The Future of Earth Observation: Standardization and Interoperability
The next evolution of optical Earth observation lies in standardized, analysis-ready data and the seamless workflows made possible by cross-satellite compatibility. This article delves into why interoperability is essential for unlocking the full potential of Earth observation and highlights how ClearSKY is helping to shape this vision.
Let’s be clear, there is no such thing as too much satellite data, which for me is evident from the number of people seeking perfect, analysis-ready, daily data. However, an increasing amount of new satellite formats, instruments, resolutions, and spectral bands mean Earth observation users must be flexible when it comes to designing their use cases and applications.
The Growing Complexity of Earth Observation
As satellite hardware has and will continue to significantly improve, it’s unreasonable to expect the specifications of satellites to standardize any time soon. What seems more likely is that satellite operators will keep asking for the best hardware at the lowest possible cost — while fixing compatibility issues through software/AI. They are after all catering to a diverse group of users in different verticals with the same hardware in orbit. Do environmental monitoring, forestry, precision agriculture need the same data products and specifications? Probably to some degree, however, one example of how these two verticals differ could be requirements to spatial resolution — and their willingness to pay for it. It’s also unlikely that the mix of spectral wavelengths is suitable for all verticals and applications.
The need for interoperability and standardization has never been more important, not only due to new satellite constellations but also due to an ever increasing data catalogue from existing satellites. These catalogues are, however, not used to the extend I would have expected before starting ClearSKY. An example of this is why so few are utilizing Landsat 8/9 for agricultural monitoring? Too low spatial resolution for small crop fields? Or because of compatibility issues using two different satellite constellations? The practical value we see from Landsat, is almost two additional images per week (in Europe) for increased temporal revisits as well as a more diverse mix of spectral bands, than what Sentinel-2 provides. As an example Sentinel-2 has no thermal bands but Landsat does, so when doing data fusion with Landsat in the mix, there is additional information not available in Sentinel-2 alone.
At ClearSKY, we believe satellite-agnostic data fusion is the natural end-game for the optical satellite imagery market. Our approach ensures that no pixel is left behind by transforming all inputs to a unified standard, enabling seamless compatibility. By embracing older satellite constellations with their unique limitations, we make affordable, daily monitoring a reality for our clients, unlocking the full potential of global satellite coverage.
Landsat and Sentinel: A Case Study in Interoperability
There are many efforts to create harmonized datasets across satellites, with Harmonized Sentinel Landsat (HLS) being one of the most accessible examples. However, from my perspective they all fall short, as the data product is neither Sentinel-2 nor Landsat 8/9 — but in practice, close to Landsat equivalent. As a result, users are asked to adjust to a new standard that doesn’t fully resolve the interoperability challenge it set out to address. For instance, HLS converts most Sentinel-2 bands to Landsat equivalents but excludes key bands like thermal and panchromatic, as Sentinel-2 lacks counterparts for these. This omission, in my perspective, creates yet another incompatible data product.
Moreover, HLS does not fuse data across satellites, meaning complementary insights from overlapping Sentinel-2 and Landsat 8/9 observations remain separate. While users can manually combine the original dataset with HLS, this adds complexity and limits the seamless integration, leaving some data richness and temporal coverage untapped
It’s also worth mentioning that HLS is designed to support Landsat users by providing increased temporal revisit, not Sentinel-2 users. This might seem obvious in hindsight, but I initially thought it served the opposite purpose: enabling Sentinel-2 users to access a historical archive of Sentinel-2-equivalent Landsat images. The clearest indicator is that HLS is a NASA-developed and produced data product, tailored to the Landsat ecosystem.
At ClearSKY, we are addressing the gap in interoperability in two key ways. First, by transforming Landsat data directly to Sentinel-2 standards, we enable Sentinel-2 users to seamlessly extend their analysis back to ~1982, when the Landsat 4/5 mission began. This transformation ensures that historical Landsat data can be integrated into modern Sentinel-2 workflows, bridging the temporal gap without sacrificing usability. While some limitations exist due to differences in spatial resolution and bit depth, our process maintains compatibility with most applications, unlocking the potential of decades of Earth observation data.
Second, we incorporate Landsat into our advanced data fusion processes, combining the unique contributions of multiple satellite constellations to create enhanced datasets. This dual approach ensures that users can either benefit from a transformed Landsat-to-Sentinel-2 workflow or indirectly leverage Landsat’s characteristics through our fusion solutions. The examples shown here highlight the first approach: single Landsat images transformed into Sentinel-2 standards. By creating a unified dataset, we maximize the value of every pixel, delivering affordable and reliable monitoring while bridging compatibility gaps.
The above example demonstrates the results of our transformation process under cloud-free conditions, producing analysis-ready imagery that aligns seamlessly with native Sentinel-2 data. It’s all about making it easy for Sentinel-2 users to work with Landsat data. However, satellite imagery is rarely captured under ideal atmospheric conditions. To illustrate how our harmonization process performs in less-than-perfect scenarios, let’s take a closer look at a Landsat image captured on a hazy day.
No matter the name — harmonized data, proxy values, synthetic data, or cloudless data fusion — the goal is always the same: to create a unified dataset. However, the contrast between proxy values and our approach at ClearSKY is stark. Proxy values, for instance, often lack native compatibility with end-user applications, and as we’ve seen, most harmonized data products struggle to cross the “compatibility finish line.” At ClearSKY, our focus is on compatibility from the ground up, ensuring that users can seamlessly integrate and apply the data they rely on.
Fewer New Standards & Satellites for Data Fusion?
In a truly interoperable ecosystem, users should have the freedom to transition seamlessly between datasets — whether from ClearSKY, native Sentinel-2, or even a competitor’s data fusion product. At ClearSKY, our mission is to foster compatibility at every level, ensuring users can rely on the data standard that best fits their needs without being locked into a proprietary system.
For instance, users working with ClearSKY’s transformed Sentinel-2 data should be able to switch back to native Sentinel-2 or another provider’s dataset without losing consistency. This level of open interoperability ensures Earth observation data remains accessible, actionable, and collaborative. Looking to the future, we envision satellites designed not for direct applications but to enhance the broader data ecosystem. These ‘fusion-first’ satellites would bridge gaps between datasets, providing calibrated, complementary inputs to create standardized formats like Sentinel-2 or other user-preferred standards. By enriching the ecosystem rather than complicating it, such satellites would ensure seamless workflows and true interoperability for users across the globe.
While this article has focused on operational satellites, the same approach can breathe new life into aging or decommissioned constellations. For instance, once MODIS ceases operations in late 2025 after 25 years of service, its unique data legacy could live on. By leveraging satellites like Sentinel-1 and Sentinel-3, we could recreate new, synthetic MODIS-equivalent images — cloud-free and operating in what would now be its fictional orbit. Though not a lucrative business, preserving such datasets is invaluable for advancing long-term scientific research — an endeavor we find both fascinating and worthwhile.
You can read more about our data transformation/harmonization at clearsky.vision/harmonization and our data fusion at clearsky.vision/cloudless-sentinel2.
