This study investigates the potential of mobile global navigation satellite system (GNSS) transmissometry (GNSS-T) measurements for estimating vegetation optical depth (VOD) in temperate forests, focusing on the Soil Moisture Active Passive (SMAP) validation experiment in 2022 (SMAPVEX22). Our methodology employed a dual-GNSS receiver setup, with one receiver positioned in open terrain to serve as a reference for direct signals, and another deployed on a mobile unit (helmet-based or robotic system) to spatially sample vegetation across expansive forested regions during SMAPVEX22. We assessed the stability of direct signal measurements over multiple days, demonstrating the reliability of the GNSS-T measurements. We reported the VOD measurement results for various sites across different forest regions during intensive observation periods and evaluated their correlation with respect to in situ vegetation parameters such as basal area, biomass, canopy height, and diameter breast height, finding a strong correlation with the basal area ($R^{2}=0.73$). In addition, with a predictive regression model, we demonstrated a strong dependence of the measured VOD on combination of such forest parameters. An evaluation of the VOD values at different satellite elevation angles highlighted an increasing trend in VOD with the incidence angle. The results showed the potential utility of mobile GNSS-T for generating large-scale VOD observations. Although spatially averaged VOD maps might not be directly comparable to spaceborne observations, combining mobile GNSS-T data with other sensors such as LiDAR can provide a reliable reference for airborne or spaceborne VOD estimates.

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.

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.

In this study, we conducted an empirical investigation on mobile global navigation satellite system (GNSS) transmissometry (GNSS-T) measurements to explore vegetation optical depth (VOD). Our approach involved using a dual-receiver setup, with one receiver located in open terrain to capture direct signals as a reference and another deployed on an unmanned ground vehicle (UGV) to sample vegetation across expansive forested regions. Noteworthy findings reveal the negligible influence of ground multipath effects within these forested terrains, effectively resulting in sampling the forest canopy rather than the ground itself as the receiver moves. The UGV-based method also uncovers VOD fluctuations inside the forest, offering insights into spatial distribution and the influence of satellite position on VOD measurements. The study further examines the effect of tree heterogeneity and seasonal dynamics on the VOD estimates. This empirical study contributes to our understanding of the VOD mapping capabilities of the mobile GNSS-T approach and can potentially lead to nonintrusive quantification of vegetation water content at a landscape scale in forest terrains. These results are significant for advancing our knowledge of forest ecosystem dynamics and sustainable resource management.

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.

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.

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.

Global soil moisture mapping at high spatial and temporal resolution is important for various meteorological, hydrological, and agricultural applications. Recent research shows that the land surface reflection in the forward direction of Global Navigation Satellite System (GNSS) signals at L-band can convey high-resolution land surface information, including surface soil moisture. However, these signals are often affected by complex land surface characteristics and the bistatic nature of the GNSS-Reflectometry (GNSS-R) technique, resulting in a nonlinear relationship between the signals and surface soil moisture. In this work, a machine learning (ML) approach is used to map quasi-global soil moisture using bistatic reflectance observations acquired from the recently launched Cyclone GNSS (CYGNSS) mission. Specifically, several land surface parameters are obtained from remote sensing products and integrated with Soil Moisture Active Passive (SMAP) enhanced soil moisture retrievals to facilitate daily quasi-global CYGNSS soil moisture mapping at 9 km. Based on cross-validation against SMAP data, the ML algorithm is shown to be suitable for retrieving soil moisture from CYGNSS. Median values of unbiased root-mean-square-difference for the quasi-global coverage or regions with vegetation water content less than 5 kg/m(2) are 0.0395 cm3/cm(3 )and 0.0320 cm(3)/cm(3), respectively. Likewise, via independent evaluation against more than 100 in-situ sites, the algorithm is shown to have an unbiased root-mean-square-error of 0.0543 cm(3)/cm(3). CYGNSS-based retrievals contain similar spatial variability as SMAP across different seasons. Moreover, through a robust triple collocation technique, the accuracy of CYGNSS soil moisture is relatively high over moderately vegetated regions with correlations ranging from 0.4 to 0.8. Based on these validation results, we argue that derived CYGNSS soil moisture estimates can supplement current global soil moisture databases and provide more frequent retrievals at 9 km.

P-band Signals of Opportunity (SoOp) is an innovative technique that shows promise for many earth observation applications including remote sensing of root-zone soil moisture (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 methodology, could provide an excellent way to map such geophysical 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 implementation 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 Aircraft 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 background 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.

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.

Agroecosystems compose large economic sectors in dominantly agriculture-based societies. Availability and management of water resources have a huge influence on the sustainability of agroecosystems. Low soil moisture is a major constraint on crop growth due to its vital role in providing crops with sufficient nutrition for root uptake. Current methodologies in precision agriculture are insufficient for direct soil moisture sensing since reflected shortwave solar radiation and infrared long-wave emission can only provide information about surface characteristics. While microwave signals are known to be highly sensitive to water within plants and soil, its implementation from small Unmanned Aircraft Systems (UAS) platforms are at relatively low technological readiness level compared to the use of shortwave/longwave optical sensors. In this paper, we summarize our efforts to apply radio frequency (RF)/microwave remote sensing from UAS for water utilization in agroecosystems. Recently, we developed a comprehensive UAS-based RF testbed, including a microwave radiometer, a scatterometer, wideband ground penetrating radar system as well as Signals of Opportunity (SoOp) receivers. These instruments operate from UAS platforms and use the microwave/radio wave portions of the spectrum. The testbed is accompanied with proximal sensing via autonomous unmanned ground vehicles that acquire insitu soil moisture and vegetation geophysical parameters to provide appropriate datasets for training and testing physics aware, machine learning-based models. In this paper, we introduce the RF sensing framework that can enable non-intrusive high-resolution soil moisture estimates at multiple depths of soil via UAS-based active/passive/SoOp RF instruments.

