Signals of opportunity (SoOp) for transmissometry is a practical method for measuring the effective impact of vegetation canopies using ubiquitously available radio frequencies with potential benefit to snow and boreal forest remote sensing as well as precision agriculture. Physical modeling of the microwave scattering within forest scenes is necessary for correctly interpreting changes in measured transmissivity. The SoOp Coherent Bistatic (SCoBi) model and simulator is updated to include explicit tree architecture information. A preliminary comparison between uniformly distributed, randomly oriented trees and fixed tree architectures is performed over a sample forest. Simulations indicate that explicit architecture information can have a strong influence on the received signal. The updated SCoBi model will be used to simulate various forest structures to understand the impact of the canopy architecture in tranmissivity estimates.

This study presents an investigation that involves comparing L-band Vegetation Optical Depth (L-VOD) obtained from Global Navigation Satellite System Transmissometry (GNSS-T) against metrics derived from airborne Light Detection and Ranging (LiDAR) data. Both data were collected during the SMAPVEX 2022 campaign in the temperate forests of the northeastern United States, covering Massachusetts and New York. From the LiDAR data, various parameters related to tree characteristics can be extracted, such as tree height, crown diameter and shape, vegetation area density, and woody volume. In this investigation, we initially computed LiDAR point cloud density as a proxy measure of vegetation structure for a given receiver position and the satellite's field of view, comparing it with L-VOD estimates at different positions within the studied forest. Our primary findings reveal a notable correlation between point density and L-VOD, despite the inherent errors in L-VOD estimates and the fact that the number of points may not be the optimal descriptor of the canopy architecture. In this paper, we will explore aforementioned LiDAR derived metrics against the GNSS-T L-VOD estimates to provide insights into the impact of canopy architecture on the L-VOD estimates, determining the specific vegetation layers that influence the measurement.

A nested facet method (NFM) of the Kirchhoff approximation (KA) is developed for land applications for signals of opportunity (SoOp) applications. This NFM follows the form of a tree data structure wherein a series of child facets are superimposed over the parent planar facet. The electric field of a parent facet is taken as the coherent sum of each child facet. The method is found to be flexible and efficient for use on consumer-grade computers, offering significant performance boosts compared to direct integration methods and is easily parallelizable. This solution to the Stratton-Chu integral can provide flexible scattering solutions in areas where analytical or statistics-based solutions may struggle to find an appropriate parameterization of the surface.

Global Navigation Satellite Systems (GNSS) Reflectometry (GNSS-R) is seeing more active interest in remote sensing for land applications. As more Signals of Opportunity (SoOp) missions emerge and as GNSS-R data is paired with machine learning (ML), it is important for SoOp researchers to clearly express physical interpretations of Delay Doppler Map (DDM) simulations over land structures to develop robust ML algorithms and mission concepts. To help illustrate the variability of SoOp measurements from space, the spaceborne variant of the SoOp Coherent Bistatic model and simulator (SCoBi) is used to display patterns and variability of DDMs over simple land structures.

Two intense observation periods (IOPs) are included in the Soil Moisture Active Passive (SMAP) Validation Experiment (SMAPVEX) 2022 in the temperate forests of the northeastern US (Massachusetts and New York). Because a sizable portion of the U.S. and the world have non-uniform forest cover at the SMAP resolution scale, the IOPs aim to test the SMAP retrieval in both fully wooded and partially forested instances. Destructive sampling is often used to assess the opacity of the forest canopy, which is intrusive and labor-intensive in forest characterization. To measure vegetative opacity directly utilizing widely accessible Global Navigation Satellite System (GNSS) signals, we have instead developed a GNSS Transmissometry (GNSS-T) approach from a mobile platform (such as a helmet wearable and quadruped ground robot). The created system gathers two simultaneous GNSS readings, one in the unobstructed open sky area and the other under the forest canopy. The difference between the two can yield information on forest transmissivity (water content). That can be used to test the SMAP retrieval methods over wooded areas. In this study, we have processed SMAPVEX’s IOP-1 GNSS-T data at selected sites, including GPS, GLONASS, Beidou and Galileo satellites, and generated forest transmissivity and vegetation optical depth (VOD) heatmaps averaged to different angular bins at both SMAPVEX’22 locations.

