GNSS hydrology: Defining a new interdiscipline integrating GNSS hydrogeodesy and remote sensing

Dear Editor,

The use of global navigation satellite system (GNSS) technologies to study the hydrological cycle has gained increasing attention. Current research primarily spans two domains: GNSS hydrogeodesy and GNSS remote sensing. However, these areas remain fragmented within hydrology-related fields. While GNSS hydrogeodesy is limited in addressing hydrological applications, GNSS remote sensing extends into broader environmental domains. To bridge this gap, we propose the formalization of “GNSS hydrology,” an interdisciplinary field that integrates the principles of both GNSS hydrogeodesy and GNSS remote sensing, with a focus on the core capabilities of GNSS technologies in hydrology. We identify three distinct branches within GNSS hydrology—GNSS positioning, reflection, and transmission hydrology, collectively called GNSS-P.R.T. hydrology—and examine their technical roles and unique applications. The proposed concept offers a unifying framework for the broader hydrology research community, positioning it as a rapidly evolving field with vast potential.

Revisiting GNSS in cross-disciplinary hydrology research

The overarching objective of studying the global hydrological cycle is to observe, understand, and forecast the storage and movement of water across spatial and temporal scales under a changing climate. To achieve this, space-based measurements of water storage and fluxes are essential, covering scales ranging from local to continental/global and spanning various temporal resolutions. Moreover, space-based hydrological observations broadly fall into two key areas: hydrogeodesy and hydrological remote sensing.

Hydrogeodesy combines geodesy—concerned with the Earth's shape, orientation, gravity field, and temporal variations—and hydrology. Satellite-based hydrogeodesy includes four primary technologies: altimetry, interferometric synthetic aperture radar (InSAR), gravimetry, and GNSS. The commonality among these methods is their ability to measure key hydrological variables such as surface water dynamics and/or total water storage (TWS) change, with the latter referring to the variation in the amount of water stored within a hydrological system over a specific period.

Hydrological remote sensing generally measures all types of water storage- and flux-related variables within the hydrological cycle, such as soil water, snow, ice, land water bodies, and specific atmospheric variables that closely interact with the land surface (e.g., precipitable water vapor [PWV]). For example, optical and SAR satellite imageries are commonly used to spatially represent hydrological variables, such as surface water extent or snow/ice cover. Passive microwave sensors, such as radiometers, are particularly effective for deriving storage-related variables, with L-band radiometers targeting soil moisture and C/X-band radiometers focused on snow and ice. Active microwave sensors like Ku/Ka-band radar are applied to surface/atmospheric profiling and precipitation measurement. A promising complement to active radar or passive radiometry is using existing, non-cooperative transmitters as illumination sources for bistatic radar. In this way, the current 100+ GNSS satellites transmitting L-band microwave signals are popular illumination sources that, with proper receiving platforms and receivers, offer a cost-effective complement to existing hydrological observation systems.

The role of GNSS: from the background above, GNSS has emerged as a versatile tool for measuring hydrological variables by leveraging geodetic and remote sensing approaches. Two key features of GNSS underpin this capability. First, its primary positioning function yields a highly accurate time series of vertical and horizontal displacements of the solid Earth, which can be linked to hydrological loading. This field, commonly called GNSS hydrogeodesy, emerged around the year 2000 and belongs to the discipline of hydrogeodesy, with a focus on monitoring the TWS change. Second, GNSS satellites transmit 1–2 GHz L-band signals, which were initially chosen for positioning due to their ability to work in clouds, snow, and rain. However, atmospheric and land surface errors—such as tropospheric delay and the land surface multipath effect—can still impact positioning accuracy. Remote sensing scientists capitalize on these positioning errors to infer atmospheric and Earth’s surface properties. This research area forms the discipline of GNSS remote sensing.