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 m 3 /m 3 and 0.034 m 3 /m 3 for global coverage and regions with vegetation water content less than 4 kg/m 2 , 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 users 1 1 https://www.gri.msstate.edu/research/ssm/.

This paper presents a machine learning (ML) framework to derive a quasi-global soil moisture (SM) product by direct use of the Cyclone Global Navigation Satellite System (CYGNSS)’s high spatio-temporal resolution observations over the tropics (within ±38° latitudes) at L-band. The learning model is trained by using in-situ SM data from the International Soil Moisture Network (ISMN) sites and various space-borne ancillary data. The approach produces daily SM retrievals that are gridded to 3 km and 9 km within the CYGNSS spatial coverage. The performance of the model is independently evaluated at various temporal scales (daily, 3-day, weekly, and monthly) against Soil Moisture Active Passive (SMAP) mission’s enhanced SM products at a resolution of 9 km × 9 km. The mean unbiased root-mean-square difference (ubRMSD) between concurrent (same calendar day) CYGNSS and SMAP SM retrievals for about three years (from 2017 to 2019) is 0.044 cm3 cm−3 with a correlation coefficient of 0.66 over SMAP recommended grids. The performance gradually improves with temporal averaging and degrades over regions regularly flagged by SMAP such as dense forest, high topography, and coastlines. Furthermore, CYGNSS and SMAP retrievals are evaluated against 170 ISMN in-situ observations that result in mean unbiased root-mean-square errors (ubRMSE) of 0.055 cm3 cm−3 and 0.054 cm3 cm−3, respectively, and a higher correlation coefficient with CYGNSS retrievals. It is important to note that the proposed approach is trained over limited in-situ observations and is independent of SMAP observations in its training. The retrieval performance indicates current applicability and future growth potential of GNSS-R-based, directly measured spaceborne SM products that can provide improved spatio-temporal resolution than currently available datasets.

Signals of opportunity (SoOp) reflectometry (SoOp-R) is a maturing field for geophysical remote sensing as evidenced by the growing number of airborne and spaceborne experiments. As this approach receives more attention, it is worth analyzing SoOp-R's capabilities to retrieve subsurface soil moisture (SM) by leveraging communication and navigation satellite transmitters. In this research, the Cramer-Rao lower bound (CRLB) is used to identify the effects of variable SoOp-R parameters on the best achievable estimation error for root-zone soil moisture (RZSM). This study investigates the use of multiple frequency, polarization, and incidence angle measurement configurations on a two-layered dielectric profile. The results also detail the effects of variable SM conditions on the capability of SoOp-R systems to predict subsurface SM. The most prevalent observation is the importance of using at least two frequencies to limit uncertainties from subsurface SM estimates. If at least two frequencies are used, the CRLB of a profile is retrievable within the root-zone depending on the surface SM content as well as the number of independent measurements of the profile. For a depth of 30 cm, it is observed that a CRLB corresponding to 4% RZSM estimation accuracy is achievable with as few as two dual-frequency-based SoOp-R measurements. For this depth, increasing number of measurements provided by polarization and incidence angle allow for sensing of increasingly wet SM profile structures. This study, overall, details a methodology by which SoOp-R receiver system can be designed to achieve a desired CRLB using a tradeoff study between the available measurements and SM profile.

A multilayer module is incorporated into the Signals of Opportunity (SoOp) Coherent Bistatic Scattering model (SCoBi) for determining the reflections and propagation of electric fields within a series of multilayer dielectric slabs. This module can be used in conjunction with other SCoBi components to simulate complex, bistatic simulation schemes that include features such as surface roughness, vegetation, antenna effects, and multilayer soil moisture interactions on reflected signals. This paper introduces the physics underlying the multilayer module and utilizes it to perform a simulation study of the response of SoOp-R measurements with respect to subsurface soil moisture parameters. For a frequency range of 100–2400 MHz, it is seen that the SoOp-R response to a single dielectric slab is mostly frequency insensitive; however, the SoOp-R response to multilayer dielectric slabs will vary between frequencies. The relationship between SoOp-R reflectivity and the contributing depth is visualized, and the results show that SoOp-R measurements can display sensitivity to soil moisture below the penetration depth. By simulation of simple soil moisture profiles with different wetting and drying gradients, the dielectric contrast between layers is shown to be the greatest contributing factor to subsurface soil moisture sensitivity. Overall, it is observed that different frequencies can sense different areas of a soil moisture profile, and this behavior can enable subsurface soil moisture data products from SoOp-R observations.