Abstract
The Copernicus Sentinel-1 satellite mission provides global coverage of the Earth’s surface with high-resolution SAR data. Sentinel-1 SLC data and the derived InSAR products have proven to constitute a valuable source of information not only for various mapping applications such as land cover [1], floods [2] and natural hazard damage [3], but also for crop monitoring [4]. However, the processing and analysis of SLC data can be complex and time-consuming, requiring specialized expertise and resources. Several studies addressed this issue with different approaches. Jacob et al. [5] produced Interferometric Coherence data cubes pre-computing all the possible master-slave pairs, resulting in an efficient user experience but with a high overhead in required resources. Ticehurst et al. [6] produced data-cubes of three Analysis Ready Data (ARD) products over Australia: backscatter, coherence and dual-polarimetric decomposition. Kellndorfer et al. [7] produced a publicly available global seasonal Interferometric Coherence data set. Finally, Agram et al. [8] created a workflow to efficiently read and process SLC data accessing single bursts but unfortunately, the implementation is closed source and the results are available only through the Descartes Labs platform.
We propose SAR2Cube as an open framework that aims to make the pre-processing and on-demand computation of InSAR products from Sentinel-1 SLC data more accessible and user-friendly. It uses openEO [9] as the client interface, which supports multiple programming languages, including R, Python, and JavaScript, enabling a wide range of users to interact with, process, and download data.
The desired datacube is a temporal stack of co-registered SLC images. One image, considered as a reference, is used to define the radar coordinate grid where all the others are aligned and resampled. The software used for the pre-processing steps is ESA SNAP. The first required steps are data unzipping and slice assembly, if the Area Of Interest (AOI) is covered by more than one slice. Subsequently the radiometric Calibration process is applied. The final co-registration step is composed by TOPSAR-Split and Apply-Orbit-File on the master and slave images, Back-Geocoding, Enhanced-Spectral-Diversity and de-bursting (TOPSAR-Deburst). Considering the S-1 IW mode, de-swathing (TOPSAR-Merge) is also required only if the AOI covers more than one subswath. Additionally, to produce the differential interferogram products with the on the-fly (OTF) operator, two Interferogram steps are required. Interferogram with geometric components (flat earth and topography) and real and imaginary part for VV and VH interferogram without geometric components that are used to obtain the basis of the geometric components per each one of the images of the dataset. These bases can be linearly combined to obtain all the possible differential interferogram pairs with the OTF interferogram operator. In this step SNAPHU unwrapping module has been used, since the two interferogram must be unwrapped before extracting the geometric component base.
This workaround is the only drawback of the pre-processing step. It is a time-consuming step that can be fixed by saving the geometrical component during the co-registration step.
The resulting stack is composed of all the aligned and calibrated images. For each date, 9 layers are generated: real and imaginary part of VV and VH for backscatter; geometric component base; and, additionally, the longitude and latitude grids, along with the Local Incidence Angle (LIA) and Digital Elevation Model (DEM), are generated only once and will be the same for each date.
In this paper, we present some general aspects of the SAR2CUBE project mainly focused on the differential interferogram and differential phase/coherence generation.
The differential interferogram computation of a dense list, it is the case of Sentinel-1, can be easily and quickly generated thanks to the Python implementation based on XArray [9] and Dask [10] and most of the processes are highly scalable.
Furthermore, SAR2CUBE offer another important feature. Due to the dense time series, it may be impractical to save all the differential phases and coherence of a stack of more than 200 images. In some cases, we can have more than 1000 interferograms. For each interferogram phase and coherence maps must be saved and stored on disk. With SAR2CUBE we can skips this storing process and compute on the fly what we really need. We also can access just a portion of the full processed area through the spatial subset that takes advantage of the geographic transformation matrices and a precise period of data through the temporal subset tool. This information can be then used in a multi temporal interferogram based process, such as Persistent Scatterer Interferometry (PSI).
SAR2Cube is a framework based on re-usable open-source components capable to provide a flexible access to Sentinel-1 SLC data, reducing the barrier for the usage of InSAR products and giving the users the possibility to work with multiple AOIs and parameters interactively thanks to openEO. Additionally, thanks to the Python based implementation of the openEO processes, it is easily extensible with new functionalities.
The European Space Agency is acknowledged for funding SAR2CUBE with the ESA Contract No. 4000129590/19/I-DT - O SCIENCE FOR SOCIE1Y PERMANENTLY OPEN CALL FOR PROPOSALS EOEP-5 BLOCK 4. The European Commission is acknowledged for the financial support within the H2020 MSCA-RISE project HERCULES (grant agreement 778360).