Estimating the landscape and soil freeze-thaw (FT) dynamics in the Northern Hemisphere is crucial for understanding permafrost response to global warming and changes in regional and global carbon budgets. A new framework is presented for surface FT-cycle retrievals using L-band microwave radiometry based on a deep convolutional autoencoder neural network. This framework defines the landscape FT-cycle retrieval as a time series anomaly detection problem considering the frozen states as normal and thawed states as anomalies. The autoencoder retrieves the FT-cycle probabilistically through supervised reconstruction of the brightness temperature (TB) time series using a contrastive loss function that minimizes (maximizes) the reconstruction error for the peak winter (summer). Using the data provided by the Soil Moisture Active Passive (SMAP) satellite, it is demonstrated that the framework learns to isolate the landscape FT states over different land surface types with varying complexities related to the radiometric characteristics of snow cover, lake-ice phenology, and vegetation canopy. The consistency of the retrievals is evaluated over Alaska, against in situ ground-based observations, showing reduced uncertainties compared to the traditional methods that use thresholding of the normalized polarization ratio.

This paper presents a convolutional autoencoder deep learning framework for probabilistic characterization of the ground freeze-thaw (FT) dynamics in the Northern Hemisphere to enhance our understanding of permafrost response to global warming and shifts in the high-latitude carbon cycle, using Soil Moisture Active Passive (SMAP) satellite brightness temperatures (TB) observations. The autoencoder recasts the FT-cycle retrieval as an anomaly detection problem in which the peak winter (summer) represents the normal (anomaly) segments of the TB time series. The results demonstrate that the new framework outperforms the widely used fixed-thresholding of the Normalized Polarization Ratio (NPR) by learning the land surface structural and radiometric complexities that might arise in TB times series due to snow cover and vegetation. Validation against ground-based measurements over Alaska shows that the accuracy of the FT-cycle retrievals can be improved by 12%, primarily due to a marked reduction in false detection of short snowmelt episodes as ground thawing by the NPR thresholding approach.

The tau-omega model is expanded to properly simulate L-band microwave emission of the soil–snow–vegetation continuum through a closed-form solution of Maxwell’s equations, considering the intervening dry snow layer as a loss-less medium. The error standard deviations of a least-squared inversion are 0.1 and 3.5 for VOD and ground permittivity, over moderately dense vegetation and a snow density ranging from 100 to 400 kgm−3, considering noisy brightness temperatures with a standard deviation of 1 kelvin. Using the Soil Moisture Active Passive (SMAP) satellite observations, new global estimates of VOD and ground permittivity are presented over the Arctic boreal forests and permafrost areas. In the absence of dense in situ observations of ground permittivity and VOD, the retrievals are causally validated using ancillary variables including ground temperature, above-ground biomass, tree height, and net ecosystem exchange of carbon dioxide. Time-series analyses promise that the new data set can expand our understanding of the land–atmosphere interactions and exchange of carbon fluxes over Arctic landscapes.

To account for the impacts of snow on surface upwelling emission at the L-band microwave, this study employs a soil-snow-vegetation emission model to examine the retrieval errors associated with the simultaneous estimation of vegetation optical depth (VOD) and ground permittivity. The findings demonstrate that neglecting snow cover in retrievals can lead to an overestimation of VOD by approximately 30% and substantial error in ground permittivity, largely depending on the ground freeze-thaw status, snow density, and VOD. We utilize the Soil Moisture Active Passive (SMAP) satellite observations to retrieve VOD and compare it to the vegetation proxies such as AGB and tree height on a global scale. The results show consistent spatial patterns with the land cover types and dependencies with above-ground biomass (AGB) values and tree heights. The preliminary results offer promising possibilities to obtain global estimates of VOD and soil permittivity over snow-covered areas, where current SMAP observations are underutilized.

Soil and its water content can remain unfrozen below an insulative snow cover and modulate snowmelt infiltration and runoff. In this article, an emission model is proposed to account for L-band microwave emission of wet soils below a dry snowpack covered with an emerging moderately dense vegetation canopy. The model links the well-known Tau–Omega emission model with the snowpack dense media radiative transfer (DMRT) theory and a multilayer composite reflection model to account for the impacts of a snow layer on the upwelling soil and the downwelling vegetation emission, respectively. It is demonstrated that even though a dry snow is a low-loss medium at the L-band, omission of its presence leads to underestimation of soil moisture (SM), especially when soil (snow) becomes wetter (denser). Constrained inversion of the proposed emission model, using brightness temperatures from the Soil Moisture Active and Passive (SMAP) satellite, shows that the retrievals of SM and vegetation optical depth (VOD) are achievable with unbiased root-mean-squared errors of 0.060 $\text {m}^{3}\cdot \text {m}^{-3}$ and 0.124 [–], when compared with the in situ data from the International Soil Moisture Network (ISMN) and VOD-derived values from the normalized difference vegetation index (NDVI) obtained from the moderate resolution imaging spectroradiometer (MODIS) observations.