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Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive climate indicator with strong biophysical importance. However, retrieval algorithms can have difficulty distinguishing the FT status of soils from that of overlying features such as snow and vegetation, while variable land conditions can also degrade performance. Here, we applied a deep learning model using a multilayer convolutional neural network driven by AMSR2 and SMAP TB records, and trained on surface (~0–5 cm depth) soil temperature FT observations. Soil FT states were classified for the local morning (6 a.m.) and evening (6 p.m.) conditions corresponding to SMAP descending and ascending orbital overpasses, mapped to a 9 km polar grid spanning a five-year (2016–2020) record and Northern Hemisphere domain. Continuous variable estimates of the probability of frozen or thawed conditions were derived using a model cost function optimized against FT observational training data. Model results derived using combined multi-frequency (1.4, 18.7, 36.5 GHz) TBs produced the highest soil FT accuracy over other models derived using only single sensor or single frequency TB inputs. Moreover, SMAP L-band (1.4 GHz) TBs provided enhanced soil FT information and performance gain over model results derived using only AMSR2 TB inputs. The resulting soil FT classification showed favorable and consistent performance against soil FT observations from ERA5 reanalysis (mean percent accuracy, MPA: 92.7%) andin situweather stations (MPA: 91.0%). The soil FT accuracy was generally consistent between morning and afternoon predictions and across different land covers and seasons. The model also showed better FT accuracy than ERA5 against regional weather station measurements (91.0% vs. 86.1% MPA). However, model confidence was lower in complex terrain where FT spatial heterogeneity was likely beneath the effective model grain size. Our results provide a high level of precision in mapping soil FT dynamics to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks, with the potential to inform Earth system model predictions. 

Abstract The timing and progression of the spring thaw transition in high northern latitudes (HNL) coincides with warmer temperatures and landscape thawing, promoting increased soil moisture and growing season onset of gross primary productivity (GPP), heterotrophic respiration (HR), and evapotranspiration (ET). However, the relative order and spatial pattern of these events is uncertain due to vast size and remoteness of the HNL. We utilized satellite environmental data records (EDRs) derived from complementary passive microwave and optical sensors to assess the progression of spring transition events across Alaska and Northern Canada from 2016 to 2020. Selected EDRs included land surface and soil freeze‐thaw status, solar‐induced chlorophyll fluorescence (SIF) signifying canopy photosynthesis, root zone soil moisture (RZSM), and GPP, HR, and ET as indicators of ecosystem carbon and water‐energy fluxes. The EDR spring transition maps showed thawing as a precursor to rising RZSM and growing season onset. Thaw timing was closely associated with ecosystem activation from winter dormancy, including seasonal increases in SIF, GPP, and ET. The HR onset occurred closer to soil thawing and prior to GPP activation, reducing spring carbon (CO₂) sink potential. The mean duration of the spring transition spanned ∼6 ± 1.5 weeks between initial and final onset events. Spring thaw timing and maximum RZSM were closely related to active layer thickness in HNL permafrost zones, with deeper active layers showing generally earlier thawing and greater RZSM. Our results confirm the utility of combined satellite EDRs for regional monitoring and better understanding of the complexity of the spring transition.