Global groundwater supplies

Groundwater accounts for 30% of Earth’s freshwater resources (Shiklomanov 1993) (Figure 1) and is estimated to globally provide 36% of potable water, 42% of irrigation water, and 24% of industrial water – indicating its significant value (Global Environment Facility 2021). Groundwater affords a host of benefits, from providing better protection against drought and microbiological contamination than surface waters, to being generally low cost and accessible to many users.

Distribution of global groundwater resources
Figure 1: Distribution of Earth’s water resources (Shiklomanov 1993)


However, the distribution of groundwater across the world is not even. Extensive work has been carried out to map global groundwater reserves in order to improve its management and sustainable use. The Worldwide Hydrological Mapping and Assessment Programme (WHYMAP) was launched in 2002 to collect, collate and visualise groundwater data (Figure 2). The maps inform on the quantity, quality, and vulnerability of groundwater resources in order for more informed decisions to be made. 

Global groundwater resources
Figure 2: Distribution of global groundwater resources within large / regional groundwater basins (blue), complex geological structures (green), and shallow / local aquifers (brown) (BGR and UNESCO 2008).


Information on the location and distribution of groundwater has traditionally come from ground-based geophysical prospecting and drilling technology, alongside clues in the landscape such as the geomorphology and vegetation. Since the 1970s, space technologies have been playing an increasing role in the monitoring of such resources. 

Despite their potential, global groundwater resources are facing major challenges. Whilst modern groundwater supplies are replenished by precipitation, fossil aquifers – deep and disconnected from our contemporary hydrological cycle and where much of the world’s groundwater reserves come from are not. Our resources are hence being depleted and degraded where rates of extraction exceed recharge over prolonged periods of time. A global groundwater depletion of 4,500km3 was estimated between 1990-2008, threatening global water security, agriculture, energy production and global peace (Frappart and Ramillien 2018).  According to Shah et al. (2001), the size of the global groundwater footprint is 3.5 times the actual area of aquifers, with 1.7 billion people living in areas where groundwater resources are under threat (Figure 3). With a growing population, urbanisation, climate change, and poor governance, these challenges are only going to grow in coming years. Improving the sustainability of use of this precious resource is of paramount importance. Decisions on groundwater management must be data driven, with space technologies showing great potential to provide this. 

Aquifer overuse
Figure 3: Groundwater footprints of aquifers (Gleeson et al. 2012)

Space-based methods for monitoring groundwater

Currently groundwater cannot be directly measured from space. Therefore, various indirect methods have been developed for progressing our knowledge of our global groundwater resources. 

Space technologies, from remote sensing to Geographic Information Systems (GIS) and the Global Positioning System (GPS), provide a rapid and cost-effective tool for detecting, extracting, conserving, and testing the vulnerability of groundwater across space and time. Remote sensing for monitoring groundwater is based on multi-spectral (Enhanced Thematic Mapper (ETM)) and spatial (Shuttle Radar Topography Mission (SRTM)) data, radar technology and thermal surveys.  Most of the Earth Observation sensors, except for radar and geophysical methods, do not penetrate the Earth’s surface. This means that a link is required between surface observations and the subsurface (Yu et al. 2009). Some of the main indirect methods of monitoring groundwater are outlined below. 

Monitoring groundwater influencing factors 

One of the oldest contributions that space technologies have been making towards groundwater monitoring is in the collection of information on groundwater influencing factors. 

Due to the lack of penetrable capacity that remote sensing offers, it is used widely to provide this indirect hydrogeological information, obtaining data on factors such as geology, geomorphology, drainage patterns, vegetation, and land use (Agarwal et al. 2013). This remotely sensed data can then be combined with GIS, geophysical techniques, and ground-truth data to prepare thematic maps in order to delineate groundwater potential zones (GWPZ) and monitor groundwater vulnerability. 

