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Hi @SarahAlidoost and @FreekvanLeijen, I drafted a JOSS paper for SARXarray according to this guideline. I put both of you as authors of this paper since you both have significant contribution in the concept and implementation. When you have time, can you review this draft? You can find a rendered paper in the artifact of the github workflow. |
paper/paper.md
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| - Installation and data preparation | ||
| - Lazy loading a SAR data stack in binary format as an Xarray Dataset | ||
| - Attaching attributes to the loaded stack | ||
| - Applying a simple SAR operation on the loaded stack |
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| - Applying a simple SAR operation on the loaded stack | |
| - Applying common SAR operations on the loaded stack such as Multi-Looking and Mean-Reflection-Map |
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can we also add an example of "complex_coherence" to the notebook?
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Added coherence computation to the bulletpoint
paper/paper.md
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| ## Acknowledgements | ||
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| 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. Special thanks to SURF for providing valuable computational resources for `SARXarray` testing. |
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Regarding SURF acknowledgement, if the project has received a grant from SURF for using resources, the Acknowledgement text is mentioned in the grant agreement. Please use that text here.
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Thanks @SarahAlidoost, I included the surf grant numbers here
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@rogerkuou thanks for preparing the draft. I left some comments, please let me know if something is unclear.
Co-authored-by: SarahAlidoost <55081872+SarahAlidoost@users.noreply.github.com>
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Hi @SarahAlidoost, thanks for reviewing the JOSS paper and apoligies for only reacting till now. Can I adapted all your suggestions. Can you give another look? |
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Hi Ou, nice story! I inserted some adaptions. Please have a look.
| affiliations: | ||
| - name: Netherlands eScience Center, Netherlands | ||
| index: 1 | ||
| - name: Delft University of Technology, Netherlands |
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| - name: Delft University of Technology, Netherlands | |
| - name: Department of Geoscience and Remote Sensing, Delft University of Technology, Netherlands |
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| ## Statement of Need | ||
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| Satellite-based SAR generates data stacks with long temporal coverage, broad spatial coverage, and high spatio-temporal resolution. [@moreira2013tutorial] Handling SAR data stacks in an efficient way is a common challenge within InSAR communities. 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. |
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| Satellite-based SAR generates data stacks with long temporal coverage, broad spatial coverage, and high spatio-temporal resolution. [@moreira2013tutorial] Handling SAR data stacks in an efficient way is a common challenge within InSAR communities. 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. | |
| Satellite-based SAR generates data stacks with long temporal coverage, wide spatial coverage, and high spatio-temporal resolution [@moreira2013tutorial]. 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. |
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| Satellite-based SAR generates data stacks with long temporal coverage, broad spatial coverage, and high spatio-temporal resolution. [@moreira2013tutorial] Handling SAR data stacks in an efficient way is a common challenge within InSAR communities. 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. | ||
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| To facilitate efficient processing of SLC SAR stacks and minimize code customization, we developed `SARXarray` for SLC SAR stack. |
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| To facilitate efficient processing of SLC SAR stacks and minimize code customization, we developed `SARXarray` for SLC SAR stack. | |
| To facilitate efficient processing of SLC SAR stacks and minimize code customization, we developed `SARXarray`. |
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| ## Summary | ||
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| Satellite-based Synthetic Aperture Radar (SAR) provides invaluable image data for Earth observation. The Interferometric SAR (InSAR) technique, 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 [@chang2014detection; @chang2017railway], and hydrocarbons extraction [@fokker2016application; @ZHANG2022102847]. To facilitate advanced data processing for InSAR communities, we developed `SARXarray`, a Xarray extension for handling SLC SAR stacks. |
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| Satellite-based Synthetic Aperture Radar (SAR) provides invaluable image data for Earth observation. The Interferometric SAR (InSAR) technique, 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 [@chang2014detection; @chang2017railway], and hydrocarbons extraction [@fokker2016application; @ZHANG2022102847]. To facilitate advanced data processing for InSAR communities, we developed `SARXarray`, a Xarray extension for handling SLC SAR stacks. | |
| Satellite-based Synthetic Aperture Radar (SAR) provides invaluable data for Earth Observation. The Interferometric SAR (InSAR) technique [@hanssen01], 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 [@chang2014detection; @chang2017railway;ozer2018applicability], and hydrocarbons extraction [@fokker2016application; @ZHANG2022102847]. To facilitate advanced data processing for InSAR communities, we developed `SARXarray`, a Xarray extension for handling SLC SAR stacks. |
I do not want to use the wording 'image data' in the first sentence, because image suggests an optical image. In the second sentence, we use 'SAR image', which I think is acceptable.
| publisher = {Elsevier}, | ||
| number = {March}, | ||
| } | ||
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| @article{ozer2018applicability, | |
| title={Applicability of satellite radar imaging to monitor the conditions of levees}, | |
| author={{\"O}zer, I{\c{s}}{\i}l E and van Leijen, Freek J and Jonkman, Sebastiaan N and Hanssen, Ramon F}, | |
| journal={Journal of Flood Risk Management}, | |
| pages={e12509}, | |
| year={2018}, | |
| publisher={Wiley Online Library} | |
| } | |
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| ## Tutorial | ||
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| We provided a tutorial as a Jupyter notebook to demonstrate the functionalities of `SARXarray`: |
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| We provided a tutorial as a Jupyter notebook to demonstrate the functionalities of `SARXarray`: | |
| We provide a tutorial as a Jupyter notebook to demonstrate the functionalities of `SARXarray`: |
| - Attaching attributes to the loaded stack | ||
| - Applying common SAR operations on the loaded stack such as: | ||
| - Multi-Looking | ||
| - Create Mean-Reflection-Map (MRM) |
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| - Create Mean-Reflection-Map (MRM) | |
| - Creation of a Mean-Reflectivity-Map (MRM) |
| - Applying common SAR operations on the loaded stack such as: | ||
| - Multi-Looking | ||
| - Create Mean-Reflection-Map (MRM) | ||
| - Calculate complex coherence |
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| - Calculate complex coherence | |
| - Calculation of coherence |
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| ## Acknowledgements | ||
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| 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. Special thanks to SURF for providing valuable computational resources for `SARXarray` testing via grant EINF-2051, EINF-4287 and EINF-6883. |
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| 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. Special thanks to SURF for providing valuable computational resources for `SARXarray` testing via grant EINF-2051, EINF-4287 and EINF-6883. | |
| 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. Special thanks to SURF for providing valuable computational resources for `SARXarray` testing via grants EINF-2051, EINF-4287 and EINF-6883. |
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| 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. Special thanks to SURF for providing valuable computational resources for `SARXarray` testing via grant EINF-2051, EINF-4287 and EINF-6883. | ||
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| We would also like to thank Dr. Francesco Nattino and Dr. Meiert Willem Grootes for the insightful discussions, which are important contributions to this work. |
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| We would also like to thank Dr. Francesco Nattino and Dr. Meiert Willem Grootes for the insightful discussions, which are important contributions to this work. | |
| We would also like to thank Dr. Francesco Nattino and Dr. Meiert Willem Grootes of the Netherlands eScience Center for the insightful discussions, which are important contributions to this work. |
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Joss paper