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.

Unmanned Aircraft Systems (UAS) play an essential role in providing high-resolution information for precision agriculture (PA). Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R) from a UAS can provide higher spatial and temporal resolution for soil moisture (SM) retrievals. This study summarizes and analyzes of a three-year-long field campaign including comprehensive GNSS-R and ancillary data from crop fields. The field data collections were conducted on 210 by 110 m (2.31 ha) corn and cotton fields over 3 years from 2021 to 2023. The results indicate that high-resolution SM measurement can be achieved with a low-cost GNSS-R system onboard a mid-size UAS platform for use in PA applications.

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.

Passive remote sensing through microwave radiometry has been utilized in Earth observation by estimating several geophysical parameters. Because of the low noise floor associated with the instrument (i.e., radiometer), the received geophysical emission is sampled in a protected band dedicated to remote sensing. This protected L-band occupying 1400-1427 MHz is also exciting and ideal for science because of lower attenuation from the atmosphere. This reason has also made this microwave region ideal for next-generation (xG) wireless communication. 5G cellular systems support two frequency ranges FR1 (0.45 GHz–6 GHz) and FR2 (24.45 GHz-52.6 GHz). Although operating bands are prohibited from conducting any up-link or down-link operations in the protected portion of the L-band, out-of-band (OOB) emissions can still have a significant impact on passive sensors because of the high sensitivity requirements related to science. This study will demonstrate a unique physical testbed that has the capability to observe in-band and OOB emissions in a protected anechoic chamber. Flexibility on transmitted waveforms and the potential to analyze raw measurements (IQ samples) of radiometers will help in designing onboard radio frequency interference (RFI) processing along with the coexistence of communication and passive sensing technologies.

Passive microwave remote sensing plays an essential role in providing valuable information about the Earth’s surface, particularly for agriculture, water management, forestry, and other environmental fields. One of the key requirements for precision agricultural applications is the availability of field-scale high-resolution remote sensing data products. With the recent development of reliable unmanned aircraft systems (UAS), airborne deployment of remote sensing sensors has become more widespread to provide such products. With this in mind, we developed a UAS-based dual H-pol (horizontal) and V-pol (vertical) polarized radiometer operating in L-band (1400-1427 MHz). The custom dual-polarized antenna acquires surface emission response through a software-defined radio (SDR). This SDR-based system provides full control over the data acquisition parameters such as bandwidth, sampling frequency, and data size. Radio frequency interference (RFI) poses a significant challenge in radiometric measurements, requiring post-processing of the full-band radiometer data to identify and eliminate RFI-contaminated measurements, thus ensuring accurate Earth emission readings.. In this paper, we implemented near-real-time RFI detection onboard during the flight to accelerate the post-processing. The altitude and the speed of the UAS can be varied to achieve desired ground resolution for the measurement. This paper presents the full custom design and development of a lightweight SDR-based UAS-borne radiometer for precision agriculture. Additionally, we introduce the concept of an agile radiometer implemented from a small UAS that can serve as a testbed for both current and future spaceborne missions.

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.

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.

This paper presents a practical technique to estimate surface reflectivity using two sets of Global navigation satellite sys-tem (GNSS) measurements. A down-facing smartphone (at-tached to a ground plate) on a Unmanned Aircraft Systems (UAS) collects reflected signals while another identical phone is located on the ground that provides reference data in an open area. Both drone and ground units are rotated with a constant speed to mitigate radiation pattern irregularities of smartphone's in-built GNSS antenna. Reflectivity at vari-ous elevation angles and locations are obtained by taking the logarithmic difference between measurements (GNSS carrier-to-noise density ratio C / No) on the UAS and in the open area. The estimated reflectivity can be utilized for quantification of surface soil moisture and vegetation water content that is needed for various precision agriculture and spaceborne product validation efforts.

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.

This paper presents a practical technique to estimate canopy transmissivity at multiple locations using two sets of Global Positioning System (GPS) measurements. One receiver is mounted on unmanned ground vehicle (UGV) that traverses on forest floor while another identical receiver is located on a tripod that provides a reference data in an open area. One-way transmissivity at various elevation angles and locations are obtained by taking the logarithmic difference between measurements (GPS carrier-to-noise density ratio C/N0) under canopy and open area under the assumption of negligible multipath. We carried out several experiments in early 2020 to test the multipath assumption where we ignore the multiple scattering involving ground reflections under forest canopy. The preliminary results indicate indeed this is the case, leading to the fact that the UGV-based GPS receiver collects mainly attenuated and scattered signal withing the vegetation. The approach can be utilized for quantification of vegetation water content that is needed for large scale spaceborne soil moisture calibration validation efforts.

Raw Global Navigation Satellites Systems (GNSS) data have been directly accessible from mass-market devices running the Android Nougat (or newer) operating system since late 2016. The availability of GNSS raw data made possible to investigate feasibility of using in-built GNSS chipsets within smartphone devices as passive radar receivers for the purpose of land remote sensing. In this study, we integrate smart-phones into small Unmanned Aircraft Systems (UAS) to collect reflected GNSS raw data for the purpose of mapping top 5-cm soil moisture. The reflected GNSS signals collected by the smartphones show high correlation with spatial features on the ground such as ponds, crops, and small creeks. To determine the quality of smartphone in-built antenna and chipset, we conducted several experiments. The results show that (1) the radiation pattern of smartphone's GNSS antenna are observed to be highly irregular, but time-invariant, and (2) internal GNSS chip produces observables of sufficient quality when the GNSS smartphone reflected signals are compared with a high quality custom-built dual channel receiver. This paper summarizes the experimental findings and challenges that need to be resolved in order to use the GNSS-Reflectometry (GNSS-R) technique via ubiquitous smartphones from small UASs.