Quantifying the transboundary environmental impacts of Earth observation in the 'big data' and constellation era
Authors: Anderson Karen , Mleczko Magdalena , Brewin Robert , Gaston Kevin , Mueller Markus , Jamie Shutler , Yan Xiaoyu , Wilkinson Ruby 
Published: March 2024

Numbers of Earth Observation (EO) satellites have increased exponentially over the past decade, fuelled by a shift towards constellation models that promise to deliver data at finer spatial, temporal and spectral resolutions compared to the past. The result is the now >1000 EO satellites in orbit, a population that is rapidly increasing because of a booming private-sector interest in space imaging. Flowing from this, EO data volumes have mushroomed in recent years, and data processing has migrated to the cloud, with scientists leveraging tools such as Google Earth Engine for information retrieval. Whilst considerable attention has been given to the launch and in-orbit environmental impacts of satellites (e.g. rocket emissions and space-junk risks), specific environmental impacts from EO missions (data infrastructures and cloud computation); have so far escaped critical scrutiny. It is urgent that the environmental science community address this gap, so that the environmental good of EO can withstand scrutiny.Data centres consume high quantities of water and energy and they may be situated in sensitive geographical situations far away from both users and launchpads (i.e. a transboundary environmental concern). There are also hidden impacts in the carbon-intensive processes of computer component manufacture, impacting places and communities far from the site of EO information retrieval. We scope the broad suite of transboundary environmental impacts that EO generates. Related to the data aspect of the EO life-cycle, we quantify the current volume of global EO data holdings (> 800 PB currently, increasing by 100 PB / year). Mapping the distribution of datasets across different data centre providers, our work shows high redundancy of datasets, with collections from NASA and ESA replicated across many data centres globally. Storage of this data volume generates annual CO2 equivalent emissions summing to >4000 tonnes/year. We quantify the environmental cost of performing EO functions on the cloud compared to desktop machines, using Google Earth Engine as an exemplar, scaling emissions using the &#8216;Earth Engine Compute Unit&#8217;. We show how large-scale analyses executed within GEE rapidly scale to produce the equivalent emissions of a single ticket on an economy flight ticket from London-Paris. Executing these processes on the cloud takes seconds, and these estimates do not account for emissions from microprocessor manufacture, nor do they account for users running processes multiple times (e.g. during code development).&#160; A major blind-spot is that the geography of GEE data centres is hidden from users, with no choice given to users about where GEE processes are executed. It is important that EO providers become more transparent about the location-specific impacts of EO work, and provide tools for measuring the environmental cost of cloud computation. Furthermore, the EO community as one which is concerned with the fate of Earth&#8217;s environment must now urgently and critically consider the broad suite of EO data life-cycle impacts that lie (a) beyond the launchpad, and (b) on Earth rather than in space; taking action to minimise and mitigate them. This is important particularly because EO data will long outlive the satellites that provided them.