Abstract
Sentinel-1 satellites provide temporally dense and high spatial resolution synthetic aperture radar (SAR) imagery. The open data policy and global coverage of Sentinel-1 make it a valuable data source for a wide range of SAR-based applications. In this regard, the Google Earth Engine is a key platform for large area analysis with preprocessed Sentinel-1 backscatter images available within a few days after acquisition. To preserve the information content and user freedom, some preprocessing steps (e.g., speckle filtering) are not applied on the ingested Sentinel-1 imagery as they can vary by application. In this technical note, we present a framework for preparing Sentinel-1 SAR backscatter Analysis-Ready-Data in the Google Earth Engine that combines existing and new Google Earth Engine implementations for additional border noise correction, speckle filtering and radiometric terrain normalization. The proposed framework can be used to generate Sentinel-1 Analysis-Ready-Data suitable for a wide range of land and inland water applications. The Analysis Ready Data preparation framework is implemented in the Google Earth Engine JavaScript and Python APIs.
Highlights
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The Sentinel-1 Analysis Ready Data (ARD) preparation framework is implemented in both the JavaScript in the Google Earth Engine (GEE) code editor (Figure 3) and the GEE Python application programming interface (API)
We proposed a framework to prepare Sentinel-1 synthetic aperture radar (SAR) backscatter ARD in GEE that is applicable for a wide range of large-scale land and inland water applications
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Global coverage satellite data are considered indispensable for earth monitoring due to their large scale availability and low cost In this regard, the Sentinel-1 synthetic aperture radar (SAR) data from the European Union’s Copernicus program is unique, as it, for the first time, provides temporally dense and high spatial resolution satellite SAR imagery freely for the entire globe. For a range of applications, CARD4L recommends additional preprocessing steps, such as speckle filtering and radiometric terrain normalization, to be implemented by the user to make the data ready for information extraction. We combine existing and new GEE implementations in a comprehensive framework for preparing Sentinel-1 SAR backscatter ARD in GEE suitable for a wide range land and inland water applications that includes additional border noise correction, speckle filtering and radiometric terrain normalization. The provided ARD preparation framework supports a wide range of monitoring and mapping applications
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