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.

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.

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.

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.

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).