While of critical importance for coastal communities, Antarctica’s future sea-level contribution remains highly uncertain. This uncertainty largely stems from the complex interaction between the ocean and the ice shelves, which is both difficult to observe and model. To better understand and constrain land-ice response to reduced buttressing exerted by ice shelves, efforts are needed to fully comprehend basal melt rates and their impact on ice shelf weakening and retreat. Here we present high-resolution basal melt maps (50 m) of vulnerable ice shelves based on a combination of stereo imagery and satellite altimetry, revealing pronounced channelized melting patterns whose melt rates were previously substantially underestimated (42–50%), which could result in faster channel breakthrough. Accurately simulating small-scale dynamics in ice-sheet models remains challenging but is essential for accurate sea-level rise projections.

Snowfall is an important climate change indicator affecting surface albedo, glaciers, sea ice, freshwater storage, cloud lifetime, and ecosystems. Precise snowfall measurements at high latitudes are particularly important for the estimation of the mass balance of ice sheets; however, the snowfall is difficult to quantify with in situ measurements in those locations. In this context, spaceborne radar and radiometer atmospheric missions can help in the assessment of snowfall at high latitudes. The decommissioned NASA CloudSat mission provided invaluable information about global snowfall climatology from 2006 to 2023. The CloudSat-based estimates of global snowfall are considered the reference for global snowfall estimates, but these data suffer from poor sampling and the inability to see shallow or retrieve heavy precipitation, which limits their use, for example, as input to surface mass balance models of the major ice sheets. WIVERN (WInd VElocity Radar Nephoscope), one of the ESA Earth Explorer 11 selected missions, is equipped with a conical scanning 94 GHz Doppler radar and a passive 94 GHz radiometer, with the main objective of measuring global in-cloud horizontal winds, but also quantifying cloud ice water content and precipitation rate. Its conically scanning system, with a 42° incidence angle, is expected to reduce the radar blind zone near the surface (especially over the ocean) and allows the mission to have a swath width of 800 km and 70 times more sampled points than a fixed-looking instrument. The proposed radar measurements tackle the current uncertainties in snowfall estimates, highly improving the sampling frequency and accuracy of snowfall measurements. The uncertainty in snowfall measurements arises from various factors, including the diurnal cycle, uncertainty in the <Z-<S relationship, and the sampling error. This study quantifies each of these contributors individually and demonstrates the improved sampling capabilities of the WIVERN conically scanning geometry for some specific regions (Antarctica, Greenland) by computing the sampling error at different spatial and temporal scales via simulations of WIVERN vs. CloudSat orbits and scanning geometry, based on the snowfall rates produced by ERA5 reanalysis. Results show that a WIVERN-like conically scanning system significantly reduces the uncertainty in polar snowfall estimates if compared to a CloudSat-like near-nadir fixed viewing geometry. While CloudSat generates acceptable errors at the annual zonal scales, WIVERN can produce estimates within the climatological variability for latitude-longitude domain larger than 0.5° × 0.5° already at the monthly timescale, making it a valuable product for regional climate model evaluation and as an input to surface mass balance models of the major ice sheets and glaciers.

Driven by rapid technological advances in cryospheric science and the emergence of new generations of remote sensing observations, this special issue of Remote Sensing of Environment, entitled “Remote sensing of the global cryosphere: status, processes, and trends”, brings together 23 studies published between 2023 and 2025. Collectively, these papers showcase how multi-sensor satellite observations, high-resolution digital elevation models (DEMs), and cutting-edge deep learning techniques are revolutionizing the monitoring of glaciers, snow, glacial lakes, permafrost, sea ice, and ice shelves across the Earth's three poles: the Arctic (including Greenland), Antarctica, and High Mountain Asia (the Third Pole). These studies integrate diverse datasets – including multisource DEMs, optical, thermal, and passive microwave imageries, as well as RADAR, LiDAR, and GRACE observations - to quantify glacier mass balance, map glacial lakes, assess permafrost thermal conditions, classify sea-ice types, and detect icebergs. We organize the publications by major cryospheric themes and their distribution across polar regions and summarize the dominant remote sensing datasets and methodologies employed. Finally, we outline future directions, emphasizing multi-sensor data fusion, physics-informed modeling, and AI-driven approaches to improve predictions of cryospheric change under a warming climate.

