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.

Radiometers play a crucial role in providing accurate geo-physical information, relying heavily on precise calibration for both radiometric accuracy and spectral consistency. Radiometers consistently allocate time and hardware resources to calibration, resources that could otherwise be utilized for environmental sensing. In addition, calibration faces challenges such as frequency dependence and environmental influences, requiring to the need for innovative solutions. In this study, advancements in deep learning (DL) techniques are utilized, using NASA’s Soil Moisture Active Passive (SMAP) satellite data to create a DL-based radiometer calibrator. The use of 2-D spectral features as input in a convolutional neural network shows promising results with high correlation and low error. Notably, ancillary features like internal thermistor temperature prove accurate for estimating antenna temperature. This compensates for changes in receiver noise temperature and short-term gain fluctuations, even when there’s no reference load or noise diode power. The proposed calibration technique, emphasizing reduced reference information, holds significant potential for a higher number of antenna scene observations within a footprint.

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.

Storage of interception water in the canopy (Sc) heavily affects measurements of vegetation optical depth (VOD) from rain, dew and fog, impeding the direct retrieval of tree physiological parameters such as biomass and plant water content. This study presents a time series decomposition of VOD from Global Navigation Satellite System-Transmissometry (GNSS-T) into biomass, plant moisture content (Mg) and Sc. The experiment was conducted at eddy covariance (EC) towers in two temperate forest types in Germany, over the entire vegetation period of 2023 and under fairly wet conditions. Sc-values were 1.5 times (needleleaf) to two times (broadleaf) higher than the average diurnal Mgcycle, allowing partitioning of interception water storage from plant water. Furthermore, we found indications that Scmaxima did not linearly increase with precipitation, suggesting sensitivity of VOD to saturation effects when canopy interception storage reaches a maximum during strong precipitation events. Results indicate the sensitivity of VOD from GNSS-T to canopy wetness. This allows partitioning of canopy water storage from other VOD components and improves the usefulness of VOD as a remote sensing metric for forest canopy water relations. Moreover, it opens pathways to quantify Scand evaporation fluxes independently from EC measurements and field experiments.

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.

A physics-based microwave emission model for layered vegetation (MEMLV) is developed to simulate the vegetation optical depth (VOD) and scattering albedo of coniferous forests from P- to Ka-band (0.4 GHz–37 GHz). This study aims to use physics-based forward modeling to guide and support multifrequency VOD retrieval. The MEMLV consists of three major components: 1) the single-layer discrete scatter model (SL-DSM) that calculates the VOD and single scattering albedo of a single layer; 2) a new Tree Structure Model (TSM) that represents forests by stratified multilayer media; and 3) the Two-Stream microwave emission model (2S-MEM) that combines SL-DSM and TSM to calculate the brightness temperature of forests and their corresponding effective VOD,$\tau _{\text {eff}}$, and effective scattering albedo,$\omega _{\text {eff}}$. Simulation of an exemplary coniferous forest shows that$\tau _{\text {eff}}$increases as frequency increases until reaching saturation at K-band. Meanwhile,$\omega _{\text {eff}}$increases rapidly at low frequencies until reaching a peak at S-band, then decreases until reaching X-band, and finally increases monotonically as frequency increases. Notably,$\omega _{\text {eff}}$is negligible at P-band for most cases. Sensitivity analyses demonstrate the saturation of$\tau _{\text {eff}}$when canopy height is substantial, and the decreasing trend of$\omega _{\text {eff}}$as canopy height or the areal fraction of canopy gap increases. The MEMLV has the potential to improve the parameterization of retrieval algorithms and to enhance the understanding of the retrieved VOD over a wide frequency range.

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.

The success of microwave radiometry depends on how accurately it can measure the natural emission of the Earth without the effects of unwanted signals. The consequence of unwanted signals in radiometers is known as radio frequency interference (RFI). The high intensity of these corrupted signals, along with wider bandwidth and longer duration, may jeopardize the overall success of a mission. These reasons resulted in a need for a robust RFI detection algorithm that will enable the mitigation of the contaminated portions of the measurements. Attributes related to RFI could be very dynamic, making it very difficult to detect with a particular algorithm. To address this issue, deep learning (DL) could be an attractive solution to detect RFI with the help of time-frequency analysis, i.e., spectrograms of the received measurement. This study aims to detect and localize RFI in a particular time-frequency bin of spectrograms with the help of DL to retrieve the non-contaminated portion of the measurements.

