"A high-resolution ground-mapping technique that effectively synthesizes a large receiving antenna by processing the phase of the reflected radar return. The along-track resolution is obtained by timing the radar return (time gating) as for ordinary radar. The crosstrack (azimuthal) resolution is obtained by processing the Doppler phase of the radar return. The cross-track dimension of the antenna is a function of the length of time over which the Doppler phase is collected." (National Aeronautics and Space Administration, 2014)

Sources

"Synthetic Aperture Radar (SAR)". NASA Glenn Research Center, National Aeronautics and Space Administration. Last modified June 12, 2014.
https://www.grc.nasa.gov/www/k-12/TRC/laefs/laefs_s.html#SAR.
Accessed February 1, 2019.

Introduction

Land subsidence is a global phenomenon and is defined as:

“a gradual settling or sudden sinking of the Earth's surface due to removal or displacement of subsurface earth materials” - National Oceanic and Atmospheric Administration (2021)

От Джакарты до Нусантары: проседание грунта и другие насущные проблемы с водой в тонущем мегаполисе

Джакарта, «тонущий город», является нынешней столицей Индонезии. Расположенный на берегу Яванского моря, этот прибрежный город является домом для почти 30 миллионов человек в районе Большой Джакарты. Джакарта десятилетиями боролась с проблемами управления водными ресурсами, что привело к некоторым нынешним кризисам, связанным с водой. Доступ к надежному питьевому водоснабжению крайне ограничен, поскольку существует значительная разница между теми, кто имеет доступ к водопроводной воде, и теми, кто его не имеет. Граждане, не имеющие доступа к водопроводной воде, в значительной степени зависят от грунтовых вод и в результате вырыли тысячи нерегулируемых колодцев. Это привело ко второму водному кризису: постоянному чрезмерному извлечению водоносных горизонтов Джакарты. Проседание грунта вызывает наибольшую обеспокоенность, поскольку этот тонущий город подвергается высокому риску наводнений со стороны окружающего океана. В результате примерно 40 процентов территории Джакарты в настоящее время находится ниже уровня моря, и прогностические модели предполагают, что к 2050 году весь город окажется под водой (Gilmartin, 2019). Эти проблемы усугубляются тем, что климатический кризис привел к значительному повышению уровня моря, поскольку ледники и ледяные шапки продолжают таять (Intergovernmental Panel on Climate Change, 2019; Lindsey, 2022). В связи с тем, что город Джакарта продолжает опускаться, а уровень моря повышается, миллионы жителей Джакарты подвергаются чрезвычайно высокому риску наводнений, особенно в сезон муссонов (рис. 1). Тысячи жителей уже были вынуждены покинуть свои дома в поисках улучшенных условий и возвышенностей (Garschagen et al., 2018).

Enhancing maritime domain awareness through ship detection in satellite imagery

Maritime Domain Awareness (MDA) confronts significant challenges in the maritime domain, leveraging satellite technologies that play a role in enabling extensive and consistent area mapping. In this case, Synthetic Aperture Radar (SAR) stands out for its all-weather capability, serving as a crucial tool for applications ranging from environmental monitoring to defense systems (Ulaby and Long, 2014).

Comment l'espace a révolutionné les affaissements?

Traduit de l'anglais par Mussa Kachunga Stanis

Introduction

L’affaissement de terrain est un phénomène mondial et se définit comme :

"Un tassement progressif ou un affaissement soudain de la surface de la Terre dû à l'enlèvement ou au déplacement de matériaux terrestres souterrains" - National Oceanic and Atmospheric Administration (2021)

