Led REC (soil moisture for crop irrigation) and ACCWA (climate change in water/agriculture), and contributed to Water-ForCE (Copernicus water cycle services) and FANFAR (flood forecasting).
ISARDSAT SL
Barcelona SME turning satellite remote sensing data into commercial environmental intelligence for water, agriculture, and air quality applications.
Their core work
isardSAT is a Barcelona-based SME specializing in Earth Observation (EO) data processing and satellite remote sensing services. They develop commercial platforms and algorithms that turn raw satellite data into actionable environmental intelligence — covering air quality monitoring, water resource management, flood forecasting, and emission tracking. Their core business sits at the intersection of satellite data, AI-driven analytics, and environmental applications, serving both public agencies (e.g., Copernicus) and commercial markets.
What they specialise in
Coordinated AirQast (commercial air quality platform using EO data) and participated in SEEDS (satellite-based emission and deposition tracking).
SnapEarth applied deep learning and NLP to EO data market access; ACCWA integrated remote sensing with management tools — signaling a move toward AI-enhanced satellite analytics.
Participated in BASE-platform (bathymetry services) and Water-ForCE (inland water remote sensing and in situ networks).
Contributed to HiFreq on high-frequency environmental sensing and distributed sensor networks for hydrological process monitoring.
How they've shifted over time
In their early H2020 period (2015–2017), isardSAT focused on foundational EO services — bathymetry, soil moisture estimation, and environmental sensor networks. From 2019 onward, they shifted decisively toward applied remote sensing for agriculture and climate adaptation (ACCWA), AI-powered EO data platforms (SnapEarth), and satellite-based emissions monitoring (SEEDS). The trajectory shows a company moving from raw data processing toward higher-value, AI-enhanced environmental intelligence products.
isardSAT is increasingly integrating AI and machine learning into their satellite remote sensing work, positioning themselves as a provider of intelligent environmental monitoring services rather than raw data processors.
How they like to work
isardSAT balances leadership and partnership — they coordinated 3 of their 10 projects, showing they can manage consortia but are equally comfortable as a specialist contributor. With 113 unique partners across 30 countries, they maintain a broad and diverse network rather than relying on a small circle of repeat collaborators. This makes them a well-connected, flexible partner who brings both technical depth and wide European network access to any consortium.
isardSAT has collaborated with 113 distinct partners across 30 countries, giving them one of the broader networks you'd expect from a 10-project SME. Their reach spans well beyond Southern Europe into a truly pan-European and international footprint, as evidenced by their West Africa flood forecasting work (FANFAR).
What sets them apart
isardSAT occupies a distinctive niche as an SME that bridges satellite science and commercial environmental services. Unlike large aerospace firms, they are agile enough to develop tailored EO products (air quality platforms, irrigation tools) while still participating in major Copernicus and Space programme initiatives. Their combination of remote sensing expertise, AI capabilities, and water/agriculture domain knowledge makes them a strong partner for anyone needing satellite data translated into decision-support tools.
Highlights from their portfolio
- AirQastTheir highest-funded project (EUR 427K) where they led development of a commercial air quality service platform using Earth Observation data — a direct path-to-market initiative.
- ACCWACoordinated a 5-year project (2019–2024) applying remote sensing to climate-smart agriculture, representing their strategic shift toward water and food security applications.
- SnapEarthMarks their entry into AI/deep learning for EO data, applying natural language processing and cloud-agnostic technologies to make satellite data more accessible to the market.