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.

Satellite-based remote sensing observations play an important role in retrieving soil moisture over the earth’s surface. NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission has gained attention as it uses the Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R) which can provide higher spatial and temporal resolution. Research is going on to improve retrieval algorithms using CYGNSS observation. In addition to the CYGNSS observations, different land surface products are leveraged to characterize the underlying surface conditions. The most commonly used features are from the Normalized Difference Vegetation Index (NDVI) and the Vegetation Water Content (VWC) from Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. Since the MODIS satellite operates on optical bands that can be greatly affected by cloud coverage, this study proposes using the SENTINEL-1 satellite which offers all-weather, day, and night measurement capability. This study utilized the SENTINEL-1 cross ratio of VH/VV as an alternative to MODIS-based vegetation indices. The results of the study showed that the SENTINEL-1 cross ratio of VH/VV can be significantly useful in CYGNSS-based SM retrieval models by including the effect of vegetation.

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.

This study focuses on modelling the scattering albedo of a vegetation canopy, which can be used in vegetation opacity depth (VOD) retrieval, over a wide frequency range (1-37 GHz). In this study, boreal tree canopy has been used as an example. A discrete scatter model has been implemented to calculate single scattering albedo and vegetation opacity depth (VOD) of a single-layer canopy consisting of a variety type of scatterers. For a more realistic parameterization, a novel tree structure model has been developed to quantify the vertical structure of a forest. For the first time, we combined the discrete scatter model with the multi-layer 2Stream model to calculate the brightness temperature of the tree canopy based on its vertical structure. This research provides a comprehensive forward modelling tool that can be parameterized with different vegetation types (e.g. tree, crops and grass) and parameters (e.g. height, density, soil condition and vertical water content distribution). The model can be used in retrieval algorithm to find effective scattering albedo and VOD over a wide range of frequencies.

Signals of opportunity reflectometry (SoOp-R), the re-utilization of non-cooperative satellite transmissions for communication and navigation, is a promising approach to remote sensing of root-zone soil moisture (RZSM). Satellite transmissions in the frequency ranges of 137–138, 240–270, and 360–380 MHz are of interest due to the increased penetration depth. These can be combined with Global Navigation Satellite System Reflectometry (GNSS-R) in L-band (1575.42 MHz) to estimate the subsurface SM profile. The objective is to define requirements (e.g. frequency and polarization combinations, observation error, and temporal coincidence of multi-source observations) for satellite-based remote sensing of RZSM. Our approach is to use synthetic observations generated from multi-year time series ofin-situSM measurements from seven United States Climate Reference Network (USCRN) sites and dynamic vegetation structure based on a simple scaling method. A multi-frequency/polarimetric retrieval algorithm is developed and applied to these synthetic observations and used to predict retrieval errors for a range of changes in system parameters. We found that the use of both high and low frequencies improves retrieval accuracy by limiting uncertainties from vegetation and surface SM and providing sensitivity to deeper layers. Moreover, the retrieval errors were found to increase linearly with the reflectivity error and inter-frequency time delays. A bivariate model derived from this linear relationship will be useful for developing requirements on reflectivity precision based upon science requirements for SM/VWC retrievals. Although orbits of specific transmitter constellations were used to generate realistic distributions of incidence angle combinations, the method and results could be applied more generally.

Soil Moisture Active Passive (SMAP) is a NASA's earth observing satellite which is used for global scale soil moisture measurement and differentiating frozen/thawed state. It is employed in 1400–1427 MHz protected band which uses L-Band radiometer for the quantification. But increasing number of wireless equipment such as air surveillance radar signals and 5G communication are making it harder to protect the radiometer microwave sensing in this secured spectrum. These technologies are responsible for the Radio Frequency Interference (RFI) in SMAP's passive observation. In this study, a novel deep learning architecture is developed that uses convolutional neural network (CNN) to predict RFI. Our model uses SMAP's level 1A raw antenna counts as well as level 1B quality flags to dynamically label these antenna raw measurements as RFI contaminated and RFI free footprints. This example study shows around 94% accuracy in detecting RFI and such result may recommend a lucrative technique in detecting RFI.

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.

