Objectives of the Product

Farming, land management and flood planning all depend on understanding the environment, yet keeping track of change across large areas is difficult. The data these sectors need sit in separate systems that do not interact with each other. Ground sensors are precise, but they cover only a small area. Satellites cover huge areas, but they cannot see conditions on the ground in detail.

Anganode closes that gap. It combines satellite imagery with data from ground-based sensors to give a single, reliable picture of what is happening in a given place and uses each source to check the other.

The activity focuses on three areas, combining ground sensors and satellite data to make the difference: using water more efficiently, measuring carbon more confidently and forecasting flood risk.

The platform is built for people who are not remote-sensing specialists. Through a simple point-and-click interface, an application programming interface (API) or a developer kit, teams set up their own monitoring without writing code or hiring Earth observation experts. The result is faster answers at a lower cost. Anganode speeds up environmental insights by up to 70% and cuts reporting costs by 50% to 80% compared with traditional consultancy work. It also supports carbon and environmental reporting, so organisations act earlier on risks such as drought, flooding, wildfire and land instability.

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Customers and their Needs

Anganode serves organisations across Europe and North America that need to monitor the environment at scale. Most face the same barrier: traditional satellite analysis needs Earth observation specialists to interpret raw imagery, which leaves teams with fragmented data, slow delivery and blind spots. Anganode focuses on three areas, where combining ground sensors and satellite data makes the biggest difference.

• Irrigation efficiency. By tracking how much water is leaving a field and where it is overwatered, Anganode helps growers decide when to irrigate, top up or hold off. Combining evapotranspiration readings from ground devices and satellites replaces guesswork based on generic weather patterns with clear, timely guidance on when to water.
• Carbon measurement. Land-management choices, such as planting or protecting vegetation, affect whether carbon is stored or released. Anganode helps measure that impact more confidently and supports Monitoring, Reporting and Verification (MRV) workflows. Verifying stored carbon accurately, for example, and proving how much carbon a stand of new trees has stored, is genuinely difficult. The activity is working towards evidence robust enough to support that process.
• Flood risk. Anganode improves the forecasting, prediction and live assessment of flood extent by combining local sensor data with satellite observations. Better local inputs sharpen satellite-based estimates, giving communities and operators earlier, clearer information to act on.

The same fusion approach extends to further use cases. For example, it supports monitoring wildfire risk, ground movement and vegetation growth around critical power infrastructure, helping network operators act before an outage occurs.

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Targeted customer/users countries

Environmental monitoring organisations in Ireland, Germany, the Netherlands, France, Belgium and the United States.

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Product description

Anganode runs on a cloud-native platform built on AWS and Kubernetes. It treats ground sensor data and satellite imagery as two independent sources, then blends them for each use case.

• Collect. Data from ground sensors arrives over standard protocols (MQTT, CoAP, AMQP) or low Earth orbit satellite networks and moves through an Apache Kafka data pipeline. Separately, users request the Earth observation layers they need from satellite constellations such as Sentinel and Landsat, which the platform downloads automatically and shows alongside the sensor data.
• Choose. Users select the sensors and satellite outputs relevant to their use case, without needing to be an Earth observation expert or data scientist.
• Blend. The two sources are combined into machine-learning models built for each use case, such as evapotranspiration and watering schedules for irrigation or flood forecasting.
• Assist. Built-in generative AI, using AWS Bedrock, on-device language models and a secure knowledge base, helps users interpret results, make decisions and work through the initial setup.
• Output. Results appear in a standard web browser as interactive 2D and 3D maps of the terrain, overlaid with environmental data and configurable time-series charts.

An orchestration layer underpins all of this. It is designed for flexibility, hiding the specialist image processing so non-experts can still make a decision backed by data. In principle, it can support any use case. The activity begins with irrigation efficiency, carbon measurement and flood risk.

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Added Value

Most alternatives fall into one of two fields. Some hand over raw satellite imagery that still needs in-house experts to interpret. Others lock you into fixed, single-purpose hardware. Rivals in data fusion tend to build one-off, consultancy-led setups for a single use case, which are hard to adopt and harder to scale. Anganode takes a different approach.

• Vendor-neutral software. It works with any sensor, satellite mission or manufacturer, built on a proven industrial IoT platform.
• Self-calibrating and cross-checked. Broad satellite observations are checked against precise local ground readings, and the two approaches validate each other. This quality control across the ground and satellite views makes results both more accurate and more trustworthy.
• Faster to set up. Preparing a new monitoring workflow drops from around 11.5 days to roughly 3.5 days.
• Reporting support. It produces structured records to support carbon and environmental reporting, including workflows aligned with Monitoring, Reporting and Verification (MRV) and disclosure rules such as the EU CSRD.

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Current Status

The activity began on 16 July 2026 and is in its early stages.