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.

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.

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.

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).