Patagonian glaciers have been rapidly losing mass in the last two decades, but the driving processes remain poorly known. Here we use two state-of-the-art regional climate models to reconstruct long-term (1940-2023) glacier surface mass balance (SMB), i.e., the difference between precipitation accumulation, surface runoff and sublimation, at about 5 km spatial resolution, further statistically downscaled to 500 m. High-resolution SMB agrees well with in-situ observations and, combined with solid ice discharge estimates, captures recent GRACE/GRACE-FO satellite mass change. Glacier mass loss coincides with a long-term SMB decline (−0.35 Gt yr⁻²), primarily driven by enhanced surface runoff (+0.47 Gt yr⁻²) and steady precipitation. We link these trends to a poleward shift of the subtropical highs favouring warm northwesterly air advections towards Patagonia (+0.14°C dec⁻¹ at 850 hPa). Since the 1940s, Patagonian glaciers have lost 1350 ± 449 Gt of ice, equivalent to 3.7 ± 1.2 mm of global mean sea-level rise.

Climate change and human-induced land degradation threaten dryland ecosystems, vital to one-third of the global population and pivotal to inter-annual global carbon fluxes. Early warning systems are essential for guiding conservation, climate change mitigation and alleviating food insecurity in drylands. However, contemporary methods fail to provide large-scale early warnings effectively. Here we show that a machine learning-based approach can predict the probability of abrupt shifts in Sudano–Sahelian dryland vegetation functioning (75.1% accuracy; 76.6% precision) particularly where measures of resilience (temporal autocorrelation) are supplemented with proxies for vegetation and rainfall dynamics and other environmental factors. Regional-scale predictions for 2025 highlight a belt in the south of the study region with high probabilities of future shifts, largely linked to long-term rainfall trends. Our approach can provide valuable support for the conservation and sustainable use of dryland ecosystem services, particularly in the context of climate change projected drying trends.

The Normalized Difference Snow Index (NDSI) is essential for accurate snow monitoring, but the widely used MODIS NDSI products generally have significant data gaps mainly due to cloud cover. Existing gap-filling methods often introduce artifact issue in regions with extensive and persistent cloud cover, where gap areas produce inaccurate results influenced by cloud shapes. To address NDSI gap-filling issue, we developed a mask-aware Transformer integrating multi-source data (MAT-MS) to effectively fill these gaps in MODIS NDSI data. The MAT-MS model leverages spatiotemporal information related to meteorology, topography, and geographic location. By incorporating a mask-aware technique, the MAT-MS can learn cloud shapes and patterns, helping to mitigate the common artifact issue. Validation using data from the Tibetan Plateau demonstrated the superior performance of the MAT-MS model, with averaged MAE, RMSE, and R² of 1.585, 5.531, and 0.868, respectively. The model reduced RMSE by over 30 % compared to traditional spatiotemporal interpolation methods, and by 9 % compared to mainstream deep learning models. Using MAT-MS, we generated a daily gap-free NDSI dataset for the Tibetan Plateau spanning from 2003 to 2020. This spatiotemporally continuous dataset is critical for detailed snow identification, enabling enhanced estimates of snow cover area, fractional snow cover, and snow depth. The flexibility of the MAT-MS model also makes it applicable to a wide range of continuous remote sensing datasets affected by data gaps.

Snow cover is a crucial driver for plant species distributions in cold environments. The primary source of snow cover data used in distribution models is remotely sensed satellite imagery, which is characterized by coarser spatial resolutions than plot-scale observations of plant distributions. This scale-mismatch was hypothesized to limit model accuracy. Here, we used a common modeling framework to assess the contribution of snow melt-out dates derived from four data sources (satellite imagery, numerical snowpack modeling, webcam imagery and in-situ soil temperature measurements) at 1 m and 20 m spatial resolution to the predictive power of distribution models of 74 plant species in an alpine landscape of the Austrian Alps. We found that >80 % of the distribution models of all species were significantly improved by at least one snow melt-out data set when considering Area Under the Curve (AUC). Satellite-based melt-out led to significantly improved models for the highest number of species (>50 % for AUC) and increased True-Skill-Statistic and AUC on average by 16 % and 5 %, respectively. Surprisingly, fine-scale and in-situ measured melt-out data did not improve models more than the coarser scale (20 m) satellite-based melt-out data. Moreover, numerical snowpack modeling delivered results comparable to the other sources, which supports its use for projecting future species distributions. We conclude that the additional effort needed for producing high resolution, in-situ datasets as compared to commonly used satellite imagery might hence be worthwhile for some species but not for plant distribution modeling in cold ecosystems in general.

