Recent advancements in the uncrewed aerial vehicles (UAVs) as remote system platforms have enabled low-cost and easy-to-use options for various applications. An example of such a remote sensing system is a software-defined radar (SDRadar) mounted on a UAV. There is a need to accurately calibrate the radar system with low-cost and field-ready schemes to utilize the full potential of the radar system. Our system is an SDRadar mounted on a small UAV. The waveform of the radar has a 1-GHz bandwidth with a center frequency set to 750 MHz. The calibration employs a 59-cm-diameter disk elevated above the ground as an external passive target. Our results demonstrate a calibration accuracy of approximately 1 dB across most of the transmitted band, in both laboratory and field conditions. However, calibration challenges persist in the lowest parts of the frequency range, which are affected by the high side lobes and grating lobes in the antenna pattern. This calibration process is adaptable to various bandwidths and center frequencies.

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.

Remote sensing using GNSS-reflectometry (GNSS-R) delay-Doppler maps (delay-Doppler maps) over land is an emerging field. Understanding the sensitivity of delay-Doppler maps to geophysical variables of interest, such as soil moisture and vegetation, is needed to understand the performance limitation of their retrievals. This work presents an analytical sensitivity analysis of GNSS-R delay-Doppler maps to land surface variables using the IGOT model and the Cyclone GNSS (CYGNSS) data over three areas with various topographical terrains. The IGOT model is an electromagnetic DDM model for land applications that is valid for topographical terrains with bare-to-intermediate vegetation cover. It divides the surface roughness into three scales. The large scale is governed by a digital elevation model (a DEM), whereas the other two are stochastic. The vegetation effect is modeled as an attenuation layer. Sensitivity to soil moisture, multiple scales of roughness, and vegetation are among the studied land surface variables. Furthermore, numerical results are obtained using realistic scenarios derived from CYGNSS observations. This work finds that the DDM peak reflectivity sensitivity to the large-scale surface roughness parameter and incidence angles varies depending on the geometry of the transmitter, receiver, and specular point. Moreover, the DDM peak reflectivity is inversely proportional to vegetation (due to attenuation effects) and small-scale surface roughness, which is consistent with the literature. Additionally, CYGNSS data over the study are analyzed to study the effect of incidence and azimuth angles. The analysis confirms that the behavior of peak reflectivity varies depending on both angles.

Sparse and unevenly distributed soil samples across the northern high-latitude region greatly limit the accuracy of soil organic carbon (SOC) mapping. Therefore, substantial discrepancies exist in SOC estimation in this region, which makes it challenging to characterize the SOC spatial variability and its potential responses to climate change and permafrost degradation. To address these challenges, we enhanced a machine learning model for SOC mapping by developing a data resampling approach that accounts for soil samples spatial heterogeneity, using Alaska as a case study. Specifically, in-situ SOC data were resampled with weights proportional to the variance within a 15-km radius, and then fitted using a random forest (RF) regression model. Multiple features, including temporal composites of Sentinel-1 C-band radar backscatter, vegetation indices from Sentinel-2, climate indices including thawing and freezing indices from moderate resolution imaging spectroradiometer (MODIS), and ancillary topography data, were selected as inputs for the RF model after recursive feature elimination to generate top-layer (0-30 cm) SOC content maps in Alaska at a 250-m resolution. The enhanced RF model with data resampling showed improved accuracy compared to the original RF model, with the coefficient of determination (R2) increased from 0.36 to 0.56 and the root mean square error (RMSE) decreased from 16% to 11% for the surface (0-10 cm) SOC content, and slightly improved accuracy for the deeper (10-30 cm) SOC content. Additionally, the enhanced RF model also better captured local-scale variability of SOC than the original RF model and SoilGrids 2.0 dataset, with high-resolution remote sensing indices playing a major role. The improved SOC content estimates were then used to estimate soil bulk density and calculate total SOC stock for Alaska. Our results suggest that Alaskan topsoil (0-30 cm) stores approximately 25.21±17.18 Pg C, with the largest SOC reserves found in shrublands. These findings highlight the importance of accounting for spatial heterogeneity in in-situ samples and leveraging high-resolution remote sensing data for regional soil mapping.

Irrigation informed by soil moisture conditions maximizes crop yield by mitigating stress from under-watering and nutrient deficiencies, stunted growth, and fruiting reduction from over-watering. Scalable methods for monitoring depth-dependent soil moisture at crop root zones are needed to fully quantify water available to crops. We aim to develop a drone-based multistatic radar system for 3D retrievals of water content and soil composition. Here, we present our current progress towards this goal which includes a trade study of penetration depth versus attenuation and our current implementation of a Finite-Difference Time-Domain Full Wave Inversion implemented in python. Once completed, we hope that this multistatic radar tomography method will enable root-zone soil moisture sensing to inform irrigation practices for maximized yield and improved food security.

Accurate assessment of areas burned in wildfires is vital for various monitoring, management, and spread modeling applications. Wildfires, especially in forested regions, pose immense challenges for precise mapping due to the inherent dynamics of fuel types and terrain complexities. While remote sensing, particularly satellite imagery, offers an approach to studying burned areas, reliance on such satellite sources introduces challenges in characterizing burned areas amidst dense vegetation and environmental variations. This paper presents a mapping of forested burned areas utilizing global navigation satellite system–reflectometry (GNSS-R) from Cyclone Global Navigation Satellite System (CYGNSS) with ancillary observations from Soil Moisture Active Passive (SMAP) mission and Shuttle Radar Topography Mission (SRTM) using machine learning approaches. We validate the results with existing burned area products and provide maps of representative California fires within CYGNSS coverage. Assimilation of GNSS-R data into the model provides near real-time and high temporal resolution, enabling rapid response and mitigation efforts to fire events.

Soil moisture is an essential climate variable that directly influences many hydrological, agricultural, and water-cycle processes. Many satellites have been launched and are still being launched to map soil moisture accurately at a global scale. Motivated by the coarse resolution of existing satellite products, many statistical, physics-based, and machine learning-based methods have been proposed to downscale soil moisture to much finer spatial scales. In this paper, we propose a novel deep learning approach that is constrained by the Tau-omega radiative transfer model to enhance the resolution of surface soil moisture. We demonstrate the proposed framework by downscaling Soil Moisture Active Passive (SMAP) L-band brightness temperature (TB) with C-band synthetic aperture radar (SAR) backscattering coefficient (σ0) imagery from Sentinel-1A/B and subsequently retrieving high spatial resolution (1km) soil moisture.

Global navigation satellite system (GNSS)-reflectometry (GNSS-R) delay-Doppler maps (DDMs) are used in various remote sensing applications over land, including retrieving surface soil moisture. Analyzing GNSS-R DDM sensitivity to such geophysical variables aids in understanding retrieval limitations and performance. This work presents a sensitivity analysis of DDMs to vegetation and soil moisture. The analysis uses the improved geometric optics with topography (IGOT) model, which computes DDMs over topographical lands with bare to intermediate vegetation. This IGOT model was validated using Cyclone GNSS (CYGNSS) DDMs over various validation sites. Analytical sensitivity results of the IGOT model are presented in this paper. Specifically, DDM is sensitive to the amplitude of the Fresnel reflection coefficient of the surface, which incorporates soil moisture. Furthermore, DDM is sensitive to the secant of the incidence angle multiplied by the surface normalized bistatic radar cross section (NBRCS), which includes an exponential term of the vegetation parameter.

Recent advancements in uncrewed aerial vehicles (UAVs) allow them to be cost-effective and easy-to-use platforms for remote sensing. The advancements in software-defined radar systems allow the use of ultra-wideband waveforms and mounting them in a hexacopter UAV. Radar cross section calibration for radar systems is essential to retrieve various geophysical variables and explore the full potential of these radar systems. In our study, we utilized a disk as an external calibration target, achieving a standard deviation ranging from 0.5 dB to 1 dB across most of our operational frequency range. Further characterizations of the system are needed to fully understand certain frequency bands and to assist the full performance potential of the calibration.

We develop and verify a space-based, distributed, adaptive intelligent, responsive New Observing System (NOS) to improve wildfire response decisions by monitoring and forecasting fuel flammability and wildfire spread and providing on-demand fire danger and burnt area maps. We use Global Navigation Satellite System Reflectometry (GNSS-R) as the NOS, informed by improvements to existing frameworks - D-SHIELD (Distributed Spacecraft with Heuristic Intelligence to Enable Logistical Decisions) and WRFx (Weather Research and Forecasting Fire Spread Model). Five new products are developed/enhanced using GNSS-R data from CYGNSS (7-sat NASA mission) and Spire Global (commercial fleet) and assimilated into WRFx, which improves existing USGS fire danger and LANDFIRE fuel layers products. These products are expected to inform observation planning and fire management via an observation value framework and a fire forecast reporter that we develop. Adaptive intelligence to dynamically task the observing (satellites) and planning (ground stations) assets, and synchronization between them, is achieved using novel Monte Carlo Tree Search (MCTS) based planner.

While microwave imaging satellites, such as the NASA Soil Moisture Active Passive (SMAP), can provide reliable estimates of surface soil moisture at km resolution, the temporal frequency of observations is on the order of days. To increase the temporal frequency of observations, a new class of approaches considers global navigation satellite system (GNSS)-reflectometry (GNSS-R) signals. In this work, we consider observations from the NASA Cyclone GNSS (CYGNSS) constellation, as well as auxiliary observations, and seek to provide instantaneous soil moisture estimates. To achieve accurate retrievals, a novel machine learning approach for probabilistic regression is considered, namely the NGBoost. In addition to achieving an accuracy comparable to previous approaches employing state-of-the-art machine learning methods, the considered framework also provides prediction intervals to quantify prediction uncertainty. Using observations from the Yanco SMAP core validation site in southeast Australia over a period of three years, we quantify the performance in terms of both retrieval accuracy and associated uncertainty. Furthermore, using noisy observations, we experimentally demonstrate the impact of input noise on the prediction uncertainty.

Retrieval of Surface Soil Moisture (SSM) over large scales at high spatial resolution is crucial for numerous applications. Existing solutions rely on the analysis of remote sensing platforms or in situ measurements that are either too coarse in their resolution or too localized to address the aforementioned need. In this work, we propose a novel deep learning approach for reliably estimating SSM at a high spatial resolution of 1 km over broad regions. To achieve this objective, the proposed framework employs a Convolutional Neural Network that can capture both multi-modal and spatial correlations. Introducing a novel loss function, the proposed scheme can leverage limited in situ observations while also generating estimates consistent with physical models. This is achieved through the utilization of coarse-resolution data assimilation estimates. For training and assessing the performance of the proposed framework, a novel dataset is generated by combining information from remote sensing, in situ measurements, and data assimilation estimates. Experimental analysis demonstrates that the proposed approach can provide accurate retrieval of SSM, significantly outperforming existing products.

This paper presents a physics-based radar modeling framework that enables joint retrievals of the Arctic tundra permafrost active layer soil organic matter content and soil moisture profile using P-band polarimetric SAR. Initially, an extensive set of field observations are used to model the subsurface soil moisture and organic matter profiles with independent model parameters. Then a new organic soil dielectric model is used to translate the soil profile properties into equivalent dielectric properties that bridge the soil field properties to their manifestation in radar measurements. Finally, a multi-layered dielectric structure is adopted by an electromagnetic scattering computational model that predicts equivalent backscattering coefficients resulting from the soil profile state parameters. These pieces together construct the forward model, which is at the heart of a physics-based radar retrieval algorithm. Finally, we integrate the developed model into a retrieval scheme, in which the soil moisture and organic profile model parameters are estimated using radar backscattering coefficients measured by AirMOSS P-band radar. We show retrieved soil moisture and soil organic matter profiles, derived pixel-wise by the algorithm providing the first airborne-driven soil organic carbon map.

Numerous satellite sources measure global soil moisture, but the spatial resolution of these products is usually coarse making it challenging to use them for various science applications. In this work, we develop a conditional generative adversarial network-based model to downscale satellite measured brightness temperature. Augmenting a physics-based Tau-omega model to the framework, high resolution soil moisture is retrieved. For training and validation, upscaled in-situ soil moisture measurements are utilized. Experimental results suggests that the proposed framework captures the soil moisture variations from in-situ sensors and helps in substantially improving the resolution of passive microwave satellite soil moisture maps.

In the last decade, multiple global navigation satellite system (GNSS)-reflectometry (GNSS-R) space missions have been launched, including the UK-DMC satellite, the TechDemoSat-1 (TDS-1) satellite, the Cyclone GNSS (CYGNSS) constellation, the FMPL-2 instrument, the Spire GNSS-R CubeSats, and the BuFeng-1 A/B twin satellites. In parallel, multiple electromagnetic models have been developed to calculate the scattered GNSS-R signals over land [1] - [5] .

Surface-to-depth profiles of soil moisture are among the most important Earth system variables due to their strong influence on the partitioning of the water cycle [1] . Yet, our directly observed knowledge of these profiles, sometimes also referred to as root-zone soil moisture (RZSM) profiles, is quite limited. Soil moisture often exhibits rapid spatial and temporal dynamics and can change significantly over a short time. For example, during the monsoon season in the US Southwest, there may be significant and sudden precipitation over localized cells.

Monitoring soil moisture enables detailed understandings of its role in the water cycle and how it is affected by climate change. Multiple airborne and spaceborne missions have been dedicated to estimating soil moisture, including Soil Moisture Active Passive (SMAP) [1] , Soil Moisture and Ocean Salinity (SMOS) [2] , and Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) [3] . Some other remote sensing missions have added soil moisture retrievals along with their primary goal. For instance, the primary objective of the Cyclone Global Navigation Satellite System (CYGNSS) mission [4] is wind speed retrieval over the ocean, but it is now also used for soil moisture retrieval, among other applications [5] , [6] . All these remote sensing systems and future systems need in situ soil moisture measurements to validate their products.

Accurate soil carbon data are important for understanding the permafrost response and potential carbon release to future climate change. However, there is a large discrepancy in current soil organic carbon (SOC) estimates in the Arctic, where sparse measurements are unable to capture SOC complexity over the vast and remote region. Polarimetric Synthetic Aperture Radar (SAR) data are sensitive to roughness and moisture conditions of soil and vegetation, and may provide useful information on surface and profile SOC properties ( Yi et al., 2021 , 2022 ). The NASA Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign acquired an abundance of full-polarimetric P- and L-band SAR data across Alaska and western Canada ( Miller et al., 2019 ), which provides opportunities to test new remote sensing applications. The main objective of this study is to investigate the potential of low-frequency polarimetric SAR data for regional SOC retrieval in the Arctic through data analysis and modeling. We chose the Alaska North Slope as our study area due to more in-situ data available in this area.

