Estimating the upper depth of subsurface water on the Greenland Ice Sheet using multi-frequency passive microwave remote sensing, radiative transfer modeling, and machine learning

As the Arctic warms, surface melt extends into the Greenland Ice Sheet's accumulation zone, where much of the water infiltrates into the snowpack. This makes monitoring the subsurface water depth and spatial extent important for accurate ice sheet runoff estimations. Subsurface water can be detected using remotely sensed microwave brightness temperatures (TB). We use vertically polarized TB at 1.4 GHz from Soil Moisture and Ocean Salinity satellite (SMOS) and at 6.9, 10.7, and 18.7 GHz from the Advanced Microwave Scanning Radiometers (AMSR-E/2) to estimate the upper depth of liquid water (UDLW) on the ice sheet accumulation area. We build a catalogue of simulated UDLW and TB: realistic UDLW are modeled by the Geological Survey of Denmark and Greenland (GEUS) snow model, forced by the Copernicus Arctic Regional Reanalysis (CARRA), and the corresponding TB are calculated by the Snow Microwave Radiative Transfer (SMRT) model at 19 sites. We train on this catalogue an ensemble of cross-validated Random Forest (RF) models to predict UDLW and its uncertainty from TB at four frequencies. On hold-out modeled data and for water within 5 m of the surface, the RF ensemble achieves a median RMSE of 0.68 m and mean error of −0.09 m. Our retrieval, when applied to observed TB, matches within 2 m UDLW inferred from subsurface temperature profiles down to 4–6 m depth. Performances decrease beyond 5 m depth and for low liquid water amounts. Our retrieval produces daily UDLW maps over the ice sheet's accumulation area during 2010–2023 which reveal the seasonal evolution of UDLW, deliver the first quantitative estimates of subsurface liquid water depth on the ice sheet and offer new insights into meltwater infiltration and storage processes.