Abstract
This study aims at assessing the feasibility of automatically producing analysis-ready radiometrically terrain-corrected (RTC) Synthetic Aperture Radar (SAR) gamma nought backscatter data for ingestion into a data cube for use in a large spatio-temporal data environment. As such, this study investigates the analysis readiness of different openly available digital elevation models (DEMs) and the capability of the software solutions SNAP and GAMMA in terms of overall usability as well as backscatter data quality. To achieve this, the study builds on the Python library pyroSAR for providing the workflow implementation test bed and provides a Jupyter notebook for transparency and future reproducibility of performed analyses. Two test sites were selected, over the Alps and Fiji, to be able to assess regional differences and support the establishment of the Swiss and Common Sensing Open Data cubes respectively.
Highlights
Global Earth systems are facing increased pressure—over-exploitation of resources, climate change, environmental and ecological degradation, and overpopulation—meaning that the ability to measure and monitor Earth surface change is of ever-increasing value [1]
A large number of options exist for preparing the digital elevation models (DEMs), adjusting its resolution, the choice of DEM resampling during processing and choice of interpolation of the Synthetic Aperture Radar (SAR) scene during geocoding
While optimizing all these processing parameters is outside the scope of this study and the results are likely different for other SAR scenes, a quick comparison was judged necessary in order to approximate optimal processing parameters
Summary
Global Earth systems are facing increased pressure—over-exploitation of resources, climate change, environmental and ecological degradation, and overpopulation—meaning that the ability to measure and monitor Earth surface change is of ever-increasing value [1]. The democratization of space and recognition of the value of Earth Observation (EO) in providing insights—e.g., for the Sustainable Development Agenda—have led to an increase in the availability of EO data worldwide, and with this, a growing interest globally in efficient exploitation of EO data at scale [2]. The launch of the set of Sentinel satellites by the European Space Agency (ESA) as part of the European Commission’s Copernicus Program has been a catalyst for this change and is generating ever-increasing interest across governments and different market sectors, each with different user requirements [4]. Local processing and data distribution methods currently exploited by industry and government are not suitable to address the challenge of scalability, increases in the size of data volumes, and the growing
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