A study by Ashokraj, Kirubakaran, and Colins Johnny (2015), demonstrates the use of GIS and remote sensing, alongside field-based measurements to map groundwater vulnerability by producing a so called SINTACS model of Palayamkottai Taluk in India. The SINTACS model was developed to determine  groundwater pollution and vulnerability index. It is based on an overlay index methodology and uses seven environmental parameters (water table depth (S), effective infiltration (I), unsaturated conditions (N), soil media (T), aquifer hydrogeological characteristics (A), hydraulic conductivity (C), and topographic slope (S)) (Kumar et al. 2013).  Space-based technologies can contribute to the collection of some of these parameters. In this particular case study, Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) data, for example, was used to generate the slope of the terrain, whilst inverse distance weighted (IDW) techniques in GIS were used both to generate depth to water table and hydraulic conductivity maps. Ratings and relative weights are then assigned to each hydro-geological parameter in order for the computation of the vulnerability index with the Raster calculator tool of GIS, creating a groundwater vulnerability map (Figure 4). 

Groundwater vulnerability map
Figure 4: Groundwater vulnerability map of Palayamkottai Taluk (Ashokraj, Kirubakaran, and Colins Johnny 2015)


Similarly, multiple studies highlight the potential of remote sensing and GIS in mapping GWPZs. Studies, such as that by Agarwal et al. (2013) in Samoda Nala Durg District in India use Analytical Hierarchy Process (AHP), a method of Multi-Criteria Decision Making (MCDM) to delineate GWPZs. In this method, thematic maps are created from various influencing factors, some of which are measured using remotely sensed data. For example, in this particular paper, drainage density, drainage, and slope were measured using Digital Elevation Models (DEMs), whilst land use, land coverage (LULC) was measured using LANDSAT data. Other themes were calculated from conventional maps and borehole data. Thematic layers are subsequently integrated in a GIS environment, from which AHP is used to calculate the Normalised weight of each theme based on their relative importance. The weights then produce five GWPZs, i.e. ‘very low’, ‘low’, ‘medium’, ‘high’ and ‘very high’ (Figure 5) from which informed decisions regarding ground-exploration can be made. 

Ground water protection zones
Figure 5: Delineation of GWPZs of Samoda Nala Durg District (Indhulekha and Jhariya 2020)

Indirectly measuring evapotranspiration 

A further method of monitoring groundwater storage from space comes in the form of indirectly measuring evapotranspiration (ET). ET reflects the exchange of mass and energy between the soil-water-vegetation system and the atmosphere. Actual ET (ETa) is affected by weather conditions, land cover and soil moisture so is an important variable when considering groundwater storage (Senay, Bohms, and Verdin 2012).   

As ETa is difficult to measure accurately, various hydrologic modelling techniques have been developed to estimate ETa, enabling its assessment at regular and large spatial scales. Estimating ETa requires various variables, some of which are collected by remote sensing. Land surface temperature, for example, was required for one of the models used by Senay, Bohms, and Verdin (2012). This was derived from thermal band observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). As ETa directly affects groundwater storage, it is an important variable to measure when considering the risk of depletion and drought.  

Directly measuring soil moisture 

Beyond these indirect measurements, microwave remote sensing can provide a direct measurement of soil moisture, which can be used to estimate groundwater levels at shallow depths if the conditions are suitable. This approach is particularly useful in dryland settings where evapotranspiration is a significant component of the water-balance equation (Ruggieri et al. 2021).  Generally, for low soil moisture levels, soil reflectance and soil moisture content are negatively correlated, making it possible to use them to develop a remote sensing monitoring model to predict groundwater level (Huo et al. 2011). Soil moisture content, reflectance and scattering properties can reflect fluctuations in groundwater levels due to the capillarity in the soil zone through which groundwater can reach the surface and through which it alters the soil moisture (Huo et al. 2011) (Figure 6).

Cross section of sub-surface
Figure 6: Cross section of the sub-surface indicating soil zone, groundwater table, and capillary action. (Mira Costa n.d.)


