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Expanding Water Monitoring Techniques: Harnessing Remote Sensing and AI for Sustainable Development


A group of birds swimming in a body of water that is coated with algae. Algae blooms are just one example of the challenges faced by wildlife, with other issues like oil spills also posing significant threats to aquatic ecosystems and the delicate balance of nature.

A group of birds swimming in a body of water that is coated with algae. Algae blooms are just one example of the challenges faced by wildlife, with other issues like oil spills also posing significant threats to aquatic ecosystems and the delicate balance of nature.


In 2015, the United Nations introduced the 17 Sustainable Development Goals (SDGs). These transformative objectives were designed to tackle the most urgent global challenges. By embracing sustainability, companies can captivate an ever-expanding customer base and attract investors who value sustainable solutions. Moreover, this approach allows businesses to optimize resource utilization, minimize waste generation, and fortify risk management and resilience strategies.


The power of remote sensing in combination with AI technology provides businesses the opportunity to set themselves on a sustainable path, by actively contributing to the achievement of two vital water-related SDG goals via water monitoring: Clean Water and Sanitation (Goal 6) and Life Below Water (Goal 14).


Data acquisition in aquatic environments has conventionally relied on collecting and analyzing water samples taken at specific time and locations. While this method offers high accuracy, it becomes resource-intensive and impractical for large-scale monitoring due to challenges such as cold temperatures, high pressures, and low visibility. On the other hand, remote sensing enables the monitoring of water changes efficiently across time and over large areas using satellite imagery, eliminating the need for extensive sample collection and measurement.


The foundation of remote sensing's application in water monitoring lies in its ability to detect changes in water substances by analyzing the spectral signature backscattering from surface water. This information can then be used to relate observed changes to specific water quality parameters using empirical or analytical models. Two notable applications of remote sensing in water monitoring are harmful algal blooms (HABs) and oil spills.



Harmful Algal Blooms (HABs)


HABs have been proliferating at an alarming rate, posing significant threats to various ecosystems and nearby communities. These blooms can lead to hypoxic events and the production of toxic substances, causing harm to aquatic life. Remote sensing plays a crucial role in addressing this challenge by:


  • Identifying, monitoring, and managing HABs in water bodies enabling timely interventions to mitigate their harmful effects.

  • Analyzing the magnitude and intensity of HABs aiding in understanding their impact on the ecosystem.

  • Improving prediction and prevention capabilities: Remote sensing data combined with advanced modeling techniques enhances our ability to predict and prevent HABs, enabling proactive measures to safeguard water resources.


An example of a successful water monitoring program developed using remote sensing is CyAN (Cyanobacteria Assessment Network), which is utilized to effectively monitor more than 2000 waterbodies in the US using Sentinel 3 data.



Oil Spills


Oil spills pose severe ecological and economic consequences, necessitating swift and accurate monitoring. Remote sensing offers valuable insights and support in addressing oil spills by:

  • Estimating the extent and impact of the spill on the surrounding environment, aiding in emergency response efforts.

  • Modeling spill expansion: predictive models can be created to anticipate the spread of the spill, assisting in containment and mitigation strategies.

  • Designing response and monitoring plans, enables authorities to allocate resources efficiently.

  • Providing baseline data: By establishing a historical record of water conditions through remote sensing, it becomes possible to compare and evaluate the impact of oil spills accurately.


Studies have presented the high accuracy performance of remote sensing and AI techniques in oil spill monitoring, an example being a method achieving 82.25% - 92.99% accuracy using Synthetic Aperture Radar (SAR) and Random Forest classification.


At Adatos, we are experienced in incorporating remote sensing and AI to visualize changes occurring on land surfaces and water bodies, assisting our clients to achieve their sustainability ambitions. Send us a message on LinkedIn and explore how Adatos can partner with you to revolutionize your environmental impact, empowering your business to make a lasting difference.

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