Satellites & Platforms

Sentinel-2

ESA's multispectral workhorse: 13 bands, 10-meter resolution, and the backbone of global land monitoring

SAT-002

Sentinel-2 is a pair of optical satellites operated by the European Space Agency (ESA) as part of the Copernicus Earth observation programme. Each satellite carries a Multispectral Instrument (MSI) that captures images in 13 spectral bands — from visible light through near-infrared to shortwave infrared — at resolutions of 10, 20, and 60 meters. With two satellites in orbit (Sentinel-2A and 2B), the combined revisit time is approximately 5 days at the equator and 2-3 days at mid-latitudes. The data is free and open access. Sentinel-2 is the most widely used optical satellite for land monitoring, agriculture, forestry, water resources, and disaster response worldwide.

Why It Matters

Before Sentinel-2, the options for free, high-resolution, multispectral satellite imagery were limited. Landsat provided 30-meter resolution with a 16-day revisit — excellent for long-term studies but too coarse and too infrequent for many operational applications. Commercial satellites offered higher resolution but at costs that excluded most researchers, governments of developing nations, and non-governmental organizations.

Sentinel-2 changed the economics and temporal density of Earth observation simultaneously. Ten-meter resolution in its visible and near-infrared bands is sufficient to monitor individual agricultural fields, detect small-scale deforestation, map urban expansion, and identify water bodies. The 5-day revisit means that even with cloud cover, usable imagery is available for most locations every 1-2 weeks — often enough for operational monitoring rather than just periodic assessment.

The combination of spatial resolution, spectral range, temporal frequency, and open data access made Sentinel-2 the default satellite for a vast range of applications. More scientific papers cite Sentinel-2 data than any other single Earth observation mission.

Fabric Connection

Sentinel-2 is the primary optical data source in Fabric's processing pipeline. Fabric's fire detection, vegetation monitoring, and water extent patterns all begin with Sentinel-2 Level-1C or Level-2A data.

Fabric adds value beyond standard Sentinel-2 processing in three ways. First, it handles the full ARD pipeline — atmospheric correction, cloud masking, geometric alignment, and quality assessment — with provenance tracking at every step. Second, it harmonizes Sentinel-2 with other sensors (Landsat, SAR, thermal) to produce composite observations that no single sensor can achieve alone. Third, through Observational Grammar, it treats every Sentinel-2 measurement as a claim with explicit uncertainty rather than as ground truth — the atmospheric correction is documented as an interpretation, not a fact.

Philosophical Thread

Sentinel-2 exemplifies a core tension in Observational Grammar: the gap between what a sensor measures and what we claim it means. The MSI measures top-of-atmosphere radiance — photons arriving at the detector after passing through the atmosphere twice. Every "surface reflectance" value is an interpretation, produced by atmospheric correction models that estimate and remove atmospheric effects. The sensor never directly observes the ground; it observes the atmosphere-ground system and then we subtract our best guess of the atmosphere.

This is why OG insists on treating every observation as a claim with provenance rather than as a fact. When we say "NDVI at this location is 0.72," what we mean is: "Given this atmospheric model, this calibration, and this processing chain, the ratio of estimated surface reflectance in these two bands produces this value." The chain of interpretation is the observation. Hiding it does not make the observation more objective — it makes it less honest.

References

Drusch, M., et al. (2012). "Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services." Remote Sensing of Environment, 120, 25-36. https://doi.org/10.1016/j.rse.2011.11.026
ESA (2024). "Sentinel-2 User Handbook." European Space Agency. https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi
Main-Knorn, M., et al. (2017). "Sen2Cor for Sentinel-2." Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII.
Claverie, M., et al. (2018). "The Harmonized Landsat and Sentinel-2 Surface Reflectance Data Set." Remote Sensing of Environment, 219, 145-161.
Phiri, D., et al. (2020). "Sentinel-2 Data for Land Cover/Use Mapping: A Review." Remote Sensing, 12(14), 2291. https://doi.org/10.3390/rs12142291