Instrument-to-Instrument translation: An AI tool to intercalibrate and homogenize observations from Earth-observing satellites
Bibliographic record
Abstract
Climate change is fundamentally altering Earth's natural systems, from shifting weather patterns and sea level rise to increasingly frequent extreme events. Understanding and responding to these changes demands continuous, reliable observations of our planet. While Earth-observing satellites have collected terabytes of data in recent decades with ever-increasing temporal, spatial, and spectral resolution, synthesizing these diverse data sources into homogeneous, long-term records remains a significant challenge for climate monitoring and situational awareness. We address this challenge with Instrument-to-Instrument Translation (ITI), an artificial intelligence framework that learns to translate between different satellite imaging domains. Building on unpaired image-to-image translation techniques, ITI overcomes a fundamental limitation in satellite data integration - it does not require the instruments to observe the same location at the same time. This flexibility enables ITI to perform instrument intercalibration, enhance image quality, mitigate sensor degradation, and achieve super-resolution asynchronously across multiple wavelength bands to enable multi-vantage point observationsBuilding on ITI's proven success in harmonizing solar observations, we extend the framework to address the unique challenges of Earth observation and atmospheric monitoring. More specifically, we demonstrate ITI’s capability by harmonizing observations from two geostationary weather satellites with complementary coverage: the Meteosat Second Generation (MSG) monitoring Europe and Africa with 11 spectral bands, and the Geostationary Operational Environmental Satellite (GOES-16) observing the Americas with 16 spectral bands. For this, we developed rs_tools, a comprehensive software package that streamlines the creation of machine learning-ready datasets, and adapted the ITI pipeline to handle the specific complexities of Earth observation data, e.g. missing observations of visible bands at night. Our results reveal good agreement between the ITI-translated imagery and actual high-quality observations, especially for infrared spectral channels. We conduct a multi-faceted performance analysis using image quality metrics (PSNR, histogram distributions, power spectra) across varying spatial scales, spectral bands, and geographic features (land/ocean). The unique overlap in MSG and GOES-16 coverage over the Atlantic Ocean enables additional validation through paired metrics (MSE, Pearson correlation, SSIM) after projecting both observing systems into a common reference frame.The ITI tool is available as open-source software for the research community, and can easily be adapted to novel datasets and research applications. This research outcome is supported by NASA award 22-MDRAIT22-0018 (No. 80NSSC23K1045) and managed by Trillium Technologies, Inc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".