Lidar elevation maps are utilized in a bistatic electromagnetic scattering model of the Cyclone Global Navigation Satellite System over San Luis Valley in Colorado, USA. The results are compared with results generated using the Shuttle Radar Topography Mission (SRTM) 1 arcsecond global digital elevation model (DEM). The high resolution lidar maps are also used to generate maps of surface roughness at length scales finer than the horizontal resolution of SRTM. Model results using these maps illustrate the importance of the spatial variation of surface roughness on reflectometry signals. Results also highlight the shortcomings of using SRTM in scattering models and show improvements when a lidar DEM is used.

In this paper, we present a new variational Born iterative method (VBIM) for real-time microwave imaging (MWI) applications. The S-parameter volume integral equation and waveport vector Green's function are implemented to utilize the measured signal of the MWI system. Meanwhile, the real and imaginary separation (RIS) approach is used at each iterative step to simultaneously reconstruct the dielectric permittivity and conductivity of unknown objects. Compared with the Born iterative method and distorted Born iterative method, VBIM requires less computational time to reach the convergence threshold. The graphics processing unit based acceleration technique is implemented for real-time imaging. To demonstrate the efficiency and accuracy of this VBIM-RIS method, synthetic analysis of a complex multi-layer spherical phantom is first conducted. Then, the algorithm is tested with measured data using our new MWI system prototype. Finally, a synthetic brain-tumor phantom model under a thermal therapy procedure is monitored to exemplify the real-time imaging with about 5 seconds per reconstruction frame.

A ground-based distributed sensing network is described in this work that leverages elements of wireless sensor networks (WSN) and uncrewed areal vehicles (UAVs) with software-defined radar payloads. Hardware and software advancements are made towards combining the operations of WSNs and UAVs for dynamic spatiotemporal monitoring of surface to subsurface soil moisture at kilometer scales. The multi-agent and distributed sensing approach demonstrates coordination, collaboration, and parallel operation of discrete assets for optimal soil moisture monitoring. Results from the first field experiment showing this coordinated operation are reported.

In this paper, a coupled hydrologic-electromagnetic approach is presented to model the behavior of organic soil dielectric properties. A detailed soil texture analysis that accounts for root biomass (RB), soil organic matter (SOM), and the mineral fraction (Min) enables characterizing the soil water retention curve (SWRC) parameters. The bound water amount is inferred from the permanent wilting point calculated from SWRC and is incorporated as a subphase into a soil dielectric mixing model. The carbon and water characteristic in the subsurface are modeled as profile functions of total organic matter (OM) and water saturation fraction (SW). This profile model is developed in support of the P- and L-band polarimetric synthetic aperture radar observations of the Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign, which will use the model to retrieve subsurface OM and SW profile functions.

In this paper we develop a Convolutional Long Short-term memory (ConvLSTM) model, a time series deep learning neural network, to predict soil moisture, with an add-on module of passive microwave (radiometer) soil moisture retrieval using the Tau omega model. We incorporate antecedent observations, landscape properties, and forcing factors such as precipitation, landcover, clay fraction, and brightness temperature in the prediction scheme. A regularization Monte Carlo Dropout layer is added to the network to remove stochasticity and avoid overfitting during the training phase. This dropout layer also provides a Bayesian approximation to quantify uncertainty during forecasting. The model is validated at four Soil Moisture Active Passive (SMAP) Cal/Val locations using performance metrics such as Root Mean Square Error (RMSE) and Bias to evaluate effectiveness of the proposed method. This model is developed as a component of the Science Simulator within the Distributed Spacecraft with Heuristic Intelligence to Enable Logistical Decisions (D-SHIELD) project.

This paper aims to retrieve the soil moisture profile above the water table using ground penetrating radar. For the forward model, the Van Genuchten soil saturation model and the generalized refractive mixing dielectric model are used to parameterize the soil saturation profile. The finite-difference time-domain method was used to simulate the electromagnetic signal. The soil moisture profile retrieval was carried out using a look-up table. We demonstrate successful retrieval of soil moisture profile in the presence of noise.

The majority of the soil moisture estimation algorithms using radars in the literature are for retrievals using a single instrument or not optimized for retrievals using multiple radars. A method for retrieving soil moisture using polarimetric radars at multiple frequencies is presented. The method uses a forward model and a hybrid local and global optimizer to retrieve soil moisture. Monte Carlo simulations of soil moisture retrieval using a maximum of four radars with different frequencies and incidence angles have been performed to assess the performance of the algorithm for various vegetation types and realistic instrument noise models. The simulation results show a mean unbiased root mean square error (ubRMSE) of less than 0.01 m3m-3, and a mean bias less than 0.005 m3m-3. The mean ubRMSE and bias values were quite small under the assumption that vegetation properties and surface roughness are known.

NASA's Cyclone Global Navigation Satellite System (CYGNSS) constellation of 8 small satellites was launched into low Earth orbit in 2016. The objectives of its initial two year mission were to study how well GPS signals that are reflected from the ocean surface can measure the winds in hurricanes and how well those measurements can improve our ability to forecast them. In the 5+ years it has been in orbit, CYGNSS has accomplished those objectives. It has also significantly expanded the scope of its scientific investigations. GPS signals reflected from the storm-fres parts of the ocean as well as the signals reflected from land have also been found to contain valuable information about surface conditions. An overview of the scientific impacts of CYGNSS observations, for hurricane prediction studies and many other applications, are presented.

A new observing strategy for floods was demonstrated and evaluated in a testbed environment. The strategy coordinates several observing platforms, including in situ and space based, to observe a flood from multiple vantage points and dynamically target predicted flood events with highresolution observations. The coordinated observations were assimilated back into the model to continuously improve forecasts and future observation selection. The demonstration shows the potential for coordinated, model-driven observing strategies and the feasibility of the NOS Testbed for demonstrating and evaluating new observing strategies.

Accurate vegetation information is essential for analyzing above-ground biomass and understanding subsurface characteristics, such as root biomasss, soilorganicmatter and soil moisture profiles. This paper investigates novel mappings of forest species and canopy height in interior Alaska. We employ Random Forests to train a regression model for canopy height mapping and a classification model for forest species mapping utilizing L-band and P-band Uninhabited Aerial Vehicle Synthetic Aperture Radar(UAVSAR). For canopy height, canopy height model (CHM) data derived from Goddard's LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) are treated as ground truth. For forest species prediction, Tanana Valley State Forest (TVSF) Timber Inventory and Forest Inventory and Analysis (FIA) data are used as reference. The experimental results show the proposed method yields a root-mean-square error of 1.90 m for forest height estimation and overall accuracy of 79.54% for forest species classification. They also demonstrate the feasibility of obtaining precise vegetation information by data-driven methods, which can be further used to enhance forest radar scattering forward models.

Adaptive and targeted allocation of mobile sensing agents, in the form of unmanned aerial vehicles (UAVs) with software defined radar (UAV-SDRadar) payloads, enable mapping of surface soil moisture in regions wherein situwireless sensor networks (WSNs) undersample soil moisture or upscaling models perform poorly. This work presents an optimization-based UAV path planning methodology that seeks to maximize UAV flight coverage over areas where a complementing WSN yields upscaled soil moisture estimates with high uncertainty. By recursively mapping soil moisture over such areas, the combined UAV and WSN instrumentation can gradually capture the domain’s true mean soil moisture. A series of numerical simulations are presented to demonstrate the algorithm’s basic function while considering real-world and feasible operational scenarios.

A comparison of three different electromagnetic scattering models for land surface delay-Doppler maps (DDMs) obtained from global navigation satellite system reflectometry (GNSS-R) along a Cyclone Global Navigation Satellite System (CYGNSS) track in the San Luis Valley, Colorado, USA, is presented. The three models are the analytical Kirchhoff solutions (AKS), the Soil And VEgetation Reflection Simulator (SAVERS), and the improved geometrical optics with topography (IGOT). Common inputs to the three models were defined by using field samples of soil moisture and texture, soil surface roughness measurements, and a digital elevation model (DEM). The resulting peak reflectivity profiles of the models and the CYGNSS data all had a range of 10 dB along the selected track, mainly due to the influence of topography. The reflectivities obtained from all three models agreed with one another to within 2.4 dB along the full length of the track. The models also showed general agreement with the corresponding CYGNSS data, although the modeled profiles were higher than CYGNSS Science Data Record Version 3.1 by an average of 5 dB and also smoother. Additional characterization of fine-scale surface roughness is identified as an area for future work to improve model fidelity. An intercomparison of DDM structure for three selected acquisitions is also provided.

We present results from a field campaign to measure seasonal snow depth at Cameron Pass, Colorado, using a synthetic ultrawideband software-defined radar (SDRadar) implemented in commercially available Universal Software Radio Peripheral (USRP) software-defined radio hardware and flown on a small hexacopter unmanned aerial vehicle (UAV). We coherently synthesize an ultrawideband signal from stepped frequency 50-MHz subpulses across 600–2100-MHz frequency bands using a novel nonuniform nonlinear synthetic wideband waveform reconstruction technique that minimizes sweep time and completely eliminates problematic grating lobes and other processing artifacts traditionally seen in stepped waveform synthesis. We image seasonal snow across two transects: a 400-m open Meadow Transect and a 380-m forested transect. We present a surface detection algorithm that fuses data from LiDAR, global navigation satellite system (GNSS)/global positioning system (GPS), and features in the radargram itself to obtain high precision estimates of both snow and ground surface reflections, and thus total snow depth, represented as two-way travel time. The measurements are validated against independent ground-based ground-penetrating radar measurements with correlations coefficients as high as$\rho = 0.9$demonstrated. Finally, we compare backscattered radar data collected by the UAV-SDRadar while hovering proximal to a known snow pit within situmeasured snow dielectric profiles and demonstrate imaging of snow stratigraphy.

We used full-polarimetric L-band and P-band synthetic aperture radar (SAR) data collected from the recent NASA Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign and Sentinel-1 C-band dual-polarization data to understand the sensitivity of radar backscatter intensity and phase to fire-induced changes in the surface and subsurface soil processes in Arctic tundra underlain by permafrost. The 2007 Anaktuvuk River fire on the Alaska North Slope was used as a case study. At ~10-year postfire, we observed a strong increase (>~3–4 dB) in the low-frequency radar backscatter in severely burned areas during the thaw season, in contrast to limited (1 dB) in burned areas than the adjacent unburned areas. Polarimetric decomposition analysis indicated a general trend toward more random surface scattering, and strong increases in double-bounce scattering and volume scattering power at both P- and L-band in the burned areas. The ice-rich yedoma region shows the largest backscatter increases in burned areas and the highest correlation with burn severity and microtopography changes. The above backscatter changes are attributed to increasing surface roughness and microtopography due to ice-wedge degradation and thermokarst development and increasing subsurface scattering due to an overall drier and deeper active layer in burned areas. Among all frequencies, P-band shows consistently larger contrast in backscatter power and phase between burned and unburned areas, which makes it potentially more useful to study fire–permafrost interactions in the Arctic over decadal time scales.

Analyses of GNSS-R data sets have revealed the occasional presence of coherent signals over land areas. Studies have been conducted of modeling such returns using classical scattering theories that consider the geometry, the frequency band, and surface parameters as inputs. The surface roughness is a crucial parameter that is not widely available from ancillary sources. This paper presents a comparison of roughness statistics derived from Digital Elevation Maps (DEMs) having different spatial resolutions, and shows the importance of having a high-resolution DEM in order to resolve small scale roughness. This is particularly important when modeling reflected signals over very flat surfaces where coherency can be significant.

A comparison of three different electromagnetic scattering models for delay-Doppler maps (DDMs) of global navigation satellite system reflectometry (GNSS-R) from land is performed along a Cyclone Global Navigation Satellite System (CYGNSS) track over a validation site in the San Luis Valley, Colorado, USA. The peak reflectivity profiles of all three models and of the corresponding CYGNSS data are found to be in general agreement and are strongly influenced by topography. An intercomparison of DDM structure for one acquisition is also included. Efforts to refine the model results using a high resolution lidar survey are ongoing.

The Permafrost Dynamics Observatory (PDO) combines L-band interferometric synthetic aperture radar (InSAR) and P-band polarimetric synthetic aperture radar (PolSAR) to simultaneously estimate the seasonal thaw depth and soil moisture profile of the active layer in permafrost regions. L-band InSAR can measure seasonal subsidence due to thawing of the active layer and P-band PolSAR backscatter is sensitive to subsurface soil moisture. A joint retrieval scheme is developed as both subsidence and soil moisture are essential to accurate active layer thickness (ALT) estimation. The PDO joint retrieval has been applied to airborne L- and P-band SAR data acquired over Arctic-boreal region during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign. In this paper, we describe the forward models and joint inversion used in the PDO retrievals and compare the results with in-situ ALT and soil moisture data estimated from ground-penetrating radar (GPR).

This article explores the tradespace for a constellation of heterogeneous smart satellites intended to measure soil moisture using a combination of L and P band radars, radiometers, and reflectometers. Orbit inclination, repeat cycle, number of satellites, and number of planes were treated as input variables to create a set of architectures for evaluation. Attempting to optimize multiple output variables (cost, average revisit time, maximum revisit time, and percent coverage) results in a complex tradespace with suitable options at various cost caps. Therefore, several cost ranges are examined to find the best constellation for a given cost cap. It was found that a relatively simple constellation of three satellites in one plane offers acceptable performance at a low cost. This preliminary submission shows results for a homogeneous constellation, while the final paper will include satellites with various instrument configurations.

The Committee on Radio Frequencies (CORF) is an independent committee of experts convened by the U.S. National Academies of Sciences, Engineering, and Medicine to consider the use of radio frequency spectrum for scientific applications and how such use may be protected amidst rising needs for the use of spectrum for numerous other purposes. This talk will provide an overview of a range of CORF activities with focus on the work of the committee in the past year.

We describe a suite of scalable software methods and frameworks to helps schedule payload operations of a large constellation, with multiple payloads per and across spacecraft, such that the collection of observational data and their downlink, constrained by the constellation constraints (orbital mechanics), resources (e.g., power) and subsystems (e.g., attitude control), results in maximum science value for a selected use case. Constellation topology, spacecraft and ground network characteristics can be imported from design tools or existing constellations and can serve as elements of an operations design tool. Our framework includes a science simulator to inform the scheduler of the predictive value of observations or operational decisions. Autonomous, realtime re-scheduling based on past observations needs improved data assimilation methods within the simulator.

We present analytic expressions for the clock phase error power spectral density (PSD) resulting from a previously reported decentralized distributed wireless synchronization protocol acting on independent sensor oscillators. We provide an overview of oscillator phase noise error modelling and examine the effects of the wireless synchronization protocol and the resulting synchronized clock phase noise PSDs. We present results from both simulation and experiment to validate the expressions derived.

