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@@ -0,0 +1,239 @@
+
+
+
+ 20260721163602-1b3982d6db221c832350d260bc6d254fbf71f02f
+ 20260721163602
+
+ JOSS Admin
+ admin@theoj.org
+
+ The Open Journal
+
+
+
+
+ Journal of Open Source Software
+ JOSS
+ 2475-9066
+
+ 10.21105/joss
+ https://joss.theoj.org
+
+
+
+
+ 07
+ 2026
+
+
+ 11
+
+ 123
+
+
+
+ SARXarray: Xarray extension for Synthetic Aperture Radar data
+
+
+
+ Ou
+ Ku
+
+ Netherlands eScience Center, Netherlands
+
+ https://orcid.org/0000-0002-5373-5209
+
+
+ Fakhereh
+ (Sarah) Alidoost
+
+ Netherlands eScience Center, Netherlands
+
+ https://orcid.org/0000-0001-8407-6472
+
+
+ Simon
+ van Diepen
+
+ Department of Geoscience and Remote Sensing, Delft University of Technology, Netherlands
+
+ https://orcid.org/0000-0002-9350-3779
+
+
+ Freek
+ van Leijen
+
+ Department of Geoscience and Remote Sensing, Delft University of Technology, Netherlands
+
+ https://orcid.org/0000-0002-2582-9267
+
+
+
+ 07
+ 21
+ 2026
+
+
+ 10492
+
+
+ 10.21105/joss.10492
+
+
+ http://creativecommons.org/licenses/by/4.0/
+ http://creativecommons.org/licenses/by/4.0/
+ http://creativecommons.org/licenses/by/4.0/
+
+
+
+ Software archive
+ 10.5281/zenodo.21338541
+
+
+ GitHub review issue
+ https://github.com/openjournals/joss-reviews/issues/10492
+
+
+
+ 10.21105/joss.10492
+ https://joss.theoj.org/papers/10.21105/joss.10492
+
+
+ https://joss.theoj.org/papers/10.21105/joss.10492.pdf
+
+
+
+
+
+ Detection of cavity migration and sinkhole risk using radar interferometric time series
+ Chang
+ Remote Sensing of Environment
+ 147
+ 10.1016/j.rse.2014.03.002
+ 0034-4257
+ 2014
+ Chang, L., & Hanssen, R. F. (2014). Detection of cavity migration and sinkhole risk using radar interferometric time series. Remote Sensing of Environment, 147, 56–64. https://doi.org/10.1016/j.rse.2014.03.002
+
+
+ Nationwide railway monitoring using satellite SAR interferometry
+ Chang
+ IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
+ 2
+ 10
+ 10.1109/JSTARS.2016.2584783
+ 2017
+ Chang, L., Dollevoet, R. P. B. J., & Hanssen, R. F. (2017). Nationwide railway monitoring using satellite SAR interferometry. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(2), 596–604. https://doi.org/10.1109/JSTARS.2016.2584783
+
+
+ A model-backfeed deformation estimation method for revealing 20-year surface dynamics of the groningen gas field using multi-platform SAR imagery
+ Zhang
+ International Journal of Applied Earth Observation and Geoinformation
+ 111
+ 10.1016/j.jag.2022.102847
+ 1569-8432
+ 2022
+ Zhang, B., Chang, L., & Stein, A. (2022). A model-backfeed deformation estimation method for revealing 20-year surface dynamics of the groningen gas field using multi-platform SAR imagery. International Journal of Applied Earth Observation and Geoinformation, 111, 102847. https://doi.org/10.1016/j.jag.2022.102847
+
+
+ Radar interferometry: Data interpretation and error analysis
+ Hanssen
+ 10.1007/0-306-47633-9
+ 978-0-7923-6945-5
+ 2001
+ Hanssen, R. F. (2001). Radar interferometry: Data interpretation and error analysis. Kluwer Academic Publishers. https://doi.org/10.1007/0-306-47633-9
+
+
+ Application of an ensemble smoother with multiple data assimilation to the bergermeer gas field, using PS-InSAR
+ Fokker
+ Geomechanics for Energy and the Environment
+ March
+ 5
+ 10.1016/j.gete.2015.11.003
+ 2352-3808
+ 2016
