Comparing Sentinel-2A with a RadCalNet Observations over GONA

Mon, 09 Nov 2026 00:00:00 +0000

🛰️ Background

How can we determine whether a satellite is measuring reflectance accurately?

One approach is to compare satellite observations against trusted ground-based reference measurements. The MetEOR Toolkit provides an end-to-end workflow for performing these comparisons while accounting for measurement uncertainty throughout the process.

This example compares Sentinel-2A Top-of-Atmosphere (TOA) reflectance with observations from the RadCalNet Gobabeb site (GONA) in Namibia during a clear-sky overpass.

📓 Full worked code example notebook:

👉 https://github.com/comet-toolkit/comet_training/blob/main/meteor_sat2insitu_example.ipynb

🎯 What does the workflow do?

The notebook demonstrates the complete satellite-to-in-situ comparison workflow:

Find satellite overpasses of a reference site.
Read satellite and reference datasets together with their uncertainties.
Prepare observations for comparison by aligning them in space, time and spectral response.
Compare measurements with propagated uncertainties.

While this example demonstrates the comparison of a single matchup, the workflow is designed to scale to large collections of satellite overpasses.

☄️ Which MetEOR tools are used?

This example combines several tools from the MetEOR toolkit:

Tool	Purpose
`scrappi`	Discover and download satellite and in-situ data products
`eomatch`	Find and catalogue satellite-to-in-situ matchups
`eoio`	Read observations, metadata and uncertainties
`meteor_maths`	Perform spectral harmonisation and mathematical processing

Together, these tools create a consistent and traceable comparison pipeline.

✓ Step 1: Find a Matchup

The first task is identifying a suitable satellite overpass.

Using eomatch, MetEOR searches both satellite and reference archives to identify observations that occur at approximately the same time and location.

For this example:

Platform: Sentinel-2A
Reference site: RadCalNet GONA
Product type: Top-of-Atmosphere Reflectance
Conditions: Clear-sky overpass with observations separated by less than 15 minutes

The resulting matchup catalogue contains all information required for subsequent analysis.

✓ Step 2: Read the Measurements and Uncertainties

After downloading the products, eoio reads both datasets into a common structure.

Importantly, the measurements are not read in isolation. Their associated uncertainty information and auxiliary data is also preserved.

Sentinel-2 Uncertainties

For Sentinel-2, eoio’s s2_rut processor is applied during the read. This uses s2_rut_python, a python port of ESA’s Sentinel-2 Radiometric Uncertainty Tool, to generate per-pixel uncertainty estimates.”

just to make clear it’s not a separate step run after eoio, but is actually handled automatically within eoio, as it’s quite a nice feature of meteor

Systematic uncertainty affecting all pixels similarly.
Random uncertainty varying between individual pixels.

RadCalNet Uncertainties

RadCalNet provides:

Spectral reflectance measurements.
Accompanying uncertainty estimates.

This allows uncertainty propagation to be maintained for both datasets throughout the comparison.

✓ Step 3: Prepare the Datasets for Comparison

Before measurements can be compared, they must represent the same spectral, temporal and spatial conditions.

MetEOR performs three key adjustments:

Spatial Alignment

Satellite pixels within the site’s region of interest are averaged to produce a representative site-scale observation.

Spectral Alignment

The RadCalNet hyperspectral measurements are integrated across the Sentinel-2 spectral response functions, producing equivalent Sentinel-2 bands.

Temporal Alignment

RadCalNet measurements are interpolated to the exact Sentinel-2 acquisition time.

Throughout these processing steps, uncertainty is propagated via punpy using the uncertainty metadata stored alongside the observations.

✓ Step 4: Compare Satellite and Reference Observations

Once both datasets have been spatially, spectrally, and temporally aligned, they can be compared directly.

The figure below shows:

The RadCalNet reflectance spectrum.
The corresponding band-integrated RadCalNet values.
Sentinel-2 observations for the same overpass.
Associated uncertainty estimates.

How well do they agree?

The final step calculates the relative difference between the satellite and reference measurements for each spectral band.

Uncertainties from the satellite and reference observations are combined to determine whether observed differences are statistically significant.