This work focuses on the problem of surface soil moisture estimation from multi-modal remote sensing observations. We focus on the scenario where both passive radiometer observations from NASA SMAP satellite, as well as active radar measurements from ESA Sentinel 1 are available. We formulate the problem as multi-source observation fusion and develop a deep learning model for SM estimation. To train and validate the performance of the proposed scheme, we consider observations from in-situ SM sensor networks over the continental USA. Experimental results demonstrate that the proposed model achieves high quality SM estimation, surpassing the performance of available products.

Detailed information on the spatial and temporal distribution of active layer thickness (ALT) throughout the North Slope of Alaska, were it available, could offer valuable insights into the effects of climate change throughout the region and facilitate the estimation of greenhouse gas emissions resulting from permafrost degradation. We are, therefore, developing extensive high-resolution maps of ALT on the North Slope of Alaska. To do this, we use a machine learning algorithm to extrapolate ALT from high resolution strips of estimated ALT derived from airborne P-band synthetic aperture radar (SAR) acquired over two sets of flights in each of three different years. Our results indicate upscaling root-mean-square error (RMSE) of about 4 cm relative to thousands of randomly-selected SAR-derived ALT validation samples, and RMSE of approximately 10 cm relative to a small number of in-situ ALT measurements.

We used the full polarimetric L-band and P-band SAR data collected from recent NASA Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign to understand the sensitivity of longwave radar backscatter intensity and phase to the post-fire recovery process of Arctic tundra. The 2007 Anaktuvuk River fire was used as a case study. At 10-years post-fire, we observed a strong increase (>∼4 dB) in both the P- and L-band radar backscatter in the severely burned areas, in contrast to limited backscatter differences (VV, VH) between burned and unburned areas at C-band. The polarimetric target decomposition analysis indicated a general trend towards more random surface scattering, and strong increases of the double-bounce and volumetric scattering power at both P- and L-band in the burned areas. Large differences were also observed in the Pauli phase angle and the dominant-scattering-type Touzi phase angle between burned and adjacent unburned areas. The above changes are likely caused by increasing surface roughness and microtopography due to thermokarst development and ice degradation, and increasing subsurface scattering due to an overall drier and deeper active layer in the burned areas.

This work presents recent field activities in support of the NASA CYGNSS missions' land applications. Land reflected GNSS signals are known to be sensitive to surface topography, vegetation cover, and soil moisture. To better understand CYGNSS sensitivity to surface conditions, especially freeze-thaw states, two SoilSCAPE wireless in situ network sites were deployed in the San Luis Valley (SLV), CO, in late Oct. 2019. These sites capture similar weather and climatic conditions but have contrasting topography and vegetation cover. Initial analysis of CYGNSS Signal-to-Noise (SNR) observations over SLV indicates the need to fully account for land-scape topography. To this end, a forward wave scattering model that incorporates a Digital Elevation Model (DEM)is currently being developed to help explain the effects of local topography on SNR observations.

Seasonal subsidence measured by repeat-pass interferometric synthetic aperture radar (InSAR) can be used to infer the active layer thickness (ALT) in permafrost regions. The differential volume of soil water undergoing the phase change over the thaw season is one of the factors impacting the seasonal subsidence and is a function of both soil moisture profile and thaw depth. Without the information about soil moisture, this InSAR approach can have large biases in the ALT estimates when soil moisture profile is below saturation. Soil moisture and ALT can also be estimated from polarimetric synthetic aperture radar (PolSAR) backscatter observations but the sensing depth of the PolSAR approach is limited when deep ALT is present. In this paper, we integrated these two approaches and applied a joint retrieval method to estimate the soil moisture profiles and ALT from the L-band InSAR and P-band PolSAR data acquired over the Arctic-boreal region during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign.

The aim of this paper is to systematically combine complementary LiDAR and synthetic aperture radar (SAR) observations to map tree canopy cover in a boreal forest. LiDAR data can provide direct measurements of vegetation structures but are limited by the sparse spatial coverage of observations. SAR systems can perform wall-to-wall high-resolution mapping without weather constraints but the information about vegetation and ground subsurface are mixed in the backscatter data. In this paper, we adopted the Random Forests algorithm to train an upscaling function using tree canopy cover (TCC) and canopy height model (CHM) derived from Goddard's LiDAR, Hyperspectral and Thermal Imager (G-LiHT) point cloud data. The regression model was then applied to the L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign to map the TCC and CHM over the Delta Junction area in interior Alaska.

In this paper we describe the development of new wireless sensor network technologies to coordinate among different ground-based and unmanned aerial vehicle (UAV)-based sensors as “Agents” who, when coordinated, deliver ground-truth at varying temporal and spatial sampling scales for NASA remote sensing science products, as well as for other potential users that may have different application requirements.

D-SHIELD is a suite of scalable software tools that helps schedule payload operations of a large constellation, with multiple payloads per and across spacecraft, such that the collection of observational data and their downlink, constrained by the constellation constraints (orbital mechanics), resources (e.g., power) and subsystems (e.g., attitude control), results in maximum science value for a selected use case. Constellation topology, spacecraft and ground network characteristics can be imported from design tools or existing constellations and can serve as elements of an operations design tool. D-SHIELD will include a science simulator to inform the scheduler of the predictive value of observations or operational decisions. Autonomous, realtime re-scheduling based on past observations needs improved data assimilation methods within the simulator.

In this work, we explore efficient ultra-wideband radar waveforms and signal processing techniques for achieving high performance radar systems in low-cost hardware. This paper describes the generation of nonlinear constant-amplitude frequency modulated (FM) waveforms with autocorrelation functions that resemble linear FM waveforms that are amplitude-apodized by an arbitrary window function. The principle of stationary phase is used to solve for a time-frequency curve that produces a waveform with a power spectral density (PSD) that is similar to a desired PSD but dependent only on waveform phase. This method is extended to nonlinear (NL) synthetic wideband waveforms (SWWs), wherein an ultra-wideband nonlinear waveform having arbitrary spectral weighting is synthesized from non-uniform stepped-frequency nonlinear frequency modulated (NLFM) sub-pulses. A novel reconstruction algorithm, Non-Uniform Frequency Stitching (NUFS) is presented for obtaining high range resolution performance from such non-uniform nonlinear SWW (NUNL-SWW) waveforms with minimal grating lobe contamination.

In this work, we propose a novel Convolutional Neural Network architecture for increasing the low spatial resolution SMAP radiometer based soil moisture estimations from 36 km to 3 km resolution by using time-series of observations from both SMAP's radiometer and Sentinel-1 radar. By simultaneously extracting features from both current low-resolution input and residuals between high and low resolution at previous time instances, the proposed network is capable of accurately estimating soil moisture using coarse resolution observations. Experimental results on three different locations demonstrate that the proposed scheme is able to estimate soil moisture with accuracy in the range of the requirements set by the SMAP science team.

We propose an inversion model for retrieving the root-zone soil moisture profile using the P-band radar observations and validate it using data collected during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission. The forward model utilized in this article uses an existing small perturbation method (SPM) to calculate the electromagnetic wave scattered from multilayered rough surface dielectric structures. To solve the inverse problem, which is formulated as a least-squares problem, we utilize a novel global optimization technique that takes the advantage of the speed of a local optimization method while ensuring convergence to the global minimum. We present the numerical results to demonstrate significant improvements in the inversion results and computational speed compared to the previously used AirMOSS baseline inversion algorithm. The presented method is validated with in situ soil moisture measurements. Finally, by utilizing a closed-form analytical solution to Richards' equation, we demonstrate improvements in the accuracy of our soil moisture retrieval results.

The Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) P-band synthetic aperture radar (SAR) was flown more than 1200 h from August 2012 to September 2015, covering regions of 2500 km2spread over nine major biomes in North America. The flights, as a part of the NASA AirMOSS Earth Venture Suborbital 1 (EVS-1) mission, collected radar data used to map root-zone soil moisture (RZSM) at 3-arcsec resolution. We previously reported the baseline retrieval algorithm and demonstrated its performance for a semiarid shrubland (Walnut Gulch, AZ, USA); we represented the RZSM profile as a continuous quadratic function and solved a radar scattering nonlinear optimization problem to obtain the unknown polynomial coefficients. In this article, we expand the retrievals to other AirMOSS sites that, in addition to the semiarid shrubland, include grassland and crops (MOISST, OK, USA), woody savanna (Tonzi Ranch, CA, USA), temperate conifer forest (Metolius, OR, USA), and boreal forest (Saskatchewan, Canada). Due to a wide range of land covers, soil types, and soil moisture regimes, we parameterize the forward model and constrain the inverse algorithm for each site separately. We present the full set of retrievals for these sites, validating the results against in situ observations. Error sources and strategies to minimize their effects are discussed. The concept of sensing depth is introduced. We find that the retrieval errors are smallest for the top 25 cm of soil with a root-mean-square error (RMSE) of less than 0.05 m3/m3. The RMSE remains around 0.06 m3/m3even for depths reaching 45 cm, which is the typical sensing depth for the sites considered. These AirMOSS RZSM products (known as Level-2/3 RZSM, or L2/3-RZSM, products) are the first of their kind in that it is the first time RZSM has been retrieved directly from remote sensing observation.

We report on recent technology advancements and developments in in situ soil moisture wireless sensor networks (WSN) in support of Earth science microwave remote sensing missions. Specifically, we discuss new hardware features, known as Wakeup-on-Radio (WoR), that enable sensor networks to respond rapidly to short-lived micrometeorological events and record measurements which may typically be lost in-between sampling periods. Additionally, we outline strategies towards WSN autonomy with the goal of enabling an in-situ sensor to "learn" soil moisture dynamics and surrounding ecohydrological processes, to then determine its optimum sampling schedule.

In this paper we advance a previously developed bistatic scattering forward model to include the circularly polarized incident and scattered waves, which is the case for Global Navigation Satellite System (GNSS) reflectometry. This model development enables retrieval of soil moisture from Signals of Opportunity (SoOp) bistatic observations, e.g., from the Cyclone Global Navigation Satellite System (CYGNSS) observations and GNSS Reflectometer Instrument for Bistatic Synthetic Aperture Radar (GRIBSAR). In order to validate the forward model with measured data, we present a method to construct the Delay Doppler Map (DDM) from simulations of the forward model. The forward model Radar Cross Section (RCS) predictions will be compared with actual measurements. The validated model is intended for use in soil moisture retrievals.

In this paper, a new set of measurements is presented to investigate the relationship between the hydraulic and electromagnetic properties of the Arctic permafrost active layer soil, which includes high organic matter content. Low-frequency dielectric permittivity and the soil water matric potential have been measured for over 50 soil samples collected from northern Alaska for a full range of soil moisture value. The organic matter content is included as a new input parameter and the hydraulic properties of soil have also been taken into account. The result is a new dielectric mixing model for soils containing organic matter and surpassing the range of validity of other existing models.

In this paper, a harmonized soil parametrization that accounts for both soil organic matter (SOM) content and degree of decomposition are used to parametrize the hydraulic properties of permafrost soils. Using the wilting point calculated from the soil-water retention curve to inform the amount of bound water in a soil medium, we construct a soil dielectric mixing model effective across the full range of SOM levels. The active layer soils are parametrized as profile functions of SOM and soil moisture in the radar retrievals. The NASA P-band Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) data acquired as part of 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign are used to demonstrate the potentials of retrieving SOM and soil moisture profiles from P-band synthetic aperture radar (SAR).

In this paper we demonstrate the capability of an ultra-wideband software defined radar (SDRadar) implemented in commercial USRP SDR hardware to produce high-resolution images of sub-surface landmine-like targets. We formulate a half-space back-projection focusing algorithm for low-altitude nadir-looking airborne altimetric ground-penetrating SAR that accounts for dispersive and refractive effects of the air-ground interface. Performance of the SDRadar and focusing algorithm are shown in experimental results. This work has applications in the development of low-cost high-resolution UAV-based radar systems for landmine detection and other sub-surface imaging tasks.

The CYGNSS constellation was successfully launched on 15 Dec 2016 and has been operating continuously in science data-taking mode since March 2017. Updates will be presented on the mission status, calibration and validation activities for its science data products, and recent scientific applications of the measurements. Those applications include the use of ocean wind measurements to estimate air-sea latent heat flux, the assimilation of wind measurements made near tropical cyclones into hurricane numerical prediction models, the retrieval of soil moisture from the scattering measurements made over land, and the imaging of inland flooding using overland measurements.

In order to calibrate and validate the SMAP soil moisture products, networks of ground-based soil moisture sensors have been deployed. Measurements collected from the networks must be upscaled to the radiometer footprint scale (30-40 km) for comparison with the SMAP radiometer-based retrievals. The upscaling is typically performed as a weighted average of individual sensor measurements within the SMAP grid. Since different weighting schemes have been found to result in different upscaled soil moisture estimates, an independent method of assessing soil moisture estimation biases is needed. We therefore present a method for calculating estimation biases at each SMAP Core Validation Site (CVS). The estimation was enabled by networks of enhanced soil moisture sampling that were deployed at four CVSs for a limited time. Based on Random Forests, our method offers a straightforward, systematic, and unified approach to bias estimation across a variety of sites. The method was applied to estimate biases at the four SMAP CVSs.

Currently, the community lacks capabilities to assess and monitor landscape scale permafrost active layer dynamics over large extents. To address this need, we developed a concept of a remote sensing based Soil Inversion Model for regional Permafrost (SIM-P) monitoring. The current SIM-P framework includes a satellite-based soil process model and a soil dielectric model. We are also working on incorporating a radar scattering model for Arctic tundra into the SIM-P framework. A unified soil parameterization scheme was developed to harmonize key soil thermal, hydraulic and dielectric parameters in the soil process and radar models that can be used in the joint soil-radar inversion framework. The soil parameter retrievals of the SIM-P framework include soil organic content (SOC) and active layer thickness (ALT). Initial tests of SIM-P using in-situ soil permittivity observations showed reasonable accuracy in predicting site-level SOC and soil temperature profiles at an Alaska tundra site and ALT in Arctic Alaska. SIM-P will be further tested using airborne P- and L-band radar data collected during NASA's Arctic Boreal Vulnerability Experiment (ABoVE) to evaluate the sensitivity of longwave radar to active layer properties.

Active microwave-based retrieval of soil moisture in vegetated areas has uncertainties due to the sensitivity of the signal to both soil (dielectric constant and roughness) and vegetation (dielectric constant and structure) properties. A multi-frequency acquisition system would increase the number of observations that may constrain soil and/or vegetation parameter retrievals. In order to realize this constraint, an understanding of microwaves interaction with the surface and vegetation across frequencies is necessary. Different microwave frequencies have varied interactions with the soil-vegetation medium and increasing penetration into the soil and canopy with the decreasing frequency. In this study, we examine the contributions of different scattering mechanisms to coincident observations from two microwave frequencies (L and P) of airborne synthetic aperture radar instruments. We quantify contributions of surface, vegetation volume, and double-bounce scattering components. Results are analyzed and discussed to guide future multi-frequency retrieval algorithm designs.

