Several studies have found that the vertical Fourier transform of lidar, interferometric Synthetic Aperture Radar (SAR), and stereo photogrammetric profiles at empirically-determined spatial frequencies enables high-performance forest aboveground biomass (AGB) estimation. Linear combinations of real and imaginary parts of Fourier transforms of Tomographic (multi-baseline) SAR (TomoSAR) profiles, from Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne data, generate ~20%-precision estimates of AGB in the Saskatchewan area of Canada. We found that this 20% precision can be improved to ~15%, a factor of 30% improvement in root mean square error (RMSE) if, in addition to using Fourier transforms of the profile itself, we use Fourier transforms of the spatial, vertical derivative of the profile. The formulation of this "derivative" algorithm is the subject of this paper.

Knowledge of the spatial and temporal distribution of active layer thickness (ALT) throughout northern Alaska would help to understand the effects of climate change in the region, as well as to quantify how much the permafrost degradation manifestly in progress there is contributing to the accumulation of greenhouse gases in the atmosphere. For this reason, we are developing extensive high-resolution maps of ALT in northern Alaska. We use machine learning along with an extensive set of spatial data layers to upscale ALT from thousands of training pixels taken from high resolution swaths of estimated ALT derived from airborne polarimetric P-band synthetic aperture radar (SAR). The resulting maps of up-scaled ALT have been compared to thousands of validation samples set aside from the PolSAR-derived swaths and to in situ ALT measurements. The maps have achieved root-mean-square errors (RMSEs) of 5–7 cm relative to validation samples, and RMSEs of approximately 10–12 cm relative to in situ ALT measurements.

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.

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.

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).

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.

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, 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).

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.

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.

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.

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.

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.

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 wireless multicast communications, the mitigation of distributed errors in different users is crucial. The corrupted packets in the transmission of multicast streaming can be recovered by retransmitting another redundancy version of the same packets or by transmitting a new parity packet generated by packet-level erasure encoders. In this paper, a hybrid automatic repeat request (HARQ) scheme is proposed for efficient multicast communications. The proposed scheme estimates the contributions of each candidate packet to the marginal recovery on the erroneous packets and then transmits the packet which generates the most marginal recovery. The length of coded bits in the redundancy versions can also be adjusted to further save the radio resources. Simulations are conducted to verify the throughput efficiency of the proposed scheme, which outperforms the conventional schemes in all the range of packet error rates.

Linear detectors such as zero-forcing (ZF) and minimum mean square error (MMSE) require only a small fraction of computational complexity compared to maximum likelihood (ML) detector. However, linear detections suffer from severe performance degradation. In this paper, we propose a novel detection scheme which obtains the initial symbol detection by MMSE detector and then perform symbol ordering by signal-to-interference-and-noise ratio (SINR). The MMSE detected symbols with higher SINR are retained as part of final solution and cancelled from the original received signals. The remaining symbols with lower SINR are detected by K-best algorithm, which selects K best nodes in each layer of the partial tree search. The small value of K is sufficient to achieve good performances, and therefore the extra computational complexity is minimal. Simulation results show the performance superiority of the proposed method compared to the conventional MMSE detection. Moreover, at the similar symbol error rates, the total number of nodes visited in the proposed approach is much smaller than the conventional K-best detection scheme.