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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.

At biotech startup Zymergen, robotic fingers are poised to pick microbe colonies in an AI-controlled quest for strains that crank out more chemicals. EMERYVILLE, CALIFORNIA--If this is the biology laboratory of the future, it doesn't look so different from today's. Scientists in white lab coats walk by with boxes of frozen tubes. The chemicals on the shelves--bottles of pure alcohol, bins of sugar, protein, and salts--are standard issue for growing microbes and manipulating their genes. You don't even notice the robots until you hear them: They sound like crickets singing to each other amid the low roar of fans.