We propose a method to estimate the active layer properties, including soil dielectric profiles and active layer thickness (ALT), in permafrost regions using time-series P-band polarimetric synthetic aperture radar (SAR) observations. The active layer and underlying permafrost are modeled as a three-layer dielectric structure with the layer dielectric constants representing the soil moisture and freeze-thaw state of the layer. To resolve the ambiguity of the retrieved layer thicknesses, an approach of finding the largest possible depths (LPDs) is combined with time-series observations, where the ALT is assumed time-invariant between the maximum thaw and before the upward freezing front rises significantly from the permafrost table. The LPD-assisted time-series retrieval algorithm is applied to the radar data acquired by the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) P-band SAR in August and October 2014 and 2015 over the Alaska North Slope. The results show that the retrieved ALT values are generally underestimated for the sites where the in situ ALT is larger than the P-band sensing depth, with a retrieval bias ranging from -0.05 to -0.24 m as validated against the in situ ALT collected at Circumpolar Active Layer Monitoring (CALM) sites. For the sites where the in situ ALT is smaller than 0.55 m, the retrieval errors are generally less than 0.1 m. The retrieval results also show that the active layer properties are strongly influenced by the land cover types at the regional scale, and not as much by the North-South temperature gradient across a 180-km-long transect along the Deadhorse flight line.

This paper presents the application of a coherent bistatic radar scattering model from vegetated terrains and the extension of the model to circularly polarized incident wave cases for GNSS signals. It will then present an analysis relating the bistatic scattering Radar Cross Sections (RCS) of vegetated land cover to the customary Delay Doppler Maps (DDMs) resulting, e.g., from GNSS reflectometry (GNSS-R) observations. This step is key in being able to use GNSS-R data in soil moisture retrieval algorithms. While some analyses exist for this purpose for scattering from sea surface, such analysis has not been carried out specifically for vegetated land surface. The proposed model and methods are envisioned to be applied to the airborne GNSS Reflectometer Instrument for Bistatic SAR (GRIBSAR) project currently under development, as well as to the CYGNSS mission.

In this study we evaluate a Random Forest (RF) model for characterizing the spatial variability of soil moisture based on model derived from in situ soil moisture samples, geophysical data and RADAR observations. The RF model is run with and without C-band SAR backscatter to understand the importance of the inclusion of SAR data for mapping of soil moisture at field scale. The inclusion of SAR data in the RF resulted in a modest improvement however the geophysical parameters (e.g. soil types and terrain properties) were of greater importance.

In this paper, a permafrost active layer retrieval algorithm using time-series P-band SAR observations is presented wherein both layer dielectric constants (representing unfrozen water content) and layer thicknesses (representing water table or thaw depths) of the active layer are retrieved. The time-series observations were acquired after the maximum thaw, so that variables such as active layer thickness (ALT) can be assumed constant as they are nearly time-invariant between the data acquisitions. Having these time-invariant variables can reduce the number of unknowns and improve the retrieval accuracy with limited number of observations. Comparison of the retrieval results with in-situ observations suggests that the retrieved ALT values are generally underestimated.

In this paper, we characterize vertical dielectric profile of permafrost soils to account for different soil horizons (or-ganic/mineral/permafrost) and seasonal variations of soil moisture and freeze/thaw state. The in-situ soil profile measurements from SoilSCAPE sites in Alaska and field observations are analyzed. We proposed three profile functions (i.e., constant, second-order polynomial, and exponential) to be used for modeling permafrost soils in radar retrievals. Constant profile has the lowest errors between in-situ and fitted profiles as well as between measured and simulated radar backscatter values. However, constant profile can only represent sharp transitions across different soil boundaries, whereas the exponential function can describe more realistic transitions at both ends of the active layer while having similar errors as constant profile. Second-order polynomial is only suitable for root-zone soil moisture (RZSM) in non-permafrost regions as permafrost soils have multiple abrupt changes throughout the soil column.

A fundamental limitation of current soil moisture remote sensing technology is that can only provide moisture information on the top 5 cm layer of soil at most, being one-tenth to one-quarter of the wavelength (21 cm at L-band; 1.4 GHz) using the current SMAP and SMOS soil moisture dedicated missions of NASA and ESA. Consequently, we have developed an airborne passive microwave sensing capability at P-band to develop a new state-of-the-art satellite concept that will provide soil moisture data for the top 10 cm layer of soil using radiometer observations at P-band (40 cm; 750 MHz). Not only would P-band provide soil moisture information on a soil layer thickness that more closely relates to that affecting crop and pasture growth, but it is expected to produce greater spatial coverage with improved accuracy to that from L-band. This is because P-band should be less affected by surface roughness conditions and have a reduced attenuation by the overlaying vegetation. This paper describes a series of small airborne field experiments at P-band, and presents some early results of P-band passive microwave observations in comparison with L-band and K-band passive microwave from initial trial flights.

In this paper, a dual-frequency permafrost active layer thickness (ALT) retrieval algorithm is presented wherein P-band AirMOSS and L-band UAVSAR data are used to retrieve the layered soil dielectric constants and layer thicknesses. Using the known radar calibration accuracy, radar sensing depth is defined to help understand the limit of radar backscatter sensitivity to subsurface soil conditions and used as a consistency check when determining ALT. Retrieved maps of soil dielectric constants and layer thicknesses show high spatial correlation with the vegetation type and organic layer thickness. It is shown that the retrieval accuracy for ALT is between 5 cm and 14 cm when the ALT is smaller than the sensing depth.

Soil moisture impacts exchanges of water, energy and carbon fluxes between the land surface and the atmosphere. Passive microwave remote sensing at L-band can capture spatial and temporal patterns of soil moisture in the landscape. Both ESA and NASA have launched L-band radiometers, in the form of the SMOS and SMAP satellites respectively, to monitor soil moisture globally, every 3-day at about 40 km resolution. However, their coarse scale restricts the range of applications. While SMAP included an L-band radar to downscale the radiometer soil moisture to 9 km, the radar failed after 3 months and this initial approach is not applicable to developing a consistent long term soil moisture product across the two missions anymore. Existing optical-, radiometer-, and oversampling-based downscaling methods could be an alternative to the radar-based approach for delivering such data. Nevertheless, retrieval of a consistent high resolution soil moisture product remains a challenge, and there has been no comprehensive intercomparison of the alternate approaches. This research undertakes an assessment of the different downscaling approaches using the SMAPEx-4 field campaign data.

NASA's Airborne Microwave Observatory of Sub-canopy and Subsurface (AirMOSS) project, with more than 1200 flight hours of a P-band synthetic aperture radar (SAR), was completed in September 2015. The goal of this mission was to improve the estimates of the North American Net Ecosystem Exchange (NEE) through providing high-resolution observations of root zone soil moisture (RZSM) over nine regions representative of the major North American biomes. AirMOSS flights covered areas of approximately 100 km by 25 km containing FLUXNET tower sites in regions that ranged from boreal forests in Saskatchewan, Canada, to tropical forests in La Selva, Costa Rica. These sites are marked in Fig. 1.

A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithm's performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3/cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.

We develop a methodology for modeling coherent electromagnetic scattering from rough surfaces with buried inhomogeneities in three dimensions. The inhomogeneities considered in this paper include random spherical media, random cylindrical media, and root-like cylindrical clusters. They are used to simulate rocks, ice particles, and vegetation roots buried beneath the ground surface that can be seen by the low-frequency radars in Earth remote sensing applications. The approach we develop first calculates volumetric scattering from the media using coherent approaches, including both the conventional recursive transition matrix (T-matrix) method as well as a new generalized iterative extended boundary condition method we developed for tilted finite cylinders, and then transforms the T-matrix to the scattering matrix, which is then used to form the full scattered field of layered structures with rough surface and subsurfaces. We validate the methodology by comparing with other numerical solutions for special cases, and show sensitivity results for scattering from rough surface with buried random spherical media and random cylindrical media of different densities. We also construct a basic root model and calculate the scattering cross sections from single and multiple root clusters with or without a subsurface interface underneath. With the approach developed in this paper, we are able to study the sensitivity of radar signals to subsurface scatterers. For example, our simulations show that, depending on their density and water content, buried roots could enhance the backscatter from a single rough surface by as much as 5 dB in co-pol components, and substantially more in cross-pol components. The results of this model are expected to enable more accurate geophysical retrievals of soil moisture as well as soil organic content.

This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active-Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, physically based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-root-mean-square error (ubRMSE) and −0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m3/m3ubRMSE, −0.015 m3/m3bias, and a correlation of 0.50, compared toin situmeasurements, thus meeting the accuracy target of 0.06 m3/m3ubRMSE. The successful retrieval demonstrates the feasibility of a physically based time series retrieval with L-band SAR data for characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation.

With emphasis on physics-based techniques, a multi-objective optimization approach to combined radar-radiometer soil moisture estimation is presented in this work. Soil moisture estimation is demonstrated via application of this method to SMAP high resolution radar and coarse resolution radiometer data. Comparisons are then made with the SMAP baseline active-passive soil moisture output data product. A strong agreement between the two techniques, especially in capturing spatial distributions of soil moisture is observed.

Improved soil moisture estimation, within a combined radar-radiometer framework, by using Joint-physics modeling is demonstrated and presented in this work. Detailed numerical simulation on bare, but rough surfaces, highlight how improved emission-scattering modeling, can outperform conventional approaches. Root-mean-squared error statistics show almost a factor of two improvement in soil permittivity predictions.

Microwave-based retrieval of soil moisture in vegetated areas have uncertainties due the sensitivity of the signal to vegetation structure and dielectric constant. In this study, we propose a framework for developing a joint active L-band and active P-band retrieval algorithm to decrease the retrieval uncertainties. The algorithm focuses on the decomposition of soil, vegetation and dihedral components to compare the observations from the two frequencies.

In this paper, an active layer thickness (ALT) retrieval algorithm is presented wherein time-series of P- and L-band radar observations are used simultaneously to retrieve the depth from ground surface to permafrost table. Several model assumptions for two active layer soil conditions (maximum thaw and partially frozen) are made based on observations of in situ soil temperature and soil moisture data. It is expected that P- and L-band radar measurements can provide different aspects of active layer soil structure for retrieving accurate ALT. Monte Carlo numerical simulations are performed to show the potentials of the proposed inversion scheme and its capability of resolving subsurface features in a layered soil structure.

Surface soil moisture was retrieved globally by systematically correcting for the effects of vegetation and soil surface roughness. The retrieval is enabled by employing physical-models of radar forward scattering for individual vegetation types to account for vegetation scattering and absorption, and by constraining the surface roughness effect using time-series observations. The L-band SMAP multi-polarized (HH/VV/HV) σ° data acquired globally every three days were used from mid-April to early July, 2015. Assessment was conducted over 13 rigorously-chosen core validation sites covering a wide range of biomass types, biomass amount, and soil conditions. The soil moisture retrieval reached an accuracy of 0.06 m3/m3RMSE, a bias of 0.003 m3/m3, and a correlation of 0.56. The successful retrieval demonstrates that the physically-based retrieval method is capable of characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation on a global scale.

We have concluded that before calculation of the AirMOSS project overall RMSE, not only the behavior and location of in-situ soil moisture probes need to be carefully evaluated but also the present error biases-associated with the in-situ probes, radar calibration, vegetation parameterization, forward model inaccuracy, and the inversion algorithm bias-need to be removed. Several measures have been taken over the course of AirMOSS mission to increase the accuracy of RZSM products. The overall RMSE was reported for each study site. We can show that the overall AirMOSS retrieval error for the top 25 cm in BERMS, Metolius, MOISST, Tonzi Ranch, and Walnut Gulch meets the mission RMSE requirement. This error would be calculated in terms of RMSE between the retrieved and actual moisture values over all qualified validation points, all sites, and all dates. The retrieval performance is expected to improve even more with reprocessing of some of the flights using recalibrated radar data.

In order to provide a reliable source of ground-based validation data for the SMAP mission at spatial scales of 3 km, 9 km and 36 km, we have developed a new regression-based method capable of yielding highly-accurate upscaled soil moisture estimates based on sparse, irregularly-spaced soil moisture measurements.

Mix-scene of forest with the combination of different land cover types, such as grass, or crop, in one pixel is common, especially for the low resolution SAR images. Various radar models have been developed through years. However, the heterogeneous effect of mix-scene is not well understood quantitative so far, which might cause substantial uncertainty in the soil moisture and biomass estimations. We investigated this effect by using a coherence 3D vegetation radar model, which is firstly parametrized and validated using the ground measurements and airborne P band SAR data at pine forest of Metolius, Oregon, USA, 2013-2014. The airborne SAR data and simulated data are in good agreement with mean difference less than 1dB and correlation coefficient above 0.96. Secondly, Simulation of several mix-scenes of the same area, tree density and height but difference tree clumping patterns and mixed with different land cover types were performed. The largest difference can up to 3dB of extreme tree clumping patterns. This study demonstrates how important it is to consider the horizontal forest heterogeneity of mix-scene. Further study with quantitative description of horizontal heterogeneity of mix land cover and the effect to soil moisture and biomass estimation are needed.

Modeling of terrain topography is crucial for vegetated areas given that even small slopes impact and alter the radar wave interactions between the ground and the overlying vegetation. Current missions either exclude pixels with large topographic slopes or disregard the terrain topography entirely, potentially accumulating substantial modeling errors and therefore impacting the retrieval performance over such sloped pixels. The underlying terrain topography needs to be considered and modeled to obtain a truly general and accurate radar scattering model. In this paper, a flexible and modular model is developed: the vegetation is considered by a multilayered multispecies vegetation model capable of representing a wide range of vegetation cover types ranging from bare soil to dense forests. The ground is incorporated with the stabilized extended boundary condition method, allowing the representation of an N-layered soil structure with rough interfaces. Terrain topography is characterized by a 2-D slope with two tilt angles (α, β). Simulation results for an evergreen forest show the impact of a 2-D slope for a range of tilt angles: a 10° tilt in the plane of incidence translates to a change of up to 15 dB in 1111, 10 dB in VV, and 1.5 dB in 11V for the total radar backscatter. Terrain topography is shown to be crucial for accurate forward modeling, especially over forested areas.

