Global Navigation Satellite Systems (GNSS) in Climate Change Research: A Comprehensive Review

Highlights

What are the main findings?

• A comprehensive cross-disciplinary synthesis of five core GNSS techniques (GNSS-RO, PPP, CORS, GNSS-R, and GNSS-IR) across all primary Earth system spheres (atmosphere, cryosphere, hydrosphere, oceans, biosphere, and lithosphere) is presented.
• Modern GNSS observations provide precise, long-term, and continuous data supporting the monitoring of atmospheric parameters, sea level, snow cover, glaciers, soil moisture, and crustal deformation.

What are the implications of the main findings?

• Integration of GNSS data with other remote sensing techniques increases the capacity to monitor climate processes and improves the quality of meteorological forecasts, environmental analyses, and geophysical models.
• The dynamic development of multi-constellation GNSSs and modern data processing methods will strengthen the role of satellite technologies in monitoring climate change and supporting adaptation and environmental management measures.
• Operational synergetic frameworks combining multi-constellation GNSS with InSAR, satellite altimetry, GRACE-FO gravity missions, and AI-driven predictive modeling for enhanced environmental monitoring and hazard nowcasting are identified.
• The capacity of GNSS to deliver high-resolution continuous physical data (e.g., millimeter-level crustal motion, PWV profiles, or snow depth retrievals) beyond traditional positioning is demonstrated.

Abstract

Global Navigation Satellite Systems (GNSSs) are playing an increasingly important role in monitoring climate change, providing precise and continuous data on processes occurring in the atmosphere, hydrosphere, cryosphere, biosphere, and lithosphere. Initially, GNSSs were used primarily for navigation and geodetic purposes, but the development of satellite signal-processing methods has significantly expanded their applications. This paper presents an overview of climate research with particular emphasis on GNSS-RO, PPP, CORS, GNSS-R, and GNSS-IR techniques. The paper discusses the possibilities for monitoring atmospheric water vapor content, sea-level changes, snow cover, glaciers, soil moisture, vegetation status, and crustal deformation associated with redistribution of the Earth’s mass induced by climate change. The analysis indicates that GNSS observations are currently an important data source for climate and environmental research, as well as in weather forecasting systems, environmental monitoring, and geodynamic analyses. Integration of GNSS data with other remote sensing techniques supports a more comprehensive assessment of changes occurring in the Earth’s climate system as well as supporting the development of methods for adaptation to ongoing climate change.

1. Introduction

GNSS (Global Navigation Satellite System) provides an important source of observations for climate monitoring. For more than 30 years, numerous GNSS permanent stations have provided continuous observations of Earth systems; moreover, these observations are used in various methods of processing GNSS data, e.g., PPP (Precise Point Positioning) or RTK (Real-Time Kinematic). For the most demanding applications, factors such as antenna characteristics, clock corrections, and atmospheric delays are accounted for, enabling centimeter- to sub-centimeter-level positioning accuracy in appropriate applications [1,2,3,4].

Climate change is one of the greatest challenges facing the modern world. Rising average surface temperatures, melting glaciers and ice sheets, rising sea and ocean levels, changes in the water cycle, and the increasing frequency of extreme weather events impact both the natural environment and the functioning of societies and economies [5,6]. Understanding the mechanisms responsible for these processes requires long-term, accurate, and global Earth observations, which enable monitoring of changes in the atmosphere, hydrosphere, cryosphere, oceans, and lithosphere [7,8].

In recent decades, satellite geodesy techniques, including GNSS, have begun to play a particularly important role in climate research. Systems such as GPS, GLONASS, Galileo, and BeiDou were originally designed for navigation and positioning but have now become valuable sources of information about processes in the Earth’s climate system [9]. Thanks to their global coverage, high measurement accuracy, and the ability to conduct continuous observations, GNSS data are being used in an increasingly broad range of environmental and climate research [10].

Table 1. Applications, monitored phenomena, and GNSS-based techniques used in climate monitoring.

Application Areas	Climate Monitoring		Tool
	Phenomena	Examples
Atmosphere	Troposphere	− precipitable water vapor (PWV) − zenith tropospheric delay (ZTD)	PPP CORS GNSS-RO
	Ionosphere	− TEC − geomagnetic storms	PPP CORS
	Weather events	− forecasting heavy rains − typhoons − atmospheric rivers	PPP CORS GNSS-RO
Cryosphere	Sea ice	− classifying ice types − determining thickness − determining concentration	GNSS-R GNSS-IR
	Snow	− measuring snow cover thickness − monitoring snow water equivalent
	Glaciers and ice sheets	− mass balance − velocity of movement	PPP RTK CORS
	Permafrost	− monitoring soil freeze–thaw cycles	PPP RTK CORS
Hydrosphere (land water)	Inland water	− terrestrial water storage (TWS)	PPP CORS GRACE
	Groundwater	− Groundwater level and storage changes (GWS)
	Hydrological phenomena	− monitoring droughts − floods
Oceans and seas	Sea level	− monitoring sea level rise − tides − non-tidal ocean loading	GNSS-R GNSS-IR CORS PPP
	Surface dynamics	− ocean wind speed − wave height	GNSS-R GNSS-IR
	Coasts	− erosion − shoreline changes − dune dynamics	PPP RTK CORS
Geodynamics and biosphere	Crustal deformation	− movements induced by mass loading − induced seismicity	PPP RTK CORS
	Biosphere	− soil moisture − vegetation water content (VWC)	GNSS-R GNSS-IR

summarizes the structure and thematic scope of this review.

GNSS satellite signals are subject to various types of delays and distortions as they propagate through the atmosphere; these were traditionally treated primarily as sources of positioning error. However, it is now known that the information contained in these effects can be used to monitor atmospheric parameters such as water vapor content in the troposphere and total electron content (TEC) in the ionosphere [11]. The development of GNSS radio occultation (GNSS-RO) technology has also enabled the acquisition of vertical profiles of atmospheric temperature, pressure, and humidity, providing high-quality data for climate research and numerical weather models [12].

However, the importance of GNSS observations extends beyond the atmosphere. Geodetic data from permanent GNSS station networks enable monitoring of crustal deformation, sea-level changes, glacier movements, and processes related to the water cycle [13]. The GNSS observations can be used in conjunction with various sensors to improve the accuracy of atmospheric monitoring and forecasting [14]. GNSS measurements are also increasingly used to study snow cover, soil moisture, changes in water resources, and other environmental parameters that are directly or indirectly related to climate change. As a result, GNSS observations have become an important element of the global Earth observation infrastructure, complementing data from satellite remote sensing missions, weather stations, and other measurement systems [15].

The paper provides an overview of the applications of GNSS observations in climate change research. Table 1 summarizes the applications of GNSS in climate monitoring and provides the organizational framework for the review. The first section discusses the basic tools and techniques for using GNSS signals, including GNSS-RO, PPP, reference station networks (CORS), and GNSS reflectometry (GNSS-R). It then presents examples of GNSS data use for monitoring the atmosphere, cryosphere, hydrosphere, oceans and seas, as well as geodynamic and biospheric processes. This review aims to demonstrate the growing role of GNSS technology as a versatile tool supporting environmental observations and advancing our understanding of climate-related processes.

Unlike previous reviews, which typically focus on single satellite techniques or limit them to selected environmental elements (e.g., GNSS meteorology or reflectometry), this paper provides unique scientific value through a holistic and multi-sphere synthesis of the applications of five major GNSS techniques (GNSS-RO, PPP, CORS, GNSS-R, and GNSS-IR) in the context of comprehensive climate change monitoring. The main scientific contribution of this paper is not only a structured summary of the capabilities and limitations of individual methods for the atmosphere, cryosphere, hydrosphere, oceans, biosphere, and geodynamics, but also a critical analysis of their measurement uncertainties, error sources, and methodological challenges. Additionally, the paper identifies niche research areas and knowledge gaps, offering the scientific community a practical guide and recommendations for integrating multi-constellation GNSS data with other remote sensing techniques (such as InSAR, altimetry, or GRACE gravity missions) to improve the reliability of long-term climate analyses.

2. Methodology

To identify and characterize the development of research on the use of Global Navigation Satellite Systems in climate change research, a bibliometric analysis of the scientific literature was conducted. The Web of Science Core Collection (WoS CC) database was used as the data source. It is one of the most frequently used databases for bibliometric analyses and enables the analysis of both bibliographic information and citation relationships between publications. The selection of WoS CC allowed for the application of uniform publication indexing criteria and the acquisition of metadata regarding, among other things, authors, affiliations, journals, keywords, year of publication, and number of citations. The literature search was conducted on 7 July 2026, and the analysis covered publications from 2000 onward. The search was conducted using the Topic (TS) field, which in the Web of Science database includes the publication title, abstract, author keywords, and Keywords Plus. The Topic field allows for the identification of publications in which the analyzed topic is present not only in the title but also in the metadata describing the thematic scope of the work.

The search strategy was developed to combine two basic thematic areas of the analyzed issue: GNSS technologies and climate change and its environmental consequences. The query utilized the Boolean operators AND and OR, as well as wildcards, to accommodate various grammatical forms and plural forms of search terms. After conducting the search, publication selection criteria were applied:

• The analysis was limited to publications in English;
• Publications from 2000 year onwards were taken into consideration;
• The resulting collection was checked for duplicates and thematic relevance to the research subject;
• Publications that did not meet the adopted criteria were removed from the final collection.

