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Intercomparison of Sentinel-1 Datasets from Google Earth Engine and the Sinergise Sentinel Hub Card4L Tool

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... earth-engine/datasets/catalog/COPERNICUS_S1_GRD, accessed on 6 January 2022). It is shown that it has similar radiometric performance within the main (0 to −30 dB) dynamic range to our product (see [4], for more details on the comparison). However, RTC is not applied by default in GEE. ...
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Digital Earth Africa is now providing an operational Sentinel-1 normalized radar backscatter dataset for Africa. This is the first free and open continental scale analysis ready data of this kind that has been developed to be compliant with the CEOS Analysis Ready Data for Land (CARD4L) specification for normalized radar backscatter (NRB) products. Partnership with Sinergise, a European geospatial company and Earth observation data provider, has ensured this dataset is produced efficiently in the cloud infrastructure and can be sustained in the long term. The workflow applies radiometric terrain correction (RTC) to the Sentinel-1 ground range detected (GRD) product, using the Copernicus 30 m digital elevation model (DEM). The method is used to generate data for a range of sites around the world and has been validated as producing good results. This dataset over Africa is made available publicly as a AWS public dataset and can be accessed through the Digital Earth Africa platform and its Open Data Cube API. We expect this dataset to support a wide range of applications, including natural resource monitoring, agriculture, and land cover mapping across Africa.
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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.
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Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection. It is unique in the field as an integrated platform designed to empower not only traditional remote sensing scientists, but also a much wider audience that lacks the technical capacity needed to utilize traditional supercomputers or large-scale commodity cloud computing resources.
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As part of the AuScope Australian Geophysical Observing System initiative, Geoscience Australia constructed a new regional-scale geodetic network that includes an array of radar corner reflectors. The purpose of the new geodetic network is to monitor crustal deformation by combining spatially dense but temporally sparse deformation maps derived from the Interferometric Synthetic Aperture Radar (InSAR) technique and temporally dense but spatially sparse point measurements from Global Navigation Satellite System (GNSS) networks. The radar corner reflector array is also designed to support calibration and validation of Synthetic Aperture Radar (SAR) products from orbiting satellites. This GA Record outlines the prototyping exercises undertaken to determine the most appropriate design of radar corner reflector that can exploit SAR acquisitions at X-, C- and L-band radar frequencies. A set of 18 corner reflector prototypes were manufactured that had different sizes and plate finishes. These prototypes had their radar signatures characterised in experiments conducted at the Defence Science and Technology Organisation ground radar reflection range in St Kilda, South Australia. Following this, the prototypes were temporarily deployed between December 2013 and May 2014 at a grazing property in Gunning, New South Wales. During this deployment the radar response of the corner reflectors was tested in SAR images from the TerraSAR-X, COSMO-SkyMed, RADARSAT-2 and RISAT-1 satellites. As a result of these experiments, a triangular trihedral corner reflector design with an inner leg dimension of 1.5 metres and powder-coated plate finish was chosen for permanent deployment in the new array. Fifteen of the prototypes and 25 new 1.5 metre corner reflectors were fully installed in the new array in the northern Surat Basin, Queensland, by 21 November 2014.
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Enabling intercomparison of synthetic aperture radar (SAR) imagery acquired from different sensors or acquisition modes requires accurate modeling of not only the geometry of each scene, but also of systematic influences on the radiometry of individual scenes. Terrain variations affect not only the position of a given point on the Earth's surface but also the brightness of the radar return as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover induced backscatter differences, and comparison of backscatter from multiple satellites, modes, or tracks loses meaning. The ASAR & PALSAR sensors provide state vectors and timing with higher absolute accuracy than was previously available, allowing them to directly support accurate tie-point-free geolocation and radiometric normalization of their imagery. Given accurate knowledge of the acquisition geometry of a SAR image together with a digital height model (DHM) of the area imaged, radiometric image simulation is applied to estimate the local illuminated area for each point in the image. Ellipsoid-based or sigma naught (σ<sup>0</sup>) based incident angle approximations that fail to reproduce the effect of topographic variation in their sensor model are contrasted with a new method that integrates terrain variations with the concept of gamma naught (γ<sup>0</sup>) backscatter, converting directly from beta naught (β<sup>0</sup>) to a newly introduced terrain-flattened γ<sup>0</sup> normalization convention. The interpretability of imagery treated in this manner is improved in comparison to processing based on conventional ellipsoid or local incident angle based σ<sup>0</sup> normalization.
Google Earth Engine: Planetary-scale geospatial analysis for everyone
  • N Gorelick
Gorelick N., et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone, Rem. Sens. of Env. Vol 202, 1 Dec 2017, 18-27. doi.org/10.1016/j.rse.2017.06.031
Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine
  • A Mullissa
Mullissa A., et al. Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine. Remote Sens. 2021, 13(10), 1954. doi.org/10.3390/rs13101954
CEOS Analysis Ready Data for Land -An Overview on the Current and Future Work
  • A Siqueira
Siqueira A., et al. CEOS Analysis Ready Data for Land -An Overview on the Current and Future Work. IGARSS 2019, Yokohama, Japan doi.org/10.1109/IGARSS.2019.8899846
The Design of Radar Corner Reflectors for the Australian Geophysical Observing System : A single design suitable for InSAR deformation monitoring and SAR calibration at multiple microwave frequency bands
  • M C Garthwaite
Garthwaite M. C., et al. The Design of Radar Corner Reflectors for the Australian Geophysical Observing System : A single design suitable for InSAR deformation monitoring and SAR calibration at multiple microwave frequency bands. Record 2015/003. Geoscience Australia, Canberra, Australia. dx.doi.org/10.11636/Record.2015.003