A prototype Open-Source data-processing pipeline to efficiently combine in-situ data with remote-sensing observations of the Earth

作者：Robert Reinecke, Annemarie Bäthge, David Noack, Matthias Zink, Simon Mischel, Stephan Dietrich · 年份：2026 · DOI：10.5194/egusphere-egu26-6238 · 研究领域：Environmental Monitoring and Data Management、Scientific Computing and Data Management、Research Data Management Practices

In situ and remote sensing data are crucial in earth sciences, as they provide complementary perspectives on environmental phenomena. In situ data, collected directly from the Earth’s surface, offer high accuracy and detailed insights into local conditions, enabling precise measurements of variables such as soil moisture, temperature, and pollutant levels. Conversely, remote sensing data provides for extensive spatial coverage and the ability to monitor changes over time across vast areas, capturing large-scale patterns and trends that in situ data alone cannot reveal. By combining these two data sources and automatically preprocessing them into Analysis-Ready Data, researchers can enhance scientific insights, improve the robustness of machine learning applications, and refine models used to predict environmental changes or assess the impacts of human activity on natural systems. This integrated approach promotes a more comprehensive understanding of complex Earth processes, enabling better-informed decision-making and effective management strategies for sustainable development. However, preprocessing and combining in situ data from different sources can be highly complex, especially for global datasets. Joining this data with remotely sensed products may require substantial computational resources, given the increased number of observational records and high temporal resolutions. Here, we present a prototype of such a pipeline, CULTIVATE, an open-source data-processing pipel...