Basic bibliometric characteristics were analyzed for the selected collection of publications, including the number of publications by year, the most frequently represented journals, authors, and research institutions, countries of origin, and the number of citations. Keyword co-occurrence was also analyzed, allowing for the identification of the main thematic areas developed in research on the use of GNSS in the context of climate change. Depending on the available data, analysis of collaboration between authors, institutions, and countries, as well as citation networks, was also considered. This approach allowed not only for the determination of the quantitative dynamics of the development of the research area but also for the identification of its thematic structure and scientific collaboration. The final set included publications that formed the basis for further quantitative and qualitative analysis. The adopted methodology enabled a comprehensive assessment of the development of research on the use of GNSS in the analysis and monitoring of processes related to climate change, as well as identification of the most important research directions, active research centers, and potential areas for further development of this topic. Figure 1 shows a bar chart of the 10 largest categories in which the articles that are the subject of this literature review were published.

Figure 2 presents the increase in the number of publications overlaid with the number of citations of the phrases “GNSS” + “climate change” in the WoS database.

Table 2. Number of publications in each analyzed GNSS category.

GNSS Technique	Number of Publications (GNSS Technique + Climate Change)	Number of Publications (GNSS Technique)
GNSS-RO	189	371
PPP	155	2679
CORS	123	1853
GNSS-R + GNSS-IR	266	577
SUM	733	5480

shows the number of publications in the WoS database with the phrase “GNSS technique” (e.g., GNSS-RO) only and with the combined phrases “GNSS technique” + “climate change”. Issues connected with climate change are only a small part of the research using the PPP and CORS techniques (less than 10%), while in the case of the GNSS-RO and both GNSS-R/-IR techniques, these issues represent approximately half of the papers.

2.1. Strengths and Limitations

To complement the literature review with a comparative analysis,

Table A1. Comparison of GNSS techniques, their applications, performance characteristics, advantages, and limitations in climate monitoring [78,237,238,239].

GNSS Technique	Main Climate/Environmental Applications	Measured/Derived Parameters	Spatial Resolution	Temporal Resolution	Typical Accuracy	Computational Requirements	Infrastructure/Cost	Main Advantages	Main Limitations
GNSS-RO	Atmosphere, climate monitoring, weather forecasting	Temperature, pressure, humidity, refractivity	Global	~100 m	High	Medium/High	High cost of LEO platform; low cost per individual observation	Global coverage, long-term stability, independence from clouds	Limited horizontal resolution; dependence on occultation geometry
PPP	Deformation monitoring, hydrosphere, glaciers, troposphere	Coordinates, ZTD/PWV	Local (point-based)	Seconds–days	mm–cm	Medium	Medium	High accuracy, no reference station required	Requires precise satellite orbits and clocks; convergence time
CORS	Deformation monitoring, atmosphere, hydrology	Coordinates, ZTD/PWV, TEC	Regional	Continuous, high frequency	mm–cm	Medium	High infrastructure cost	Continuous observations, high accuracy, multiple applications	Network maintenance costs; dependence on station density
GNSS-R	Oceans, ice, soil moisture, floods	Soil moisture, water level, wind speed, wave height	Regional/global, depending on platform (ground/satellite)	High	method-dependent (ground, satellite)	High	Medium/High	Large spatial coverage, uses existing GNSS signals	Complex processing; dependence on surface properties
GNSS-IR	Snow, water level, soil, coastal areas	Snow depth, water level, soil moisture	Point-based/local	Very high	cm–dm, application-dependent	Low/Medium	Low	Can use existing GNSS antennas; low cost	Limited spatial representativeness; dependence on reflection geometry

(Appendix A) summarizes the most important GNSS techniques used in research related to climate change monitoring in this article, namely GNSS-RO, PPP, CORS, GNSS-R, and GNSS-IR. Individual techniques differ not only in their principles of observation acquisition and processing but also in the range of possible applications, infrastructure requirements, spatial and temporal resolution, accuracy of the obtained products, and computational effort and costs [16,17]. Therefore, the selection of the appropriate technique should depend on the characteristics of the climate process being analyzed, the required spatial and temporal scale of observations, and the availability of appropriate measurement infrastructure.

GNSS-RO is characterized primarily by its ability to obtain global profiles of atmospheric parameters, particularly temperature, pressure, and humidity, making it particularly useful for climate research conducted on a global scale. Its significant advantages include high long-term stability and the ability to conduct observations regardless of cloud cover and lighting conditions. At the same time, the use of GNSS-RO requires the use of satellite platforms in low Earth orbit, and the information obtained is primarily profile-based, which limits its direct application to monitoring processes with very high spatial resolution. GNSS-RO is therefore particularly advantageous in atmospheric and climate analyses conducted on regional and global scales [18].

PPP and CORS techniques are particularly important for monitoring changes in the Earth’s surface, crustal deformation, and atmospheric parameters. PPP allows for determining the position of a single receiver without the need for a local reference station but requires access to precise orbits and satellite clocks, as well as appropriate data processing. CORS networks, on the other hand, provide continuous, long-term observations and enable high-accuracy positioning, but their operation requires the maintenance of appropriate ground infrastructure [19]. Both techniques are particularly useful in studying changes in the Earth’s surface elevation and horizontality, glacier movements, and changes related to hydrological loading, and they also allow for monitoring of tectonic and geodynamic processes.

A distinct group of techniques is constituted by GNSS-R and GNSS-IR, which utilize signals reflected from the Earth’s surface. Their main advantage is the ability to leverage existing GNSS signals to obtain information on surface properties without the need for active radar systems. GNSS-R can be used, among other things, to monitor soil moisture, ocean surface moisture, water level, ice, and hydrological phenomena. This technique offers the ability to conduct observations over large areas, but signal interpretation is more complex and depends on the electromagnetic and geometric properties of the observed surface. GNSS-IR, on the other hand, utilizes interference between the direct and reflected signals recorded by a ground-based receiver and is particularly useful for monitoring local parameters such as snow cover height, water level, and soil moisture changes. Its significant advantages include relatively low hardware requirements and the ability to leverage existing GNSS infrastructure. However, the limited spatial representativeness of a single station is a significant limitation of this technique.

The presented comparison demonstrates that there is no single universal GNSS technique suitable for all climate change monitoring applications. GNSS-RO is particularly valuable for global atmospheric observations, and PPP and CORS for precise monitoring of position, deformation, and atmospheric parameters, while GNSS-R and GNSS-IR extend GNSS capabilities to include observations of the Earth’s surface properties. These differences have direct implications for the design of climate monitoring systems, as they determine both the scope of information that can be obtained and the requirements for infrastructure, data processing, and costs. The comparison presented in Table A1 thus provides a basis for further critical evaluation of the suitability of individual techniques for specific components of the climate system and environmental observational variables.

2.2. Methodological Recommendations

This section critically discusses which techniques are most appropriate for specific climate variables or environmental applications. Based on the reviewed literature, the paper provides recommendations regarding the most suitable methods, explaining why they outperform alternative approaches under different study conditions.

Table 3. Comparison of the climate variables, recommended GNSS technique, and alternatives and advantages of GNSS methods over alternatives.

Climate Variable	GNSS Technique	Alternative or Complementary Technique	Main Advantage over Alternative Methods
Vertical temperature and pressure profiles	GNSS-RO (e.g., MetOp)	Radiosondes, microwave radiometers	No requirement for instrumental calibration; high vertical resolution (~100–300 m)
PWV	CORS/PPP	Satellite infrared and microwave sensors	Continuous measurement (24/7) regardless of cloud cover and time of day; high temporal resolution
Snow depth and/or snow water equivalent (SWE)	GNSS-IR	LiDAR, UAV photogrammetry	Utilization of existing GNSS antennas without additional costs; non-invasive, continuous winter monitoring
Ground deformation and subsidence	CORS+InSAR	Piezometric wells, GRACE mission	Absolute, continuous 3D geodetic control, serving as calibration points for InSAR phase
Glacier mass balance and post-glacial rebound	PPP/CORS	Laser altimetry (ICESat-2), GRACE	Millimeter vertical accuracy is necessary to separate elastic rebound effects from GIA processes
Ocean wind speed and cyclones	Satellite GNSS-R (e.g., CYGNSS)	Scatterometers, oceanographic buoys	L-band signal penetrates torrential rainfall without signal saturation
Inland water extent and inundation	Satellite/Airborne GNSS-R	MODIS, Landsat, Sentinel-1	High reflectivity from the water surface; complete insensitivity to cloud cover

provides a comprehensive summary of leading and alternative GNSS techniques, focused on the precise monitoring of key climate variables. This synthesis clearly demonstrates that the selection of the appropriate measurement method is closely dependent on the specificity of the phenomenon being analyzed and the required spatial and temporal resolution. In atmospheric research, GNSS-RO stands out for its unique ability to vertically profile temperature and pressure without the need for instrumental calibration, making it a key tool in monitoring climate change in oceanic and polar regions. Ground-based CORS networks and real-time PPP techniques play a fundamental role in operational meteorology, enabling continuous detection of water vapor content, essential for forecasting heavy precipitation events. In cryosphere and terrestrial hydrology research, GNSS-IR deserves particular attention, enabling cost-free and continuous measurements of snow cover depth using existing reference station infrastructure.

Measurements of ground deformation and glacier mass balance, however, require the highest vertical accuracy, down to the millimeter level, which is guaranteed by combining dual-frequency PPP/CORS measurements with satellite data such as InSAR or gravity missions. In the context of ocean dynamics and extreme phenomena, a key advantage of GNSS-R satellite reflectometry is the ability of the L-band signal to penetrate heavy rainfall, enabling direct measurement of wind speed at the core of tropical cyclones. This method also performs excellently for mapping the extent of inland waters in densely clouded tropical regions, where traditional optical sensors are rendered ineffective. This comprehensive analysis confirms that modern environmental monitoring increasingly relies on the integration and synergy of diverse remote sensing techniques.

