Concepts for Open Access Interdisciplinary Remote Sensing with ESA Sentinel-1 SAR Data

Earth observation with advanced, large-scale technologies as satellite piloted Synthetic Aperture Radar (SAR) appear essential to monitor agricultural ecosystems in near future. Radar backscatter e.g. allows insights to crop conditions, soil properties and direct mapping of vegetation growth. Precise SAR pre-processing is a substantial prerequisite to perform machine learning on SAR data, e.g. for early prediction of optimal sowing, harvesting and fertilization time points. Not only for a successful, resource-efficient and environmentally friendly farming but for a wide range of other fields concerning environmental observations. Open access technologies offer the best solutions for collaborative efforts, thus minimizing financial and legal constraints in comparison to technologies residing in the commercial sector. Here, we combine expertise from the area of computer science, data science, software engineering, agriculture and geo-information-systems to build on state-of-the-art, open source (OS) tools and technologies in Germany. Our goal is to provide an easy to employ Sentinel-1 SAR pre-processing tool as well as a Germany wide, open access, pre-processed, analysis-ready database of Sentinel-1 SAR data. With the employment of modern software developing methods including the Model View Controller (MVC) architecture and a procedural and object-oriented design, these solutions can be extended, adapted and tested. This solution is available and accessible here (Jennifer, JenniferMcCl/Sentinel-1_SAR-Data-Processing: Sentinel-1_SAR-Data-Processing_V.1.0-beta, https://zenodo.org/record/8214935 ).