Machine learning of Antarctic firn density by combining radiometer and scatterometer remote-sensing data

Abstract

Firn density plays a crucial role in assessing the surface mass balance of the Antarctic ice sheet. However, our understanding of the spatial and temporal variations in firn density is limited due to (i) spatial and temporal limitations of in situ measurements, (ii) potential modelling uncertainties, and (iii) lack of firn density products driven by satellite remote-sensing data. To address this gap, this paper explores the potential of satellite microwave radiometer (Special Sensor Microwave Imager/Sounder (SSMIS)) and scatterometer (Advanced Scatterometer (ASCAT)) observations for assessing spatial and temporal dynamics of dry-firn density over the Antarctic ice sheet. Our analysis demonstrates a clear relation between density anomalies at a depth of 40 cm and fluctuations in satellite observations. However, a linear relationship with individual satellite observations is insufficient to explain the spatial and temporal variation in snow density. Hence, we investigate the potential of a non-linear random forest (RF) machine learning approach trained on radiometer and scatterometer data to derive the spatial and temporal variations in dry-firn density. In the estimation process, 10 years of SSMIS observations (brightness temperature) and ASCAT observations (backscatter intensity) is used as input features to a random forest (RF) regressor. The regressor is first trained on time series of modelled density and satellite observations at randomly sampled pixels and then applied to estimate densities in dry-firn areas across Antarctica. The RF results reveal a strong agreement between the spatial patterns estimated by the RF regressor and the modelled densities. The estimated densities exhibit an error of ±10 kg m−3 in the interior of the ice sheet and ±35 kg m−3 towards the ocean. However, the temporal patterns show some discrepancies, as the RF regressor tends to overestimate summer densities, except for high-elevation regions in East Antarctica and specific areas in West Antarctica. These errors may be attributed to underestimations of short-term or seasonal variations in the modelled density and the limitations of RF in extrapolating values outside the training data. Overall, our study presents a potential method for estimating unknown Antarctic firn densities using known densities and satellite parameters.