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Abstract The addition and refreezing of liquid water to Greenland’s accumulation area are increasingly important processes for assessing the ice sheet’s present and future mass balance, but uncertain initial conditions, complex infiltration physics and limited field data pose challenges. Satellite-based L-band radiometry offers a promising new tool for observing liquid water in the firn layer, although further validation is needed. This paper compares time series of liquid water amount (LWA) from three percolation zone sites generated by a localized point-model, a regional climate model,in situmeasurement, and L-band radiometric retrievals. LWA integrates the interplay of liquid water generation and refreezing, which often occur simultaneously and repeatedly within firn layers on diurnal, episodic, and seasonal scales offering insights into methods for measuring and modeling meltwater processes. The four LWA records showed average discrepancies of up to 62% nRMSE, reflecting shortcomings inherent to each method. Better agreement between series occurred after excluding the regional climate model record, lowering nRMSE to 8–13%. The agreement between L-band radiometry and other LWA records inspires confidence in this observational tool for understanding firn meltwater processes and serving as a validation target for simulations of water processes in Greenland’s melting firn layer. 

Abstract. Quantifying the total liquid water amounts (LWAs) in the Greenland ice sheet (GrIS) is critical for understanding GrIS firn processes, mass balance, and global sea level rise. Although satellite microwave observations are very sensitive to ice sheet melt and thus can provide a way of monitoring the ice sheet melt globally, estimating total LWA, especially the subsurface LWA, remains a challenge. Here, we present a microwave retrieval of LWA over Greenland using enhanced-resolution L-band brightness temperature (TB) data products from the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite for the 2015–2023 period. L-band signals receive emission contributions deep in the ice sheet and are sensitive to the liquid water content (LWC) in the firn column. Therefore, they can estimate the surface-to-subsurface LWA, unlike higher-frequency signals (e.g., 18 and 37 GHz bands), which are limited to the top few centimeters of the surface snow during the melt. We used vertically polarized TB (V-pol TB) with empirically derived thresholds to detect liquid water and identify distinct ice sheet zones. A forward model based on radiative transfer (RT) in the ice sheet was used to simulate TB. The simulated TB was then used in an inversion algorithm to estimate LWA. Finally, the retrievals were compared with the LWA obtained from two sources. The first source was a locally calibrated ice sheet energy and mass balance (EMB) model, and the second source was the Glacier Energy and Mass Balance (GEMB) model within NASA's Ice-sheet and Sea-level System Model (ISSM). Both models were forced by in situ measurements from six automatic weather stations (AWSs) of the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Climate Network (GC-Net) located in the percolation zone of the GrIS. The retrievals show generally good agreement with both the references, demonstrating the potential for advancing our understanding of ice sheet physical processes to better project Greenland's contribution to the global sea level rise in response to the warming climate. 

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.