3. Geodetic Tools for Climate Monitoring Applications

This section contains specific GNSS-related tools for a variety of climate monitoring applications. It is divided into four different types of tools/methods:

3.1. GNSS-RO

GNSS-RO is one of the most important techniques, an established satellite-based method of remote atmospheric sensing used in recent climate research. Its principle of operation is based on observing signals emitted by GNSS satellites as they pass through successive layers of the atmosphere during sunset or sunrise, relative to a low-orbiting (LEO) satellite. Figure 3 shows a schematic diagram of GNSS-RO observation geometry. Changes in the air’s refractive index cause deflection and delay in signal propagation, enabling the determination of vertical profiles of atmospheric refractivity, temperature, pressure, and humidity. A major advantage of GNSS-RO is its high measurement accuracy and low susceptibility to instrumental degradation. Atmospheric parameters are determined without external radiometric calibration, thereby maintaining the consistency of multi-year measurement series. Consequently, GNSS-RO data are used as an independent observational source for assessing tropospheric and stratospheric temperature changes and to validate products from other satellite techniques and atmospheric reanalysis models [20,21].

It is worth emphasizing that a key element of modern GNSS observation processing in meteorological and climate research is the precise modeling of the tropospheric delay using contemporary empirical and numerical atmospheric models. Advanced mapping functions, such as VMF1 (Vienna Mapping Function 1) and its latest generation, VMF3 (Vienna Mapping Function 3), are widely used in operational analyses and geodetic software [23,24] alongside empirical atmospheric model GPT3 (Global Pressure and Temperature 3). By utilizing these mapping functions and atmospheric models—which are often based on data from numerical weather prediction (NWP) models—it allows for a determination of both hydrostatic and wet components of the tropospheric delay. This approach drastically reduces errors resulting from observation geometry and atmospheric parameter variability. Incorporating these modern mapping models is essential for achieving sub-centimeter accuracy in determining the total tropospheric delay (ZTD) and total water vapor content (PWV), which provides the foundation for a reliable assessment of long-term climate trends [25].

Monitoring the vertical structure of the atmosphere is particularly important in climate research. Temperature and refractive index profiles obtained using the GNSS-RO method enable analysis of changes in tropopause height, atmospheric stability, and long-term temperature trends associated with global warming [26]. These data are used to identify changes in both the troposphere and the stratosphere, providing information on the processes that modify Earth’s energy balance [27,28]. An important direction for development is the use of GNSS-RO observations to improve numerical weather and climate models. Atmospheric profiles obtained from the COSMIC, COSMIC-2, and MetOp missions are routinely assimilated into forecast models, thereby increasing the accuracy of global temperature, humidity, and pressure representations. This particularly beneficial effect is observed over the oceans and in regions with limited classical meteorological measurements. As a result, GNSS-RO technology contributes to improving forecasts of extreme weather events and the quality of long-term climate analyses [29,30].

The integration of GNSS-RO data with observations from microwave radiometers, radiosondes, and CORS networks is also becoming increasingly important. Combining multiple information sources enables a more comprehensive analysis of atmospheric changes and reduces uncertainty in climate models. GNSS-RO technology is currently considered an important tool for atmospheric and climate monitoring, providing reliable data used both in scientific research and by global meteorological centers and institutions responsible for the development of Earth observation systems [31,32].

3.2. PPP

PPP is a widely used method for processing GNSS observations, enabling the determination of a single receiver’s coordinates with centimeter-level positioning accuracy without requiring a nearby reference station [33]. This accuracy is achieved by utilizing precise satellite orbits and clocks, atmospheric models, and advanced methods for estimating systematic errors. Although PPP was developed primarily for geodetic applications, it now also plays a significant role in monitoring environmental processes and climate change research [34,35]. One important application of PPP in climate research is monitoring the Earth’s surface deformations caused by changes in the mass of glaciers, ice sheets, and snow cover [36,37]. Multi-year observation series enable the detection of vertical and horizontal displacements of reference stations with an accuracy of a few millimeters. This information is used to analyze Glacial Isostatic Adjustment and associated crustal uplift, monitor changes in the ice sheet mass balance, and assess the impact of global warming on polar regions [38]. The PPP technique is also used to monitor land subsidence resulting from changes in water resources and intensive groundwater exploitation. In many regions of the world, long-term deformations associated with droughts, changes in groundwater levels, and human activity are observed. Combining PPP observations with hydrological and satellite data enables the assessment of the impact of climate change on the water cycle and the identification of areas particularly vulnerable to environmental degradation [39,40].

An important direction of development is the use of PPP to determine tropospheric parameters, primarily the zenith total delay (ZTD), which forms the basis for estimating atmospheric water vapor content. Thanks to its high accuracy and the ability to conduct continuous observations, this technique provides data used in both climate research and numerical weather forecast models. Integrating PPP with multi-constellation observations increases the number of available measurements, thereby improving the accuracy of the atmospheric parameters determined [41,42]. Real-time PPP solutions are becoming increasingly important, enabling ongoing monitoring of environmental processes. These solutions are used to observe deformations caused by extreme hydrological events, monitor infrastructure exposed to climate change, and support early warning systems. The development of multi-constellation GNSSs, improved error-modeling strategies, and artificial intelligence methods is making PPP one of the fundamental tools for geodetic environmental monitoring [43,44].

3.3. CORS

Continuously Operating Reference Station (CORS) networks constitute an important component of geodetic Earth-observation infrastructure [45]. They consist of permanent GNSS stations with precisely determined coordinates that continuously observe satellite signals and provide data for both precise positioning and monitoring environmental changes [46]. Combined with differential techniques such as DGPS, RTK, and RTN, CORS networks are widely used in research on the effects of climate change [47]. One of the most important applications of CORS networks is the long-term monitoring of surface deformation associated with climate-related sediment redistribution. Continuous observations enable the detection of vertical and horizontal displacements associated with melting ice sheets, glacier retreat, changes in hydrological loading, or isostatic processes [48]. Millimeter-level positioning precision can support the analysis of the rate of crustal uplift following the retreat of ice sheets and the assessment of the impact of mass balance changes on geodynamic deformation. Data from national and global CORS networks currently constitute one of the primary sources of information for validating geophysical models [43,49]. Differential GNSS techniques also play a crucial role in monitoring climate-related threats. High positioning accuracy enables the observation of landslides, deformation of dams, levees, and infrastructure exposed to the hydrometeorological hazards [50]. Continuous monitoring allows detection of small movements that precede structural failures or the activation of mass movements, thereby supporting the operation of early warning systems [51]. Integrating GNSS data with geotechnical, radar, and satellite measurements increases the effectiveness of environmental hazard assessments [52]. CORS networks also provide data used for atmospheric monitoring. Observations from reference stations are used to determine tropospheric and ionospheric parameters used in climate research and numerical weather models. A dense network of stations increases the spatial resolution of atmospheric products, enabling more precise analysis of water vapor changes, ionospheric disturbances, and their relationship with extreme weather events. As a result, the CORS infrastructure now serves a dual purpose: providing precise positioning and contributing to a global atmospheric observation system [53]. CORS are increasingly being integrated as a component of integrated environmental monitoring systems, combining geodetic, meteorological, and hydrological observations. This enables comprehensive assessments of the impacts of climate change, monitoring processes at local and global scales, and providing data for scientific analyses, crisis management, and adaptation planning [54,55].

3.4. GNSS-R and GNSS-IR

GNSS-R and GNSS interferometric reflectometry (GNSS-IR) utilize satellite navigation system signals reflected from the Earth’s surface to retrieve environmental properties [56]. Unlike traditional GNSS applications, which analyze signals arriving directly from satellites, reflectometric methods utilize changes in the amplitude, phase, and delay of the reflected signal [56,57]. Figure 4 shows a simple reflection model for snow depth retrieval using GNSS-IR.

This technique enables determination of land, water and snow surface parameters without active radar sensors. Thanks to the global availability of GNSS signals and low infrastructure costs, GNSS-R and GNSS-IR techniques are becoming increasingly important for monitoring climate-related processes [53,59]. One of the most important applications of GNSS-R is water surface observation. Analysis of the reflected signal characteristics enables determination of wind speed over the sea, wave heights, and changes in sea and ocean levels. Data from satellite missions such as CYGNSS are used to monitor tropical cyclones and assess energy exchange between the atmosphere and the ocean. In the context of climate change, these observations provide information on the intensification of extreme weather events and long-term changes in ocean dynamics [60,61]. On land, GNSS-R is used to monitor soil moisture, flooding, and surface and land-cover changes. Signal reflection properties depend on soil water content and surface roughness, enabling tracking of the effects of droughts, heavy rainfall, and changes in the water cycle. This information is used to validate hydrological models and satellite products, supporting the assessment of the impact of climate change on water resources and ecosystem functioning [62,63]. GNSS-IR technology is based on the analysis of interference between the direct and reflected signals recorded by a single ground-based GNSS antenna. This method enables determination of snow cover height, water level, soil moisture, and coastal zone changes without additional sensors. Multi-year observation series allow for monitoring snowmelt rates, seasonal changes in water retention, and sea and lake level fluctuations, which are among the most important indicators of ongoing climate change [64,65,66].