We present a method to combine same-resolution measurements of active radar and passive radiometer microwave remote sensing to build a framework for soil moisture estimation in support of the Soil Moisture Active and Passive (SMAP) mission. A unified active-passive soil moisture estimation algorithm is developed within a global optimization scheme, using a joint cost function with adaptive regularization, where, unlike traditional methods, both radar and radiometer measurements are utilized at the same time to retrieve soil moisture. Monte Carlo numerical simulations and optimization to retrieve soil moisture are performed for Corn, Soybean, and Grass landcover types for active-only, passive-only, and active-passive scenarios. These numerical experiments show that the proposed combined active-passive (CAP) soil moisture estimation approach outperforms either the single-sensor technique, particularly for higher vegetation water content (VWC) values (VWC > 3 kg/m2). For example, for the case of Corn with VWC of 5 kg/m2, retrieval error is reduced to 0.035 cm3/cm3for the active-passive method from 0.08 cm3/cm3for active scenarios. Furthermore, tests of this new algorithm on the Passive and Active Land S-band Sensor (PALS) Soil Moisture Experiment 2002 (SMEX02), as well as the Combined RadarRadiometer (ComRad) collocated active and passive data, demonstrate the applicability of this method to actual data, even with potentially inaccurate forward models and noisy data. Results indicate that the best soil moisture estimates over a large range of soil moisture (0.04-0.4 cm3/cm3) and vegetation (0-5 kg/m2) conditions are achievable when the adaptive regularization parameter γ is chosen to give slightly more weight to the radiometer forward model without discarding the complementary radar measurement points.

Spatial and temporal variability in landscape freeze- thaw (FT) status at higher latitudes and elevations significantly impacts land surface water mobility and surface energy partitioning, with major consequences for regional climate, hydrological, ecological, and biogeochemical processes. With the development of new-generation spaceborne remote sensing instruments, future L-band missions, including the NASA Soil Moisture Active and Passive mission, will provide new operational retrievals of landscape FT state dynamics at moderate (~3 km) spatial resolution. We applied theoretical simulations of L-band radar backscatter using first-order radiative transfer models with two and three-layer modeling schemes to develop a modified seasonal threshold algorithm (STA) and FT classification study over Alaska using 100-m-resolution satellite Phased Array L-band Synthetic Aperture Radar (PALSAR) observations. The backscatter threshold distinguishes between frozen and nonfrozen states, and it is used to classify the predominant frozen or thawed status of a grid cell. An Alaska FT map for April 2007 was generated from PALSAR (ScanSAR) observations and showed a regionally consistent but finer FT spatial pattern than an alternative surface air temperature-based classification derived from global reanalysis data. Validation of the STA-based FT classification against regional soil climate stations indicated approximately 80% and 75% spatial classification accuracy values in relation to respective station air temperature and soil temperature measurement-based FT estimates. An investigation of relative spatial scale effects on FT classification accuracy indicates that the relationship between grid cell size and classified frozen or thawed area follows a general logarithmic function.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite is scheduled for launch in January 2015. In order to develop robust soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, algorithm developers had identified a need for long-duration combined active and passive L-band microwave observations. In response to this need, a joint Canada-U.S. field experiment (SMAPVEX12) was conducted in Manitoba (Canada) over a six-week period in 2012. Several times per week, NASA flew two aircraft carrying instruments that could simulate the observations the SMAP satellite would provide. Ground crews collected soil moisture data, crop measurements, and biomass samples in support of this campaign. The objective of SMAPVEX12 was to support the development, enhancement, and testing of SMAP soil moisture retrieval algorithms. This paper details the airborne and field data collection as well as data calibration and analysis. Early results from the SMAP active radar retrieval methods are presented and demonstrate that relative and absolute soil moisture can be delivered by this approach. Passive active L-band sensor (PALS) antenna temperatures and reflectivity, as well as backscatter, closely follow dry down and wetting events observed during SMAPVEX12. The SMAPVEX12 experiment was highly successful in achieving its objectives and provides a unique and valuable data set that will advance algorithm development.

We propose a new model for estimating subsurface soil moisture using P-band radar data over barren, shrubland, and vegetated terrains. The unknown soil moisture profile is assumed to have a second-order polynomial form as a function of subsurface depth with three unknown coefficients that we estimate using the simulated annealing algorithm. These retrieved coefficients produce the value of soil moisture at any given depth up to a prescribed depth of validity. We use a discrete scattering model to calculate the radar backscattering coefficients of the terrain. The retrieval method is tested and developed with synthetic radar data and is validated with measured radar data and in situ soil moisture measurements. Both forward and inverse models are briefly explained. The radar data used in this paper have been collected during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission flights in September and October of 2012 over a 100 km by 25 km area in Arizona, including the Walnut Gulch Experimental Watershed. The study area and the ancillary data layers used to characterize each radar pixel are explained. The inversion results are presented, and it is shown that the RMSE between the retrieved and measured soil moisture profiles ranges from 0.060 to 0.099 m3/m3, with a Root Mean Squared Error (RMSE) of 0.075 m3/m3over all sites and all acquisition dates. We show that the accuracy of retrievals decreases as depth increases. The profiles used in validation are from a fairy dry season in Walnut Gulch and so are the accuracy conclusions.

A combined radar-radiometer soil moisture estimation framework is presented in this work, which utilizes both radar backscatter and radiometer brightness temperature using physics-based models that fundamentally couple the scattering and emission processes. A regularization or tuning parameter is introduced within the optimization algorithm to enable flexibility and adaptability. It is observed that by finding the right balance between radar and radiometer contributions within a “joint” cost function, best estimates over a larger range of surface soil moisture and roughness are achievable. Monte Carlo numerical simulations are performed to highlight how this novel Active-Passive method is capable of fully utilizing emission and scattering sensitivities to surface soil moisture.

Terrain topography is of great importance for vegetated areas given that even small slopes impact and alter the radar wave interactions between the ground and the overlying vegetation. Current missions generally exclude pixels with large topographic slopes or disregard the terrain topography entirely, potentially accumulating substantial modeling errors and therefore impacting the retrieval performance over such sloped pixels. A flexible and modular radar scattering model has been developed: it describes multispecies vegetation over an N-layered soil with rough interfaces and considers a two-dimensional slope. Simulation results show the impact of a two-dimensional slope for a range of tilt angles: a 3 degree tilt in the plane of incidence translates to a change of up to 6 dB in HH, 2 dB in VV and 1 dB in HV for the total radar backscatter. The terrain topography is shown to be crucial for accurate forward modeling, especially over forested areas.

The focus for geophysical parameter retrieval from space, such as soil moisture, is shifting towards lower frequencies allowing better penetration through vegetation (rendering it less visible) and into the soil (allowing soil moisture sensing into depth). But the ionosphere becomes increasingly opaque at lower frequencies, which intensifies the need for mitigation of ionospheric effects to allow the use of spaceborne radar signals. This work focuses on a radar-only method to predict and mitigate the Faraday rotation effect, which is considered to be the remaining dominant effect. A novel method to retrieve the Faraday rotation angle by using three different optimization techniques in the presence of other system distortion terms with no external targets is presented. The retrieval performance with synthetic spaceborne radar data is very promising; it allows a successful retrieval of the Faraday rotation angle if it is initiated within ±25° of the ground truth.

Northern peatlands are estimated to hold about 30 % of the total global pool of soil carbon or 13 % of the total terrestrial carbon in the biosphere [1]. The warmer, drier conditions being experienced throughout the Arctic appear to be accelerating both aerobic and anaerobic decomposition of northern peatland soils, thereby increasing emissions of methane (CH4) and carbon dioxide (CO2) [2]. If continued, this trend could cause northern peatlands to become major sources of atmospheric carbon, with existing models predicting large increases in CH4emissions as CO2levels continue to rise [3]. To better understand sources, sinks, and net fluxes of atmospheric CO2and CH4validated high-resolution maps of the extent and distribution of northern wetlands are needed [4].

Radar backscattering coefficients for heterogeneous pixels are traditionally assumed to be the average of the coefficients for the constitutive homogeneous pixels. We investigate the validity of this assumption for bare rough surfaces by using the 2-D finite-element method to compute the ensemble averaged “true” coefficients for heterogeneous pixels and compare these values with the computed averages for a variety of surfaces. We quantify the impact of heterogeneity in both soil moisture and surface roughness on the averaging assumption. We find that the validity of the assumption rests crucially on the surface correlation type (exponential or Gaussian) and length. In particular, when considering pixels with either heterogeneous soil moisture or roughness, we find that for high-contrast pixels, the backscatter averaging assumption breaks down by as much as 11 dB for Gaussian correlated surfaces for the longest correlation lengths considered (regardless of the source of heterogeneity), whereas for exponentially correlated surfaces, it breaks down by 6 dB for pixels with heterogeneous roughness and 2 dB for pixels with heterogeneous moisture. We attribute this behavior to Gaussian correlated surfaces possessing higher cross-pixel coherent interactions. Furthermore, conditions of validity for the backscatter averaging assumption are identified.

Physical models for radar backscattering coefficients are developed for the global land surface at L-band (1.26 GHz) and 40°incidence angle to apply to the soil moisture retrieval from the upcoming soil moisture active passive mission data. The simulation of land surface classes includes 12 vegetation types defined by the International Geosphere-Biosphere Programme scheme, and four major crops (wheat, corn, rice, and soybean). Backscattering coefficients for four polarizations (HH/VV/HV/350611873VH) are produced. In the physical models, three terms are considered within the framework of distorted Born approximation: surface scattering, double-bounce volume-surface interaction, and volume scattering. Numerical solutions of Maxwell equations as well as theoretical models are used for surface scattering, double-bounce reflectivity, and volume scattering of a single scatterer. To facilitate fast, real-time, and accurate inversion of soil moisture, the outputs of physical model are provided as lookup tables (with three axes; therefore called datacube). The three axes are the real part of the dielectric constant of soil, soil surface root mean square (RMS) height, and vegetation water content (VWC), each of, which covers the wide range of natural conditions. Datacubes for most of the classes are simulated using input parameters from in situ and airborne observations. This simulation results are found accurate to the co-pol RMS errors of to 3.4 dB (six woody vegetation types), 1.8 dB (grass), and 2.9 dB (corn) when compared with airborne data. Validated with independent spaceborne phased array type L-band synthetic aperture radars and field-based radar data, the datacube errors for the co-pols are within 3.4 dB (woody savanna and shrub) and 1.5 dB (bare surface). Assessed with spaceborne Aquarius scatterometer data, the mean differences range from ~ 1.5 to 2 dB. The datacubes allow direct inversion of sophisticated forward models without empirical parameters or formulae. This capability is evaluated using the time-series inversion algorithm over grass fields.

Radar measurements at P-band are sensitive to profile soil moisture. Associated backscatter measurements depend on the distribution and variation of the soil moisture profile. Existing scattering models account for this variation by approximating the soil moisture profile as consisting of a number of homogeneous layers. Since the inversion of the scattering models during the retrieval process can be based on only a few polarimetric backscatter measurements, the number of obtainable independent layers in the profile representation is limited. The purpose of this paper is to gain insights into the effects of the layering representation on the resulting modeled forward scattering. These insights form the rational basis for the design of retrieval algorithms. The effects of reflections between layers and other sources of error on simulated backscattering coefficients are first illustrated using several case studies. To determine the combined effect of different error sources for realistic soil moisture profiles, ten years of conditions at a grassland in California are studied. Depending on the layering strategy and the polarization, the root-mean-square error (RMSE) of backscattering coefficients due to misrepresenting the profile alone can be up to 2 dB, although errors can be up to 10 dB in particular cases. The error generally decreases as additional layers are added. The HH-polarization is more sensitive to the subsurface than the VV-polarization and has greater errors. Using a profile-dependent layer placement strategy decreases the RMSE of the backscatter simulation by less than 1 dB relative to a strategy with fixed layering.

A compact polarimetric (CP) radar system requires fewer measurements than a fully polarimetric (FP) system, thus allowing added flexibility in radar system design. Previous studies have shown the potential of using compact polarimetry for radar remote sensing of soil moisture. This paper extends previous studies by considering a time series data cube retrieval algorithm and measurements in the presence of vegetation. Vegetation information is assumed to be provided by an ancillary data source in the retrieval process. The performance of an algorithm for reconstructing FP information from CP measurements of vegetated soil surfaces is also examined. The results of the study show that only a modest degradation in soil moisture retrieval performance occurs when compact-pol measurements are used in place of full-pol data.

A soil moisture retrieval algorithm is presented whereby both radar and radiometer measurements are used simultaneously in an optimization scheme to retrieve for surface soil moisture in the presence of vegetation. Further, using fine-resolution radar measurements, a disaggregated brightness temperature product at the radar resolution is developed to reconcile the spatial resolution discrepancy between the two measurements. Numerical simulations are performed to synthesize SMAP data, and then via the method of Simulated Annealing, optimization is accomplished to retrieve high resolution soil moisture.

Most traditional radar retrieval techniques utilize radar forward models that assume or require homogeneous scenes. NASA's upcoming Soil Moisture Active Passive (SMAP) mission launching in October 2014 will carry an L-band radar delivering a pixel size of 3 km × 3 km. Even though this resolution is finer than the SMAP 36 km radiometer pixels, it is still coarse enough that modeling such pixels assuming homogeneous scenes is not realistic. This highlights the need to develop spatial aggregation and disaggregation techniques using radar forward scattering models that assume homogeneity over fine-scale sub-pixels and derive tailor-made models for their contribution to the coarse-scale satellite pixel for successful soil moisture retrieval. For radar based soil moisture retrieval to be a strong candidate for future products, this uncertainty has to be successfully mitigated. To study the heterogeneity at landscape level, high-resolution P-band data from the Earth Venture 1 (EV-1) Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission is used as its pixel resolution is fine enough to assume homogeneity over a pixel. In this work, a sampling area over a well-known AirMOSS study site is chosen, an aggregation study based on landcover type is carried out and a coarse resolution radar backscatter function over the sampling area is found to be a weighted linear average of the contributions of the fine-scale pixels.

The inversion of physics-based models presents a promising alternative to empirical relationships for the retrieval of forest structure from Synthetic Aperture Radar (SAR) data, one particular advantage being the ability to include information on soil and vegetation moisture. This paper describes a two-stage estimation algorithm for retrieving canopy and stem parameters, applied to AIRSAR data over a site in Queensland, Australia and validated using eight field plots.

A model of 3-D electromagnetic scattering from multiple rough surfaces within homogeneous-layered or vertically inhomogeneous media is developed in this work. This model, aimed at radar remote sensing of surface-to-depth profiles of soil moisture, computes total bistatic radar cross sections from the multilayer structure based on the scattering matrix approach, cascading the scattering matrices of individual rough interfaces and the layer propagation matrices. We have recently developed the single-surface scattering matrix obtained using the stabilized extended boundary condition method (SEBCM) providing both large validity range over the surface roughness and higher computational efficiency compared to fully numerical solutions. In the presence of a vertical dielectric profile, the aggregate scattering matrix of the profile is obtained from the model of stratified homogeneous layers. Results of this multilayer SEBCM model are validated with small perturbation method of up to third order and the method of moments. Additionally, the model is used to perform a sensitivity analysis of the scattering cross section with respect to perturbations in ground parameters such as subsurface layer separation, roughness of surface and subsurface layers, and moisture content of subsurface layers. The multilayer SEBCM model developed in this work presents a realistic and computationally feasible method for solving scattering from multilayer rough surfaces of realistic roughness, providing an accurate and efficient tool for future retrievals of soil moisture profiles.

The Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) was carried out in Saskatchewan, Canada, from 31 May to 16 June, 2010. Its main objective was to contribute to Soil Moisture and Ocean Salinity (SMOS) mission validation and the prelaunch assessment of the proposed Soil Moisture Active and Passive (SMAP) mission. During CanEx-SM10, SMOS data as well as other passive and active microwave measurements were collected by both airborne and satellite platforms. Ground-based measurements of soil (moisture, temperature, roughness, bulk density) and vegetation characteristics (leaf area index, biomass, vegetation height) were conducted close in time to the airborne and satellite acquisitions. Moreover, two ground-based in situ networks provided continuous measurements of meteorological conditions and soil moisture and soil temperature profiles. Two sites, each covering 33 km × 71 km (about two SMOS pixels) were selected in agricultural and boreal forested areas in order to provide contrasting soil and vegetation conditions. This paper describes the measurement strategy, provides an overview of the data sets, and presents preliminary results. Over the agricultural area, the airborne L-band brightness temperatures matched up well with the SMOS data (prototype 346). The radio frequency interference observed in both SMOS and the airborne L-band radiometer data exhibited spatial and temporal variability and polarization dependency. The temporal evolution of the SMOS soil moisture product (prototype 307) matched that observed with the ground data, but the absolute soil moisture estimates did not meet the accuracy requirements (0.04 m3/m3) of the SMOS mission. AMSR-E soil moisture estimates from the National Snow and Ice Data Center more closely reflected soil moisture measurements.

We present an analytical solution for coherent scattering of electromagnetic waves from a two-layer rough surface structure with uncorrelated random rough interfaces. The Kirchhoff approximation is used to predict the coherent (specular) component of the scattered wave from a layered rough surface that is assumed to have radii of curvature much larger than the wavelength to allow the application of the method. The roughness on both boundaries is assumed small such that the coherent component of the scattered wave is dominant. The derived solution includes all orders of the scattered wave and has a simple algebraic expression that can be readily computed. We validate the solution against a numerical method and present simulation results for various cases.

An integrated soil moisture retrieval algorithm is presented in this work wherein both radar and radiometer measurements are used simultaneously to retrieve surface soil moisture. This method is applied to bare rough surfaces with varying soil moisture and roughness distributions. A thresholding method based on physical models of scattering and emission is presented to obtain equivalent estimated brightness temperatures from active radar measurements. This technique is used as a constraint to link active and passive data within the optimization scheme. Numerical simulations are performed to investigate the proposed inversion technique using the method of Simulated Annealing.

A generalized radar scattering model to predict backscattering for different frequencies and polarizations is crucial in the endeavor to understand the relationship between radar measurement and properties of both the vegetation and soil, and for allowing the estimation of both vegetation and soil information from radar data. In this work, we develop a combined radar scattering model of vegetation and multilayered soil structure to represent realistic soils with multiple layers of various textures, depths, and moisture contents. A multilayered soil scattering model based on first order small perturbation method (SPM) is integrated [1] to account for direct ground scattering. Furthermore, the coherent interaction between the vegetation and layered ground is included using a recently developed model based on the Kirchhoff approximation [2].

The inversion of physics based models presents an alternative to empirical relationships for the retrieval of forest structure from Synthetic Aperture Radar (SAR) data. A major disadvantage of such techniques is instability in the presence of moderate levels of noise. The effects of noise on the accuracy with which parameters can be retrieved is evaluated in this study under a number of conditions.

The Airborne Observatory of Subcanopy and Subsurface (AirMOSS) is one of the five Earth Venture-1 missions selected in May 2010, and seeks to improve the estimates of the north American net ecosystem carbon exchange (NEE) through high-resolution observations of root zone soil moisture (RZSM). To obtain estimates of RZSM and assess its heterogeneities, AirMOSS will fly a P-band (430 MHz) synthetic aperture radar (SAR) over 2500 km2areas within nine major biomes of north America. Retrieval of RZSM in the presence of substantial vegetation requires the construction of accurate radar scattering models that account for diverse vegetation conditions as well as subsurface inhomogeneities. In this paper we provide a summary of recent advances in this area, emphasizing several coherent and incoherent models of scattering from multilayered inhomogeneous rough surfaces, as well as strategies for using these forward models in retrieval algorithms.

We develop the stabilized extended boundary condition method (SEBCM) based on the classical EBCM to solve the 3-D vector electromagnetic scattering problem from arbitrary random rough surfaces. Similar to the classical EBCM, we expand the fields in terms of Floquet modes and match the extended boundary conditions at test surfaces away from the actual rough surface to retrieve the surface currents and therefore the scattered fields. However, to solve long-standing stability problems of the classical EBCM, we introduce a z-coordinate transformation to restrict and control the test surface locations explicitly. We also introduce the concepts of moderated test surface locations and balanced k-charts for further stabilization and optimization of the solutions. The computational efficiency is optimized by judicious submatrix decomposition. The resulting bistatic scattering cross sections are validated by comparing with analytical and numerical solutions. Specifically, the solutions are compared with those from the small perturbation method and small-slope approximation within their validity region, and with those from the method of moments outside the validity domains of analytical solutions. It is shown that SEBCM gives accurate, numerically efficient, full-wave solutions over a large range of surface roughnesses and medium losses, which are far beyond the validity range of analytical methods. These properties are expected to make SEBCM a competitive forward solver for soil moisture retrieval from radar measurements.

In this paper, we study the radar retrieval of soil moisture as well as canopy parameters in a range of boreal forests. The retrieval is formulated as an optimization problem where the difference between data and prediction of a forward scattering model is minimized. The forward model is a discrete scatterer radar model, and the optimization algorithm is a global optimization scheme known as simulated annealing. The inversion method is first applied to synthetic data assuming hypothetical allometric relationships to make the retrieval possible by reducing the number of unknown vegetation parameters. The inversion algorithm is then validated using the data acquired with the National Aeronautics and Space Administration (NASA)/Jet Propulsion Laboratory (JPL) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) in June 2010 in central Canada boreal forests in support of the prelaunch calibration and validation activities of NASA's Soil Moisture Active and Passive (SMAP) mission. The inversion results for synthetic data show that the absolute retrieval error in soil moisture and relative retrieval error in canopy height are small, while the relative output error in trunk density could be large. The inversion results for actual field data show a great accuracy in soil moisture retrieval for Old Jack Pine and Young Jack Pine forests but show large retrieval errors for many of the radar pixels in the Old Black Spruce site. This paper shows that L-band radar is capable of retrieving surface soil moisture in forests with a high biomass where the forest structure allows soil moisture information to be carried by scattering mechanisms.

In this paper, a heterogeneous landscape simulator is introduced, which will facilitate lifelike forward modeling of various landscapes by taking additional information and ancillary data into account. First results of simple spatial aggregation techniques are discussed and the uncertainty of soil moisture retrieval is quantified. A scaling analysis is performed by investigating above and below-ground modeling parameters for their influence on the radar model output and their impact on simple aggregation problems.

In this work, a 3D scattering model from layered arbitrarily random rough surfaces with embedded discrete random scatterers is constructed based on the scattering matrix approach. Scattering matrix of a single rough surface is found using the stabilized extended boundary condition method (SEBCM). Meanwhile, by finding the T matrices of discrete scatterers using analytical or numerical methods, the random medium scattering solution is found based on the recursive T-matrix approach and near-to-far field transformed numerical plane wave expansion of the vector spherical harmonics. Solutions provided in this work include multiple scattering effects among the medium scatterers and between subsurfaces and sublayers. The high computational efficiency and accuracy of this method enable it to serve as a powerful tool for studying the role of vegetation roots and other sublayer inhomogeneities in retrieval of subsurface and root zone soil moisture from radar measurements.

In this paper, we study the radar retrieval of soil moisture and canopy parameters in a forested area. The for ward model is a discrete scatterer radar model and the inverse model implements a global optimization scheme known as simulated annealing. Inversion is first applied to synthetic data assuming hypothetical allometric relationships to make the retrieval possible by reducing the number of unknowns. The inversion algorithm is then validated using data from the Canadian Experiment for Soil Moisture in 2010 (CanEx-SMlO) acquired with the NASA/JPL UAVSAR in June 2010 in central Canada boreal forests in support of the pre-launch calibration and validation activities of NASA's Soil Moisture Active and Passive (SMAP) mission.

We study the retrieval of surface and deep moisture of bare soil from noisy radar observations using simulated annealing. Due to moisture variations with depth, we model bare soil with a stratified dielectric profile with a rough surface on top. Small perturbation method (SPM) is used as the forward model. We use the full moisture profile for radar data synthesis and study the retrieval accuracy by varying the number of layers that represent the soil profile during inversion. The effect of measurement frequency on the accuracy of deep moisture retrieval is investigated. This work is intended for assessing the effect of subsurface profile on soil moisture retrieval from radar observations of NASA's Soil Moisture Active and Passive (SMAP) mission and future lower frequency airborne or spaceborne systems that may follow SMAP.

A generalized radar scattering model based on wave theory is described. The model predicts polarimetric radar backscattering coefficients for structurally complex vegetation comprised of multiple species and layers. Compared to conventional two-layer crown-trunk models, modeling of actual forests has been improved substantially, allowing better understanding of microwave interaction with vegetation. The model generalizes an existing single-species discrete scatterer model and, by including scattering and propagation effects through judiciously defined vegetation layers, enables its application to an arbitrary number of species types. The scatterers within each layer are modeled as finite cylinders or disks having arbitrary size, density, and orientation, as in the predecessor model. The distorted Born approximation is used to represent the propagation through each layer, while scattering from each is modeled as a linear superposition of scattering from its respective random collection of scatterers. Interactions of waves within and between each layer and direct scattering from the ground are accounted for. Validation of the model is presented based on its application to 23 wooded savanna sites located in Queensland, Australia, and comparison with Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) and National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) Airborne Synthetic Aperture Radar (AIRSAR) data. Results indicate good agreement between simulated and actual backscattering coefficients, particularly at HH and VV polarizations. More discrepancies are found at HV polarizations and can be explained by uncertainties in the knowledge of input parameters, such as inaccuracies in the surface model, surface roughness parameterization, and soil moisture.

On 27 May 2010, NASA announced the first Earth Venture (EV-1) selections in response to a recommendation made by the National Research Council for low-cost investigations fostering innovation in Earth Science. The five EV-1 investigations span the Earth science focus areas of atmosphere, weather, climate, water and energy and, carbon and represent Earth science researchers from NASA as well as other government agencies, academia and industry from around the world. The five EV-1 missions are (1) Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS); (2) Airborne Tropical TRopopause Experiment (ATTREX); (3) Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE); (4) Deriving Information on Surface Conditions from Column and VERtically Resolved Observations Relevant to Air Quality (DISCOVER-AQ), and (5) Hurricane and Severe Storm Sentinel (HS3). The Earth Venture missions are managed out of the Earth System Science Pathfinder (ESSP) Program Office [3]

The use of a non-linear estimation algorithm for retrieving the biomass and structure of vegetation from polarimetric Synthetic Aperture Radar (SAR) data is demonstrated for woody regrowth in Queensland, Australia dominated by Acacia harpophylla (Brigalow). By varying the size and density of trees and associated woody components (branches and trunks), multiple simulations of the backscattering coefficient (σ0) were performed based on the SAR simulation model of. Functions relating σ0to these variables were subsequently used to generate spatial estimates from NASA JPL airborne SAR (AIRSAR) data. Above ground biomass was estimated from stem density and size measurements using available allometric relationships. The study demonstrates potential for retrieval of regrowth structure and biomass through nonlinear estimation.

In this paper, the stabilized extended boundary condition method (SEBCM) is developed based on the classical EBCM to solve both 2D and 3D electromagnetic scattering from arbitrary random rough surfaces. The SEBCM gives accurate full wave solutions over large range of surface roughnesses and medium losses, which are far beyond the validity range of analytical methods, and perform with much higher efficiency than numerical methods. These properties make SEBCM a competitive forward model in the inverse problem for soil moisture retrieval from radar measurements.

The solution to the inverse problem for a three-layer medium representing a large class of natural subsurface structures is developed in this paper using radar data. The retrieval of the layered medium parameters is accomplished as a sequential nonlinear optimization starting from the top layer and progressively characterizing the layers below. The optimization process is accomplished by an efficient iterative technique built around the solution of the forward scattering problem. The forward scattering process is formulated by using the Extended Boundary Condition Method (EBCM) and constructing reflection and transmission matrices for each interface. These matrices are then combined into the generalized scattering matrix for the entire system, from which radar scattering coefficients are then computed. To be efficiently utilized in the inverse problem, the forward scattering model is simulated over a wide range of unknowns to obtain a complete set of subspace-based equivalent closed form models that relate radar cross section coefficients to the sought-for parameters including dielectric constants of each layer and separation of the layers. The inversion algorithm is implemented as a modified conjugate-gradient-based nonlinear optimization. It is assumed that multifrequency radar measurements are available from tower-mounted or airborne platforms, for example at typical radar frequencies of L-band and P-band (UHF). It is shown that this technique results in accurate retrieval of surface and subsurface parameters, even in the presence of noise.

In this study, we develop a combined active/passive technique to estimate surface soil moisture with the focus on the short vegetated surfaces. We first simulated a database for both active and passive signals under SMAP's sensor configurations using the radiative transfer model with a wide range of conditions for surface soil moisture, roughness and vegetation properties that we considered as the random orientated disks and cylinders. Using this database, we developed 1) the techniques to estimate surface backscattering and emission components and 2) the technique to estimate soil moisture with the estimated surface backscattering and emission components. We will demonstrate these techniques with the model simulated data and its validation with the airborne PALS image data from the soil moisture SGP'99 and SMEX'02 experiments.

We have been developing high-resolution thematic maps of wetlands throughout the North American boreal regions. We assemble a wetlands map for each region based on data collected during the late 1990s, then construct a second map based on data collected during the late 2000s. Comparison of the two maps then makes it possible to assess changes that have occurred over the course of the intervening decade..

We previously derived the bistatic scattering coefficients of a 3-D two-layer dielectric structure with slightly rough boundaries using the small perturbation method (SPM). The use of SPM raises the question about its region of validity, which pertains to the conditions on each layer roughness, slope, and permittivity for which the first-order SPM is accurate within a specified error bound. To this end, the SPM solution needs to be compared with an accurate solution that does not impose roughness restrictions. We use the method of moments to solve an integral equation to analyze electromagnetic scattering from a large ensemble of two-layer structures. Simulations are performed for 1-D rough surfaces represented by zero-mean stationary random processes, separating homogeneous dielectric layers. Observations are reported on the accuracy of the first-order SPM for TM incidence at a fixed incidence angle of 45°.

