LEBANON - AI-enhanced satellite monitoring is opening new ways to track water scarcity, land degradation and climate risks. Its value, however, will depend on whether environmental data becomes part of public decision-making.
Environmental change rarely occurs at a single location or on a convenient schedule. A reservoir may lose water over several seasons; vegetation can become increasingly dry before a wildfire, and agricultural or natural land can gradually disappear beneath construction.
Traditional monitoring, through field visits, measuring stations, sensors, and laboratory testing, remains essential. Yet it can be expensive, limited to specific locations and vulnerable to equipment failures, funding shortages or restricted access. In Lebanon, these pressures are compounded by institutional fragmentation, economic crisis and conflict.
Satellite imagery, combined with artificial intelligence, offers a broader view. It can help scientists repeatedly observe large areas, compare current conditions with decades of historical data, and identify changes that require closer investigation on the ground.
How a Satellite Image Becomes Environmental Data
Earth-observation satellites collect information beyond what is visible in an ordinary photograph. Water, vegetation, dry soil, burned land, and buildings reflect light differently, creating distinct signals within satellite images.
Machine-learning models can be trained to recognize these signals. They can separate water from land, distinguish healthy vegetation from stressed crops, map wildfire-affected areas, or detect changes in forests, coastlines, and urban development.
Technology becomes particularly useful when thousands of images must be compared. Instead of manually examining every image, researchers can use AI to identify patterns across years or decades and translate them into estimates, maps, and warnings.
This does not mean that an algorithm automatically understands an environmental problem. Models must be trained using reliable information, tested against real conditions, and interpreted by specialists. Satellite monitoring is therefore most valuable when it complements field measurements rather than attempting to replace them.
Why Lebanon Needs a Wider View
Lebanon already has experience using satellite data to monitor environmental change. The National Center for Remote Sensing at CNRS-L has led several projects covering land, water, agriculture, and ecosystems. Recent work includes a nationwide high-resolution land-cover map developed with the World Food Programme between 2022 and 2024, satellite-based crop and evapotranspiration modelling through the China–Lebanon Joint Laboratory, and an ongoing FAO-supported project mapping crops and areas at risk from the plant disease Xylella fastidiosa.
The center's 2025–2030 priorities include using GeoAI to detect land-use change, expanding the monitoring of vegetation, soil moisture and temperature anomalies, and developing a national platform that combines satellite, climate and field data. These applications show how Earth observation can provide a broader and regularly updated view of environmental conditions, while helping researchers identify where more detailed investigation is needed.
Qaraoun Shows What Is Possible
A recent study on the Qaraoun Reservoir demonstrates how these tools can be applied in Lebanon.
Researchers from the American University of Beirut, Lebanese University and CNRS-L combined more than 50 years of Landsat and Sentinel satellite images with information about the shape of the reservoir basin. A machine-learning model was then trained to estimate the reservoir’s water volume from the surface area visible in each image.
The method matched verified observations across more than 95% of the shoreline, while the model explained more than 98% of the variation in stored water. Its estimated error remained below 1.5% of the reservoir’s full capacity.
The long-term record also captured the severity of the recent water decline. In July 2025, Qaraoun was estimated to contain 49.2 million cubic meters, representing a 66% decrease from July 2024 and a 56% decrease from July 2023. The researchers described the result as an emerging drought signal.
This is more than a technical achievement. Regular reservoir estimates could support drought preparedness, irrigation planning, and decisions related to water storage and hydropower. The researchers also created an interactive dashboard to make the long-term record easier for stakeholders to examine.
Yet the study also illustrates the limits of satellite monitoring. Satellites can show that a reservoir is shrinking, but they cannot independently identify every pollutant in the water. Laboratory analysis, field sampling and inspections remain necessary to determine environmental and public-health risks.
From Maps to Decisions
Across the region, governments and research organizations are beginning to move from producing environmental maps to building tools that support routine management.
In May 2026, Iraq received an FAO WaPOR-based monitoring tool that uses satellite data to assess irrigation performance, crop-water deficits, water productivity and possible illegal water abstraction. WaPOR itself provides a publicly accessible, near-real-time database covering agricultural water use across Africa and the Near East.
Meanwhile, the active e-ReWater MENA initiative is combining AI, Earth observation and data analytics to map wastewater availability and reuse potential in Egypt, Saudi Arabia and the United Arab Emirates. The platform is intended to provide decision-ready information for policymakers and investors, and it builds partly on earlier assessments of water-reuse potential in Lebanon.
These examples point to the real opportunity for Lebanon. AI can help detect that a reservoir is shrinking, farmland is under stress, or forest cover has been damaged.
The technology can give Lebanon a clearer and more continuous view of its environment. Whether that view improves environmental governance will depend on what happens after the data reaches the ground.