Using space-based technologies to monitor marine oil pollution

Deepwater Horizon Oil Spill, Gulf of Mexico: boat in the middle of an oil spill

Oil spills are a critical form of environmental pollution that have far-reaching negative impacts. They severely degrade marine ecosystems, introducing toxic chemicals into the oceans and harming sea life. They also have significant financial impacts through the diminishment of ecotourism as well as the killing of commercially viable species. Despite these negative impacts, oil spills are notoriously difficult to track and monitor given the general lack of surveillance over the vastness of the Earth’s oceans. Space-based technologies are evolving as a tool to aid in the detection of oil spills worldwide. Two primary technologies have been optimized for oil spill monitoring: optical satellite imagery and synthetic aperture radar (SAR). Optical satellite imagery functions somewhat like taking a photograph of the Earth’s surface and requires clear skies and daylight to produce imagery. SAR imagery, on the other hand, relies on microwaves to produce images, and therefore can function regardless of weather, as well as at night. The combination of these two technologies has allowed scientists an increased ability to monitor where and when oil pollution is happening, providing an eye-in-the-sky to survey marine activities. While these space-based technologies are aiding in the detection of a variety of oil spill incidents, they are particularly helpful to monitor the illegal dumping of oil and effluent from shipping vessels as ships are no longer able to dump oily bilgewater into the ocean under the veil of darkness. Unfortunately, the enforcement of environmental and marine law remains an issue and ships are rarely prosecuted. It will be important for space-based technologies to continue to evolve and provide evidence of marine pollution in the effort to provide protection for Earth’s marine ecosystems.

SAR backscatter to monitor under tree cover

Forest cover refers to the extent of land area covered by forests. It can be expressed either as a percentage relative to the total land area or in absolute terms measured in square kilometers or square miles (ScienceDirect). As of 2020, globally, forests account for 31 percent of the land area with roughly half of this area considered relatively intact. The total forest coverage is 4.06 billion hectares.

Utilizando tecnologías espaciales para monitorear la contaminación marina por petróleo

Remote sensing techniques for observing snow and ice

Introduction

Snow has a crucial contribution to Earth’s climate and helps to maintain the Earth’s temperature. When snow melts, it aids in providing water to people for their livelihood and affects the survival of animals and plants (National Snow and Ice Data Center). Approximately 1.2 billion people - constituting one-sixth of the global population - depend on snowmelt water for both agricultural activities and human consumption (Barnett et al., 2005).

Interview with Mina Konaka, Satellite engineer at JAXA

Mina Konaka works at the Japan Aerospace Exploration Agency (JAXA) as a satellite engineer and is currently working on the satellite ALOS-4, which can detect changes in groundwater on land. She attended the International Space University, participating in the project AWARE (Adapting to Water and Air Realities on Earth), in which participants aimed to provide solutions for flood and air quality risks due to climate change, using earth observation data and ground-based sensors. Mina feels strongly about the need to talk more globally about water management solutions, rather than on an individual country basis. Mina also hopes that in the future there will be more female engineers who pursue dreams of space, and that gender balance is no longer an issue.

Interview with Ailin Sol Ortone Lois, Director of SAR Research group, at Universidad Tecnológica Nacional, Specialist at the Argentinian Air Force, Teacher and Researcher at Universidad Nacional de Luján and the UTN

Interview with Prof. Wolfgang Wagner

Photo of Prof. Wolfgang Wagner lecturing a seminar

Interview with Mina Konaka, Satellite engineer at JAXA

ARSET - Crop mapping using synthetic aperture radar (SAR) and optical remote sensing

Overview

Monitoring crop growth is important for assessing food production, enabling optimal use of the landscape, and contributing to agricultural policy. Remote sensing methods based on optical and/or radar sensors have become an important means of extracting information related to crops. Optical data is related to the chemical properties of the vegetation, while radar data is related to vegetation structure and moisture. Radar can also image the Earth’s surface regardless of almost any type of weather condition.

ARSET - Mapping crops and their biophysical characteristics with polarimetric SAR and optical remote sensing

Overview

Mapping crop types and assessing their characteristics is critical for monitoring food production, enabling optimal use of the landscape, and contributing to agricultural policy. Remote sensing methods based on optical and/or microwave sensors have become an important means of extracting information related to crops. Optical data is related to the chemical properties of the vegetation, while radar data is related to vegetation structure and moisture. Radar can also image the Earth’s surface regardless of almost any type of weather condition.

Rapid Impact Assessment Using Open-source Earth Observation - on the example of the Kachowka Dam Break

The Jupyter notebook demonstrates how EOdal can be used for disaster relief after the break of the Kachowka using open-source Earth Observation data.

On June 6, 2023, the Kakhovka Dam in Ukraine broke. We do not yet know who or what was responsible for the collapse of the dam. What we do know, however, are the devastating consequences for the region downstream - especially for the local population.