The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey. SMAP will make global measurements of the soil moisture present at the Earth's land surface and will distinguish frozen from thawed land surfaces. Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy, and carbon transfers between the land and the atmosphere. The accuracy of numerical models of the atmosphere used in weather prediction and climate projections are critically dependent on the correct characterization of these transfers. Soil moisture measurements are also directly applicable to flood assessment and drought monitoring. SMAP observations can help monitor these natural hazards, resulting in potentially great economic and social benefits. SMAP observations of soil moisture and freeze/thaw timing will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes. The SMAP mission concept will utilize L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days. In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon. SMAP is scheduled for launch in the 2014-2015 time frame.

In this paper, we consider the problem of monitoring soil moisture evolution using a wireless network of in situ sensors. Continuously sampling moisture levels with these sensors incurs high-maintenance and energy consumption costs, which are particularly undesirable for wireless networks. Our main hypothesis is that a sparser set of measurements can meet the monitoring objectives in an energy-efficient manner. The underlying idea is that we can trade off some inaccuracy in estimating soil moisture evolution for a significant reduction in energy consumption. We investigate how to dynamically schedule the sensor measurements so as to balance this tradeoff. Unlike many prior studies on sensor scheduling that make generic assumptions on the statistics of the observed phenomenon, we obtain statistics of soil moisture evolution from a physical model. We formulate the optimal measurement scheduling and estimation problem as a partially observable Markov decision problem (POMDP). We then utilize special features of the problem to approximate the POMDP by a computationally simpler finite-state Markov decision problem (MDP). The result is a scalable, implementable technology that we have tested and validated numerically and in the field.

In this work we develop a methodology for imaging subsurface point targets using a single-pass strip-map synthetic aperture radar (SAR). The point targets are embedded in an arbitrary homogeneous half space, and are located at arbitrary depths. It is assumed that the radar frequency is low enough and system sensitivity high enough to allow the required two-way penetration depth to target. The succession of steps required to form the image of the subsurface point targets are described, including the estimation of the subsurface wave velocity, estimation of the depth of the point target, and the modified range and azimuth filters to achieve optimum resolution. The theoretical approach is described and results are presented for a range of point target depths, subsurface velocities, and radar system parameters. It is found that with the assumptions made it is possible to image the point targets in 3D with good range, azimuth, and depth accuracy.

This work addresses the noise sensitivity of the simulated annealing method in inversion of subsurface parameters of layered rough surfaces to measurement noise. We consider two different noise models and assess the noise response of the inversion algorithm for each of the models. Conclusions are made based on the calculated average and standard deviation of the output error in the retrieved model parameters.

Previously, we have developed a robust algorithm for mapping boreal wetlands using L-band satellite radar imagery, and in particular have used the method to produce a complete vegetated wetlands map of Alaska using the JERS radar data. In this work, we apply this algorithm to produce a static map of Canadian wetlands from the 1997-98 era JERS radar data at 100-m resolution, to be followed in the future by 2007-era ALOS/PALSAR maps.

We have been developing a continental-scale map of the North American boreal wetlands based on L-Band SAR imagery collected in 1997-1998 by the Japanese Earth Resources Satellite (JERS). The map currently covers the entire state of Alaska, identifying up to nine wetlands classes and two uplands classes. We have also recently obtained and classified a region of L-Band SAR imagery collected in 2007 by the Advanced Land Observing Satellite (ALOS) Phased Array L-Band SAR (PALSAR). Herein, we compare the results of the PALSAR classification to those of the JERS classification in order to detect changes in wetlands type or extent during the decade-long interval between the two sets of SAR imagery.

In this paper, the model parameters of a two-layer dielectric structure with random slightly rough boundaries are retrieved from data that consist of the backscattering coefficients for multiple polarizations, angles, and frequencies. We use the small perturbation method to solve the forward problem. The inversion problem is then formulated as a least square problem and is solved using a global optimization method known as simulated annealing, which is shown to be a robust retrieval algorithm for our purpose. The algorithm performance depends on several parameters. We make recommendations on these parameters and propose a technique for exiting local minima when encountered. We test the sensitivity of the inversion scheme to measurement noise and present the noise analysis results.

We have developed a new concept for a smart sensor web technology for measurements of soil moisture that include spaceborne and in-situ assets. The objective of the technology is to enable a guided/adaptive sampling strategy for the in-situ sensor network to meet the measurement validation objectives of the spaceborne sensors with respect to resolution and accuracy. One potential application is the Soil Moisture Active/Passive (SMAP) mission. The science measurements considered are the surface-to-depth profiles of soil moisture estimated from satellite radars and radiometers, with calibration and validation using in-situ sensors. Installing an in-situ network to sample the field for all ranges of variability is impractical. However, a sparser but smarter network can provide the validation estimates by operating in a guided fashion with guidance from its own sparse measurements. The feedback and control take place in the context of a dynamic data assimilation system subject to energy and accuracy constraints. The overall design of the smart sensor web including the control architecture, assimilation framework, and actuation hardware are presented in this paper. We also present results of initial numerical and laboratory demonstrations of the sensor web concept, which includes a small number of soil moisture.

We have shown that the method of simulated annealing (SA) is capable of inverting subsurface parameters of a three-dimensional, two-layer dielectric structure with slightly rough interfaces. In this work, we address the sensitivity of the Simulated Annealing to measurement noise. We also study the impact of the number of measurement parameters, i.e., number of frequency points and number of observation angles, on sensitivity of the inversion algorithm.

Modeling of electromagnetic scattering from discrete random media in layered rough surfaces finds various applications, including depth retrieval of layered snow-covered ice and remote sensing of vegetation canopy. In this paper, a coherent technique for solving scattering from discrete random media in layered rough surfaces is presented. The significance of the development of a full-wave solution to this problem stems from the fact that both co-polarized phase difference and polarized scattering coefficients can only be accurately determined using a coherent approach. Therefore, the objective of this paper is to formulate a full-wave solution for scattering from discrete random media in layered rough surfaces as well as to demonstrate the potential in the retrieval of subsurface parameters pertaining to the physical properties of rough surfaces and discrete random media using polarized scattering coefficients and co- polarized phase difference. The core of our technique lies in the use of plane wave decomposition. Plane wave solution for the scattered field due to a rough surface is obtained using extended boundary condition method (EBCM). The recursive T-matrix algorithm together with cylindrical-waves-to-plane-waves transformation matrices is employed to deal with scattering from discrete random media. Subsequently, plane wave solutions for the scattered fields due to rough surfaces and discrete random media are then cast into reflection and transmission matrices. These reflection and transmission matrices facilitate the application of scattering matrix technique which coherently accounts for electromagnetic interactions between layered rough surfaces and discrete random media. Various numerical results are examined and it is shown that the subsurface parameters may significantly impact backscattering coefficients and co-polarized phase difference even when the subsurface ground is covered by a rough layer of discrete random media.

We previously applied an optimization technique known as Simulated Annealing (SA) to the inverse problem associated with a 3D two-layer dielectric structure with slightly rough interfaces and showed that simulated annealing methods are capable of globally minimizing cost functions with many local minima [1]. Nonlinearity of the cost function is a major factor that decreases the performance of the inversion algorithm. With a fixed set of measurement and inversion parameters, as the number of unknown model parameters increases, the cost function nonlinearity becomes more severe, decreasing the efficiency of inversion and making the annealing process perform like an inefficient brute force search. The focus of this work is on strategies to choose the optimal set of measurement parameters for retrieval of the largest possible number of parameters of a layered dielectric structure.

We have used two seasons of L-band SAR imagery to produce a thematic map of wetlands throughout Alaska. The classification was developed using the Random Forests statistical decision tree algorithm. Input data included mosaics of summer and winter JERS-1 SAR imagery with associated image collection dates, summer and winter SAR backscatter texture, elevation, slope, proximity to water, and geographic latitude. The accuracy of the resulting thematic map was quantified using extensive ground reference data. The overall aggregate accuracy calculated based on all classified pixels was 89.5%, with individual per-tile aggregate accuracies ranging from 80% to 97%. As the first high-resolution large-scale synoptic wetlands map of Alaska, this product provides the basis for improved characterization of land- atmosphere CH4and CO2fluxes and climate change impacts associated with thawing soils and changes in extent and drying of wetland ecosystems.

Radar remote sensing of soil moisture content at low frequencies requires an accurate scattering model of realistic soils, which often involves multilayer rough surfaces and dielectric profiles. In this paper, a hybrid analytical/numerical solution to two-dimensional scattering from multilayer rough surfaces separated by arbitrary dielectric profiles based on the extended boundary condition method (EBCM) and scattering matrix technique is presented. The reflection and transmission matrices of rough interfaces are constructed using EBCM. The dielectric profiles are modeled as stacks of piecewise homogeneous dielectric thin layers, whose scattering matrices are computed by recursively cascading reflection and transmission matrices of individual dielectric interfaces. The interactions between the rough interfaces and stratified dielectric profiles are taken into account by applying the generalized scattering matrix technique. The scattering coefficients are obtained by combining the powers computed from the resulting Floquet modes of the overall system. The bistatic scattering coefficients are validated against existing analytical and numerical solutions. Field-collected soil moisture data are then used for numerical simulations to investigate the penetration capability at different frequencies and to address the potential of low-frequency radar systems in estimating deep soil moisture. In particular, soil moisture profiles during dry ground, wet ground, and wet subsurface layer conditions are examined. The results show that both backscattering coefficients and copolarized phase difference at low frequencies are sensitive to the roughness of subsurface interfaces and deep soil moisture. Also, much larger depth sensitivity can be achieved using copolarized phase difference than scattering coefficients

The Microwave Observatory of Subcanopy and Subsurface (MOSS) is a mission concept for a spaceborne synthetic aperture radar (SAR) system that provides global observations of soil moisture under substantial vegetation cover (exceeding 20 kg/m2) and at useful depths (1-5 m). The concept was developed and a number of new required technologies were demonstrated through a National Aeronautics and Space Administration Earth Science Technology Office Instrument Incubator Program project. This very high frequency (VHF)/ultrahigh frequency (UHF) polarimetric SAR is designed to provide 7-10-day observations of soil moisture at 1-km resolution. The rapid repeat cycle mandates swath widths in the range of 300-400 km, which must be realized by a 30-m-long antenna. Conventional array implementations would result in a mass of more than 4000 kg, whereas with the technology proposed and demonstrated in this project, the total antenna mass is less than 500 kg. The antenna concept is a dual-stacked patch array feed illuminating a 30-m mesh reflector to synthesize the long apertures and achieve the wide swath. The feed system prototype was fabricated and its performance demonstrated. Other major project components were: (1) system-level SAR and mission design; (2) demonstration of science data and products, using a tower-based VHF/UHF radar; (3) spacecraft and mesh reflector antenna mechanical design; (4) developing mitigation strategies for ionospheric effects; and (5) assessing frequency interference effects. Experimental science data were generated from the tower radar for soil moisture profiling in Arizona and for forest penetration in Oregon. The soil moisture products were demonstrated through an integrated inversion-processing algorithm. This paper summarizes the results from the MOSS project and demonstrates the feasibility of the spaceborne mission.

Radar remote sensing of soil moisture content at low frequencies requires an accurate scattering model of realistic soils, which often involves multilayer rough surfaces and inhomogeneous dielectric profiles. In this paper, a hybrid analytical/numerical solution to two- dimensional scattering from multilayer rough surfaces separated by arbitrary dielectric profiles based on the extended boundary condition method (EBCM) and scattering matrix technique is presented. The reflection and transmission matrices of a rough interface are constructed using EBCM. The inhomogeneous dielectric profile is modeled as a stack of piecewise homogeneous dielectric thin layers. The scattering matrices of an inhomogeneous dielectric profile are computed by recursively cascading reflection and transmission matrices of individual dielectric interfaces from the bottom dielectric interface to the top interface. The interactions between the rough interfaces and the inhomogeneous dielectric profile are taken into account by applying the generalized scattering matrix technique, hi numerical simulations, the actual field-collected soil moisture data are used, hi particular, the dielectric profiles during both dry and wet ground conditions are examined. The numerical simulations are performed to investigate both bistatic scattering coefficients and copolarized phase difference due to different subsurface roughness parameters and ground conditions. Simulation results show that the bistatic scattering coefficients at low frequencies are sensitive to subsurface roughness parameters and copolarized phase difference strongly depends on soil moisture contents.

We use the simulated annealing (SA) method to find the global minimum of a cost function associated with scattering from a layered dielectric structure with rough boundaries. In the case of a two layer structure, even a simple implementation of the SA algorithm is capable of retrieving more parameters than is possible with local optimization methods. We discuss different strategies that can improve the performance of this algorithm for our purpose.

An analytical method to calculate the bistatic-scattering coefficients of a three-dimensional layered dielectric structure with slightly rough interfaces is presented. The interfaces are allowed to be statistically distinct, but possibly dependent. The waves in each region are represented as a superposition of an infinite number of up- and down-going spectral components whose amplitudes are found by simultaneously matching the boundary conditions at both interfaces. A small-perturbation formulation is used up to the first order, and the scattered fields are derived. The calculation intrinsically takes into account multiple scattering processes between the boundaries. The formulation is then validated against known solutions to special cases. New results are generated for several cases of two- and three-layer media, which will be directly applicable for modeling of the signals from radar systems and subsequent estimation of a layered medium subsurface properties, such as moisture content and layer depths

The electromagnetic scattering of rough surface has been investigated extensively for the past decades. There exist analytical solutions to rough surface scattering such as small perturbation method (SPM) or Kirchhoff approximation (KA). These analytical solutions, however, are limited to either small or large roughness regimes. Recent efforts have been put forth to study scattering from multilayer rough surface in the application of deep soil moisture estimation. In this paper, multilayer rough surface scattering is analyzed based on an approach which combines extended boundary condition formulation (EBC) and transition matrix method. From the Floquet theorem, the Floquet modes translate into the coherent reflection and transmission coefficients in their corresponding scattering directions. To account for coherent multiple interactions between rough surface layers, transition matrix approach is used. Each Floquet mode direction is considered a port of a multiport system and network theory is used to characterize cascaded rough surface layers. Bistatic scattering coefficients are then obtained by incoherently averaging the power computed from the resultant Floquet modes in the scattering directions. Therefore, the electromagnetic wave interactions in multilayer rough surfaces are analyzed in a very efficient way by applying EBC to each rough surface and then cascading iteratively transition matrix of each layer interface. Finally, the results are validated against the analytical SPM solution to two-layer rough surface scattering.