The retrieval of soil moisture (SM) under forest canopy has long been an important goal for low frequency remote sensing. The NASA Soil Moisture Active Passive (SMAP) mission is engaged at three separate experiment sites to improve its SM retrieval algorithm in forested areas. Two of the sites are located in the deciduous forest region in Massachusetts and New York, US and one is located in southern boreal forest zone in Saskatchewan, Canada. Each site has a SM measurement network of 20-25 stations spread out over an area of about 30 km, which covers the SMAP radiometer footprint. In 2022, intensive observations will be carried out at each site which involve deployments of an airborne instrument, which is similar to the SMAP instrument, and intensive manual measurements of SM, surface and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. Here we show some early results using the networks and SMAP measurements to analyze the sensitivity of the SMAP L-band measurements to SM changes in forested area and the impact of the vegetation to the signal. The results suggest an upper limit for vegetation attenuation accounting for surface roughness effect and relate that to the values used in the current SMAP SM products.

SigNals Of Opportunity: P-band Investigation (SNOOPI) will be the first in-space validation of P-band (240–380 MHz) SoOp techniques and a prototype science instrument. These techniques have the potential to enable remote sensing of root-zone soil moisture (RZSM) and snow water equivalent (SWE). SNOOPI technology validation goals will be met by targeting observations within 9 km of the SMAP calibration/validation sites in the continental United States. A second priority is collection of continuous phase data over snow-covered regions. These goals are evaluated under constraints of a limited data budget and mission lifetime, with a launch readiness in August 2022. This presentation will review the instrument science plans aimed at achieving the validation objectives defined for the mission. Mission planning and data processing approaches are described.

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.

NASA Cyclone Global Navigation Satellite System (CYGNSS) mission has gained attention within the land remote sensing community for estimating soil moisture (SM) by using the Global Navigation System Reflectometry (GNSS-R) technique. CYGNSS constellation generates Delay-Doppler Maps (DDM) that contain valuable earth surface information from GNSS reflection measurements. Existing approaches use predefined features from DDMs to estimate SM. This pa-per presents a deep-learning framework to learn optimal features from DDMs for estimating SM. The proposed approach is applied over the Continental United States (CONUS) by leveraging CYGNSS DDM observations with ancillary re-motely sensed geophysical data. The model is trained and evaluated using the Soil Moisture Active Passive (SMAP) mission's enhanced SM products at a$9\text{km}\times 9\text{km}$resolution with vegetation water content less than$5kg/m^{2}$. The mean unbiased root-mean-square difference (ubRMSD) between CYGNSS and SMAP SM retrievals from 2017 to 2020 is 0.0362$m^{3}/m^{3}$with a correlation coefficient of 0.9309 over 5-fold cross-validation.

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.

The retrieval of soil moisture under forest canopy has long been an important goal for low frequency remote sensing. The NASA mission started a dedicated field experiment in May 2019 by deploying two temporary soil moisture networks in northeast US that cover two separate SMAP pixels with variable degree of forest cover. The measurements will be augmented with two intensive observation periods (IOP). The first IOP is planned for April 2022 and the other one for July 2022. The IOPs will entail a deployment of the airborne PALS (Passive Active L-band sensor) instrument, which is similar to the SMAP instrument, and intensive manual measurements of soil moisture and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. The early results have shown that the SMAP measurement signal at L-band is sensitive to soil moisture changes observed on the ground.

SigNals Of Opportunity: P-band Investigation (SNOOPI) will be the first on-orbit demonstration of remote sensing using Signals of Opportunity (SoOp) in P-band (240–380 MHz). P-band is needed to penetrate through dense vegetation and into the root zone. The longer wavelength of P-band also increases the unwrapping interval for phase observations. These observations hold the potential for spaceborne remote sensing of root-zone soil moisture (RZSM) and snow water equivalent (SWE), two variables identified as priorities in the 2017–2027 Decadal Survey for Earth Science and Applications from Space. SNOOPI will provide in-space validation of both the P-band SoOp technique and a science instrument prototype. SNOOPI technology validation goals will be met by targeting observations within 9 km of the SMAP calibration/validation sites in the continental United States. A secondary priority is collection of continuous phase data over snow-covered regions. These goals are evaluated under constraints of a limited data budget and mission lifetime, with a launch readiness in early 2022. Updates on the development of measurement models and mission planning to support SNOOPI are provided. A ground-based station will be deployed to monitor the noncooperative sources, in order to reduce risk due to uncertainty in knowledge of the broadcast power, spectrum shape, and orbital position.