Recommended Practice: Flood Mapping and Damage Assessment using Sentinel-1 SAR data in Google Earth Engine

Floods, as natural disasters, are most commonly caused by storms and torrential rain or by overflowing lakes, rivers or oceans; this type of natural disaster is one of the most common and effects nearly every demographic and area on Earth. As they are wide-ranging disasters, floods leave disaster managers with a wide-range of concerns. The immediate concern during a disaster is that of human life and the infrastructure needed to offer emergency response through. Floods can wash away bridges and buildings, can destroy electricity systems and can even disconnect portions of cities or rural communities from the first responders who need to reach them. Long-term concerns caused by major floods focus on systemic damage; food is often the most serious concern as crops are destroyed and livestock drowns in major flood disasters. This Recommended Practice aims to create important disaster information for both the short- and long-term concerns of floods. The tool produces a flood extent map using Sentitnel-1 SAR images, as well as displays information about cropland and population centers affected in order to address the totality of major concerns that floods cause.

43rd International Geoscience and Remote Sensing Symposium 2023

The International Geoscience and Remote Sensing Symposium (IGARSS) is the world’s premier symposium on geoscience, remote sensing and related topics. The 43rd annual IGARSS is going be to held in person with the theme of “Reconnecting” with our peers and community. This is an ideal forum for obtaining up-to-date information about the latest developments, exchanging ideas, identifying future trends and making networking with the international geoscience and remote sensing community.

14th Geospatial World Forum 2023

Now in its 14th edition, Geospatial World Forum (GWF) has become a not-to-be-missed conference for the professionals engaged in geospatial and allied sectors. It is a collaborative and interactive platform that demonstrates the collective and shared vision of the global geospatial community.

Webinar: Global flood monitoring and Surface Soil Moisture anomalies

The EO AFRICA R&D Facility Space Academy has a series of webinars covering topics related to the use of EO to assess and monitor the state of water and food security-related spatial components.

The 9th webinar will describe ways to monitor flood events and surface soil moisture on a global scale, using microwave Earth Observation data. Current research topics will be described that are relevant for use cases in Africa (e.g. subsurface scattering in arid regions). The validation of flood monitoring and of soil moisture products will be discussed.

12th Advanced Training Course on Land Remote Sensing

The European Space Agency (ESA) is organizing an Advanced Training Course on Land Remote Sensing with a focus on Hydrology and Hazards in collaboration with the Wrocław University of Environmental and Life Sciences. This course is dedicated to train the next generation of Earth Observation (EO) scientists and experts, working in the domains of hydrology or related hazards, in using data from Copernicus Sentinels and other satellite missions for science and applications development. The course is part of the EO Science for Society element of ESA’s Future EO programme.

Stakeholder

Stimson Center

The Energy, Water, & Sustainability Program at the Stimson Center addresses important and timely policy issues and technical opportunities concerning energy, water, and sustainable development in the Global South from a multidisciplinary perspective.

Our work on transboundary river basins identifies pathways towards enhancing water security and optimizing tradeoffs between water, energy, and sustainable development options in the Mekong, Ganges-Brahmaputra, Indus, Aral Sea and Euphrates-Tigris river basins.

Takami

Jumpei Takami

Associate Expert in Remote Sensing United Nations Office for Outer Space Affairs

Proficient in Remote Sensing and Geographic Information Systems with Machine Learning approach: Analysis of disaster risk reduction and management associated with climate change using remote sensing and geographic information system technologies and implementation of disaster-oriented projects; landslide, flooding, drought, and land subsidence, optionally with machine learning approaches; forest inventory for canopy height and above ground biomass, and planning, design, construction, and maintenance of civil engineering construction projects.

Kasiti

Felix Kasiti

PhD Researcher University of Stirling

Felix is a PhD researcher at the University of Stirling, Stirling, UK, researching on the use of Synthetic Aperture Radar (SAR) in mapping floods. He recently worked as a hydrologist with SERVIR Eastern and Southern Africa project at the Regional Centre for Mapping of Resources for Development, Nairobi, Kenya from 2019 to 2022.

In 2018, he obtained his M.Sc. degree on Water Science (Policy) from the Pan African University Institute of Water and Energy Sciences (PAUWES). Attained his B.Sc.