+ Fokker, P., Wassing, B., van Leijen, F., Hanssen, R., & Nieuwland, D. (2016). Application of an ensemble smoother with multiple data assimilation to the bergermeer gas field, using PS-InSAR. Geomechanics for Energy and the Environment, 5(March), 16–28. https://doi.org/10.1016/j.gete.2015.11.003
+
+
+ Applicability of satellite radar imaging to monitor the conditions of levees
+ Özer
+ Journal of Flood Risk Management
+ S2
+ 12
+ 10.1111/jfr3.12509
+ 2019
+ Özer, I. E., Leijen, F. J. van, Jonkman, S. N., & Hanssen, R. F. (2019). Applicability of satellite radar imaging to monitor the conditions of levees. Journal of Flood Risk Management, 12(S2), e12509. https://doi.org/10.1111/jfr3.12509
+
+
+ A tutorial on synthetic aperture radar
+ Moreira
+ IEEE Geoscience and remote sensing magazine
+ 1
+ 1
+ 10.1109/MGRS.2013.2248301
+ 2013
+ Moreira, A., Prats-Iraola, P., Younis, M., Krieger, G., Hajnsek, I., & Papathanassiou, K. P. (2013). A tutorial on synthetic aperture radar. IEEE Geoscience and Remote Sensing Magazine, 1(1), 6–43. https://doi.org/10.1109/MGRS.2013.2248301
+
+
+ Peat subsidence and dynamics in midden-delfland, the netherlands, from time series InSAR analysis and the SPAMS model
+ Lumban-Gaol
+ Geoderma
+ 463
+ 10.1016/j.geoderma.2025.117551
+ 0016-7061
+ 2025
+ Lumban-Gaol, Y., Conroy, P., van Diepen, S., van Leijen, F., & Hanssen, R. (2025). Peat subsidence and dynamics in midden-delfland, the netherlands, from time series InSAR analysis and the SPAMS model. Geoderma, 463, 117551. https://doi.org/10.1016/j.geoderma.2025.117551
+
+
+ On the definition of an independent stochastic model for InSAR time series
+ Brouwer
+ IEEE Transactions on Geoscience and Remote Sensing
+ 63
+ 10.1109/TGRS.2025.3600893
+ 2025
+ Brouwer, W. S., & Hanssen, R. F. (2025). On the definition of an independent stochastic model for InSAR time series. IEEE Transactions on Geoscience and Remote Sensing, 63, 1–11. https://doi.org/10.1109/TGRS.2025.3600893
+
+
+ First wide-area dutch peatland subsidence estimates based on InSAR
+ Conroy
+ IGARSS 2024 - 2024 IEEE international geoscience and remote sensing symposium
+ 10.1109/IGARSS53475.2024.10642504
+ 2024
+ Conroy, P., Lumban-Gaol, Y., Diepen, S. van, Leijen, F. van, & Hanssen, R. F. (2024). First wide-area dutch peatland subsidence estimates based on InSAR. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 10732–10735. https://doi.org/10.1109/IGARSS53475.2024.10642504
+
+
+ Xarray: N-D labeled arrays and datasets in Python
+ Hoyer
+ Journal of Open Research Software
+ 1
+ 5
+ 10.5334/jors.148
+ 2017
+ Hoyer, S., & Hamman, J. (2017). Xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software, 5(1). https://doi.org/10.5334/jors.148
+
+
+ Dask: Parallel computation with blocked algorithms and task scheduling
+ Rocklin
+ SciPy 2015
+ 10.25080/Majora-7b98e3ed-013
+ 2015
+ Rocklin, M. (2015). Dask: Parallel computation with blocked algorithms and task scheduling. SciPy 2015. https://doi.org/10.25080/Majora-7b98e3ed-013
+
+
+ Xarray-sentinel
+ Amici
+ 2024
+ Amici, A., & others. (2024). Xarray-sentinel. https://github.com/bopen/xarray-sentinel
+
+
+
+
+
+
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+
+
+
+
+
+
+
+Journal of Open Source Software
+JOSS
+
+2475-9066
+
+Open Journals
+
+
+
+10492
+10.21105/joss.10492
+
+SARXarray: Xarray extension for Synthetic Aperture Radar
+data
+
+
+
+https://orcid.org/0000-0002-5373-5209
+
+Ku
+Ou
+
+
+
+
+https://orcid.org/0000-0001-8407-6472
+
+(Sarah) Alidoost
+Fakhereh
+
+
+
+
+https://orcid.org/0000-0002-9350-3779
+
+van Diepen
+Simon
+
+
+
+
+https://orcid.org/0000-0002-2582-9267
+