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.

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.

The retrieval of soil moisture under forest canopy has long been an important goal for low frequency remote sensing. The NASA mission started a dedicated field experiment in May 2019 by deploying two temporary soil moisture networks in northeast US that cover two separate SMAP pixels with variable degree of forest cover. The measurements will run through 2021 and they will be augmented with two intensive observation periods (IOP). The first IOP will be conducted in April 2021, and a second one in July 2021. The IOPs will see deployment of the airborne PALS (Passive Active L-band sensor) instrument, which is similar to the SMAP instrument, and intensive manual measurements of soil moisture and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. The early results have shown that the SMAP measurement signal at L-band is sensitive to soil moisture changes observed on the ground.

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.

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.

Modeling L-band backscatter from a corn canopy continues to be a challenge due to the complex dynamics in both plant phenology and the underlying soil. An experiment has been conducted to better understand the relationship between L-band backscatter and canopy parameters such as soil moisture, vegetation water content, dew, and periodic rows. The experiment consists of field measurements that take into account plant phenology and are concurrent with L-band backscatter returns from a corn canopy over a full growing season. The field measurements of the corn plants' constituents highlight modeling complexities, such as an inhomogeneity in the dielectric constant of the stalk and cobs. A simple method to replace the stalk and cob with a homogeneous dielectric constant is validated. Using the field measurements in a scattering model developed at George Washington University (GW), both coherent and incoherent backscatter are computed. The results show coherent effects contributing to enhanced backscatter by up to 2.7 dB for both HH-pol and VV-pol. The coherent model and the detailed measurements, especially, the dielectric constant of the stalks, resulted in good agreement with the measurements. These measurements have an average root mean square difference (RMSD) with the results from the coherent model of around 1 dB for both HH-pol and VV-pol over the entire growing season. The incoherent mode does not perform as well.

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.

SCoBi-Veg stands for Signals of opportunity Coherent Bistatic scattering model for Vegetated terrains. It simulates polarimetric reflectometry of vegetation canopy over a flat ground using a Monte Carlo scheme. The model is aimed at assessing the value of navigation and communication satellite Signals of Opportunity in a range of frequencies from P- to S-bands for remote sensing of a number of geophysical land parameters such as soil moisture and biomass. A fully polarimetric expression for bistatic scattering from a vegetation canopy is first formulated for a general case and is then specialized to the practical case of ground-based/low-altitude platforms with passive receivers overlooking vegetation using the signals transmitted from large distances. Using analytical wave theory in conjunction with distorted Born approximation, the transmit and receive antenna effects (i.e., polarization crosstalk/mismatch, orientation, and altitude) are explicitly accounted for. The forward model developed here enables the understanding of the effect of different geophysical parameters and system configurations on the coherent and incoherent components of the reflected signatures. It can thus help developing robust inverse algorithm for extraction of soil moisture and biomass. The model is applied to P-band signals of geostationary communication satellites to describe polarimetric reflections from tree canopies as observed from down-looking platforms at various altitudes. The relative contributions of diffuse and specular scattering on total reflected power and reflectivity are quantified for various observing scenarios.

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.

Root zone soil moisture (RZSM) is an essential variable in meteorology, hydrology, and agriculture. A penetration depth sufficient to sense RZSM requires frequencies below about 500 MHz (I- and P-band). Active or passive microwave sensing in these bands presents substantial technical challenges due to antenna size, radio frequency interference (RFI) and competition for spectrum. Bistatic radar using Signal of Opportunity (SoOp) (e.g. digital satellite transmitters) offers an alternative approach, through reutilizing powerful signals already occupying bands allocated for communications. Airborne experiments using 240-270 MHz sources were conducted in October 2016, followed by a campaign using 360-380 MHz from a fixed tower location in an agricultural research site during the 2017 growing season. A new campaign that will also include I-band (137 MHz) is presently being installed in advance of the 2018 season. This paper will summarize activities to support the reduction of data from these campaigns and development of soil moisture profile retrievals.

A coherent bistatic vegetation scattering model, based on a Monte Carlo simulation, is being developed to simulate polarimetric bi-static reflectometry at VHF/UHF-bands (240-270 MHz). The model is aimed to assess the value of geostationary satellite signals of opportunity to enable estimation of the Earth's biomass and root-zone soil moisture. An expression for bistatic scattering from a vegetation canopy is derived for the practical case of a ground-based/low altitude platforms with passive receivers overlooking vegetation. Using analytical wave theory in conjunction with distorted Born approximation (DBA), the transmit and receive antennas effects (i.e., polarization, orientation, height, etc.) are explicitly accounted for. Both the coherency nature of the model (joint phase and amplitude information) and the explicit account of system parameters (antenna, altitude, polarization, etc) enable one to perform various beamforming techniques to evaluate realistic deployment configurations. In this paper, several test scenarios will be presented and the results will be evaluated for feasibility for future biomass and root-zone soil moisture application using geostationary communication satellite signals of opportunity at low frequencies.

This paper reports on a recent field campaign that aims to collect time-series multi-frequency microwave data over winter wheat during the entire growth cycle. The data are being collected to characterize vegetation dynamics and to quantify its effects on soil moisture retrievals. A C-band radar was recently incorporated within the existing L-band radar/radiometer system called ComRAD (SMAP's ground based simulator) and an additional VHF receiver is being constructed as well. With C-band's ability to sense vegetation details and VHF's root-zone soil moisture within ComRAD's footprint, we will have an opportunity to test our `discrete scatterer' vegetation models and parameters at various surface conditions. The purpose of this investigation is to determine optical depth and effective scattering albedo of vegetation of a given type (i.e. winter wheat) at various stages of growth that are needed to refine soil moisture retrieval algorithms for the SMAP mission.

The ability to quantify forest fire severity levels is of great importance for fire monitoring, management, and research. Optical observations have been found to be well suited to estimate the impact of fire on the forest canopy. In this letter, C-band VV polarization synthetic aperture radar (SAR) backscatter response to a forest fire was evaluated against burn severity estimated by the Landsat difference Normalized Burn Ratio (dNBR) products. It was observed that the difference between pre- and postfire back-scatters (ratios in power) increased with the dNBR index. Temporal evaluation of backscatter change with respect to various pre- and postfire conditions showed a noticeable sensitivity on the state of a burned stand and its local fire impact levels in wet conditions but, to a lesser degree, in dry conditions. A SAR approach that combines pre- and postfire backscattering could be used to quantify the fire impacts if the effects induced by geometry, weather, and other human activities are accounted for in the analysis.

Once launched in late 2014, NASA's Soil Moisture Active Passive (SMAP) mission will use a combination of a four-channel L-band radiometer and a three-channel L-band radar to provide high resolution global mapping of soil moisture and landscape freeze/thaw state every 2-3 days. These measurements are valuable to improved understanding of the Earth's water, energy, and carbon cycles, and to many applications of societal benefit. In order for soil moisture to be retrieved accurately from SMAP microwave data, prelaunch activities are concentrating on developing improved geophysical retrieval algorithms for each of the SMAP baseline products. The ComRAD truck-based SMAP simulator collected active/passive microwave time series data at the SMAP incident angle of 40° over corn and soybeans during 2012 for use in refining SMAP retrieval algorithms.

This study reports on the utilization of microwave modeling, together with ground truth, and L-band (1.4-GHz) brightness temperatures to investigate the passive microwave characteristics of a conifer forest floor. The microwave data were acquired over a natural Virginia Pine forest in Maryland by a ground-based microwave active/passive instrument system in 2008/2009. Ground measurements of the tree biophysical parameters and forest floor characteristics were obtained during the field campaign. The test site consisted of medium-sized evergreen conifers with an average height of 12 m and average diameters at breast height of 12.6 cm. The site is a typical pine forest site in that there is a surface layer of loose debris/needles and an organic transition layer above the mineral soil. In an effort to characterize and model the impact of the surface litter layer, an experiment was conducted on a day with wet soil conditions, which involved removal of the surface litter layer from one half of the test site while keeping the other half undisturbed. The observations showed detectable decrease in emissivity for both polarizations after the surface litter layer was removed. A first-order radiative transfer model of the forest stands including the multilayer nature of the forest floor in conjunction with the ground truth data are used to compute forest emission. The model calculations reproduced the major features of the experimental data over the entire duration, which included the effects of surface litter and ground moisture content on overall emission. Both theory and experimental results confirm that the litter layer increases the observed canopy brightness temperature and obscure the soil emission.