Almeer

Shaima Almeer

Senior Space Data Analyst Bahrain Space Agency

Shaima Almeer is a young Bahraini lady that works as a senior space data analyst at the National Space Science Agency. At NSSA she is responsible for acquiring data from satellite images and analyzing them into meaningful information aiming to serve more than 21 governmental entities. Shaima is also committed to publishing scientific research papers, aiming to support and spread the knowledge to others.

Space-based Solution

Identification of potential locations/recharge for shallow groundwater in geographically small countries - mapping completed - need in situ data

Map of potential shallow groundwater locations or recharge areas in Bahrain

Identification of potential locations/recharge for shallow groundwater in geographically small countries - mapping completed - need in situ data

Published on 12 May 2025

Groundwater Digital Elevation Model (DEM)Height Above Nearest Drainage (HAND)Synthetic Aperture Radar (SAR)Soil moisture Bahrain, Kingdom of

Identification of potential locations/recharge for shallow groundwater in geographically small countries - mapping completed - need in situ data

Published on 12 May 2025 , by

Addressed challenge(s)

The extraction of information on groundwater for a geographically small, water-scarce and groundwater reliant region

Collaborating actors (stakeholders, professionals, young professionals or Indigenous voices)

Bahrain Space Agency

National Water and Sanitation Agency of Brazil

University of Stirling

Aisha Aljawder

- at Bahrain Space Agency

Alexandre De Amorim Teixeira

GIS Specialist at National Water and Sanitation Agency of Brazil

Felix Kasiti

PhD Researcher at University of Stirling

Solution summary

To address the challenge of water security in Bahrain, this solution integrates space-based technologies and geospatial analysis to identify and monitor potential water resources, particularly shallow groundwater. The methodology involves the use of satellite-derived datasets and terrain modelling tools to analyse hydrological behaviour, soil moisture, and elevation-based drainage characteristics.

Three main data sources were incorporated into the solution:

GRACE (Gravity Recovery and Climate Experiment) data is used to assess changes in terrestrial water storage at the regional scale by detecting gravity anomalies related to mass variations in groundwater. GRACE data is retrieved and visualised through platforms such as Google Earth Engine and ArcGIS Pro, enabling temporal monitoring of water resources.
HAND (Height Above Nearest Drainage) modelling was employed to identify topographic wetness and assess the hydrological potential of the landscape. HAND normalises elevation relative to the nearest drainage, highlighting areas where water is more likely to accumulate or infiltrate. This method supports the identification of suitable zones for groundwater recharge, such as infiltration basins or artificial wetlands, especially in an arid environment like Bahrain. The HAND model was derived using the GLO-30 Copernicus DEM (2023_1 DGED version), processed through the TerraHidro platform, and included the generation of essential layers such as flow direction (D8), contributing area (D8CA), slope, and drainage networks with thresholds of 10, 100, and 300 pixels.
Soil moisture analysis was conducted using two approaches:

SAR (Synthetic Aperture Radar) data from the Sentinel-1 constellation, which provides all-weather, day-and-night measurements of surface moisture conditions.
Optical-based soil moisture estimation, calculated from Landsat-8 imagery using vegetation and thermal indices (e.g., Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST)). This dual approach allows for consistent monitoring of surface moisture, which is crucial for assessing recharge potential and supporting irrigation planning.

Together, these tools provide a multi-faceted view of Bahrain's hydrological landscape, enabling decision-makers to strategically identify areas with groundwater potential and implement more sustainable water resource management practices.

Solution requirements

Gravity Recovery and Climate Experiment (GRACE)

GRACE is a joint mission by the National Aeronautics and Space Administration (NASA) and the German Aerospace Center (DLR) to measure Earth's gravity field anomalies from its launch in March 2002 to the end of its mission in October 2017. The GRACE Follow-On (GRACE-FO) is a continuation of the mission launched in May 2018. GRACE provides information on how mass is distributed and is varied over time through its detection of gravity anomalies. Because of this, a significant application of GRACE is groundwater anomalies detection. Hence, GRACE data has been explored as a solution for this challenge.

Two software platforms have been utilised to download and visualise GRACE data for Bahrain:

Google Earth Engine (GEE): A cloud-based platform that facilitates remote sensing analysis with a large catalogue of satellite imagery and geospatial datasets. The platform is free for academic and research purposes.
QGIS: A desktop application that allows the exploration, analysis and visualisation of geospatial data. This application is open source.

Height Above Nearest Drainage (HAND)

The Height Above Nearest Drainage (HAND) is a terrain model that normalises elevation data relative to the local drainage network, offering a hydrologically meaningful representation of the landscape. By calculating the vertical distance between each point on the terrain and the nearest drainage channel, HAND allows for the identification of topographic wetness zones and the classification of soil water environments. It has shown strong correlation with water table depth and has been effectively validated in various catchments, particularly in the Amazon region. The HAND model supports physically based hydrological modelling and has broad applicability in areas such as flood risk assessment, soil moisture mapping, and groundwater dynamics, using only remote sensing-derived topographic data as input.

Soil moisture using Synthetic Aperture Radar (SAR) imagery

SAR data from Sentinel-1 constellation was used to generate relative soil moisture values. Seninel-1 is a radar-based satellite which acquires data with 6 days repeat cycle, and is neither affected by clouds, weather nor time of the day. Being a dual-polarimetric platform, it acquires data in VV (Vertical-transmit and Vertical received) polarization and VH (Vertical-transmit and Horizontal received) polarization. The data was analysed in GEE.

Soil moisture using multispectral and thermal imagery (Optical)

The data utilised to detect soil moisture are satellite imagery from Landsat-8 downloaded through GEE. Landsat-8 provides multispectral and thermal satellite imagery with 16 days repeat cycle. The specific bands required to calculate soil moisture index are the red, near-infrared bands and thermal infrared bands.

Solution outline and steps

GRACE

Figure 1 illustrates the steps taken to extract the recent GRACE Monthly Mass Grids Version 04 - Global Mascon (CRI Filtered) Dataset from GEE.

HAND

The elevation data downloaded and processed for the region of interest were derived from the GLO-30 dataset. The Copernicus DEM, a Digital Surface Model (DSM), represents the Earth's surface, including features such as buildings, vegetation, and infrastructure. This DSM is based on the WorldDEM product, which has undergone extensive editing to ensure the flattening of water bodies, consistent river flow representation, and correction of terrain anomalies, including shorelines, coastlines, and features like airports. The WorldDEM itself was generated using radar satellite data from the TanDEM-X mission, a Public Private Partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space. The GLO-30 data used in this work corresponds to the 2023_1 version of the Defence Gridded Elevation Data (DGED), provided via ESA’s https PRISM service and made accessible through OpenTopography.

The following products were processed using the TerraHidro software from the GLO-30 dataset: removepits.tif, d8.tif, d8ca.tif, slope.tif, drainage_10.tif, drainage_100.tif, and drainage_300.tif, as well as the HAND-derived products hand_10.tif, hand_100.tif, and hand_300.tif. Each product has a specific role in hydrological modeling:

removepits: This process modifies the original Digital Elevation Model (DEM) to eliminate depressions or pits that are not hydrologically realistic, ensuring that every cell has a defined downstream flow direction.
d8: The D8 (Deterministic 8) flow direction model calculates the steepest descent path from each pixel to one of its eight neighbors, indicating the primary direction of surface water flow.
d8ca: The D8 Contributing Area represents the number of upstream cells that contribute flow to each cell, allowing the identification of areas of potential accumulation and drainage.
slope: This product calculates the slope of the terrain in degrees, essential for understanding runoff velocity and erosion potential.
drainage_10, drainage_100, and drainage_300: These are drainage networks derived from the D8 contributing area, using threshold values of 10, 100, and 300 pixels, 0.9ha, 9ha and 27ha, respectively. They represent streams formed when the contributing area exceeds the specified number of pixels, with higher thresholds resulting in more generalised drainage networks.

From these products, the following HAND (Height Above Nearest Drainage) models were generated:

hand_10, hand_100, and hand_300: These datasets represent the vertical distance (in meters) from each pixel to the nearest drainage cell identified in the corresponding drainage network (with thresholds of 10, 100, and 300 pixels, respectively). These HAND maps are used to characterise terrain wetness, identify flood-prone areas, and support soil moisture and hydrological modeling.

All processing followed the methodology and toolset provided by the TerraHidro system, developed by INPE, and detailed at http://www.dpi.inpe.br/terrahidro/doku.php.

