EOdal: An open-source Python package for large-scale agroecological research using Earth Observation and gridded environmental data

Abstract

Earth Observation by means of remote sensing imagery and gridded environmental data opens tremendous opportunities for systematic capture, quantification and interpretation of plant–environment interactions through space and time. The acquisition, maintenance and processing of these data sources, however, requires a unified software framework for efficient and scalable integrated spatio-temporal analysis taking away the burden of data and file handling from the user. Existing software products either cover only parts of these requirements, exhibit a high degree of complexity, or are closed-source, which limits reproducibility of research. With the open-source Python library EOdal (Earth Observation Data Analysis Library) we propose a novel software that enables the development of fully reproducible spatial data science chains through the strict use of open-source developments. Thanks to its modular design, EOdal enables advanced data warehousing especially for remote sensing data, sophisticated spatio-temporal analysis and intersection of different data sources, as well as nearly unlimited expandability through application programming interfaces (APIs).