This paper explores the retrieval of snow water equivalent (SWE) through the use of machine learning techniques and active radar data collected over the 2020 SnowEx campaign. The retrieval makes use of active radar measurements provided by NASA's SWESARR instrument for direct sensing of snowpack sensitivity to SWE. The example results show that an RMSE of 1.93 cm can be obtained through a combined use of SAR data with sufficient ancillary data. Such results may indicate successful SWE estimation by means of pairing spaceborne SAR measurements with sufficient auxiliary information.

Mapping forest transmissivity on a large scale is needed for soil moisture and vegetation optical depth (VOD) calibration validation efforts led by passive microwave remote sensing missions. To this end, we recently introduced a Global Navigation Satellite System (GNSS) Transmissometry (GNSS-T) technique from a mobile platform to measure vegetation opacity directly using readily available GNSS signals, which assumes negligible ground multipath. In order to better assess the limitation of such an approach, our previously developed Signals of Opportunity (SoOp) Coherent Bistatic Scattering model (SCoBi) is modified to simulate first-order scattering contributions when the receiver is located above ground but below canopy. This paper describes the advancement of SCoBi from the case of a passive receiver overlooking vegetation to below-canopy upward receivers. This extension allows for fully polarimetric, complex simulations through evaluation of the coherent superposition of electric fields interacting within the canopy and with the forest floor. The simulation results shed light on errors associated with measurement configurations and site characteristics on the VOD measurements.

P-band Signals of Opportunity (SoOp) is an innovative technique that shows promise for many earth observation ap-plications including remote sensing of root-zone soil mois-ture (RZSM), above-ground biomass (AGB), and snow water equivalent (SWE). The combination of long wavelength and bistatic configuration, which is unique to P-band SoOp meth-odology, could provide an excellent way to map such geo-physical variables globally. To leverage such potential, the development of ground-based testbeds are needed to test and refine both algorithms and forward models. However, its im-plementation from small Unmanned Aircraft Systems (UAS) platforms is at a relatively low technological readiness level. In this paper, we summarize our efforts on implementing a P-band SoOp receiver from a multi-copter Unmanned Air-craft Systems (UAS) platform. The receiver has gone through several iterations in the lab and field. In this paper, we will provide experimental results as well as the pertinent back-ground and theoretical derivations supporting the design and implementation of the UAS-based instrument.

Following the launch of multiple global navigation satellite system (GNSS) reflectometry (GNSS-R) missions, the Signals of Opportunity (SoOp) method has proven to be a powerful tool for geophysical parameter retrieval for land applications such as soil moisture. Having demonstrated the feasibility of the SoOp techniques at P- and S-band, the development of SoOp measurements beyond the GNSS frequency regime is highly anticipated. The SoOp Coherent Bistatic (SCoBi) model and simulator, developed in 2017 and open-sourced in 2018, has been made available to provide multifrequency, fully polarimetric SoOp simulations for ground-based applications through the joint use of analytical wave theory and distorted Borne approximation to evaluate land contributions from multilayer dielectric profiles composed of soil moisture, vegetation, and surface roughness effects. This paper describes the advancement of SCoBi from a ground-and airborne-based model to a spaceborne model. This extension allows for fully polarimetric, complex delay-Doppler map (DDM) simulations through evaluation of the coherent superposition of electric fields emerging from a grid of oriented facets. The model generates a grid of facets by determining the geometry of contributing elements from digital elevation models, with each element providing its contribution under a flat-earth assumption. This module will enable the analysis of fully polarimetric scattering from frequencies available across the ultra-high frequency (UHF) regime.

