Jeffrey D. Ouellette

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.

An analysis of a Level-2 (L2) soil moisture record extending from May 1st, 2021 to January 1st, 2024 derived from Spire, Inc.'s Global Navigation Satellite System Reflectometry (GNSS-R) observatories is presented. The product's sensitivity to large scale soil moisture variability is demonstrated using an example of a 2022 flood in Pakistan. Product consistency among the constellation's multiple satellites is also investigated; no clear evidence of inter-satellite biases is observed. Further comparisons are performed with soil moisture datasets from the SMAP (Soil Moisture Active Passive) and CYGNSS (Cyclone Global Navigation Satellite System) missions, from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5), and from in situ ISMN (International Soil Moisture Network) sites. Although an overall product correlation with SMAP soil moistures of approximately 85% is determined, per-pixel correlations vary significantly and per-pixel Root Mean Square Errors (RMSE) can range from 0.02-0.09 (cm $^{3}$ /cm $^{3}$ ) depending on land class. The importance of applying the product's quality flags is also demonstrated. The influence of other calibration effects and inland water body contamination on these results is also discussed.

This works reports updates to a previously-reported time-series ratio (TSR) algorithm for surface soil moisture retrieval from Cyclone Global Navigation Satellite System (CYGNSS) reflectivity data. Both the original and updated algorithms are based on ratios between consecutive, anglecorrected CYGNSS normalized radar cross section or reflectivity measurements, but the new algorithm implements a de-noising approach which involves the application of a fractional exponent term to these ratios. Additionally, modifications have been made to several quality control parameters and to the bounds used in the algorithm's bounded linear least-squares solver. Error statistics for several variations of the TSR algorithm are reported using cross-validation with soil moisture retrievals from the Soil Moisture Active Passive sensor. The final results show that the updated algorithm produces similar retrieval errors to the previously reported approach, but increases the data retained in the retrieval so that coverage is improved.

Ocean wave periods are estimated using measurements of S-band signals-of-opportunity reflections from the sea surface. The measurements were collected on an oil platform and show a time evolving Doppler centroid frequency and spectral width due to the vertical motion of ocean waves within the glistening zone observed. Previous results for estimating the significant wave height are also re-examined using the Doppler spectral width. For the one month dataset considered, the period of oscillations in the Doppler centroid frequency are found highly correlated ( $\mathbf {R \approx 0.78}$ and $\mathbf {Bias \approx 1.02 (s)}$ ) to ocean wave period information from a nearby buoy.

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.

Methods for improving efficiency in computing bistatic scattering from ocean-like surfaces are presented. The methods focus on approximations for the Kirchhoff integral required in evaluating the predictions of multiple theories of bistatic scattering from the ocean surface including the physical optics (PO) and small slope approximation (SSA) approaches. A representation of the integral in terms of the probability density function of a symmetric alpha stable random variable is examined for near-specular scattering as has been proposed previously in the literature. Methods for evaluating this form are presented for azimuthally varying surfaces having a general spectrum model as well as a fully analytical expression for surfaces described by an isotropic Pierson-Moskowitz spectrum. The resulting forms are also compared and contrasted with the standard geometrical optics (GO) theory of surface scattering to provide insight into the cutoff wavenumber used in the standard GO method. Additional two-scale and small roughness approximations to the integral are also investigated for the prediction of scattered fields outside the near-specular region.

This paper extends the application of an existing change-detection-based, time-series soil moisture retrieval algorithm to non-concurrent active and passive measurements from WindSat/AMSR2 and the Soil Moisture Active Passive radar, which was active from late April until mid-July on 2015. A time-series of L-band radar backscatter observations was used to populate an under-determined matrix equation whose optimal solution was derived via a bounded linear least squares estimator, and whose bounds were derived from a time-series of radiometer-derived soil moisture estimates (taken by either WindSat or AMSR2). Surface soil moisture estimates are compared with in-situ measurement probes, which were treated as ground truth. Error statistics and time-series results for the validation sites are presented here and conclusions derived therefrom. The overall RMSE and un-biased RMSE for the retrieval algorithm, taken across all reference pixels considered in the study, were 0.070 $\mathbf{m}^{3}/\mathbf{m}^{3}$ and 0.067 $\mathbf{m}^{3}/\mathbf{m}^{3}$ respectively, when using WindSat to constrain the algorithm. When using AMSR2 to constrain the algorithm, the RMSE and un-biased RMSE were 0.093 $\mathbf{m}^{3}/\mathbf{m}^{3}$ and 0.090 $\mathbf{m}^{3}/\mathbf{m}^{3}$ respectively.

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.

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).