Damage features, such as rifts and crevasses, are the first signs of a weakened ice shelf and the precursor for retreat. Yet, damage changes are not widely quantified on Antarctic ice shelves, leaving future ice shelf weakening poorly understood. Here we use satellite imagery to detect both long-term (24-year) and short-term (annual, 2015–2021) Antarctic-wide damage changes, revealing a multiyear damage development cycle strongly correlated to ice shelf area changes, and a net decline in damaged area from 1997 to 2021. We establish a data-driven link between damage and ice flow characteristics, which shows that ice flow acceleration, strain rate increases and thinning lead to more damage development, in particular under high-emission climate scenarios. This sensitivity to warming suggests that without quantification of damage impacts by detailed physical models the (timing of) ice shelf retreat and Antarctic mass loss may currently be underestimated.

Rivers and slush fields on the Greenland Ice Sheet increasingly develop in locations where the accumulation zone hosts near-impermeable ice slabs. However, the division between runoff versus retention in these areas remains unmeasured. We present field measurements of superimposed ice formation onto slabs around the visible runoff limit. The quantity of superimposed ice varies by proximity to visible surface water and the surface slope, highlighting that meltwater can flow laterally before refreezing. We use heat conduction modelling and radar observations of autumn wetness to show that in our field area in 2022, 65% of superimposed ice formed during summer and the rest during autumn in the relict supraglacial hydrological network. Overall, 84% of melt around the visible runoff limit refroze. Ice-sheet-wide we estimate that slabs refroze 56 gigatonnes of melt (26-69 gigatonnes according to slab extent) between 2017 and 2022. Slabs are thus both hotspots of refreezing and emerging zones of runoff.

To investigate the drivers of lake drainages in Antarctica, we analyzed optical remote sensing data from the Shackleton Ice Shelf in East Antarctica over seven melt seasons, 2016 to 2023. Our study identified seven drainage event in 2016-2017, one in 2018-2019, fifteen in 2019-2020, and two in 2020-2021. All identified drainages occurred in regions with relatively medium to high levels of satellite-derived ice shelf damage and, except one, all with active damage development. Additionally, 17 out of 25 drainages coincided with increases in tidal heights. These findings provide insights into the factors influencing current lake drainages in Antarctica in both timing and distribution.

Correction to: Nature Reviews Earth & Environment https://doi.org/10.1038/s43017-023-00507-9, published online 23 January 2024.

In the version of the article initially published, in Fig. 5, under “Radar altimeter”, “O(16–160 m)” previously read “O(16–160 km)”. This has now been corrected in the HTML and PDF versions of the article.

Dynamic ecosystems, such as the Amazon forest, are expected to show critical slowing down behavior, or slower recovery from recurrent small perturbations, as they approach an ecological threshold to a different ecosystem state. Drought occurrences are becoming more prevalent across the Amazon, with known negative effects on forest health and functioning, but their actual role in the critical slowing down patterns still remains elusive. In this study, we evaluate the effect of trends in extreme drought occurrences on temporal autocorrelation (TAC) patterns of satellite-derived indices of vegetation activity, an indicator of slowing down, between 2001 and 2019. Differentiating between extreme drought frequency, intensity, and duration, we investigate their respective effects on the slowing down response. Our results indicate that the intensity of extreme droughts is a more important driver of slowing down than their duration, although their impacts vary across the different Amazon regions. In addition, areas with more variable precipitation are already less ecologically stable and need fewer droughts to induce slowing down. We present findings indicating that most of the Amazon region does not show an increasing trend in TAC. However, the predicted increase in extreme drought intensity and frequency could potentially transition significant portions of this ecosystem into a state with altered functionality.

More than 60% of meteorite finds on Earth originate from Antarctica. Using a data-driven analysis that identifies meteorite-rich sites in Antarctica, we show climate warming causes many extraterrestrial rocks to be lost from the surface by melting into the ice sheet. At present, approximately 5,000 meteorites become inaccessible per year (versus ~1,000 finds per year) and, independent of the emissions scenario, ~24% will be lost by 2050, potentially rising to ∼76% by 2100 under a high-emissions scenario.