Microwave remote sensing can be both active (radar) and passive and offers advantages over visible and infrared spectra. They can make observations through cloud coverage and their measurements are not dependent on solar illumination. A range of instruments attached to trucks, aircraft, and spacecraft have been deployed over the years, including the Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI), the Advanced Microwave Scanning Radiometer (AMSR) satellite systems, the Soil Moisture Ocean Salinity (SMOS) mission and Soil Moisture Active Passive (SMAP) mission, as well as multiple other Synthetic Aperture Radar (SAR) series (Jackson 2002). 

For example, passive and active microwave observations from the Electronically Scanned Thinned-Array Radiometer (ESTAR) were made across 8 days in  a study in Oklahoma (Jackson 2002). The ESTAR data were processed to produce brightness temperature maps. Using retrieval algorithms of soil water content, the temperature data were converted to images of soil water content (Figure 7). Spatial patterns are associated with soil textures and temporal patterns are associated with drainage and evaporative processes. Information on the spatial and temporal variations can therefore be used to estimate groundwater recharge.

Soil moisture
Figure 7: Maps of soil water content for the Little Washita watershed, Oklahoma, USA, derived from passive microwave observations. (Jackson 2002)

GRACE Missions 

The final method of groundwater monitoring explored in this article, and possibly the most exciting, is GRACE TELLUS. The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions are able to observe temporal and spatial variations of Terrestrial Water Storage (TWS) from space. Unlike most missions, GRACE does not carry independent science instruments but relies on slight changes in distances between the two co-orbital satellites, dependant on Earth’s gravity field, which water, as it has a mass, affects. The amount of water stored in a region can then be estimated by observing these tiny variations in gravity field recorded by the satellites. TWS is the sum of multiple storage components: groundwater, glaciers, snow, soil moisture, and storage in surface water bodies. Hence, changes in groundwater storage can be calculated when these other variables are estimated from other satellites, models, or in-situ data. 

GRACE data can therefore provide information on climatic events and basin-scale hydrological fluxes, allowing the detection of shortages, the identification of water sources, and the monitoring of groundwater levels. As the GRACE data is an estimate of bulk water storage changes and cannot distinguish between different components of stored water mass, it must be interpreted using hydrological modelling (Bonsor et al. 2010). 

GRACE data has been used extensively since the first mission was launched in 2002. Whilst the data is of lower spatial resolution compared to in-situ methods, it provides huge cost and efficiency benefits. GRACE data, for example, has been used successfully in the Indus River basin to map groundwater storage changes – indicating where supplies are being depleted and where they’re being adequately recharged (Figure 8). The data highlighted the basin as the second-most overstressed aquifer globally. GRACE and GRACE-FO data continues to be used extensively worldwide to understand groundwater patterns and to predict future vulnerabilities. 

Groundwater changes in the Indus River Basin
Figure 8:  NASA GRACE satellite data map of monthly groundwater changes in the Indus River Basin. Orange and yellow indicates areas where groundwater might be depleted; blue and green highlights areas where groundwater is being replenished. Credits: Pakistan Council of Research in Water Resources (NASA 2016). 


Groundwater is evidently not easy to monitor. There is no single method combining high spatial and temporal resolution, with low cost and workload. However, by combining methods of data collection, from ground-based geophysics and borehole readings to DEMs and remote sensing, a clearer picture of global groundwater reserves emerges.    

Space-based technologies, from earth observation to navigation satellite systems, provide vital data for the sustainable management of groundwater resources. Numerous instruments and missions have provided indirect data on groundwater, such as those of groundwater influencing factors, soil moisture, evapotranspiration, and gravity changes. Despite the indirect nature, by informing on GWPZs, groundwater storage, levels, vulnerability, and recharge, these technologies still provide a lot of potential and data that wouldn’t otherwise be available.


Agarwal, Etishree, Rajat Agarwal, R. D. Garg, and P. K. Garg. 2013. “Delineation of Groundwater Potential Zone: An AHP/ANP Approach.” Journal of Earth System Science 122 (3): 887–98.

