We examine data continuity between the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On (FO) missions over Greenland and Antarctica using independent data from the mass budget method, which calculates the difference between ice sheet surface mass balance and ice discharge at the periphery. For both ice sheets, we find consistent GRACE/GRACE‐FO time series across the data gap, at the continental and regional scales, and the data gap is confidently filled with mass budget method data. In Greenland, the GRACE‐FO data reveal an exceptional summer loss of 600 Gt in 2019 following two cold summers. In Antarctica, ongoing high mass losses in the Amundsen Sea Embayment of West Antarctica, the Antarctic Peninsula, and Wilkes Land in East Antarctica cumulate to 2130, 560, and 370 Gt, respectively, since 2002. A cumulative mass gain of 980 Gt in Queen Maud Land since 2009, however, led to a pause in the acceleration in mass loss from Antarctica after 2016.

The continuous and precise mapping of glacier calving fronts is essential for monitoring and understanding rapid glacier changes in Antarctica and Greenland, which have the potential for significant sea level rise within the current century. This effort has been mostly restricted to the slow and painstaking manual digitalization of the calving front positions in thousands of satellite imagery products. Here, we have developed a machine learning toolkit to automatically detect glacier calving front margins in satellite imagery. The toolkit is based on semantic image segmentation using Convolutional Neural Networks (CNN) with a modified U-Net architecture to isolate the calving fronts from satellite images after having been trained with a dataset of images and their corresponding manually-determined calving fronts. As a case study we train our neural network on a varied set of Landsat images with lowered resolutions from Jakobshavn, Sverdrup, and Kangerlussuaq glaciers, Greenland and test the results on images from Helheim glacier, Greenland to evaluate the performance of the approach. The neural network is able to identify the calving front in new images with a mean deviation of 96.3 m from the true fronts, equivalent to 1.97 pixels on average, while the corresponding error for manually-determined fronts on the same resolution images is 92.5 m (1.89 pixels). We find that the trained neural network significantly outperforms common edge detection techniques, and can be used to continuously map out calving-ice fronts with a variety of data products.

Sea level contributions from the Greenland Ice Sheet are influenced by the rapid changes in glacial terminus positions. The documentation of these evolving calving front positions, for which satellite imagery forms the basis, is therefore important. However, the manual delineation of these calving fronts is time consuming, which limits the availability of these data across a wide spatial and temporal range. Automated methods face challenges that include the handling of clouds, illumination differences, sea ice mélange, and Landsat 7 scan line corrector errors. To address these needs, we develop the Calving Front Machine (CALFIN), an automated method for extracting calving fronts from satellite images of marine-terminating glaciers, using neural networks. The results are often indistinguishable from manually curated fronts, deviating by on average 86.76 ± 1.43 m from the measured front. Landsat imagery from 1972 to 2019 is used to generate 22 678 calving front lines across 66 Greenlandic glaciers. This improves on the state of the art in terms of the spatiotemporal coverage and accuracy of its outputs and is validated through a comprehensive intercomparison with existing studies. The current implementation offers a new opportunity to explore subseasonal and regional trends on the extent of Greenland's margins and supplies new constraints for simulations of the evolution of the mass balance of the Greenland Ice Sheet and its contributions to future sea level rise.

Delineating the grounding line of marine-terminating glaciers—where ice starts to become afloat in ocean waters—is crucial for measuring and understanding ice sheet mass balance, glacier dynamics, and their contributions to sea level rise. This task has been previously done using time-consuming, mostly-manual digitizations of differential interferometric synthetic-aperture radar interferograms by human experts. This approach is no longer viable with a fast-growing set of satellite observations and the need to establish time series over entire continents with quantified uncertainties. We present a fully-convolutional neural network with parallel atrous convolutional layers and asymmetric encoder/decoder components that automatically delineates grounding lines at a large scale, efficiently, and accompanied by uncertainty estimates. Our procedure detects grounding lines within 232 m in 100-m posting interferograms, which is comparable to the performance achieved by human experts. We also find value in the machine learning approach in situations that even challenge human experts. We use this approach to map the tidal-induced variability in grounding line position around Antarctica in 22,935 interferograms from year 2018. Along the Getz Ice Shelf, in West Antarctica, we demonstrate that grounding zones are one order magnitude (13.3 ± 3.9) wider than expected from hydrostatic equilibrium, which justifies the need to map grounding lines repeatedly and comprehensively to inform numerical models.

Totten and Moscow University glaciers, in the marine‐based sector of East Antarctica, contain enough ice to raise sea level by 5 m. Obtaining precise measurements of their mass balance is challenging owing to large area of the basins and the small mass balance signal compared to West Antarctic glaciers. Here we employ a locally optimized processing of Gravity Recovery and Climate Experiment (GRACE) harmonics to evaluate their mass balance at the sub‐basin scale and compare the results with mass budget method (MBM) estimates using regional atmospheric climate model version 2.3 (RACMO2.3) or Modèle Atmosphérique Régional version 3.6.4 (MAR3.6.4). The sub‐basin mass loss estimate for April 2002 to November 2015 is 14.8 ± 4.3 Gt/yr, which is weakly affected by glacial isostatic adjustment uncertainties (±1.4 Gt/yr). This result agrees with MBM/RACMO2.3 (15.8 ± 2.0 Gt/yr), whereas MBM/MAR3.6.4 underestimates the loss (6.6 ± 1.6 Gt/yr). For the entire drainage, the mass loss for April 2002 to August 2016 is 18.5 ± 6.6 Gt/yr, or 15 ± 4% of its ice flux. These results provide unequivocal evidence for mass loss in this East Antarctic sector.

This study investigates the spatial and temporal variability of the soil moisture in India using Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) gridded datasets from June 2002 to April 2017. Significant relationships between soil moisture and different land surface–atmosphere fields (Precipitation, surface air temperature, total cloud cover, and total water storage) were studied, using maximum covariance analysis (MCA) to extract dominant interactions that maximize the covariance between two fields. The first leading mode of MCA explained 56%, 87%, 81%, and 79% of the squared covariance function (SCF) between soil moisture with precipitation (PR), surface air temperature (TEM), total cloud count (TCC), and total water storage (TWS), respectively, with correlation coefficients of 0.65, −0.72, 0.71, and 0.62. Furthermore, the covariance analysis of total water storage showed contrasting patterns with soil moisture, especially over northwest, northeast, and west coast regions. In addition, the spatial distribution of seasonal and annual trends of soil moisture in India was estimated using a robust regression technique for the very first time. For most regions in India, significant positive trends were noticed in all seasons. Meanwhile, a small negative trend was observed over southern India. The monthly mean value of AMSR soil moisture trend revealed a significant positive trend, at about 0.0158 cm³/cm³ per decade during the period ranging from 2002 to 2017.