, such information may not be reliable or available during the radar overpass of the observed scene (e.g., due to cloud coverage if derived from an optical sensor). Thus, the approach described herein is an extension of a change-detection method for soil moisture estimation, which does not require ancillary vegetation information, nor does it make use of a complicated forward scattering model. Novel modifications to the original algorithm include extension to multiple polarizations and a new technique for bounding the radar-derived soil moisture product using radiometer-based soil moisture estimates. Soil moisture estimates are generated using data from the Soil Moisture Active/Passive (SMAP) satellite-borne radar and radiometer data, and are compared with up-scaled data from a selection ofin situnetworks used in SMAP validation activities. These results show that the new algorithm can consistently achieve rms errors less than 0.07 m3/m3over a variety land cover types.

A study of the polarimetric properties of near specular scattered fields from rough surfaces is reported using a circular polarization basis. Such studies are relevant for Global Navigation Satellite System- Reflectometry (GNSS-R) remote sensing of the sea surface. Particular emphasis in the study is placed on the use of field correlations for the remote sensing of wind direction over the sea surface.

A dual-copolarization algorithm to classify inland open water bodies free of flooded vegetation using an L-band radar is presented and evaluated, with a view to applying the method to the Soil Moisture Active Passive (SMAP) mission for hydrological science and soil moisture retrieval applications. Past radar-based water body detection algorithms have applied a threshold to a single-polarization measurement, with water body detection declared if the observed cross section is less than the specified threshold. However, such methods are subject to ambiguities associated with scene variability and terrain slopes, making a universal threshold value difficult to derive and complicating the global application of such methods. Because SMAP will provide measurements in both HH and VV polarizations, the copolarization ratio is also available for water body detection. A threshold of -3 dB applied to the HH/VV polarization ratio is found effective in detecting water bodies at 40° incidence angle based on analysis of theoretical model predictions and measurements from airborne synthetic aperture radar and the spaceborne Aquarius scatterometer. When the water surface is calm and its radar response is very small (i.e., at the radar thermal noise level), the HH/VV ratio method fails. However, a combination of an HH/VV threshold (at -3 dB) and an HH threshold (at -25 dB) is shown to allow water body classification even in this situation. This proposed “combined” algorithm is assessed in four different geophysical scenarios. The resulting water body detection error is shown to be less than 10% for these cases, which satisfies SMAP requirements to allow accurate soil moisture retrieval, and the corresponding false alarm rate is smaller than 2%. The robustness of the proposed approach to subpixel heterogeneity has been also investigated. The performance of the algorithm remains sensitive to the noise level of the radar observations: for SMAP, a radar noise-equivalent sigma 0 of -28.5 dB or less is required in order to facilitate acceptable performance.

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

A study of the bistatic scattering patterns of rough surfaces is reported. We utilize both approximate and numerically exact models for surface scattering, and focus in particular on the location of local minima in the normalized radar cross-section as a function of the polar and azimuthal scattering angles. It is shown that these minima locations are distinct for HH and VV polarized returns in the case of slight roughness, and that the minimum locations for VV polarization are dependent for this case on the permittivity of the surface. It is also shown that these behaviors are modified as the surface roughness increases. These results may be useful in the design of future bistatic methods for sensing rough surface properties; initial consideration of the remote sensing of surface permittivity for small height surfaces is provided as an example.

A compact polarimetric (CP) radar system requires fewer measurements than a fully polarimetric (FP) system, thus allowing added flexibility in radar system design. Previous studies have shown the potential of using compact polarimetry for radar remote sensing of soil moisture. This paper extends previous studies by considering a time series data cube retrieval algorithm and measurements in the presence of vegetation. Vegetation information is assumed to be provided by an ancillary data source in the retrieval process. The performance of an algorithm for reconstructing FP information from CP measurements of vegetated soil surfaces is also examined. The results of the study show that only a modest degradation in soil moisture retrieval performance occurs when compact-pol measurements are used in place of full-pol data.

The possibility of recovering sea surface wind vectors from near-nadiral cross-track Ku- or Ka-band backscatter measurements using a measuring geometry similar to that of the Airborne Precipitation Radar 2 is discussed. The cross-track scanning geometry includes diversity in both elevation and azimuth angles, allowing the possibility of both wind speed and direction retrieval to within a 180°direction ambiguity. A simulation study of wind vector retrieval accuracy is performed based on the use of the “cutoff-invariant two-scale model” of sea surface backscatter returns. The results show that a maximum likelihood retrieval approach utilizing data from the cross-track scan can provide reasonable wind vector retrieval accuracy.

The objective of this paper is to investigate the sensitivity of cross-polarized backscatter to soil moisture content (mv) using experimental and simulated data. The experimental data set consists of co- and cross- C- and L band radar and ground data collected over bare fields by the University of Michigan in 1992 and during the Italian ENVISAT 2003 campaign over Matera (Italy), and over wheat fields during the European Space Agency AgriSAR 2006 and 2009 campaigns over DEMMIN (Germany) and Flevoland (The Netherlands), respectively. The simulated data set has been generated by merging a first-order Radiative Transfer model with a second-order Small Slope Approximation model. Preliminary results show that the model can reproduce the observed sensitivity of cross-polarized backscatter to mv. However, the modelled cross-polarized backscatter is biased with respect to the observations, suggesting that a single scale soil roughness is not sufficient to reproduce the backscatter level observed over fairly smooth surfaces.