Ashokraj, Chinnasamy, Muniraj Kirubakaran, and J Colins Johnny. 2015. “Estimation of Groundwater Vulnerability Using Remote Sensing and GIS Techniques.” International Journal for Innovative Research in Science and Technology 1 (9): 118–25.

BGR, and UNESCO. 2008. “World-Wide Hydrological Mapping and Assessment Programme (WHYMAP).” 2008.….

Bonsor, H C, M M Mansour, A M Macdonald, A G Hughes, R G Hipkin, and T Bedada. 2010. “Interpretation of GRACE Data of the Nile Basin Using a Groundwater Recharge Model.” Hydrology and Earth System Sciences 7 (4): 4501–33.

Frappart, Frédéric, and Guillaume Ramillien. 2018. “Monitoring Groundwater Storage Changes Using the Gravity Recovery and Climate Experiment (GRACE) Satellite Mission: A Review.” Remote Sensing 10 (6).

Gleeson, Tom, Yoshihide Wada, Marc F.P Bierkens, and Ludovicious P.H. van Beek. 2012. "Water Balance of Global Aqufers Revealed by Groundwater Footprint." Nature, 488: 197-200. DOI:10.1038/nature11295

Global Environment Facility. 2021. “Grounwater.” 2021.

Huo, Aidi, Xunhong Chen, Huike Li, Ming Hou, and Xiaojing Hou. 2011. “Development and Testing of a Remote Sensing-Based Model for Estimating Groundwater Levels in Aeolian Desert Areas of China.” Canadian Journal of Soil Science 91: 29–37.

Indhulekha, K., and D. C. Jhariya. 2020. “Delineation of Groundwater Potential Zones in Samoda Watershed, Chhattisgarh India, Using Remote Sensing and GIS Techniques.” IOP Conference Series: Earth and Environmental Science 597.

Jackson, Thomas J. 2002. “Remote Sensing of Soil Moisture: Implications for Groundwater Recharge.” Hydrogeology Journal 10 (1): 40–51.

Kumar, Sathees, D. Thirumalaivasan, Nisha Radhakrishnan, and Samson Mathew. 2013. “Groundwater Vulnerability Assessment Using SINTACS Model.” Geomatics, Natural Hazards and Risk 4 (4): 339–54.

Mira Costa. n.d. “Chapter 11: Rivers & Streams and Groundwater.” Introduction to Geology. Accessed April 12, 2021.

NASA. 2016. “NASA Data Used to Track Groundwater in Pakistan.” NASA. 2016.….

Ruggieri, Giovanni, Vincenzo Allocca, Flavio Borfecchia, Delia Cusano, Palmira Marsiglia, and Pantaleone De Vita. 2021. “Testing Evapotranspiration Estimates Based on Modis Satellite Data in the Assessment of the Groundwater Recharge of Karst Aquifers in Southern Italy.” Water 13 (118).

Senay, Gabriel B., Stefanie Bohms, and James P. Verdin. 2012. “Remote Sensing of Evapotranspiration for Operational Drought Monitoring Using Principles of Water and Energy Balance.” In Remote Sensing of Drought: Innovative Monitoring Approaches, 123–44.

Shah, Tushaar, David Molden, R Sakthivadivel, and David Seckler. 2001. “Global Groundwater Situation: Opportunities and Challenges.” Economic And Political Weekly 36 (43): 4142–50.

Shiklomanov, Igor. 1993. “World Fresh Water Resources.” In Water in Crisis: A Guide to the World’s Fresh Water Resources.

Yu, Dehao, Zhengdong Deng, Fan Long, Hongjun Guan, Daqing Wang, and Yizheng Gou. 2009. “Study on Shallow Groundwater Information Extraction Technology Based on Multi-Spectral Data and Spatial Data.” Science in China, Series E: Technological Sciences 52 (5): 1420–28.