In the field of land observation, the capabilities of GNSS-R technology are extremely extensive. These systems enable, among other things, monitoring of soil moisture, salinity, vegetation status (including forest biomass), and ground freezing and thawing phenomena, as well as flood detection and mapping of inland waters and wetlands. Satellite constellations such as CYGNSS and the Chinese Tianmu-1 (TM-1) system have been shown to provide high temporal and spatial resolution, which is indispensable in resource management and early warning systems for disasters. Furthermore, the use of data from FY-3 satellites (models E, F, and G) enables extremely precise mapping of terrestrial water bodies [67]. An example is the detection of major rivers and smaller tributaries in the Amazon and Congo basins using the Z-score algorithm. This operation allowed for an overall water detection accuracy of over 95% and 97% to be achieved [68]. Another priority aspect for land is the accurate estimation of soil moisture (SM), which is strongly linked to the global hydrological cycle. The use of global models that account for local geographic variation significantly improves the reliability of measurements [69]. By integrating data from the CYGNSS constellation (effective reflectance) and SMAP ancillary products (including temperature and optical vegetation density), it is possible to create dedicated polynomial models for each grid cell. This independence of the algorithms from the introduction of redundant (and often misleading) parameters has resulted in a reduction of the root mean square error (RMSE) to a very low value of 0.040 cm³/cm³ [70].

Another important area for GNSS-R research is the cryosphere, with particular emphasis on sea ice. Information about ice is critical for maritime navigation, offshore resource exploration, and research on climate change on Earth. Satellite missions such as the British TechDemoSat-1 (TDS-1) have demonstrated that parameters extracted from GNSS-R delay and Doppler maps (DDMs) can be successfully used to detect sea ice areas, estimate the concentration and thickness of this ice, and determine elevation profiles [71]. Developments in this field are strongly coupled with machine learning algorithms. The study prepared by Hu et al. (2024) shows an innovative approach using the Local Linear Embedding (LLE) dimensionality reduction technique coupled with a Support Vector Machine (SVM) classifier [72]. This particular method was shown to be excellent at isolating useful signal features in the presence of noise, which in turn allows for the reliable discrimination of sea ice from open water. This method achieved an outstanding accuracy of over 99.7% for selected data while significantly reducing the computational costs of training processes compared to classical convolutional neural networks (CNNs).

Recent methodological development increasingly integrates GNSS-R and GNSS-IR observations with radar and optical data, as well as hydrological and climate models. The use of multi-constellation GNSSs and machine learning algorithms improves the accuracy of environmental parameter estimation and enables automatic monitoring of changes across large areas [73,74].

4. Climate Monitoring

Section 4 presents the most important applications of GNSS observations in climate change monitoring. Thanks to their high measurement accuracy, global coverage, and the ability to conduct continuous observations, GNSSs have become an important element of modern environmental monitoring systems [15,75]. Data obtained from satellite navigation systems enable the analysis of processes occurring in the atmosphere, cryosphere, hydrosphere, oceans, biosphere, and lithosphere, providing information essential for assessing the effects of climate change and evaluating and validating climate models. The following sections discuss the most important applications of GNSS techniques in specific components of the Earth system [76,77].

4.1. Atmosphere

The atmosphere is one of the most dynamic components of the Earth’s climate system and the environment through which GNSS signals propagate. Signal propagation delays, once considered solely a source of positioning error, now constitute a valuable source of information about the state of the atmosphere. GNSS observations allow for monitoring of water vapor content, ionospheric changes, and meteorological phenomena, supporting both climate research and weather forecasting [78,79,80].

4.1.1. Ionosphere

The ionosphere is an ionized region of the upper atmosphere in which the presence of free electrons affects the propagation of GNSS signals. This effect is a major source of positioning errors, but it also enables monitoring of the upper atmosphere. Dual-frequency satellite observations enable determination of TEC, a fundamental parameter used to analyze ionospheric processes and assess the impact of solar and geomagnetic activity on the Earth’s environment [81,82]. The development of global and regional networks of reference stations has enabled the creation of TEC models with high temporal and spatial resolution. Figure 5 shows an hourly VTEC (Vertical TEC) using GNSS observations for one day in 2007.

Multi-year observation series allow for the identification of diurnal, seasonal, and multi-annual variability, as well as the analysis of the impact of the solar activity cycle on the ionosphere’s structure. GNSS data are currently a major source of information used to validate ionospheric models and improve the quality of precise satellite positioning [84,85,86]. Studies of ionospheric disturbances caused by phenomena occurring in the atmosphere and on the Earth’s surface are becoming increasingly important. Passing typhoons, strong convective systems, volcanic eruptions, and earthquakes generate acoustic-gravity waves that cause local changes in TEC. Analyzing the timing and propagation of these disturbances allows for a better understanding of the coupling mechanisms between the lower atmosphere and the ionosphere and the development of methods for monitoring extreme phenomena [25,87,88]. An important direction for development is the use of multi-constellation observations, including GPS, GLONASS, Galileo, and BeiDou. A larger number of satellites increases the density of observations, improving the accuracy of reconstruction of the spatial distribution of TEC and the effectiveness of detecting short-term disturbances. At the same time, methods are being developed that integrate GNSS data with physical models and machine learning algorithms, enabling the forecasting of ionospheric changes and limiting their impact on navigation and telecommunications systems [89,90,91].

In the context of climate change research, ionospheric observations provide information on long-term changes in the upper atmosphere and on the interrelationships among solar activity, processes in the neutral atmosphere, and the space environment. The growing number of reference stations and the development of data-processing methods ensure that GNSS remains a key observational technique for ionospheric monitoring for monitoring the ionosphere, enabling continuous global observations and supporting research into the functioning of the entire atmosphere-ionosphere system.

4.1.2. Troposphere

The troposphere is the lowest layer of the atmosphere, where processes responsible for shaping weather and climate occur the most. Unlike ionospheric delays, GNSS signal delays in the troposphere depend not on frequency but on pressure, temperature, and water vapor content [92,93,94]. A parameter derived from satellite observations is the ZTD, consisting of hydrostatic and wet components. The wet component is of particular importance, enabling the determination of the integrated PWV, one of the fundamental indicators describing the moisture cycle in the atmosphere [18,95]. Continuous observations from networks of permanent GNSS stations provide data with high temporal resolution data, surpassing traditional radiosonde measurements in this respect. This enables near-real-time monitoring of changes in water vapor content and analysis of short-term and seasonal changes in atmospheric humidity. GNSS data are now widely used for validating satellite products and atmospheric reanalyses, constituting one of the most comprehensive sources of information on the state of the troposphere [96,97,98].

A significant application of GNSS observations is the assimilation of tropospheric parameters into numerical weather forecast models. Incorporating ZTD and PWV values into meteorological models improves the representation of the humidity field, resulting in greater accuracy in precipitation and convection forecasts. Figure 6 shows an example of daily variations in ZTD at a permanent GNSS station over more than half a year, red line indicates a mean value of the ZTD StdDev.

Tropospheric tomography, using observations from multiple satellites and numerous reference stations, is also playing an increasingly important role. It enables reconstruction of the three-dimensional distribution of atmospheric humidity, allowing tracking of air mass transport and the development of precipitation-producing structures [89,100].

Multi-year series of PWV observations are particularly valuable in climate change research, enabling the analysis of changes in the intensity of the hydrological cycle and the assessment of the impact of rising temperature on atmospheric water vapor content [101]. Integrating GNSS data with climate models enables a better description of the interrelationships among humidity, energy balance, and atmospheric circulation. Thanks to its high accuracy and continuous measurements, GNSS technology has become a fundamental tool for monitoring the troposphere, supporting both climate research and the development of modern weather forecasting systems [102,103,104].

4.1.3. Weather Events

The development of GNSS techniques has allowed not only the determination of atmospheric parameters but also the monitoring and forecasting of weather phenomena [105]. Continuous measurements of water vapor content and changes in tropospheric delay are particularly important, as they enable the identification of conditions favorable to the development of intense convection, heavy precipitation, and thunderstorms [106,107]. Unlike classical observational methods, networks of permanent GNSS stations provide data frequently and independently of the time of day or cloud cover, making them a valuable complement to weather radars and satellite observations [47,108]. One of the most important applications is the monitoring of heavy precipitation. Numerous studies have shown that rapid increases in atmospheric water vapor can precede heavy precipitation events. Integrating GNSS observations with numerical models improves the accuracy of precipitation forecasts, especially in regions with limited classical meteorological measurements [109,110,111]. GNSS technology is also used to analyze tropical cyclones (including hurricanes and typhoons). The movement of such systems causes dynamic changes in the atmospheric moisture distribution, which are recorded by networks of reference stations. Analysis of the spatial distribution of PWV enables tracking the development of cyclones and assessing their potential intensity. GNSS observations are also used to validate satellite data and improve models forecasting the trajectory and development of severe weather systems [112,113]. Research using artificial intelligence methods to analyze GNSS data is becoming increasingly important. Machine learning algorithms enable the detection of relationships between changes in atmospheric parameters and the occurrence of hazardous weather phenomena, such as storms, heavy precipitation, or heat waves. Combining GNSS observations with radar, satellite data, and meteorological models improves the effectiveness of forecasting systems and may support more timely responses by emergency-management services [114,115].

