Ruben Remelgado , Christopher Conrad, Carsten Meyer

Satellite remote sensing is vital for monitoring, research, and policy addressing sustainability challenges from climate and ecosystem changes to food and water security. Here, Landsat satellite data play a crucial role, thanks to their unique global, long-term, and high-resolution coverage. Yet, gaps and quality limitations in the Landsat data archive may propagate into derived remote-sensing products and thereby threaten the validity of downstream applications, especially when data users have limited training in remote sensing. To improve awareness of these issues, we here demonstrate that global, historical Landsat data are spatially and temporally uneven, frequently interrupted, and have seasonally incomplete coverage and quality. Using a causal-discovery framework, we moreover show that these limitations are inherited in several state-of-the-art, global time-series products, biassing perceptions of changes in forests, arable-lands, and water resources. These biases can impair reliable assessments of environmental and human development issues targeted by the Sustainable Development Goals (SDG) framework, and disproportionately affect lower-income countries. We provide global data-quality information to support the explicit consideration of potential biassing effects in future uses of remote-sensing products derived from Landsat data, and discuss avenues towards better uncertainty reporting and bias control in satellite-based sustainability monitoring and related applications.