+van Leijen
+Freek
+
+
+
+
+
+Netherlands eScience Center, Netherlands
+
+
+
+
+Department of Geoscience and Remote Sensing, Delft
+University of Technology, Netherlands
+
+
+
+
+22
+12
+2024
+
+11
+123
+10492
+
+Authors of papers retain copyright and release the
+work under a Creative Commons Attribution 4.0 International License (CC
+BY 4.0)
+2026
+The article authors
+
+Authors of papers retain copyright and release the work under
+a Creative Commons Attribution 4.0 International License (CC BY
+4.0)
+
+
+
+Python
+Synthetic Aperture Radar
+SAR
+InSAR
+Dask
+Xarray
+
+
+
+
+
+ Summary
+
Satellite-based Synthetic Aperture Radar (SAR) provides invaluable
+ data for Earth Observation. The Interferometric SAR (InSAR) technique
+ (Hanssen,
+ 2001), which utilizes a stack of SAR images in Single Look
+ Complex (SLC) format, plays a significant role in various surface
+ motion monitoring applications, e.g. civil-infrastructure stability
+ (Chang
+ et al., 2017;
+ Chang
+ & Hanssen, 2014;
+ Özer
+ et al., 2019), and hydrocarbon extraction
+ (Fokker
+ et al., 2016;
+ Zhang
+ et al., 2022). To facilitate advanced data processing for InSAR
+ communities, we developed SARXarray, a Xarray
+ extension for handling co-registered SLC SAR stacks.
+
+
+ Statement of Need
+
Satellite-based SAR generates data stacks with long temporal
+ coverage, wide spatial coverage, and high spatio-temporal resolution
+ (Moreira
+ et al., 2013). Handling SAR data stacks in an efficient way is
+ a common challenge within the InSAR community. To address this
+ challenge, High-Performance Computing (HPC) is often used to process
+ data in a parallel and distributed manner. However, to fully leverage
+ HPC capabilities, data processing workflows need to be customized for
+ each specific use-case.
+
To facilitate efficient processing of SLC SAR stacks and minimize
+ code customization, we developed SARXarray.
+
SARXarray leverages two well-established
+ Python libraries Xarray
+ (Hoyer
+ & Hamman, 2017) and Dask
+ (Rocklin,
+ 2015) from the
+ Pangeo
+ community. It utilizes Xarray’s support on labeled
+ multi-dimensional datasets to stress the space-time character of an
+ SLC SAR stack. Dask is used to perform lazy
+ evaluation of operations and block-wise computations. SARXarray can be
+ integrated into existing Python workflows of InSAR processing and
+ deployed on a variety of compute infrastructures.
+
+
+ State of the field
+
A similar open-source library
+ xarray-sentinel(Amici
+ & others, 2024) exists for handling raw Sentinel-1 GRD and
+ SLC data as lazy-loaded Xarray Datasets. Despite the similar goals of
+ digesting SAR data into lazy-loaded Xarray Datasets,
+ SARXarray and
+ xarray-sentinel are designed for different
+ applications:
+
+
+
SARXarray is designed to handle
+ co-registered SLC stacks, instead of raw Sentinel-1 data products
+ from European Space Agency (ESA). It is able to lazily read the
+ output of two common SLC co-registration tools:
+ DORIS
+ and
+ SNAP
+ into Xarray objects. The two tools perform the necessary
+ coregistration step, which aligns the SLCs in a stack to a common
+ reference frame. The generated outputs can be further used in
+ Time-Series InSAR (TS-InSAR) processing.
+
+
+
SARXarray supports interferometric
+ stacks from other sensors than Sentinel-1, as long as they can be
+ co-registered by SNAP or DORIS. It reads the output from the two
+ co-registration tools, and relies on them to handle the
+ specificities of different sensors.