Bands whose uncertainty intervals include zero can be considered consistent within the stated measurement uncertainties.

📂 Results

This example demonstrates how MetEOR can:

Discover satellite-to-in-situ matchups,
Read observations together with uncertainty information,
Align measurements across space, time and spectral response,
Propagate uncertainty through each processing stage,
Quantify agreement between satellite and reference observations.

Although the notebook analyses a single Sentinel-2 overpass, the same workflow can be extended to multiple sites, sensors and time periods.

📚 Learn More

This page provides a high-level overview of the workflow.

For the complete code, figures and detailed explanations of each processing step, see the full Jupyter Notebook:

👉 https://github.com/comet-toolkit/comet_training/blob/main/meteor_sat2insitu_example.ipynb

Satellite-to-In-Situ Radiometric Validation

Mon, 09 Nov 2026 00:00:00 +0000

📈 Background

The Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) coordinates international activities to assess and improve the quality of Earth observation (EO) satellite measurements.

Key outcomes of this activity are the Product Validation Platform (CEOS-PVP) and the RadVAL Dashboard, which provides satellite-to-in-situ validation results and long-term radiometric stability monitoring for EO missions.

The comparison system is implemented using the NPL MetEOR Toolkit, which performs matchup processing, uncertainty propagation and statistical analysis.

For a full overview of the CEOS PVP activity, visit:

👉 https://www.ceos-pvp.org/

☄️ MetEOR Processing

The RadVAL workflow compares satellite observations with highly characterised reference measurements from ground-based calibration sites.

To create comparable observations, MetEOR performs three key processing steps:

Spectral integration of hyperspectral reference measurements using the satellite spectral response function.
Temporal interpolation to the satellite overpass time.
Spatial aggregation of satellite pixels within the site’s region of interest.

The resulting matchup pairs allow the bias between satellite and reference observations to be evaluated for each sensor band and overpass.

A detailed description of the methodology is available at:

👉 https://www.ceos-pvp.org/methods

✓ Uncertainty Analysis

Alongside the measurement comparison, MetEOR evaluates uncertainty contributions from the satellite observations, reference measurements and comparison process.

The uncertainty structure used within the RadVAL analysis is summarised below.

Further details are available in the report:

👉 Multispectral Satellite-to-In-Situ Comparison Uncertainty Budget
https://www.ceos-pvp.org/download/multispectral-uncertainty-report

📂 Results

The processed comparisons are published through the RadVAL Dashboard, where time series of satellite-reference bias can be explored for different sensors, spectral bands and validation sites.

The resulting records provide an indicator of sensor radiometric stability through time:

Consistent bias indicates stable sensor performance.
Gradual changes may indicate sensor drift.
Abrupt changes can reveal calibration updates or instrument anomalies.

The dashboard also displays uncertainty estimates arising from the satellite measurements, reference observations and comparison methodology.

✓ Explore the Results

Interactive RadVAL products are available at:

👉 https://www.ceos-pvp.org/radval-dashboard

✓ Summary

This case study demonstrates how MetEOR enables traceable, uncertainty-aware satellite-to-in-situ radiometric validation within the CEOS RadVAL framework.

By combining robust matchup processing with uncertainty analysis, the system supports the monitoring of radiometric stability across Earth observation satellite missions.****

Validation | CoMet Toolkit

Comparing Sentinel-2A with a RadCalNet Observations over GONA

🛰️ Background

🎯 What does the workflow do?

☄️ Which MetEOR tools are used?

✓ Step 1: Find a Matchup

✓ Step 2: Read the Measurements and Uncertainties

Sentinel-2 Uncertainties

RadCalNet Uncertainties

✓ Step 3: Prepare the Datasets for Comparison

Spatial Alignment

Spectral Alignment

Temporal Alignment

✓ Step 4: Compare Satellite and Reference Observations

How well do they agree?

📂 Results

📚 Learn More

Satellite-to-In-Situ Radiometric Validation

📈 Background

☄️ MetEOR Processing

✓ Uncertainty Analysis

📂 Results

✓ Explore the Results

✓ Summary