This paper investigates tree scattering effects at L-band by using a first-order radiative transfer (RT) model and truck-based measurements of brightness temperature over natural conifer stands to assess the performance of the τ - ω (tau-omega) model, a zero-order RT solution, over forest canopies. The tau-omega model accounts for vegetation effects in terms of "effective" vegetation parameters (single-scattering albedo and vegetation opacity) which represent the canopy as a whole. This approach inherently ignores multiple-scattering effects and it thus has a limited validity depending on the level of scattering within the canopy. The fact that the scattering from large forest components such as branches and trunks is significant at L-band requires that retrieved vegetation parameters be evaluated (compared) with their theoretical definitions to provide better understanding of these parameters in the soil moisture (SM) retrievals over moderately to densely vegetated landscapes. In this paper, the tau-omega model is fitted to a first-order RT model with an "effective" albedo assuming that "effective" vegetation optical depth is same as the "theoretical" opacity [1]. The "effective" albedo is found to be less than half of the "theoretical" one, which is generally around 0.5-0.6 for tree canopies at L-band. The "effective" albedo differs from the albedo of a single forest canopy element and becomes a global parameter which depends on all the processes taking place within the canopy including multiple-scattering and ground reflection.

In this study, a first-order radiative transfer (RT) model is developed to more accurately account for vegetation canopy scattering by modifying the basic τ-ω model (the zero-order RT solution). In order to optimally utilize microwave radiometric data in soil moisture (SM) retrievals over vegetated landscapes, a quantitative understanding of the relationship between scattering mechanisms within vegetation canopies and the microwave brightness temperature is desirable. The first-order RT model is used to investigate this relationship and to perform a physical analysis of the scattered and emitted radiation from vegetated terrain. This model is based on an iterative solution (successive orders of scattering) of the RT equations up to the first order. This formulation adds a new scattering term to the τ-ω model. The additional term represents emission by particles (vegetation components) in the vegetation layer and emission by the ground that is scattered once by particles in the layer. The model is tested against 1.4-GHz brightness temperature measurements acquired over deciduous trees by a truck-mounted microwave instrument system called ComRAD in 2007. The model predictions are in good agreement with the data, and they give quantitative understanding for the influence of first-order scattering within the canopy on the brightness temperature. The model results show that the scattering term is significant for trees and modifications are necessary to the τ-ω model when applied to dense vegetation. Numerical simulations also indicate that the scattering term has a negligible dependence on SM and is mainly a function of the incidence angle and polarization of the microwave observation.

This paper discusses the results from a series of field experiments using ground-based L-band microwave active/passive sensors. Three independent approaches are applied to the microwave data to determine vegetation opacity of coniferous trees. First, a zero-order radiative transfer model is fitted to multi-angular microwave emissivity data in a least-square sense to provide “effective” vegetation optical depth. Second, a ratio between radar backscatter measurements with a corner reflector under trees and in an open area is calculated to obtain “measured” tree propagation characteristics. Finally, the “theoretical” propagation constant is determined by forward scattering theorem using detailed measurements of size/angle distributions and dielectric constants of the tree constituents (trunk, branches, and needles). The results indicate that “effective” values underestimate attenuation values compared to both “theoretical” and “measured” values.

A first order scattering model is developed and tested at 1.4 GHz by using microwave brightness temperature data acquired over deciduous tree canopies in Maryland during 2007. Microwave measurements at several incident angles and supporting ground truth data (including size/angle distributions of tree constituents) have been collected over stands of deciduous Paulownia trees under full canopy and leaf-drop conditions. Detailed ground truth data obtained during this experiment have been used to compute the additional radiation due to scattering and emission by the vegetation components. The preliminary model predictions are in good agreement with the data and they give quantitative understanding for the influence of the first order scattering within the canopy on the radiometer brightness temperature. The model results using tree ground truth show that the scattering term is significant for trees and that the tau-omega model needs modification to account for additional scattering contribution. Numerical simulations also indicate that the single scattered radiation increases the canopy brightness temperature considerably. These simulations show that the scattering term has a negligible dependence on soil moisture and is only function of angle and polarization.