Summary form only given. This paper details our recent development of a system prototype for a spaceborne sensor that addresses the current NASA science priority of measuring soil moisture "under a substantial vegetation canopy and reaching a useful depth within the uppermost soil layer". The mission is named MOSS, for the Microwave Observatory of Subcanopy and Subsurface. It will enable measurement and derivation of data products not obtained from any other current, planned, or proposed instrument, with a solution that offers high science value through a low-mass and, in the long-term, low-cost approach. The proposed system is a spaceborne synthetic aperture radar (SAR) operating at the two low frequencies of 435 MHz (UHF, P-band) and 137 MHz (VHF) to enable sensing through vegetation and down into soil. The future mission scenario is achieved from a Sun synchronous orbit of 1313 km altitude, with a swath width of 430 km, incidence angle ranges of 17-30 degrees, resolution of 1 km, and a 7-day exact repeat consistent with the temporal scale of variations of the subcanopy and subsurface soil moisture. We describe a tower-based test-bed that we have developed to validate the spaceborne measurement concept. The processing software, including polarimetric calibration aspects, is detailed, and example science products are presented. The procedures and algorithms to estimate subsurface moisture are presented in detail, and the use an iterative estimation procedure is explained. The results are validated by comparing against detailed ground truth.

A multilayer canopy scattering model is developed for mixed-species forests. The multilayer model provides a significantly enhanced representation of actual complex forest structures compared to the conventional canopy-trunk layer models. Multilayer Michigan Microwave Canopy Scattering model (Multi-MIMICS) allows overlapping layer configuration and a tapered trunk model applicable to forests of mixed species and/or mixed growth stages. The model is the first-order solution to a set of radiative transfer equations and includes layer interactions between overlapping layers. It simulates SAR backscattering coefficients based on input dimensional, geometrical, and dielectric variables of forest canopies. The Multi-MIMICS is an efficient realization of actual forest structures and can be shaped for specific interest of forest parameters. We present the model's application and validation in the paper. The model is parameterized using data collected from a 220,000-ha area of forests in central Queensland, Australia. Fifteen 50/spl times/50 m test sites representing the general forest diversity and growth stages are chosen as ground truth. Polarimetric backscattering airborne SAR (AIRSAR) data of the same area are acquired to validate the model simulations. The model predicts SAR backscattering coefficients of the test areas. Simulation results show a good agreement with AIRSAR data at most frequencies and polarizations. The simulated backscattering coefficient from the multilayer model and the standard MIMICS are also compared and significant improvements are observed.

A bistatic forest scattering model is developed to simulate scattering coefficients from forest canopies. The model is based on the Michigan Microwave Canopy Scattering (MIMICS) model (hence called Bi-MIMICS) and uses radiative transfer theory, where the first-order fully polarimetric transformation matrix is used. Bistatic radar systems offer advantages over monostatic radar systems because of the additional information provided by the diversity of the geometry. By simulating the forest canopy scattering from multiple viewpoints, we can better understand how the forest scatterers' shape, orientation, density, and permittivity affect the canopy scattering. Bi-MIMICS is parametrized using selected forest stands with different canopy compositions and structure. The simulation results show that bistatic scattering is more sensitive to forest biomass changes than backscattering. Analyzing scattering contributions from different parts of the canopy gives us a better understanding of the microwave's interaction with the tree components. The ground effects can also be studied. Knowledge of the canopy's bistatic scattering behavior combined with additional synthetic aperture radar measurements can be used to improve forest parameter retrievals. The simulation results of the model provide the required information for the design of future bistatic radar systems for forest sensing applications.

In this paper, we introduce a multilayer MIMICS for nonuniform forest canopies and present the model applications. The Michigan Microwave Canopy Scattering model (MIMICS) has been developed to simulate microwave backscattering from tree canopies. However, the crown-trunk canopy model is too restrictive for nonuniform canopy coverage. Multilayer MIMICS is developed to remove the two-layer canopy restriction. The model is a simulation solution to an array of radiative transfer equations and it includes the layer interactions between overlapping layers. This paper is focused on the model validation and application of multilayer MIMICS. Our collaborators have supplied us with extensive ground truth data from a 220,000 ha woodland and forest study areas within central Queensland, Australia. The field measurement is at the individual tree level. We can simulate the polarimetric backscattering for the ground measurement. AIRSAR data of the same area are obtained to validate the model simulation. The results show good agreement between the model simulation and SAR measurements. Analyzing an individual layer's contribution offers better understanding of canopy composition effects on backscattering. The multilayer canopy configuration improves the estimate accuracy over that from a crown-trunk layer model, as in the older version of MIMICS.

An analytical method to calculate the scattering coefficients of a three-layer 3D rough surface is introduced. The two rough interfaces are assumed to be distinct. The waves in each region are represented as a superposition of an infinite number of up- and down-going spectral wave components, whose amplitudes are found by applying the boundary conditions. A small-perturbation formulation is used in the process and the scattering coefficients are derived to the first order. The formulation is validated against known solutions for special cases of flat interfaces as well as the single rough surfaces. Results are then generated for several cases of the three-layer rough interface medium, to be used for modeling of the backscattered signals from a tower-based radar system and subsequent estimation of multilayered soil properties such as moisture content and subsurface layer height

The potential of synthetic aperture radar (SAR) data for retrieving the above-ground and component (e.g., branch, trunk) biomass of mixed-species forests (including woodlands) typical to subtropical Queensland, Australia, was evaluated using a wave scattering model based on that of Durden et al. (1989). The model was parameterized using field data collected for nine forest types, which were selected through combined analysis of 1 : 4000 aerial photographs and light detection and ranging data. The simulated SAR backscatter data demonstrated a good correspondence at most frequencies and polarizations with Airborne SAR data. Analysis of scattering mechanisms revealed dominance of C-band horizontal-vertical (HV) volume scattering and increases with small-branch/foliage biomass, dominance of L- and P-band HH trunk-ground scattering and increases with trunk biomass, and dominance of L-band HV volume (branch) scattering and increases with large-branch biomass. The study concluded that above-ground biomass estimated using empirical relationships with selected SAR channels will be more reliable for forests of similar structural form due to dominance of microwave interaction with particular biomass components and the strength and consistency of relationships between these and the affiliated components that represent the total. In mixed-species forests, retrieval will be compromised by interaction with a greater diversity of structures and variability in relationships between structural components. Although empirical relationships with selected combinations of channels (e.g., L-band HH/HV) might allow retrieval of component and total biomass of forests containing trees of similar form (e.g., as mapped using Landsat sensor data), the use of SAR inversion models was considered a more appropriate route for retrieving the biomass of forests containing a mix of structural forms.

Abstract- This paper presents an approach to quantifying the biomass (crown and stem) and structure of mixed species woodlands in Australia that is based on an inversion algorithm for SAR data. When applied to AIRSAR data acquired in 2000 over selected woodlands in Queensland, a close correspondence with ground-based estimates of biomass was observed. 1.

The accurate assessment of spatial and temporal distributions of wetlands can have a large impact in improving the estimates of the global net carbon exchange. This paper presents the methodology and sample results for the first large-scale wetlands map of the North American boreal zone, derived from JERS-1 and ERS-2 SAR imagery. The finished product will be a consistent baseline map, which can be subsequently used for time-series analyses when continuous satellite radar observations become available. The wetlands class maps are generated using a combination of optimization-based class rule definitions and a supervised classification algorithm. The wetlands class types are those defined by the Canadian Wetlands Classification System. Results are validated at a number of study sites and compared to existing local-scale wetlands maps.

Measurements of deep and subcanopy soil moisture are critical in understanding the global water and energy cycle, as well as the interaction of the carbon and water cycles, but are presently not available on a synoptic basis. In this paper, a low-frequency UHF/VHF radar mission concept is presented and technology challenges to implement it are discussed. This mission concept is currently being studies under a NASA/ESTO instrument incubator program (IIP) project. The progresses of several aspects of the project are discussed.

Biomass information is needed by research activities relating to natural resources sustainability, carbon cycle, and forest fire fuel loading. Yet, spatially and temporally consistent biomass data distribution over large scales is often not available. In this study, we explore the potential of developing such a forest biomass data sets by integrating traditional yield tables and satellite data.

Radar and optical remote sensing data are used in a unified algorithm to estimate forest variables. The study site is the H. J. Andrews experimental forest in Oregon, which has significant topography and several mature and old-growth conifer stands with biomass values sometimes exceeding 1000 tons/ha. Polarimetric multifrequency Airborne Synthetic Aperture Radar (AIRSAR) backscatter, interferometric C-band Topographic Synthetic Aperture Radar (TOPSAR) coherence, and multispectral Landsat Thematic Mapper (TM) digital numbers are used in a regression analysis that relates them to forest variable measurements on the ground. Parametric expressions are derived and used to estimate the same variables(s) at other locations from the combination of AIRSAR and TM data. It is shown that the estimation accuracy is significantly improved when the radar and optical data are used in combination compared to estimating the same variable from a single data type alone.

Characterization of boreal forests in ecosystem models requires temporal and spatial distributions of water content and biomass over local and regional scales. The authors report on the use of a semi-empirical algorithm for deriving these parameters from polarimetric synthetic aperture radar (SAR) measurements. The algorithm is based on a two layer radar backscatter model that stratifies the forest canopy into crown and stem layers and separates the structural and biometric attributes of forest stands. The structural parameters are estimated by training the model with SAR image data over dominant coniferous and deciduous stands in the boreal forest such as jack pine, black spruce, and aspen. The algorithm is then applied on AIRSAR images collected during the Boreal Ecosystem Atmospheric Study (BOREAS) over the boreal forest of Canada. The results are verified using biometry measurements during BOREAS-intensive field campaigns. Field data relating the water content of tree components to dry biomass are used to modify the coefficients of the algorithm for crown and stem biomass. The algorithm was then applied over the entire image generating biomass maps. A set of 18 test sites within the imaged area was used to assess the accuracy of the biomass maps. The accuracy of biomass estimation is also investigated by choosing different combinations of polarization and frequency channels of the AIRSAR system. It is shown that polarimetric data from P-band and L-band channels provide similar accuracy for estimating the above-ground biomass for boreal forest types. In general, the use of P-band channels can provide better estimates of stem biomass, while L-band channels can estimate the crown biomass more accurately.

During several field campaigns in spring and summer of 1994, the NASA/JPL airborne synthetic aperture radar (AIRSAR) collected data over the southern and northern study sites of BOREAS. Among the areas over which radar data were collected was the young jack pine (YJP) tower site in the south, which is generally characterized as having short (2-4 m) but closely spaced trees with a dense crown layer. In this work, the AIRSAR data over this YJP stand from six different dates were used, and the dielectric constant and hence the moisture content of its branch layer components were estimated. The approach was to first derive a parametric scattering model from a numerical discrete-component forest model, which is possible if the predominant scattering mechanism can be identified. Here, a classification algorithm was used for this purpose, concentrating on areas where the volume scattering mechanism from the branch layer dominates. The unknown parameters mere taken to be the real and imaginary parts of the dielectric constant, from which the moisture content can be derived. Once the parametric model was derived, a nonlinear estimation algorithm was employed to retrieve the model parameters from SAR data. This algorithm is iterative, and takes the statistical properties of the data and unknown parameters into account. The inversion process was first verified using synthetic data. It was observed that the algorithm is robust with respect to the a priori estimate. The estimation algorithm was then applied to AIRSAR data of BOREAS. The results show how the environmental conditions affected the moisture state of this forest stand over a period of six months. It is observed that canopy moisture increased during the thaw season, was stable starting from the end of the thaw season throughout most of the growing season, after which a period of dry-down was observed at the end of the growing season.

A spaceborne inflatable antenna concept is evaluated for passive microwave sensing of soil moisture and ocean salinity. The concept makes use of a large-diameter, offset-fed, parabolic-torus antenna with multiple feeds in a conical pushbroom configuration. An inflatable structure provides the means for deploying the large-aperture, low-mass, and low-cost antenna system in space, suitable for operation in the 1-3-GHz frequency range needed for soil moisture and salinity sensing. The concept is designed to provide multichannel, constant-incidence-angle, wide-swath, and high-radiometric-precision observations of the Earth's surface. These capabilities facilitate estimation of soil moisture and salinity, with global coverage every two to three days. Simulations show that a 25-m diameter, 1.41- and 2.69-GHz, dual-polarized system should be capable of providing surface soil moisture estimates with an accuracy of /spl sim/0.04 g-cm/sup -3/ (where vegetation water content is less than /spl sim/5 kg-m/sup -2/) at a spatial resolution of /spl sim/30 km. Although inflatable systems represent a new and untested technology for remote-sensing applications, the advantages of low packaged volume, low manufacturing cost, and low mass provide an incentive for their study. This paper evaluates one possible concept for incorporating the capabilities of inflatable systems into a scientific mission and for demonstrating these capabilities for remote-sensing applications.

As part of the intensive field campaign (IFC) for the Boreal forest ecosystem-atmosphere research (BOREAS) project in August 1993, the NASA/JPL AIRSAR covered an area of about 100 km/spl times/100 km near the Prince Albert National Park in Saskatchewan, Canada. At the same time, ground-truth measurements were made in several stands which have been selected as the primary study sites. This paper focuses on an area including jack pine stands in the Nipawin area near the park. Upon examining the AIRSAR data from stands of old and young jack pine (OJP and YJP), distinct signatures are observed for each of the forest types at various frequencies and polarizations, in particular, at P-band HH. The authors use a forest scattering model in conjunction with the ground-truth measurements to explain such behavior. The forest model includes the major scattering mechanisms by taking the forest component interactions into account. The contribution from each of the scattering mechanisms to the total backscatter is calculated and their differences for OJP and YJP stands are evaluated. The results are used to discuss the effect of the physical properties of the forest components in each stand on radar backscatter. They are also used to show that it is not only the backscatter level but also the relative contribution from various scattering mechanisms that will help in quantitative interpretation of SAR data. This work is mainly intended as a precursor to the authors ongoing work which uses a mechanism-specific inversion technique to retrieve forest parameters from SAR data for these BOREAS sites.>

An iterative algorithm is developed to solve the nonlinear inverse scattering problem for two-dimensional lossless dielectric inhomogeneities using time-domain scattering data. The method is based on performing Born-type iterations on a volume integral equation and, hence, successively calculating higher-order approximations to the unknown object profile. Both the full-angle and the limited-angle problems are considered. Solutions are obtained for cases where the first-order Born approximation is severely violated. Wideband time-domain scattered field measurements make it possible to use sparse data sets and thus reduce experimental complexity and computation time. Several examples are given to show the ability of this method to invert arbitrarily shaped permittivity profiles using few transmitters and receivers. The high-resolution capability of the algorithm is also demonstrated.>