+
+
+
+
+ Software design
+
SARXarray is designed as an extension of
+ Xarray using accessors. This design is motivated by
+ Xarray
+ community’s recommendation, in order to isolate the
+ extension from API changes of the core Xarray library.
+
The software has three main components:
+
+
+
an I/O module that lazily loads/writes binary SLCs and related
+ metadata
+
+
+
a Stack accessor that provides basic SAR-specific operations
+ (amplitude/phase extraction and Mean-Reflection-Map
+ generation)
+
+
+
a utility module that provides functions for multi-Looking and
+ coherence calculation.
+
+
+
+
+ Research impact statement
+
SARXarray is a dependency of
+ CAROLINE
+ (Contextual and Autonomous processing of satellite Radar Observations
+ for Learning and Interpreting the Natural and built Environment),
+ which is an InSAR processing framework developed by the InSAR group of
+ Delft University of Technology. It has facilitated the data processing
+ of multiple InSAR research projects
+ (Brouwer
+ & Hanssen, 2025;
+ Conroy
+ et al., 2024;
+ Lumban-Gaol
+ et al., 2025).
+
SARXarray is a recognized related project of
+ the Xarray ecosystem, see the
+ Xarray
+ user-guide page.
+
+
+ Tutorial
+
We provide a tutorial as a Jupyter notebook to demonstrate the
+ functionalities of SARXarray:
+
Tutorial
+ Jupyter notebook
+
The tutorial includes the following steps:
+
+
+
Installation and data preparation
+
+
+
Lazy loading a SAR data stack in binary format as an Xarray
+ Dataset
+
+
+
Attaching attributes to the loaded stack
+
+
+
Applying common SAR operations on the loaded stack such as:
+
+
+
Multi-Looking
+
+
+
Creation of a Mean-Reflectivity-Map (MRM)
+
+
+
Estimation of coherence
+
+
+
+
+
+
+ Acknowledgements
+
The authors express sincere gratitude to the Dutch Research Council
+ (Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO) for
+ their generous funding of the SARXarray
+ development through the Collaboration in Innovative Technologies (CIT
+ 2021) Call, grant NLESC.CIT.2021.006. We would like to extend special
+ thanks to SURF for providing valuable computational resources for
+ SARXarray testing via grants EINF-2051,
+ EINF-4287 and EINF-6883.
+
We would also like to thank Dr. Francesco Nattino, Dr. Meiert
+ Willem Grootes and Dr. Pranav Chandramouli of the Netherlands eScience
+ Center for the insightful discussions, which are important
+ contributions to this work.
+
+
+ AI usage disclosure
+
In writing the software documentation and the JOSS paper, GPT-5 was
+ used for language improvements for grammar correction and style
+ edits.
+
Specifically, in the documentation process, an agent skill (under
+ path ../.github/skills/release-changelog) is
+ used to generate changelog entries from git tags and commit history.
+ The generated content was always reviewed and edited by a human before
+ being committed to the repository.
+
Additionally, various language models were used via
+ GitHub
+ Copilot in Pull Requests reviews, for pre-filtering obvious
+ issues such as typos, small logical errors, and for suggesting code
+ improvements. All suggestions from AI were reviewed and verified by
+ the authors before merging into the codebase. The correctness of the
+ code was ensured by the unit tests.
+
+
+
+
+
+
+
+
+ ChangLing
+ HanssenRamon F.
+
+ Detection of cavity migration and sinkhole risk using radar interferometric time series
+ Remote Sensing of Environment
+ 2014
+ 147
+ 0034-4257
+ https://www.sciencedirect.com/science/article/pii/S0034425714000674
+ 10.1016/j.rse.2014.03.002
+ 56
+ 64
+
+
+
+
+
+ ChangLing
+ DollevoetRolf P. B. J.
+ HanssenRamon F.
