Long-term altimetry data are one of the major sources to analyze the change in global ice reserves. This study focuses on the elevation and volume changes in the Greenland ice sheet (GrIS) from 2010 to 2019 derived from altimetry observations. In this study, the methods for determining surface elevation change rates are discussed, and specific strategies are designed. A new elevation difference method is proposed for CryoSat-2 synthetic aperture interferometric (SARin) mode observations. Through validation with Airborne Topographic Mapper (ATM) data, this new method is proved to be effective for slope terrains at the margins of the ice sheet. Meanwhile, a surface fit method is applied for the flat interior of the ice sheet where low resolution mode (LRM) observations are provided. The results of elevation change rates in the GrIS from 2010 to 2019 are eventually calculated by combining CryoSat-2 and ATM observations. An elevation change rate of −11.83 ± 1.14 cm·a⁻¹ is revealed, corresponding to a volume change rate of −200.22 ± 18.26 km³·a⁻¹. The results are compared with the elevation changes determined by Ice, Cloud, and Land Elevation Satellite (ICESat) from 2003 to 2009. Our results show that the overall volume change rate in the GrIS slowed down by approximately 10% during the past decade, and that the main contributor of GrIS ice loss has shifted from the southeast coast to the west margin of the ice sheet.

Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has been proven to be a cost-effective and efficient tool for monitoring the Earth’s surface soil moisture (SSM) with unparalleled spatial and temporal resolution. However, the accuracy and reliability of GNSS-R SSM estimation are affected by surface vegetation and roughness. In this study, the sensitivity of delay Doppler map (DDM)-derived effective reflectivity to SSM is analyzed and validated. The individual effective reflectivity is projected onto the 36 km × 36 km Equal-Area Scalable Earth-Grid 2.0 (EASE-Grid2) to form the observation image, which is used to construct a global GNSS-R SSM retrieval model with the SMAP SSM serving as the reference value. In order to improve the accuracy of retrieved SSM from CYGNSS, the effective reflectivity is corrected using vegetation opacity and roughness coefficient parameters from SMAP products. Additionally, the impacts of vegetation and roughness on the estimated SSM were comprehensively evaluated. The results demonstrate that the accuracy of SSM retrieved by GNSS-R is improved with correcting vegetation over different types of vegetation-covered areas. The retrieval algorithm achieves an accuracy of 0.046 cm³cm⁻³, resulting in a mean improvement of 4.4%. Validation of the retrieval algorithm through in situ measurements confirms its stability.

The urban heat island (UHI) effect is one of the typical characteristics of urban climate, which makes a lot of harmful gases accumulate and cannot be diffused. The UHI effect will cause air pollution, affecting the health and living conditions of humans. In this paper, a new method for calculating UHI intensity (UHII) by using the Global Navigation Satellite Systems (GNSS) derived zenith tropospheric delay (GNSS-ZTD) dataset and atmospheric empirical model was proposed and tested by using UHII observations from ground meteorological sensors for monitoring UHII in the Hong Kong city. The method requires accurate GNSS-ZTD datasets, priori atmospheric parameters (i.e. ground pressure and water vapour partial pressure) and the coefficient of the refractive function as inputs. The GNSS-ZTD datasets were obtained by using observations from the Hong Kong GNSS continuously operating reference stations network, and priori atmospheric parameters were obtained from a prior atmospheric model based on ERA5 (fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting) dataset and the coefficient of the refractive function can be obtained from the historical radiosondes data. The ground refractivity, pressure and water vapour partial pressure can be estimated by using the above-mentioned datasets, then the time series of UHII can be obtained by using a refractivity model proposed by Smith and Weintraub (SW-UHII), or Thayer (Thayer-UHII model). The results showed that the accuracy of the SW-UHII is better than the accuracy of the Thayer-UHII, and the root mean square (RMS) of the SW-UHII is 14% lower than the RMS of the Thayer-UHII. The mean bias, RMS and standard deviation of the SW-UHII compared with meteorological data derived UHII over GNSS stations are 1.82°C, 1.9°C and 1.41°C in all seasons, respectively, and the largest RMS is in winter (day of year 1–61 and 331–365). All the above results indicated that the GNSS technique has a certain prospect for monitoring UHII.

Mountain glaciers are the most direct and sensitive indicators of climate change. In the context of global warming, monitoring changes in glacier elevation has become a crucial issue in modern cryosphere research. The Global Ecosystem Dynamics Investigation (GEDI) is a full-waveform laser altimeter with a multi-beam that provides unprecedented measurements of the Earth’s surface. Many studies have investigated its applications in assessing the vertical structure of various forests. However, few studies have assessed GEDI’s performance in detecting variations in glacier elevation in land ice in high-mountain Asia. To address this limitation, we selected the Southeastern Tibetan Plateau (SETP), one of the most sensitive areas to climate change, as a test area to assess the feasibility of using GEDI to monitor glacier elevation changes by comparing it with ICESat-2 ATL06 and the reference TanDEM-X DEM products. Moreover, this study further analyzes the influence of environmental factors (e.g., terrain slope and aspect, and altitude distribution) and glacier attributes (e.g., glacier area and debris cover) on changes in glacier elevation. The results show the following: (1) Compared to ICESat-2, in most cases, GEDI overestimated glacier thinning (i.e., elevation reduction) to some extent from 2019 to 2021, with an average overestimation value of about −0.29 m, while the annual average rate of elevation change was relatively close, at −0.70 ± 0.12 m/yr versus −0.62 ± 0.08 m/yr, respectively. (2) In terms of time, GEDI reflected glacier elevation changes at interannual and seasonal scales, and the trend of change was consistent with that found with ICESat-2. The results indicate that glacier accumulation mainly occurred in spring and winter, while the melting rate accelerated in summer and autumn. (3) GEDI effectively monitored and revealed the characteristics and patterns of glacier elevation changes with different terrain features, glacier area grades, etc.; however, as the slope increased, the accuracy of the reported changes in glacier elevation gradually decreased. Nonetheless, GEDI still provided reasonable estimates for changes in mountain glacier elevation. (4) The spatial distribution of GEDI footprints was uneven, directly affecting the accuracy of the monitoring results. Thus, to improve analyses of changes in glacier elevation, terrain factors should be comprehensively considered in further research. Overall, these promising results have the potential to be used as a basic dataset for further investigations of glacier mass and global climate change research.

Due to the presence of sea ice, determining the sea surface height in the Arctic Ocean remains a significant challenge. State-of-the-art Arctic Mean Sea Surface (MSS) products are primarily derived from radar altimetry missions like CryoSat-2. However, the ICESat-2 laser altimeter can offer valuable sea surface observations up to 88° N latitude, extending the observational reach. This paper analyses the performance of combined CryoSat-2 and ICESat-2 data in determining the Arctic sea surface. Comparisons of overlapping observations from both missions reveal excellent consistency, with an inter-mission bias of less than 1 cm in the Arctic. Different geophysical corrections are considered, and the results suggest that only the ocean tide correction needs to be unified, while other corrections show minimal discrepancies. The MSS derived from combined data boasts both superior spatial coverage and precision compared to individual missions. The impact of summer melt pond is also discussed. The data from June, July and August are seriously contaminated, but only have limited effect on the mean sea surface calculation. Overall, the combined use of CryoSat-2 and ICESat-2 data offers a promising approach to accurately determining the Arctic sea surface, paving the way for improved understanding of sea level change and its implications in this critical region.