During 2007-2009 field experiments have been conducted using the ComRAD microwave truck instrument system with a goal of optimizing microwave soil moisture retrieval algorithms for small to medium deciduous and coniferous trees. A joint effort of NASA / GSFC and George Washington University, ComRAD consists of a quad-polarized 1.25 GHz radar and a dual-polarized 1.4 GHz radiometer sharing the same antenna. In the current study, ComRAD microwave data and ground truth measurements of soil moisture, temperature, soil texture, and vegetation water content and geometry statistics have been used to assess whether the zero-order tau-omega model can be employed successfully to retrieve soil moisture under tree canopies using effective values for tau (the vegetation opacity) and omega (the single scattering albedo). In addition, the tau-omega model has been modified to include a first-order scattering term, which will be discussed in a companion paper.

In the radiometric sensing of soil moisture through a forest canopy, knowledge of canopy attenuation is required. Active sensors have the potential of providing this information since the backscatter signals are more sensitive to forest structure. In this paper, a new radar technique is presented for estimating canopy attenuation. The technique employs details found in a transient solution where the canopy (volume-scattering) and the tree-ground (double-interaction) effects appear at different times in the return signal. The influence that these effects have on the expected time-domain response of a forest stand is characterized through numerical simulations. A coherent forest scattering model, based on a Monte Carlo simulation, is developed to calculate the transient response from distributed scatterers over a rough surface. The forest transient-response model for linear copolarized cases is validated with the microwave deciduous tree data acquired by the Combined Radar/Radiometer (ComRAD) system. The attenuation algorithm is applicable when the forest height is sufficient to separate the components of the radar backscatter transient response. The frequency correlation functions of double-interaction and volume-scattering returns are normalized after being separated in the time domain. This ratio simply provides a physically based system of equations with reduced parameterizations for the forest canopy. Finally, the technique is used with ComRAD L-band stepped-frequency data to evaluate its performance under various physical conditions.

Truck-based measurements of brightness temperature at L- band over small deciduous stands located in Maryland were made in 2006 and 2007. Ground truth data related to forest stands and the ground were also collected. The deciduous trees were modeled by the Distorted Born Approximation (DBA) in conjunction with Peak's principle. The ground was modeled as a half space with surface roughness. The model has been used to investigate the sensitivity of L-band radiometers to soil moisture under forest stands. It was observed that it is possible to see through the forest to sense the underlying soil moisture and to see the seasonal changes.

During 2007 a field experiment was conducted with a goal of optimizing microwave soil moisture retrieval algorithms for small to medium deciduous trees. After initial field checkout in Fall 2006, the ComRAD microwave truck instrument system was deployed to a test site with several stands of deciduous paulownia trees. A joint effort of NASA/GSFC and George Washington University, ComRAD consists of a quad-polarized 1.25 GHz radar and a dual-polarized 1.4 GHz radiometer sharing the same antenna. ComRAD can function as a ground-based instrument simulator for L band space missions such as SMOS, SMAP, and Aquarius. In the current study, ComRAD acquired data from April to November 2007 to monitor the seasonal difference in microwave response to soil moisture under deciduous trees. To conclude the three-year planned field measurement effort, ComRAD will deploy to a natural coniferous pine tree site in 2008.

The NASA/GSFC and George Washington University network analyzer-based multifrequency truck- mounted radar system has recently been upgraded with the addition of a dual-polarized 1.4 GHz total power radiometer. The system, now called ComRAD for Combined Radar/Radiometer, can function as a ground-based instrument simulator for L band space missions such as Hydros, Aquarius, and SMOS. In late summer 2006 ComRAD was deployed to the field to begin a series of coordinated active/passive L band measurements over small stands of deciduous and coniferous trees in order to improve our understanding of the microwave properties of trees and their effect on soil moisture retrieval algorithms. This paper describes the preliminary measurements obtained at the start of a three-year planned field measurement effort.

This paper studies the feasibility of making backscatter measurements from rainfall with the NASA/Microwave Link system at Wallops Island, VA. The research entails the implementation of an FMCW radar at the Link frequencies to enable simultaneous forward and backscatter measurements from rain. The backscatter measurements will be used in conjunction with the forward measurements and the measurements from a ground-based network of disdrometers and rain gauges located under the propagation path to develop new microwave retrieval techniques, and to test established single-frequency and dual-frequency radar retrieval-algorithms relevant to the ongoing TRMM and up coming GPM missions