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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. 

Although multiple efforts have been made to model global navigation satellite system (GNSS)-reflectometry (GNSS-R) delay-Doppler maps (DDMs) over land, there is still a need for models that better represent the signals over land and can enable reliable retrievals of the geophysical variables. Our paper presents improvements to an existing GNSS-R DDM model by accounting for short-wave diffraction due to small-scale ground surface roughness and signal attenuation due to vegetation. This is a step forward in increasing the model fidelity. Our model, called the improved geometric optics with topography (IGOT), predicts GNSS-R DDM over land for the purpose of retrieving geophysical parameters, including soil moisture. Validation of the model is carried out using DDMs from the Cyclone GNSS (CYGNSS) mission over two validation sites with in situ soil moisture sensors: Walnut Gulch, AZ, USA, and the Jornada Experimental Range, NM, USA. Both the peak reflectivity and the DDM shape are studied. The results of the study show that the IGOT model is able to accurately predict CYGNSS DDMs at these two validation sites. 

Abstract The changing thermal state of permafrost is an important indicator of climate change in northern high latitude ecosystems. The seasonally thawed soil active layer thickness (ALT) overlying permafrost may be deepening as a consequence of enhanced polar warming and widespread permafrost thaw in northern permafrost regions (NPRs). The associated increase in ALT may have cascading effects on ecological and hydrological processes that impact climate feedback. However, past NPR studies have only provided a limited understanding of the spatially continuous patterns and trends of ALT due to a lack of long-term high spatial resolution ALT data across the NPR. Using a suite of observational biophysical variables and machine learning (ML) techniques trained with availablein situALT network measurements (n= 2966 site-years), we produced annual estimates of ALT at 1 km resolution over the NPR from 2003 to 2020. Our ML-derived ALT dataset showed high accuracy (R²= 0.97) and low bias when compared within situALT observations. We found the ALT distribution to be most strongly affected by local soil properties, followed by topographic elevation and land surface temperatures. Pair-wise site-level evaluation between our data-driven ALT with Circumpolar Active Layer Monitoring data indicated that about 80% of sites had a deepening ALT trend from 2003 to 2020. Based on our long-term gridded ALT data, about 65% of the NPR showed a deepening ALT trend, while the entire NPR showed a mean deepening trend of 0.11 ± 0.35 cm yr⁻¹[25%–75% quantile: (−0.035, 0.204) cm yr⁻¹]. The estimated ALT trends were also sensitive to fire disturbance. Our new gridded ALT product provides an observationally constrained, updated understanding of the progression of thawing and the thermal state of permafrost in the NPR, as well as the underlying environmental drivers of these trends. 

Abstract. Permafrost-affected ecosystems of the Arctic–boreal zone in northwestern North America are undergoing profound transformation due to rapid climate change. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) is investigating characteristics that make these ecosystems vulnerable or resilient to this change. ABoVE employs airborne synthetic aperture radar (SAR) as a powerful tool to characterize tundra, taiga, peatlands, and fens. Here, we present an annotated guide to the L-band and P-band airborne SAR data acquired during the 2017, 2018, 2019, and 2022 ABoVE airborne campaigns. We summarize the ∼80 SAR flight lines and how they fit into the ABoVE experimental design (Miller et al., 2023; https://doi.org/10.3334/ORNLDAAC/2150). The Supplement provides hyperlinks to extensive maps, tables, and every flight plan as well as individual flight lines. We illustrate the interdisciplinary nature of airborne SAR data with examples of preliminary results from ABoVE studies including boreal forest canopy structure from TomoSAR data over Delta Junction, AK, and the Boreal Ecosystem Research and Monitoring Sites (BERMS) area in northern Saskatchewan and active layer thickness and soil moisture data product validation. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band airborne SAR data (https://uavsar.jpl.nasa.gov/cgi-bin/data.pl).