The application of the CYGNSS mission in the context of weather monitoring and its phenomena is relatively new [116]. Since the data release began in 2017, this constellation of eight microsatellites launched by NASA has become the subject of intensive research, going far beyond its original purpose of measuring marine cyclonic winds without rain attenuation [117]. A groundbreaking development direction was the application of machine learning (ML) and deep learning (DL) algorithms to process complex reflectometric data, such as delay-Doppler scatter maps (DDMs) from the CYGNSS mission [118]. Neural network and random forest models enabled precise, continuous, high-resolution estimation of soil moisture, effectively reducing the noise resulting from dense biomass attenuation and terrain roughness [119,120]. The use of machine learning methods in combination with CYGNSS data has also revolutionized land hydrological mapping, enabling rapid detection and classification of inland waters, dynamic monitoring of floods in river basins, and determination of the extent of flooding under dense tree canopy [121]. Furthermore, innovative deep learning using multimodal residual networks utilizes CYGNSS data to measure ocean surface height (altimetry) and estimate wind speed, demonstrating higher accuracy than traditional analytical methods [11,122]. The integration of passive satellite reflectometry techniques with machine learning is currently setting a new standard in environmental remote sensing, fully exploiting the physical limitations of reflected signal complexity [74].

Researchers also investigate how tropospheric parameters affect GNSS measurements at Antarctic continuous reference stations, focusing on non-tidal atmospheric loading (NTAL) [123]. By analyzing daily coordinate time series and atmospheric models from 2016 to 2024, the research identifies a persistent positive correlation between vertical residuals and atmospheric loading at many stations. These results help identify stations where accounting for NTAL is essential, improving the accuracy of geodynamic analyses in polar environments. Figure 7 illustrates that the spatial distribution of the correlation coefficients is uneven. The map clearly highlights stations with higher correlation values, indicated in green, whereas lower values are predominantly found among stations marked in orange and red.

These results indicate that tropospheric parameters have a substantial impact on the results of GNSS measurements even in the Antarctic region.

In the context of climate change, GNSS observations contribute to analyses of long-term atmospheric conditions associated with extreme weather events [103,124]. Multi-year data series enable assessment of changes in atmospheric moisture availability, the intensification of the hydrological cycle, and the impact of rising temperatures on the development of convective phenomena. Thanks to their high accuracy, measurement continuity, and global coverage, GNSSs have become an important element of modern atmospheric monitoring systems, supporting both climate research and the development of operational forecasting systems and early warnings of meteorological hazards [78,125].

4.2. Cryosphere

The cryosphere encompasses all components of the climate system in which water exists in solid form, including sea ice, snow cover, glaciers and ice sheets, and permafrost. Changes in these components are among the most pronounced indicators of contemporary climate change [7,126]. In recent years, GNSS observations have been widely used to monitor processes in the cryosphere, enabling both direct measurements of surface deformation and indirect determination of environmental parameters using techniques such as GNSS Interferometric Reflectometry (GNSS-IR) and precise satellite positioning. The most important GNSS applications in the study of individual cryosphere components are presented below [56,127,128].

4.2.1. Sea Ice

Sea ice plays a key role in the Earth’s global energy balance by regulating surface albedo and the exchange of energy and moisture between the ocean and the atmosphere [129]. The increase in air temperature observed in recent decades has contributed to a long-term reduction in the extent and thickness of sea ice, particularly in the Arctic. Accurate monitoring of these changes is essential for both climate research and forecasting future changes in the polar environment [130,131,132]. GNSS observations are increasingly complementing traditional satellite methods for monitoring sea ice. Using GNSS signal reflections from the ice surface enables determination of the physical properties of the ice cover, its roughness, and changes in surface height. GNSS-IR technology enables continuous measurements with ground-based receivers, while observations from satellite platforms extend the monitoring range to difficult-to-access areas [61]. Research by Wan et al. indicates that signals reflected from the ice surface enable the differentiation of sea ice types and the tracking of seasonal changes in its physical properties [133]. The authors emphasize that GNSS-R is a valuable complement to radar and radiometric data, particularly under cloudy conditions and during the polar night. Belloni et al. (2022), in turn, point to the growing importance of multi-system GNSS observations in monitoring the polar regions [134]. Integrating GNSS data with satellite observations from other missions improves the accuracy of ice surface parameter estimates and reduces the uncertainty of models describing sea ice changes. Figure 8 shows delay Doppler maps (DDMs) collected by the UK TechDemoSat-1 (TDS-1) satellite mission for three different surface types.

The growing number of GNSS satellites and advances in data processing methods mean that reflectometric techniques will play an increasingly important role in monitoring changes in the Arctic and Antarctic. The ability to conduct continuous observations regardless of lighting conditions is a significant advantage over many traditional remote sensing methods.

4.2.2. Snow

Snow cover is one of the most dynamic elements of the cryosphere and a crucial component of the water cycle. Its thickness and duration influence the energy balance of the Earth’s surface, river supply, and the functioning of mountain and polar ecosystems [135]. A warming climate is shortening the snow cover period and decreasing snow depth in many regions of the world. GNSS-IR technology enables precise monitoring of snow cover depth by analyzing the interference between the direct signal and the signal reflected from the snow surface [136]. An advantage of this method is the ability to use existing permanent GNSS stations without installing additional measurement equipment [137].

Research by Hu et al. (2022) demonstrated that the GNSS-IR method allows for highly accurate measurements of snow cover height while maintaining continuous observations throughout the winter season [138]. The authors emphasize the feasibility of conducting automated monitoring even in difficult-to-access mountainous regions. Similar conclusions were reported by Altuntaş et al. (2022), demonstrating the good agreement between GNSS-IR results and conventional field measurements [139]. Figure 9 shows an example of reflector-height estimates obtained using GNSS-IR (red) with reference measurements (blue) at one of the stations in Greenland.

The use of multi-frequency GNSS observations further reduces the impact of environmental disturbances and increases the accuracy of snow depth estimates. GNSS data are increasingly being integrated with meteorological measurements and satellite observations, creating comprehensive snow cover monitoring systems [140]. Such solutions enable a better assessment of the impact of climate change on water resources and support the development of hydrological models for forecasting floods and droughts [141,142].

4.2.3. Glaciers

Glaciers and ice sheets are among the most sensitive indicators of recent climate warming. Their systematic mass loss contributes to sea level rise and affects the local water balance. Monitoring changes in ice thickness, glacier movement velocity, and mass balance is one of the main applications of GNSS techniques [143,144].

According to Wells et al. (2024), the use of low-cost GNSS receivers enables high-accuracy direct monitoring of changes in glacier mass balance [145]. The authors demonstrated that combining GNSS-IR observations with classic ablation poles and time-lapse images allows for effective separation of the influence of climate processes from glacier movement dynamics. Research by Lambrecht et al. (2018), in turn, shows that permanent GNSS stations enable continuous monitoring of glacier movements, providing information on changes in their movement velocity in response to meteorological conditions and melting processes [146]. Figure 10 shows the vertical-position time series from four permanent GNSS stations located at the Polish Polar Station at Hornsund (SW Svalbard), orange line represent seasonal function fitted into data.

Such data are crucial for calibrating numerical models describing glacier evolution. In recent years, advances in GNSS technology have enabled the use of small, energy-efficient receivers that can operate for months in extreme polar conditions [148,149]. This enables long-term observations even on glaciers in Greenland and Antarctica. Combining GNSS data with satellite observations and airborne laser scanning supports a more comprehensive assessment of glacier volume changes and their impact on global sea level [150,151].

4.2.4. Permafrost

Permafrost is defined as soil or underwater sediment that continuously remains below 0 °C (32 °F) for two years or more. Its degradation is one of the most important consequences of climate warming at high latitudes [152]. Thawing permafrost leads to land subsidence, changes in water conditions, and the release of significant amounts of greenhouse gases. GNSS techniques enable highly accurate monitoring of vertical and horizontal ground surface movements related to cyclical freezing and thawing of the subsoil [153]. Continuous observations enable determination of the rate of permafrost degradation and identification of areas particularly susceptible to deformation. Zou et al. (2019) demonstrated that GNSS observations enable detection of seasonal changes in surface elevation associated with ground freezing and thawing [154]. These data are a valuable complement to geotechnical measurements and satellite observations. Still et al. (2023) emphasize that the development of a network of permanent GNSS stations enables long-term monitoring of surface changes in the Arctic [151]. Integrating GNSS data with InSAR methods significantly improves the accuracy of analyses of permafrost-related deformation. GNSS-IR measurements allow for continuous monitoring of permafrost based on reflector height changes (Figure 11).

GNSS observations are expected to play an increasingly important role as climate warming continues, because surface deformations are among the first signals of permafrost destabilization. This information is crucial for both climate research and risk assessments for infrastructure located in polar regions.

4.3. Hydrosphere

The hydrosphere plays a key role in the functioning of the climate system through its participation in the global water cycle. GNSS observations are used to monitor surface and groundwater, soil moisture, and hydrological phenomena. Integrating geodetic data with remote sensing methods enables assessment of the effects of droughts, floods, and long-term changes in water resources associated with climate change [156,157].

4.3.1. Inland Water

GNSSs are used in inland water research, both in direct geodetic measurements and in remote sensing using reflected satellite signals. The development of satellite technologies has enabled the monitoring of lake, river, and wetland extents with high observation frequency, providing significant support for water resource management and climate change analysis [158,159]. One of the most promising areas is the use of the CYGNSS constellation, which records GPS signals reflected from the Earth’s surface. As demonstrated by Pavur et al. (2024), the properties of electromagnetic wave reflection from water surfaces allow for effective differentiation between dry and flooded areas [160]. The analyses compared water extent maps derived from CYGNSS data with those from MODIS and Landsat products (Figure 12). The results indicated high agreement between the methods, and the appropriate selection of classification thresholds enabled significant improvements in the accuracy of water body identification. The authors emphasize that GNSS-R is a valuable complement to traditional optical observations, especially in cloudy conditions when optical imagery is limited. The use of GNSS technology also encompasses precise geodetic measurements on inland waters. High-accuracy positioning systems are used during hydrographic surveys, shoreline delineation, and monitoring changes in the morphology of water bodies. The use of RTK receivers and differential techniques allows for centimeter-level positioning of vessels and measurement platforms, significantly improving the quality of data used in cartography, water management and many other fields [161,162].