Global soil moisture mapping at high spatial and temporal resolution is important for its related meteorological, hydrological, and agricultural applications. Using the L-band signals, several satellite-based microwave sensors are providing global soil moisture retrievals at a spatial resolution of about 40 km and a revisit time of 2–3 days. Recent research shows that the forward scattered Global Navigation Satellite System (GNSS) signals at L-band can convey high-resolution information of land surface conditions, including surface soil moisture. However, these signals are often affected by complex land surface characteristics and the bistatic nature of GNSS-R technique, leading to nonlinear relation between the signals and surface soil moisture. In this work, a machine learning (ML) approach is used to map quasi-global soil moisture from Cyclone GNSS (CYGNSS) observables. Specifically, several land surface parameters are obtained and used in combination with CYGNSS data in the ML model by using the Soil Moisture Active Passive (SMAP) data as reference. A good performance of the ML method is achieved with median ubRMSDs of 0.0426 m3/m3and 0.034 m3/m3for global coverage and regions with vegetation water content less than 4 kg/m2, respectively. Moreover, an independent evaluation of the CYGNSS data against in-situ measurements suggests that the overall accuracy of CYGNSS soil moisture is comparable with SMAP data. With an increased sampling frequency of CYGNSS, the generated products can supplement current global soil moisture database. In addition, the ML-based CYGNSS products are published via a website portal for future users11https://www.gri.msstate.edu/research/ssm/.

High Spatio-temporal soil moisture is essential for many meteorological, hydrological, and agricultural applications and studies. Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) provides a promising opportunity for high-resolution soil moisture retrievals. NASA's Cyclone Global Navigation Satellite System is a preeminent GNSS-R application that offers high spatial and temporal resolution observations from Earth's surface. However, the quasi-random sampling of land surface by the CYGNSS constellation circumvents obtaining fully observed daily soil moisture predictions. This work investigates multidimensional spatial and temporal interpolation of the CYGNSS soil moisture estimates using methods such as linear, nearest, and natural interpolation. The results indicate that the interpolation error (RMSE) was 0.032$m^{3}/m^{3}$, 0.038$m^{3}/m^{3}$, and 0.030$m^{3}/m^{3}$for linear, nearest, and natural interpolation, respectively. The results also show that interpolated and observed CYGNSS SM values have the similar performance metrics when validated with the SMAP 9-km gridded SM product.

Frequencies in the Very-High (VHF) to Ultra-High (UHF) range show potential for the remote sensing of soil moisture within the root-zone. This paper analyzes the Cramer-Rao Lower Bound (CRLB) for estimating soil moisture parameters using the SoOp Coherent Bistatic Scattering Model (SCoBi). CRLB defines the best achievable estimation variance for any unbiased estimator, hence allowing to identify optimal measurement configurations for soil moisture estimation. For different frequency, polarization and direction values SCoBi can model specular scattering surface reflection coefficients. Initial CRLB analysis are carried out using different combinations of a maximum of 120 measurements. The results indicate that surface soil moisture can reliably be measured while soil moisture values at 40 cm depth can be estimated within ± 4% accuracy if the surface and subsurface soil moisture is below 32.5% VSM. Its also shown that dual frequency measurements of soil moisture can greatly reduce the CRLB compared to using a single frequency.

High spatio-temporal soil moisture is critical for the understanding of land-atmosphere interactions and affects meteorological, hydrological and agricultural applications. Currently, most satellite-based microwave sensors provide global soil moisture retrievals at ~40 km spatial and 2-3 days temporal resolution. Using the forward scattered L-band Global Navigation Satellite System (GNSS) signals, surface soil moisture can be estimated at higher spatial and temporal scales. However, due to the complex land surface characteristics and bistatic nature of GNSS signals, the retrieval algorithms for deriving surface soil moisture from GNSS signals are still under development. In this work, a machine learning (ML) algorithm has been used for estimating soil moisture from Cyclone Global Navigation Satellite System (CYGNSS) measurements. The in-situ data from International Soil Moisture Network and global soil moisture data from Soil Moisture Active Passive (SMAP) have been deployed as the reference data in the ML algorithm. In particular, various remote sensing-based land surface parameters have been included and facilitate a robust soil moisture retrieving process. The proposed approach has achieved an ubRMSD of 0.0523 m3/m3between the retrieved soil moisture from CYGNSS and in-situ measurements in a 5-fold cross-validation over 129 ground-based soil moisture sites, suggesting a satisfactory performance of the ML-based approach. Moreover, the global median ubRMSD of 0.042 m3/m3is obtained between SMAP and CYGNSS ML predictions. Surface soil moisture can be retrieved at ~9 km spatial and 1-2 days temporal scales through the presented framework.

