Sentinel-2 vs. ClearSKY vs. AlphaEarth: Choosing the Right Satellite Data
2025-08-21 · 4 min read · Sentinel-2 · Data Fusion · Mosaics
TL;DR: Sentinel-2 is free and science-grade but often cloudy. ClearSKY fuses multiple same-day passes (and, if needed, prior days) across satellites to deliver date-faithful imagery without using future data. AlphaEarth produces globally consistent, model-based layers excellent for mapping and analytics, but they are not tied to a single capture date.
Why this comparison matters
“Cloud-free” imagery can be built in very different ways. Some products show what the surface looked like on a particular day; others show a best-possible view aggregated over time; still others are model-derived layers trained on large multi-source archives. If you need daily operational decisions or auditability by date, these differences aren’t cosmetic as they determine whether a pixel is evidence from a given day or a learned/composited estimate.
Sentinel-2: the free original
What it is. The Copernicus Sentinel-2 mission is a pair of optical satellites with 13 bands at 10–60 m and a revisit of ~5 days for the constellation (2–3 days at mid-latitudes). It’s the backbone of modern open EO. Learn more: Copernicus Sentinel-2 mission overview
What it means for you. Sentinel-2 is free and well-documented, but single scenes are frequently cloudy and require additional processing before analysis. For basemaps, you’ll usually turn to multi-date mosaics published by Copernicus or third parties. For date-critical analysis, you either wait for a clear scene or use a fusion approach to avoid gaps. Examples of official mosaic products: Copernicus Sentinel-2 Global Mosaic.
ClearSKY: same-day data fusion with no future data
What it is. ClearSKY is an operational data fusion service. We combine multiple optical satellites on the same day to maximize the chance that at least one pass catches your area between clouds. Because clouds move and overpasses are staggered, same-day multi-satellite inputs substantially raise the probability of clean pixels in any given location. When coverage is still thin, we may use nearby prior-day observations. This is clearly labeled in our metadata. However, never borrow from future dates**. That preserves a defensible nominal date for each pixel.
Why it’s different. The output is designed for daily operations and audits: one nominal date per product, per-pixel provenance/quality layers, and timeliness that aligns with end-of-day or early-morning decisions. The method reduces holes without blurring time, because fusion happens within the day first, not across long windows. Learn more: ClearSKY Webpage.
AlphaEarth: learned, globally consistent layers
What it is. AlphaEarth Foundations (Google DeepMind) is a geospatial AI that learns representations from many sources, optical, radar, lidar, simulations, and more, to produce globally coherent layers and analyses. These are powerful for large-scale mapping, monitoring, and research. Learn more: DeepMind’s AlphaEarth Foundations · AlphaEarth Foundations (arXiv)
What it means for you. AlphaEarth outputs are not tied to a single acquisition time in the way a daily satellite scene is. They’re model-derived from multi-date, multi-sensor corpora to achieve consistency and completeness. That makes them excellent for thematic mapping and broad-area context, but they are not the same thing as a single-day observation you can audit to a specific timestamp.
What really differs under the hood
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Temporal truth. Sentinel-2 scenes are single acquisitions (often cloudy). ClearSKY fuses same-day observations first (and may include prior-day where needed, always labeled), keeping the date meaningful. AlphaEarth provides model-derived layers optimized for consistency and coverage rather than strict per-day snapshots.
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Completeness vs. date fidelity. Cloudless mosaics (e.g., Copernicus S2 quarterly/seasonal layers) reach high coverage by mixing dates. AlphaEarth reaches coverage via learned estimates across time and sensors. ClearSKY’s promise is coverage today without pulling in tomorrow’s pixels.
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Operational use. For daily change detection, compliance evidence, and time-critical field decisions, date fidelity and provenance dominate. For visualization, mapping, and modeling at regional to global scales, long-window mosaics or model-derived layers can be ideal.
Which should you pick?
Use Sentinel-2 when you need free, raw inputs and you’re prepared to handle cloud detection and gap-filling yourself.
Use ClearSKY when you need near-real-time, date-faithful imagery for operations and compliance, built from same-day multi-satellite fusion and, if necessary, prior-day pixels with clear QA, never future data.
Use AlphaEarth when you need globally consistent, model-based layers for mapping, screening, and research at scale and don’t require per-day auditability.
Quick view
| Feature | Sentinel-2 | ClearSKY | AlphaEarth |
|---|---|---|---|
| Cost | Free (open data) | Commercial service | Research/partnered platform |
| What you get | Single scenes (often cloudy) | Same-day fusion (prior-day fallback; no future data) | Model-derived global layers |
| Temporal fidelity | Single acquisition | Nominal date per pixel | Not a single-day snapshot |
| Completeness | Weather-limited | High for day-to-day ops | Very high, globally |
| Best for | DIY processing, open projects | Daily ops, audits, change detection | Mapping, broad-area context, modeling |