The integration of GNSS with unmanned aerial vehicles (UAV) is also playing an increasingly important role. Studies on shoreline monitoring in inland lakes have shown that combining low-cost UAV platforms with precise GNSS positioning enables rapid acquisition of high-resolution spatial data. This solution enables accurate assessment of water-level changes, coastal erosion, and seasonal changes in water bodies, providing an effective tool for environmental monitoring [163].

These examples demonstrate that GNSS technologies, both in classical satellite geodesy and GNSS-R reflectometry, are becoming an important element of contemporary hydrological research. Their advantage is the ability to conduct continuous observations over large areas, regardless of lighting conditions, which increases the potential for using these data in climate change analyses and water resource monitoring [164].

4.3.2. Groundwater

GNSS technologies are playing an increasingly important role in groundwater research, although their applications are primarily indirect [165]. Unlike hydrogeological methods, which enable direct measurement of groundwater levels, GNSSs are primarily used to monitor ground-surface deformation associated with water resource exploitation [166]. This information provides valuable data on changes in aquifers and allows assessment of the impact of human activity on the environment. One of the most important applications is monitoring land subsidence caused by intensive groundwater extraction. Over-exploitation reduces pressure in aquifers, resulting in aquifer compaction and a permanent lowering of the ground surface. According to the results presented by Zhang et al. (2025), continuous GNSS observations enable precise determination of the rate and spatial extent of subsidence, providing an effective tool for monitoring geological hazards [167]. The authors emphasize that integrating GNSS data with InSAR radar interferometry observations and hydrogeological measurements enables a much better understanding of the mechanisms responsible for surface deformation. Another important development is the use of permanent GNSS stations to analyze seasonal changes in ground surface elevation [138]. Changes in the Earth’s crustal load resulting from fluctuations in groundwater volumes cause small but measurable vertical displacements of geodetic stations. Analysis of multi-year time series enables the identification of cycles related to aquifer recharge and drainage and the assessment of hydrogeological systems’ responses to prolonged periods of drought and heavy rainfall. This approach is a valuable complement to traditional measurements taken in piezometer networks.

Integrating GNSS observations with satellite data from the GRACE and GRACE Follow-On missions is also becoming increasingly important [168]. While gravity satellites provide information on changes in total water resources at large spatial scales, GNSS data enable local model verification by analyzing surface deformations caused by changes in water mass [169]. Combining both sources of information improves the accuracy of estimates of changes in groundwater resources and allows for a better assessment of the effects of long-term exploitation and climate change. Figure 13 shows a sample of terrestrial water storage changes from GPS inversion, GRACE, and the GLDAS hydrological model with a precipitation level.

A review of available research indicates that GNSS is not an alternative to classical hydrogeological methods but a valuable complement. The ability to conduct continuous, precise measurements of surface deformation and to integrate them with other remote sensing techniques makes GNSSs an important element in monitoring groundwater resources and assessing their long-term changes amid increasing climate change [170,171,172].

4.3.3. Hydrological Phenomena

GNSS satellite navigation systems are increasingly used to monitor hydrological phenomena, providing information on processes occurring in the atmosphere, hydrosphere, and lithosphere [173]. Of particular importance is the use of GNSS signals to determine atmospheric parameters affecting the water cycle, monitor soil moisture, and observe the effects of extreme hydrological phenomena, such as floods or landslides triggered by heavy rainfall [174]. While atmospheric sensing tracks moisture in the air, GNSS applications in hydrology focus on terrestrial surface processes, terrestrial water storage (TWS), and land-based hazard monitoring. Using GNSS-R and GNSS-IR, reflected signal characteristics deliver continuous surface soil moisture estimations and track surface inundation extent during flood events. On larger basin scales, vertical crustal deformation recorded by CORS networks serves as an absolute scale for tracking seasonal groundwater storage changes and regional hydrological mass loading. Furthermore, high-precision GNSS positioning provides critical deformation monitoring for hydrometeorological hazards, such as rainfall-induced landslides, dam deformation, and levee instability. By bridging soil moisture sensing, surface water mapping, and geodetic loading responses, GNSS techniques provide a comprehensive framework for monitoring terrestrial water cycle dynamics and mitigating land-based hydrological risks [82].

Another important application area is determining soil moisture using GNSS-R [62]. This method analyzes the characteristics of signals reflected from the ground surface, which depend on its dielectric properties and, consequently, on its water content. Studies have shown that GNSS-R enables monitoring of changes in soil moisture over large areas with high observation frequency, providing a valuable complement to traditional field measurements and satellite data. Information on soil moisture is crucial for both surface runoff modeling and agricultural drought analysis and flood risk assessment. GNSS technologies are also used to monitor the effects of extreme hydrological phenomena [175]. Heavy rainfall increases soil moisture, which can trigger mass movements and landslides. Multi-station networks of permanent GNSS stations record even small surface movements preceding landslide activation, enabling the identification of areas at risk. GNSS data are also used to monitor deformation of levees, dams, and other hydraulic structures, increasing infrastructure safety during periods of extreme weather events [53,176].

A literature review indicates that the development of GNSS techniques has significantly expanded the possibilities of observing hydrological processes. Integrating geodetic observations with meteorological and satellite data and hydrological models enables increasingly accurate monitoring of the water cycle, assessment of the effects of climate change, and development of early warning systems for hydrological hazards [177,178,179].

4.4. Oceans and Seas

Oceans and seas constitute the primary reservoir of thermal energy in the Earth’s climate system and play a crucial role in regulating global atmospheric circulation. GNSS techniques enable monitoring of sea level, ocean surface dynamics, and changes in coastal zones. These data are used to assess sea level rise rates, coastal erosion processes, and the impact of climate change on the marine environment [180,181].

4.4.1. Sea Level

Sea level changes are one of the most unambiguous indicators of recent climate change. Rising temperatures lead to thermal expansion of ocean waters and the rapid melting of ice sheets and glaciers, resulting in a systematic rise in mean sea level [182]. GNSSs are becoming increasingly important for studying these processes, enabling precise monitoring of vertical crustal motion, a crucial element in interpreting observed sea-level changes. Unlike traditional tide gauges, which record relative sea level relative to land, GNSS observations allow us to determine whether changes result from actual ocean-level rise or from vertical coastal movements [183,184]. One of the main applications of GNSS is determining the vertical displacements of stations located near tide gauges [185]. This data allows for correction of sea level observations for the effects of land subsidence or uplift, which is crucial for assessing the rate of sea level rise. As demonstrated in a study comparing methods for estimating vertical crustal movements, integrating GNSS observations with satellite altimetry yields more reliable estimates of regional sea level changes than using a single measurement technique [186]. The authors emphasize that failure to account for local deformations can lead to significant errors in the analysis of long-term trends.

Another important element of recent research is the analysis of Glacial Isostatic Adjustment (GIA) [187]. This process causes long-term vertical movement of the Earth’s crust in response to the melting of ice sheets after the last glacial period. The SELEN4 model, developed by Spada and Melini, enables the simultaneous modeling of sea-level changes, Earth’s surface deformation, and signals observed by GNSS networks. The authors point out that accounting for GIA effects is essential for the correct interpretation of geodetic data and determining the actual rate of contemporary sea level rise.

Observations of the polar regions are also a significant source of information on ocean level changes. Analysis of GNSS, GRACE, and ICESat data for Antarctica revealed significant mass loss from the West Antarctic Ice Sheet, accompanied by significant vertical crustal deformation. These results confirm that GNSS observations are an important element in monitoring changes in glacier mass balance and their impact on global sea level [154]. GNSS data are increasingly being integrated with modern altimetry missions, such as Sentinel-6A. Precise satellite orbit determination using GPS and Galileo receivers directly translates into improved accuracy of satellite measurements of ocean surface height [188]. Integrating satellite geodetic techniques now enables monitoring of sea-level changes with centimeter-level accuracy, which is fundamental for forecasting the impacts of climate change, assessing coastal hazards, and developing climate models.

4.4.2. Surface Dynamics

The dynamics of the sea and ocean surface play a key role in the functioning of the Earth’s climate system, influencing heat transport, gas exchange between the ocean and the atmosphere, and global water circulation [189]. In recent years, GNSSs have found widespread use in studying ocean surface processes, both through the precise positioning of measurement platforms and through satellite reflectometry. These methods enable the acquisition of sea surface data with high temporal and spatial resolution, providing a valuable complement to traditional oceanographic observations [190]. One of the most important applications of GNSS-R is the analysis of ocean surface roughness, which is closely related to wind speed and wave height. Reflected navigation signals contain information about sea surface characteristics, enabling determination of hydrodynamic parameters without active radar sensors. As the authors point out, this technique enables continuous observations even during periods of heavy cloud cover and precipitation, when traditional optical methods are significantly less effective [191]. By leveraging existing GPS, Galileo, and BeiDou signals, global observational coverage can be achieved at relatively low satellite mission costs. Significant progress has also been made in monitoring wind speed over the ocean. Data obtained from the CYGNSS mission have demonstrated high accuracy in determining wind fields, particularly during extreme atmospheric phenomena such as tropical cyclones. According to the results presented in the study, GNSS-R observations enable more accurate determination of hurricane structure than traditional microwave radiometers because GNSS signals are not significantly attenuated by heavy precipitation [192]. This information is crucial for weather forecasting models and analyses of energy exchange between the atmosphere and the ocean. GNSS technologies are also used to study surface currents and short-term sea level changes. Integrating GNSS observations with satellite altimetry and oceanographic buoys enables the analysis of spatial variation in wave action and surface circulation. The authors indicate that combining multiple data sources significantly improves the accuracy of oceanographic models for predicting heat transport, pollutants, and sea ice drift [193].

A literature review indicates that GNSS-R techniques are becoming one of the most promising satellite-based tools for monitoring sea surface dynamics amid ongoing climate change [11]. The growing importance of GNSS observations also stems from their use in monitoring climate change. Long-term data series on sea surface roughness, wind speed, and wave height enable the identification of changes in global atmospheric and ocean circulation. This information is used to validate climate models and assess the impact of rising ocean surface temperature on the intensity of extreme weather events [118,194].

4.4.3. Coasts

Coastal areas are among the most vulnerable to the effects of recent climate change; rising mean sea level, more frequent storms, and increasing coastal erosion necessitate systematic coastal monitoring. In recent years, GNSSs have become a fundamental tool supporting coastal observations, enabling precise determination of changes in shoreline position, vertical land movement, and coastal deformation [195,196,197,198]. Integrating geodetic data with oceanographic observations allows for a more reliable assessment of the impact of climate change on the functioning of coastal zones. One of the most important applications of GNSS is the interpretation of long-term sea level changes recorded by tide gauges [199,200]. Because these devices measure relative sea level relative to the land, simultaneous monitoring of vertical ground movement is essential. As Marcos et al. (2019) emphasize, only about a quarter of the world’s tide gauges have GNSS stations in their immediate vicinity, which remains a significant limitation for global monitoring [201]. Considering vertical displacements of the Earth’s crust enables the separation of changes resulting from actual sea-level rise from those due to local tectonic or anthropogenic processes, which is crucial for accurately assessing the rate of climate change. GNSS technologies are also used in coastline mapping and monitoring changes. Nugraha et al. (2019) indicate that precise GNSS receivers form the basis of modern methods for determining the coastline’s course, whose position depends on tidal conditions [202]. Figure 14 shows sea-level residuals from GNSS-IR and tide gauge measurements.

GNSS data, integrated with satellite imagery and photogrammetric measurements, enable the generation of accurate coastal models used in spatial planning, maritime administration, and the management of areas prone to flooding. Of particular importance is the ability to perform cyclic measurements, enabling assessment of erosion rates and sediment accumulation over long time series [203]. Mobile measurement systems are also increasingly used in studies of coastal environments. Huang et al. (2018) developed a platform based on an UAV equipped with a lidar and an RTK GNSS receiver, enabling the measurement of tide levels and wave heights with an accuracy of a few centimeters [204]. The use of low-altitude UAV platforms enables observations in difficult-to-access areas and rapid documentation of changes following storms and other extreme weather events. This solution significantly increases the spatial resolution of measurements compared to traditional monitoring methods [205]. Another important research direction is the analysis of coastal ecosystem responses to sea level rise. Anderson et al. (2022) used geodetic GNSS measurements combined with precise leveling to determine the altitudinal boundaries of vegetation in coastal marshes [206,207,208]. Results allowed for the identification of altitudinal thresholds that determine the migration of the boundary between wetlands and land. The authors point out that such observations are an important tool for forecasting the impact of accelerated sea level rise on the functioning of coastal ecosystems and for planning adaptation measures. The ability to continuously and precisely measure position, elevation, and land deformation enables not only the assessment of current sea-level changes but also the analysis of erosion processes, shoreline changes, and the response of coastal ecosystems to ongoing climate change. Integrating GNSS data with tide-gauge, remote-sensing, and photogrammetric measurements is currently one of the most important tools for coastal monitoring [201,209].

4.5. Geodynamics

Climate change affects not only the atmosphere and hydrosphere but also geodynamic processes occurring in the lithosphere. Melting ice sheets, redistribution of water masses, and changes in crustal loading cause deformations of the Earth’s surface, which are recorded by precise GNSS observations [48,210]. Analysis of multi-year time series allows assessment of the impact of climate processes on crustal movements and improvement of geodynamic models. Ongoing climate change impacts not only the atmosphere and hydrosphere but also geodynamic processes occurring in the lithosphere [48,211,212]. Melting ice sheets, redistribution of water masses, changes in crustal loading, and sea level rise lead to deformations of the Earth’s surface, which can be recorded with millimeter precision using GNSS [213,214]. Multi-year observation series acquired by permanent GNSS stations enable analysis of both long-term trends and short-term deformations associated with tectonic, hydrological, and oceanic processes. Satellite geodesy has become a fundamental tool for monitoring the impact of climate change on crustal dynamics [215]. Figure 15 shows a velocity and height vector field calculated from GNSS observations over an area of approximately 100 × 100 km using 9 CORSs.

One of the most important research areas is the observation of vertical and horizontal crustal displacements. Analyses conducted using geodynamic GNSS networks indicate that multiple measurement campaigns and continuous observations allow the identification of deformations with amplitudes of several millimeters per year. Kaftan et al. (2019) demonstrated that properly designed geodynamic networks enable the detection of long-term crustal movements even in areas with low tectonic activity [217]. The authors emphasize the importance of precise GNSS data processing and the elimination of seasonal measurement errors, which translates into greater reliability in deformation analyses. Modeling of GNSS time series is also gaining importance. A paper on the application of the Monte Carlo MSSA (Multichannel Singular Spectrum Analysis) method demonstrated that advanced statistical techniques enable effective separation of seasonal signals from actual geodynamic trends [218]. This allows for a more accurate determination of crustal deformation associated with climatic processes, such as changes in hydrological loading or ice sheet melting. Similar conclusions were presented in studies on real-time deformation modeling, which showed that incorporating uncertainty into GNSS observations improves the effectiveness of monitoring short-term deformation changes [219].

GNSSs also play a significant role in the study of tectonic and seismic activity. Analysis of data from western China revealed a close relationship between observed crustal deformation and the distribution of strong earthquakes [220]. A study in Ukraine highlighted how machine learning techniques reveal significant links between water level fluctuations, crustal deformation, and earthquakes. The findings suggest hydrological variations influence seismicity, providing valuable insights for risk management in regions with hydroelectric infrastructure [221]. Furthermore, research conducted in Japan confirmed that slow slip events can be identified from continuous GNSS observations, even before the onset of more intense seismic activity [222]. Although these phenomena are tectonic in nature, their interpretation is increasingly being expanded to include analysis of changes in crustal load caused by the redistribution of water and ice masses.

Observations of ocean dynamics using GNSS-A (GNSS-Acoustic) technology are a particularly interesting research direction. Unlike conventional ground-based stations, this method enables monitoring of tectonic plate movements beneath the ocean surface. Studies conducted in the subduction zones around Japan have shown that multi-year GNSS-A measurement series enable determination of the rate of seafloor deformation and the accumulation of stresses that drive the generation of strong earthquakes [223]. Subsequent studies have also demonstrated that the influence of internal tides can limit the accuracy of observations and that modeling them significantly improves the quality of the determined displacements [224]. Climate change also influences deformations associated with the redistribution of ocean water masses. Albarici et al. (2019) demonstrated that modeling ocean and crustal tides is essential for the correct interpretation of GNSS data and tide-gauge observations [225]. Taking these effects into account allows for more accurate determination of vertical movements of geodetic stations and elimination of errors in determining mean sea level. The results of this study indicate that integrating geodynamic models with GNSS observations significantly increases the accuracy of analyses of environmental changes occurring in coastal zones. Integration of geodynamic GNSS observations with large spatial datasets from mining areas requires meeting specific organizational and technological conditions, as discussed in the context of mining [226]. In mining areas, it is also essential to account for surface deformations resulting from exploitation and backfilling processes, which can be analyzed using integrated geodetic and geoinformatics models [227]. Modern GNSSs are among the fundamental tools for monitoring geodynamic processes related to climate change [36,228,229].

4.6. Biosphere

The biosphere is a component of the climate system particularly sensitive to environmental changes [230]. The development of GNSS-R and GNSS-IR techniques has enabled the use of satellite signals to monitor soil moisture, vegetation water content, vegetation cover height, and land use changes [231]. The acquired information supports the assessment of ecosystem health and the analysis of the relationship between climate change and the functioning of the natural environment [144]. Changes occurring in the biosphere are strongly linked to climatic, hydrological, and geodynamic processes. In recent decades, the increasing impact of climate change on ecosystem functioning has been observed, as evidenced by changes in vegetation cover, forest degradation, shifts in soil moisture, and the transformation of wetlands. Satellite technologies, including GNSS, are playing an increasingly important role in monitoring these processes. They provide precise data on the Earth’s surface and enable continuous environmental observations. For example, Figure 16 shows daily surface volumetric soil moisture (VSM) derived from GPS L2C observations (red) compared with in situ measurements (green) and precipitation values (blue). Thus, remote sensing techniques show a very high correlation with expensive and time-consuming in situ measurements.

Classical applications of GNSSs were primarily related to geodesy and monitoring crustal deformation. The development of remote sensing methods based on GNSS signals, such as GNSS-R/IR, has significantly expanded their scope of applications to include studies of the biosphere and the natural environment. As pointed out by Martin et al. (2020), the development of multi-constellation GNSS systems and interferometric techniques has enabled precise environmental observations using widely available satellite receivers [232]. These techniques use satellite signals reflected from Earth’s surface to determine land-cover properties, soil moisture, vegetation height, and biomass water content.