+
+ Nationwide railway monitoring using satellite SAR interferometry
+ IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
+ 2017
+ 10
+ 2
+ 10.1109/JSTARS.2016.2584783
+ 596
+ 604
+
+
+
+
+
+ ZhangBin
+ ChangLing
+ SteinAlfred
+
+ A model-backfeed deformation estimation method for revealing 20-year surface dynamics of the groningen gas field using multi-platform SAR imagery
+ International Journal of Applied Earth Observation and Geoinformation
+ 2022
+ 111
+ 1569-8432
+ https://www.sciencedirect.com/science/article/pii/S1569843222000498
+ 10.1016/j.jag.2022.102847
+ 102847
+
+
+
+
+
+
+ HanssenR. F.
+
+ Radar interferometry: Data interpretation and error analysis
+ Kluwer Academic Publishers
+ Dordrecht
+ 2001
+ 978-0-7923-6945-5
+ 10.1007/0-306-47633-9
+
+
+
+
+
+ FokkerPA
+ WassingBBT
+ van LeijenFJ
+ HanssenRF
+ NieuwlandDA
+
+ Application of an ensemble smoother with multiple data assimilation to the bergermeer gas field, using PS-InSAR
+ Geomechanics for Energy and the Environment
+ Elsevier
+ 2016
+ 5
+ March
+ 2352-3808
+ 10.1016/j.gete.2015.11.003
+ 16
+ 28
+
+
+
+
+
+ ÖzerIşıl E.
+ LeijenFreek J. van
+ JonkmanSebastiaan N.
+ HanssenRamon F.
+
+ Applicability of satellite radar imaging to monitor the conditions of levees
+ Journal of Flood Risk Management
+ 2019
+ 12
+ S2
+ https://onlinelibrary.wiley.com/doi/abs/10.1111/jfr3.12509
+ 10.1111/jfr3.12509
+ e12509
+
+
+
+
+
+
+ MoreiraAlberto
+ Prats-IraolaPau
+ YounisMarwan
+ KriegerGerhard
+ HajnsekIrena
+ PapathanassiouKonstantinos P
+
+ A tutorial on synthetic aperture radar
+ IEEE Geoscience and remote sensing magazine
+ IEEE
+ 2013
+ 1
+ 1
+ 10.1109/MGRS.2013.2248301
+ 6
+ 43
+
+
+
+
+
+ Lumban-GaolYustisi
+ ConroyPhilip
+ van DiepenSimon
+ van LeijenFreek
+ HanssenRamon
+
+ Peat subsidence and dynamics in midden-delfland, the netherlands, from time series InSAR analysis and the SPAMS model
+ Geoderma
+ 2025
+ 463
+ 0016-7061
+ https://www.sciencedirect.com/science/article/pii/S0016706125003921
+ 10.1016/j.geoderma.2025.117551
+ 117551
+
+
+
+
+
+
+ BrouwerWietske S.
+ HanssenRamon F.
+
+ On the definition of an independent stochastic model for InSAR time series
+ IEEE Transactions on Geoscience and Remote Sensing
+ 2025
+ 63
+
+ 10.1109/TGRS.2025.3600893
+ 1
+ 11
+
+
+
+
+
+ ConroyPhilip
+ Lumban-GaolYustisi
+ DiepenSimon van
+ LeijenFreek van
+ HanssenRamon F.
+
+ First wide-area dutch peatland subsidence estimates based on InSAR
+ IGARSS 2024 - 2024 IEEE international geoscience and remote sensing symposium
+ 2024
+
+ 10.1109/IGARSS53475.2024.10642504
+ 10732
+ 10735
+
+
+
+
+
+ HoyerS.
+ HammanJ.
+
+ Xarray: N-D labeled arrays and datasets in Python
+ Journal of Open Research Software
+ Ubiquity Press
+ 2017
+ 5
+ 1
+ https://doi.org/10.5334/jors.148
+ 10.5334/jors.148
+
+
+
+
+
+ RocklinMatthew
+
+ Dask: Parallel computation with blocked algorithms and task scheduling
+ SciPy 2015
+ 2015
+ https://doi.org/10.25080/Majora-7b98e3ed-013
+ 10.25080/Majora-7b98e3ed-013
+
+
+
+
+
+ AmiciA
+ others
+
+ Xarray-sentinel
+ 2024
+ https://github.com/bopen/xarray-sentinel
+
+
+
+
+