Soil moisture (SAR)

Several steps were executed to derive the mean soil moisture conditions over the study area between 2017 and 2024. A step-by-step guide is shown in Figure 2. The values of soil moisture estimated is relative to the maximum soil moisture recorded in the region such that the wettest will be the maximum and the driest will be the minimum. These are used to normalise the final output into values between 0 and 1 where 0 is the driest and 1 is the wettest.

Steps for processing SAR soil moisture — Figure 2. Processing steps for SAR soil moisture

Soil moisture (Optical)

Similar to the soil moisture calculation with SAR, an average of the soil moisture from 2017 to 2024 has been derived. The interrelations between the derived vegetation through the Normalized Difference Vegetation Index (NDVI) as well as Land Surface Temperature (LST) have been the basis for generating the soil moisture map. Figure 3 demonstrates the steps followed to generate optical soil moisture.

Steps for processing optical soil moisture — Figure 3. Processing steps for optical soil moisture

Shallow groundwater locations/recharge areas

To estimate potential suitable locations for shallow groundwater or groundwater rechange, the results from the HAND, SAR and optical soil moisture have been aggregated to formulate a final classification map. To perform this, the following has been done:

Classification of HAND, SAR and optical soil moisture results to ranges from 1-5, with 5 being the most suitable region based on the related values.
Spatial modelling of these three classifications to formulate a final suitability value from 1-5 with 5 being the most suitable region overall. HAND has been given a weightage of 50 per cent while SAR and optical soil moisture have been given a weightage of 25 per cent each to represent 50 per cent overall for soil moisture.

Map generation

Different maps have been generated for each component of this solution (HAND, SAR soil moisture, optical soil moisture, shallow groundwater locations/recharge areas). The subsequent steps illustrate the steps needed to develop the maps for this solution:

A basemap is added to the map for visualisation purposes. This is done through using the QGIS plugin called QuickMapServices. To install plugins, go to the Plugins tab and select Manage and Install Plugins.

Installing plugins in QGIS — Figure 4. Map generation - Step 1

In the search box of the Plugins window, search for QuickMapServices and install the plugin.

QGIS plugin QuickMapServices — Figure 5. Map generation - Step 2

The plugin logo should appear in the QGIS panel. Click on the logo for Search QMS Panel. This label would appear if you hovered over the logo.

Finding plugin in QGIS panel — Figure 6. Map generation - Step 3

In the Search QMS Panel on the right, search for Google Satellite and add the basemap. It should appear in the list of layers.

Adding a basemap with the QGIS plugin — Figure 7. Map generation - Step 4

Now we have a base layer that we can place our analysis on top of. Add the layer to the QGIS project if it is not already added. This can be done through drag and drop.

Adding a layer to QGIS project — Figure 8. Map generation - Step 5

Right click on the layer and select Properties to adjust visualisation parameters.

Adjusting parameters in Properties of layer in QGIS — Figure 9. Map generation - Step 6

In the Layer Properties window, click on Symbology and discover the most appropriate visualisation method for the data layer. This is an example for the set classifications for the HAND.

Adjusting symbology of a layer in QGIS — Figure 10. Map generation - Step 7

Once the layer visualisation has been set, the map layout can be generated. Go to Project > New Print Layout and name the layout.

Creating a new print layout in QGIS — Figure 11. Map generation - Step 8

Naming the print layout in QGIS — Figure 12. Map generation - Step 8

In the Layout window, items such as the layers map, legend, scales can be added. This is accessed through the Add Item tab.

Adding items to print layout in QGIS — Figure 13. Map generation - Step 9

The items added to the map can then be moved and arranged by selecting the Edit tab then either Select/Move Content to move the locations of the specific content or Move Content to move the position/scale of the map.

Moving and scaling the map in the print layout in QGIS — Figure 14. Map generation - Step 10

Each item’s properties such as size, colour and fonts can also be edited in the Item Properties panel in the right.

Adjusting the properties of an item in the print layout in QGIS — Figure 15. Map generation - Step 11

The final generated layout is then exported in the desired format: png, pdf or svg. This is achieved through clicking on the Layout tab.