One of the most important research directions is monitoring vegetation health. Wu et al. (2023) developed the LAGRS-Veg model, which simulates the interaction between GNSS-R signals and vegetation and accounts for biophysical parameters such as biomass, plant water content, soil moisture, and surface roughness [120]. The results of these studies indicate that GNSS-R technology enables effective monitoring of ecosystem changes, especially where classical radar methods suffer from signal saturation. Vegetation water content is also a crucial element of biosphere functioning. This parameter can serve as an indicator of vegetation condition and drought stress. Wang et al. (2026) demonstrated that combining GNSS-IR with hyperspectral data enables accurate determination of vegetation water content [233]. This solution could be useful for monitoring the effects of climate change, especially in agricultural and forested areas, where changes in plant water content are among the first signs of deteriorating environmental conditions. Determining soil moisture, which directly affects vegetation development and the water balance of ecosystems, is equally important. Rahmani et al. (2026) demonstrated the potential of GNSS-R satellite observations for simultaneously monitoring soil moisture and vegetation health [234]. Thanks to the high frequency of observations and the global coverage of GNSSs, regular monitoring of environmental changes is possible even in difficult-to-access areas. GNSS-R is also used to determine vegetation height. Zhang et al. (2017) demonstrated that signal-to-noise ratio (SNR) analysis allows for the simultaneous determination of soil moisture and vegetation height [235]. This method is characterized by relatively low cost and the ability to leverage existing permanent GNSS stations, making it an attractive complement to traditional observation methods. GNSS technologies are also used to map vegetated areas. As described in the paper by Hoang and Luu (2026), integrating GNSS measurements with uncrewed aerial vehicles (UAVs) enables the creation of high-resolution terrain models and detailed vegetation cover maps [236]. Such solutions are used in forest management, monitoring the effects of natural disasters, and assessing landscape changes influenced by human activity. Biosphere monitoring is also important for geodynamic studies. Changes in land cover, soil moisture, and biomass influence local changes in the Earth’s crustal load, which precise GNSS stations can record. Xiang et al. (2022) emphasized that advanced GNSS time-series analysis methods enable more effective separation of actual geodynamic signals from environmental disturbances, thereby increasing the reliability of interpretations of observed Earth surface deformations [218]. Consequently, biosphere data are becoming an important component of analyses of the impact of climate change on geodynamic processes.

Combining GNSS data with optical, radar, and hyperspectral observations enables comprehensive analyses of environmental changes, supporting both scientific research and actions for environmental protection and climate change adaptation. The analyzed studies reviewed here indicate that GNSS techniques are becoming an increasingly useful observation tool for monitoring the biosphere, providing high-resolution spatial and temporal data for both climate and geodynamic analyses.

5. Knowledge Gaps and Future Directions

Although GNSSs have established themselves as an indispensable tool for Earth observation and climate change research, there remain significant knowledge gaps and unresolved scientific questions. The expansion of GNSS applications from positioning to multidisciplinary climate monitoring reveals fundamental methodological, physical, and observational limitations that require systematic investigation.

A major unresolved problem in atmospheric GNSS applications is the determination of total PWV and the vertical structure of moisture during severe hydrometeorological events. While the total ZTD under stable weather conditions is determined with subcentimeter accuracy, dynamic convective processes induce strong horizontal tropospheric gradients that standard mapping functions cannot fully reproduce. Furthermore, GNSS-RO suffers from signal degradation and multipath in the lowest boundary layer (1–2 km above the Earth’s surface), limiting its ability to detect low-level moisture changes.

In cryosphere studies, GNSS-R and GNSS-IR offer enormous potential for monitoring snow depth, sea ice, and surface melting. However, a key physical gap remains parameter separation; current algorithms struggle to isolate the effects of surface roughness, soil/ice permittivity, vegetation cover, and liquid water content from the actual snow depth signal. During transitional periods, the presence of wet snow strongly attenuates microwave signals, causing significant misestimates. Developing physically consistent electromagnetic scattering models that account for inhomogeneous surface geometry remains an open challenge in GNSS applications. Using GNSS station position time series to monitor surface loads (e.g., hydrological loads, glacier mass loss) encounters limitations in signal separation due to their type. Separating local anthropogenic deformations—such as land subsidence due to excessive groundwater extraction—from tectonic subsidence and regional Glacial Isostatic Adjustment (GIA) may not be precise without additional techniques/sensors. Furthermore, systematic noise must be considered, which can distort long-term geodynamic and climatic trends.

Although the integration of GNSS data with InSAR, altimetry, GRACE missions, and optical remote sensing technologies is widely recommended, consistent standards for spatial-temporal data fusion are lacking. Differences in spatial resolution and sampling rate often introduce some uncertainties.

Summary of key knowledge gaps to address in future GNSS climate research:

• Standardization of higher-order ionospheric corrections and horizontal gradients in climate time series.
• Separation of the effects of dielectric permittivity from surface roughness in GNSS-R measurements of snow and permafrost.
• Rigorous physical separation of local anthropogenic subsidence from regional mass loads induced by climate change.
• Development of AI models incorporating the laws of physics into early warning and weather forecasting systems.

6. Conclusions

This review demonstrates that GNSSs have transitioned from traditional geometric positioning tools into an indispensable, multi-sphere Earth observation infrastructure for climate change monitoring. Beyond spatial positioning, GNSS observations now deliver vital physical proxies across the atmosphere, cryosphere, hydrosphere, oceans, biosphere, and lithosphere. Specifically, atmospheric soundings via GNSS-RO and ground-based Precipitable Water Vapor (PWV) tracking provide calibration-free vertical profiles and continuous moisture monitoring over data-sparse oceanic and polar regions. Concurrently, reflectometric techniques (GNSS-R and GNSS-IR) exploit existing navigation signals to offer cost-effective, non-invasive observations of snow depth, sea ice, and soil moisture without requiring dedicated radar transmitters. In geodynamic and sea-level studies, millimeter-level dual-frequency positioning via PPP and CORS networks delivers the absolute 3D geodetic framework necessary to isolate actual ocean rise from local vertical land motion, mass loading, and Glacial Isostatic Adjustment.

Despite these advances, several critical methodological limitations and physical uncertainties remain to be resolved. Reflectometric methods continue to face parameter-decoupling ambiguities, as reflected signal characteristics simultaneously depend on surface roughness, terrain geometry, soil moisture, and vegetation water content. Furthermore, the presence of liquid water during snowmelt severely distorts dielectric permittivity estimates. In the lower troposphere, GNSS-RO suffers from multipath and horizontal moisture gradients, while ground-based PWV estimates remain sensitive to weighted mean atmospheric temperature ($T_m$) model uncertainties. Additionally, geodetic coordinate time series remain vulnerable to time-correlated noise, unmodeled phase center variations, and non-tidal atmospheric loading, complicating the separation of local anthropogenic subsidence from regional climate-driven mass redistribution.

Looking forward, the rapid expansion of multi-constellation GNSS (GPS, GLONASS, Galileo, BeiDou) coupled with multi-sensor data fusion—integrating GNSS with InSAR, satellite altimetry, GRACE-FO gravity, and optical remote sensing—presents significant opportunities to overcome single-technique resolution boundaries. Furthermore, incorporating artificial intelligence and physics-informed machine learning into processing pipelines will drastically enhance real-time precipitation nowcasting, automated signal decoupling, and hazard early-warning capabilities. To maximize the scientific impact of GNSS in climate research, the scientific community must prioritize the standardization of data processing protocols for time-correlated noise and higher-order ionospheric corrections, expand hybrid monitoring networks in under-represented high-altitude and polar environments, and establish open-access, real-time data frameworks to better support global climate adaptation and environmental management.

Although GNSS techniques are widely used in climate research, the reliability and precision of the resulting products (e.g., PWV, temperature profile, snow cover depth, and crustal deformation) are subject to a number of measurement uncertainties and methodological limitations. Accurate identification and mitigation of these errors is crucial for assessing long-term climate trends. In the GNSS-RO technique, the main limitations in the lower troposphere (0–2 km a.s.l.) are the multipath phenomenon and strong horizontal humidity gradients, which lead to negative refractivity bias. Despite the use of ionospheric-free combinations, higher-order ionospheric corrections and local asymmetry during geomagnetic storms introduce residual errors in the derived stratospheric temperature profiles. In the case of PWV analyses from the CORS network or the PPP technique, the accuracy of estimating the total tropospheric delay (ZTD) and the total water vapor content (PWV) depends on the adopted mapping functions (e.g., VMF3) and errors in the mean atmospheric temperature (T_m). The uncertainty of the T_m model translates directly into a systematic error in the PWV determination of 1–3%. In the case of PPP and CORS surface deformation analyses, a distinction must be made between local anthropogenic subsidence (e.g., due to intensive groundwater extraction) and regional loading deformation (hydrological/ice loading) as well as tectonic and isostatic subsidence (GIA). However, GNSS time series analysis is subject to time-correlated noise (flicker noise, color noise), as well as technical barriers such as jumps caused by antenna changes or changes in phase center models. In reflectometric measurements (GNSS-R and GNSS-IR), there is significant ambiguity in separating the influence of individual ground features on the recorded signal. Changes in the amplitude and phase of the reflected signal are a simultaneous function of soil moisture, terrain geometry, and vegetation water content. However, in snow and permafrost thickness measurements, melting snow and the presence of liquid water (wet snow) alter the ground’s permittivity, which can lead to erroneous estimates of the GNSS antenna height.