The potentiality of Signals of Opportunity (SoOp) over land can be investigated by advanced forward and inverse modeling and simulation tools to provide viable measurements for Earth science data products over land. This research investigates various inversion techniques that can leverage SoOp sources for land-based Earth science measurements by applying them to simulated soil moisture profiles over bare- and vegetated- soils. Forward modeling is accomplished using Mississippi State University’s Signals of Opportunity Coherent Bistatic Scattering Model (SCoBi), a new, open-source electromagnetic scattering model that can determine coherent received signals at a receiving antenna through application of Maxwell’s equations at discrete scattering soil layer boundaries in conjunction with the distorted Born approximation to describe vegetation propagation and scattering. The results of the forward model are used in various inverse methods to investigate the potentiality of using multiple SoOp sources for Soil Moisture Profile (SMP) retrieval. Multiple SMPs are analyzed by SCoBi to determine the sensitivity of soil moisture variation to SoOp transmitter characteristics such as polarization and elevation angle. Simultaneously, SoOp measurements conducted at Purdue University’s Agronomy Center for Research and Education (ACRE) are used to determine the impact that changes in both physical SMPs and vegetation canopies have on the scattered SoOp. The characteristics of the scattering surfaces, vegetation, and SMPs at the ACRE facility are modeled within SCoBi to observe patterns and relationships captured in reflectivity measurements that are caused by vegetation growth periods as well as rain and drought effects manifested by changing SMPs.

NASA’s Cyclone Global Navigation Satellite System (CYGNSS) receives the forward scattered L-band GNSS signals between ±37° latitudes. The received signals over land are previously shown to be highly sensitive to surface soil moisture (SM). Assuming coherent reflections over land, the CYGNSS bistatic radars can provide a spatial resolution of around 7 × 0.5 km and a revisit time of 1-2 days. SM retrieval at such a high spatio-temporal resolution could help advance hydrometeorology and agriculture applications. This study examines case scenarios for determining the relations of CYGNSS-deliverables and available SM data as well as specifying the requirements for CYGNSS-derived SM retrieval. Preliminary results demonstrate moderate correlation between CYGNSS measurements and SMAP SM. However, the results also show that accurate derivation of high spatio-temporal SM products from CYGNSS measurements is a challenging problem due to the heterogeneous land covers, varying topography, and surface roughness.

Conventional microwave remote sensing has been performed with mono-static active radars for decades. However, SoOp (Signal of Opportunity) has been gaining a great interest among researchers in recent years because it removes costs for a transmitter antenna by reception of existing direct and/or reflected signals. Although SoOp has produced encouraging results for the remote sensing of ocean surface roughness and wind vectors, the concept is still emerging and requires exhaustive analysis in order to be applied on land observations such as retrieval of biomass, soil moisture, surface topography, and snow depth. Bistatic analytical models and simulators can fulfill the need for analysis. They create environments that enable computation, validation, and examination of methods for future missions, which are difficult to perform in the real world experiments. Being motivated by this phenomenon, we have developed a generalized coherent forward model of bistatic scattering from vegetation cover for SoOp applications with the name SCoBi-Veg (SoOp Coherent Bistatic Scattering Model for Vegetated Terrains), which is currently under review by IEEE Transactions on Geoscience and Remote Sensing [1] [2]. We have also developed a simulator that employs SCoBi-Veg model, for the sake of creating a medium for a community of researchers, scientists, and users with little-or-no electromagnetic background to study new methods with varying configurations, to analyze such methods, to determine the optimal cases for specific missions, to generate, visualize, and analyze test data. In fact, SCoBi is a framework that implements only the simulator for vegetated terrains (SCoBi-Veg) for now. The simulator is being open-sourced in the Matlab/Octave development environment. It takes many inputs for vegetation, antennas, ground, and preferences. It generates received field and power, reflectivity, and/or NBRCS (normalized bistatic radar cross-section) for direct, coherent (specular), and incoherent (diffuse) contributions. This paper describes the ongoing open sourcing and the capabilities of the SCoBi simulator.