Exporting the print layout in QGIS — Figure 16. Map generation - Step 12

Results and maps

GRACE

The GRACE data has been downloaded and analysed through GEE. The main limitation of this dataset is its course resolution of 55.6 km2 as downloaded from the platform. This is due to the small geographical area of Bahrain at around 800 km2, causing water storage monitoring in specific locations to be a difficult task. Figure 17 demonstrates the span of GRACE data relative to the area of Bahrain.

HAND

The HAND model shown in the figure 18 provides valuable insights for addressing water scarcity in Bahrain. The low-lying areas highlighted in blue indicate regions where water tends to accumulate or water table is relatively shallow, suggesting potential zones for managed aquifer recharge (MAR) or stormwater harvesting. These areas could be prioritised for infiltration basins, recharging wells, or constructed wetlands to enhance groundwater storage. Conversely, the higher elevation zones in grey are less likely to retain surface water but could be strategically used for runoff collection and diversion to recharge areas. Given Bahrain’s arid climate and dependence on non-conventional water sources, integrating HAND-based terrain analysis into water resource planning can support more resilient, localised, and efficient water management strategies, particularly in optimising land use for recharge, storage, and flood mitigation purposes.

Soil moisture (SAR)

Figure 19 shows the mean soil moisture values of different regions of Bahrain. The southern regions seem to be drier while most central regions are wet. The analysis excluded urban regions.

Map with SAR soil moisture results — Figure 19. SAR soil moisture results map

Soil moisture (Optical)

Figure 20 illustrates the soil moisture map with optical imagery for Bahrain. The results here highlight the northern west regions with high soil moisture values and the central, southern regions as dry with some specific location in the central and southern regions as wet.

Map with optical soil moisture results — Figure 20. Optical soil moisture results map

Shallow groundwater locations/recharge areas

Through Figure 5, the combinations of HAND, SAR and optical soil moisture has yielded to the potential locations for shallow groundwater locations/recharge areas. The areas highlighted in red represent the locations with highest potential.

Map showing potential shallow groundwater locations and recharge areas in Bahrain — Figure 21. Shallow groundwater locations/recharge areas results map

Solution impact

With the establishment of a methodology that identifies locations of shallow groundwater or recharge, significant information is being derived about the hydrological state of the country. This importance is placed due to the lack of remote sensing data that enables direct measurement of groundwater in the area. Hence the information extracted from this methodology can be initially integrated with sample in-situ data to calibrate the model; and then, be relied on solely for future measurements. Additionally, with the country’s rigorous focus on addressing groundwater scarcity, this type of information can greatly support decision-making when it comes to the formulation and execution of different projects and policies related to this matter.

Future work

To enhance the accuracy, applicability, and long-term impact of this solution in addressing water scarcity in Bahrain, several future developments are proposed:

Integration of additional remote sensing products: Incorporate higher-resolution satellite data to improve spatial resolution in soil moisture and elevation analyses, enabling finer-scale hydrological modeling and more localised identification of recharge zones. Moreover, the inclusion of land cover and geological characteristics can enhance the spatial modelling conducted.
Validation with in-situ data: Collaborate with local water authorities to collect and integrate ground-truth data such as groundwater levels, soil profiles, and well yields to validate and calibrate the HAND model and soil moisture outputs. This is also vital to assess the suitable weightage and classification for spatial modelling to be done to combine all three products generated.
Development of a Decision Support System (DSS): Create an interactive platform or dashboard that integrates HAND, GRACE, and soil moisture maps to assist policymakers in identifying priority areas for groundwater recharge, stormwater harvesting, and drought preparedness.
Temporal analysis and trend monitoring: Implement time-series analyses of GRACE and soil moisture data to detect trends, seasonal variations, and anomalies in water availability, supporting early warning systems and long-term planning.
Hydrological modelling coupling: Link HAND-derived terrain data with physically based hydrological models (e.g., SWAT, DHSVM) to simulate runoff, infiltration, and recharge scenarios under different land use and climate conditions.
Community engagement and capacity building: Conduct training workshops and knowledge-sharing activities with national institutions and stakeholders to build local capacity in geospatial water resource monitoring using open-source and space-based tools.

By pursuing these developments, the solution can evolve into a comprehensive and replicable model for sustainable groundwater resource management in water-scarce regions worldwide.

Relevant publications