Activity at Port Hedland, Australia, one of the world’s largest bulk export ports, captured by EDC-01 in February 2026.
Achieving daily global coverage is only meaningful if the measurements behind it are scientifically reliable. Maintaining that standard requires continuous calibration, validation, and engineering discipline to preserve radiometric accuracy, geometric stability, and signal quality across a multi-satellite Earth observation constellation.
This is part of a four-part series adapted from a feature first published in SpaceNews: Engineering Behind EarthDaily: Solving for Global Daily Coverage, Scientific Quality, and High-Spectral Diversity.)
If customers build models, analytics, or operational systems on data that later shifts — due to recalibration, processing changes, or stability improvements — the downstream cost is significant.
Archives must be reprocessed; models retrained; analytics revalidated; and baselines adjusted. Those costs compound.
We are treating data release as a quality milestone, not simply a “we have pixels” milestone. The objective is to address the 80/20 reality most data scientists face: too much time correcting, normalizing, masking clouds, and transforming imagery, and too little time building applications.
We know this problem because we have experienced it.
Cloud masking illustrates the point. Even with high-quality public missions such as Sentinel-2, subtle clouds and haze can escape detection, leading to false positives in change detection.
Sentinel-2 does not detect the presence of small clouds and haze in this image, EarthDaily accurately labels these atmospheric effects enabling the end user to ignore these areas to minimize false positives in change detection.
Zoom of previous image. EarthDaily’s Cloud Mask developed with Sentinel-2 picks up even subtle clouds that can give rise to false positives. This quality of the cloud mask is necessary for reliable change detection.
We have developed an AI-based cloud masking solution using Sentinel-2 and Landsat-8/9 that will be applied directly to EarthDaily data to label cloud, shadow, and haze more precisely.
The objective is not incremental improvement over time. It is to deliver stable, analysis-ready measurement from the start.
When EarthDaily data becomes fully operational, it will deliver:
There is no tasking required over land; coverage is continuous.
Over oceans, we maintain tens of millions of square kilometers of additional daily capacity for maritime tasking. Demand for persistent data is clear, and customers are already securing coverage.
The objective is not to produce isolated, spectacular images. It is to provide a continuous, stable measurement system — a data infrastructure for change detection, forecasting, risk management, environmental monitoring, and AI-driven insight. Infrastructure, not simply imagery.
There are different ways to build in space. Some missions are designed to build, launch, learn, and iterate. Others obsess over traceability, uncertainty budgets, calibration campaigns, and long-term measurement stability.
From the beginning, EarthDaily chose the latter.
When customers build analytics, risk models, and automated decisions on your data, you don’t get to “ship it and patch it later.” The payload, calibration, algorithms, and ground segment must align from Day 1 — and they must hold steady.
That philosophy takes time.
Download the PDF to explore early imagery from the EarthDaily Constellation.
More in this series: