Cold regions, including high-latitude and high-altitude landscapes, are experiencing profound environmental changes driven by global warming. With the advance of earth observation technology, remote sensing has become increasingly important for detecting, monitoring, and understanding environmental changes over vast and remote regions. This paper provides an overview of recent achievements, challenges, and opportunities for land remote sensing of cold regions by (a) summarizing the physical principles and methods in remote sensing of selected key variables related to ice, snow, permafrost, water bodies, and vegetation; (b) highlighting recent environmental nonstationarity occurring in the Arctic, Tibetan Plateau, and Antarctica as detected from satellite observations; (c) discussing the limits of available remote sensing data and approaches for regional monitoring; and (d) exploring new opportunities from next-generation satellite missions and emerging methods for accurate, timely, and multi-scale mapping of cold regions.

The Tibetan Plateau houses numerous lakes, the phenology and duration of lake ice in this region are sensitive to regional and global climate change, and as such are used as key indicators in climate change research, particularly in environment change comparison studies for the Earth three poles. However, due to its harsh natural environment and sparse population, there is a lack of conventional in situ measurement on lake ice phenology. The Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Snow Index (NDSI) data, which can be traced back 20 years with a 500 m spatial resolution, were used to monitor lake ice for filling the observation gaps. Daily lake ice extent and coverage under clear-sky conditions was examined by employing the conventional SNOWMAP algorithm, and those under cloud cover conditions were re-determined using the temporal and spatial continuity of lake surface conditions through a series of steps. Through time series analysis of every single lake with size greater than 3 km² in size, 308 lakes within the Tibetan Plateau were identified as the effective records of lake ice extent and coverage to form the Daily Lake Ice Extent and Coverage dataset, including 216 lakes that can be further retrieved with four determinable lake ice parameters: Freeze-up Start (FUS), Freeze-up End (FUE), Break-up Start (BUS), and Break-up End (BUE), and 92 lakes with two parameters, FUS and BUE. Six lakes of different sizes and locations were selected for verification against the published datasets by passive microwave remote sensing. The lake ice phenology information obtained in this paper was highly consistent with that from passive microwave data at an average correlation coefficient of 0.91 and an RMSE value varying from 0.07 to 0.13. The present dataset is more effective at detecting lake ice parameters for smaller lakes than the coarse resolution passive microwave remote sensing observations. The published data are available in https://data.4tu.nl/repository/uuid:fdfd8c76-6b7c-4bbf-aec8-98ab199d9093 and http://www.sciencedb.cn/dataSet/handle/744.

The reliable knowledge of seasonal snow volume and its trend is very important to understand Earth’s climate system. Thus, a long-time snow water equivalent (SWE) dataset is necessary. This work presents a daily SWE product of 1980–2020 with a linear unmixing method through passive microwave data including SMMR, SSM/I and SSMIS over China after cross-calibration and bias-correction. The unbiased root-mean-square error of snow depth is about 5–7 cm, corresponding to 10–15 mm for SWE, when compared with stations measurements and field snow course data. The spatial patterns and trends of SWE over China present significant regional differences. The overall slope trend presented an insignificant decreasing pattern during 1980–2020 over China; however, there is an obvious fluctuation, i.e. a significant decrease trend during the period 1980–1990, an upward trend from 2005 to 2009, a significant downward trend from 2009 to 2018. The increase of SWE occurred in the Northeast Plain, with an increase trend of 0.2 mm per year. Whereas in the Hengduan Mountains, it presented a downward trend of SWE, up to −0.3 mm per year. In the North Xinjiang, SWE has an increasing trend in the Junggar Basin, while it shows a decreasing trend in the Tianshan and Altai Mountains.

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate–cryosphere–hydrology–hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze–thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole.

With global warming, the decrease in sea ice creates favorable conditions for Arctic activities. Sea surface temperature (SST) is not only an important driven factor of sea ice concentration (SIC) changes but also an important medium of the ocean–atmosphere interaction. However, the response of sea surface temperature to Arctic sea ice varies in different sea areas. Using the optimal interpolated SST data from the National Centers for Environmental Information (NCEI) and SIC data from the University of Bremen, the temporal and spatial characteristics of SST and SIC in the Arctic above 60°N and their relationship are studied, and the melting and freezing time of sea ice are calculated, which is particularly important for the prediction of Arctic shipping and sea ice. The results show that (1) the highest and lowest monthly mean Arctic SST occur in August and March, respectively, while those of SIC are in March and September. The maximum trends of SST and SIC changes are in autumn, which are +0.01 °C/year and −0.45%/year, respectively. (2) There is a significant negative correlation between the Arctic SST and SIC with a correlation coefficient of −0.82. (3) The sea ice break-up occurs on Day of the Year (DoY) 143 and freeze-up occurs on DoY 296 in the Arctic. The melting and freezing processes lasted for 27 days and 14 days, respectively. (4) The Kara Sea showed the strongest trend of sea ice melting at −1.22 d/year, followed by the Laptev Sea at −1.17 d/year. The delay trend of sea ice freezing was the most significant in the Kara Sea +1.75 d/year, followed by the Laptev Sea +1.70 d/year. In the Arctic, the trend toward earlier melting of sea ice is smaller than the trend toward later freezing.

How climate change influences lake ice phenology is important in understanding the climate‐lake interactions. This study investigates the response of lake ice phenology to future climate scenarios. Thirty‐five lakes in the Tibetan Plateau were studied. Firstly, we applied a random forest (RF) model to simulate lake ice condition under different representative concentration pathways (RCPs). The results of the virtual experiments show that lake ice freeze‐up start date (FUSD) and break‐up end date (BUED) are well simulated by the RF model, with R²_FUSD > 0.91 under different RCPs, and R²_BUED > 0.84, except in RCP2.6 (R² = 0.74). Secondly, two non‐parametric methods (Mann‐Kendall and Sen's slope estimator) were used for analyzing the trends in lake ice phenology and its response to various emission scenarios. Lake ice phenology was largely affected by temperature changes under different RCPs, and it had a larger inter‐annual variability in the early period (2002–2050) than in the later period (2050–2099). FUSD of 35 lakes delayed by an average of 0.01, 0.04, and 0.04 days/yr from 2002 to 2098 under RCP4.5, RCP6.0, and RCP8.5, whereas BUED advanced by an average of 0.04, 0.11, and 0.21 days/yr from 2003 to 2099. Both delays in FUSD and advances in BUED contributed to shortened average ice duration of 35 lakes, which shortened by 0.05, 0.14, and 0.25 days/yr from 2002 to 2098 under RCP4.5, RCP6.0, and RCP8.5, respectively. These findings could help to tackle the impacts of climate change on the lake systems.