The Arctic-boreal zone (ABZ) is warming due to climate change. Current spaceborne remote sensing techniques and retrieval methodologies need to be complemented to improve systematic monitoring of the cryosphere. To that end, this article presents a new investigation of the use of the global navigation satellite system reflectometry (GNSS-R) remote sensing technique by a SmallSat constellation. A new freeze/thaw (F/T) seasonal multithresholding algorithm (STA) is developed using high-inclination orbit near-Nadir Spire Global GNSS-R data acquired through the National Aeronautics and Space Administration (NASA) Commercial Smallsat Data Acquisition (CSDA) Program. Five different soil surface reflectivity Gamma models are proposed to account for the impact of vegetation cover and small-scale surface roughness on Earth-reflected GNSS signals. The sensitivity of the Gamma models to F/T surface state transitions is evaluated, and the optimum model is selected to construct a seasonal scale factor. Then, a multithresholding matrix is obtained for F/T classification using a specific threshold for every surface grid cell. Results for the annual frozen soil duration (days yr(-1)) are compared with those by the Soil Moisture Active Passive (SMAP) mission. Additionally, freezing and thawing periods are analyzed to determine when the moisture exchange with the atmosphere is locked, which is an important climatic factor. A novel metric is introduced to characterize the freeze intensity moving beyond classical F/T binary classifications. Results are evaluated using air and soil temperature, snow depth and temperature, and soil moisture content (SMC) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5-Land reanalysis product.

Modeling Arctic-Boreal vegetation is a challenging but important task, since this highly dynamic ecosystem is undergoing rapid and substantial environmental change. In this work, we synthesized information on 18 dynamic vegetation models (DVMs) that can be used to project vegetation structure, composition, and function in North American Arctic-Boreal ecosystems. We reviewed the ecosystem properties and scaling assumptions these models make, reviewed their applications from the scholarly literature, and conducted a survey of expert opinion to determine which processes are important but lacking in DVMs. We then grouped the models into four categories (specific intention models, forest species models, cohort models, and carbon tracking models) using cluster analysis to highlight similarities among the models. Our application review identified 48 papers that addressed vegetation dynamics either directly (22) or indirectly (26). The expert survey results indicated a large desire for increased representation of active layer depth and permafrost in future model development. Ultimately, this paper serves as a summary of DVM development and application in Arctic-Boreal environments and can be used as a guide for potential model users, thereby prioritizing options for model development.