Instrument-to-Instrument translation: An AI tool to intercalibrate and homogenize observations from Earth-observing satellites

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 observations

Building 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.